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Navigating the Hedge Fund Landscape: Start-ups, Mid-sized Firms, or Big Funds?

Wed, 21 May 2025 07:17:56 GMT

How to choose between Start-ups, Mid-sized Firms, or Big Funds?

Navigating the Hedge Fund Landscape: Start-ups, Mid-sized Firms, or Big Funds?

Introduction

The landscape of quantitative trading is vast and varied, encompassing dynamic start-ups, mid-sized organisations, and established giant hedge funds. Each of these presents unique opportunities and challenges.

This guide aims to accomplish two goals: to shed light on the characteristics, advantages, and challenges of various buy-side groups; and to provide you with the insights needed to navigate this multifaceted industry.

This guide offers a high-level overview of these broad categories, providing an understanding of the potential advantages and disadvantages. By exploring the differences and similarities between these groups, I hope to empower you to make an informed career decision that aligns with your personal and professional goals.

To make sense of this landscape, I've classified organisations based on size - Start-up vs Mid-size vs Big Fund.

I'll classify mid-sized as having assets under management (AUM) of between 1 to 10 billion and big funds, those with AUM exceeding 10 billion. Although further subdivisions are possible, based on styles and structures, for clarity, I'll focus on a tripartite comparison: start-ups, mid-sized firms, and big funds.

It's essential to note that, in reality, the lines between these categories can blur, with many firms exhibiting characteristics that span multiple categories.

This guide does not delve into individual motivations, specific trading strategies, or personal skills. These aspects are intimately tied to your unique circumstances. Instead, we'll take a bird's eye view, focusing on the broad characteristics of various buy-side groups.

FYI - when I use the word "quants" to refer to all quantitatively skilled people - Portfolio Managers (PMs), Researchers, Developers, Data Scientists, etc.

Let's get into it!

Start-up vs Mid-size vs Big Funds

Start-ups – advantages

Start-ups are undoubtedly exciting enterprises, heralded for their potential to reshape industries and can lead to impressive returns. The allure of a start-up, particularly in the financial sector, can be nearly irresistible, especially given the media spotlight that tends to focus on the few success stories, thus painting a picture of easily attainable success.

Some of the positive attributes are:

Blank Canvas: The appeal of a start-up lies in its pristine nature. You are given a unique opportunity to create something meaningful on a blank canvas. This is incredibly appealing to many, as the untapped potential seems limitless, especially in the initial stages.
Make a mark: A start-up offers a unique opportunity to create something meaningful and substantial from the ground up. You can truly make your mark and influence the company's direction in a way that's often impossible in larger, more established organisations.
Ownership: The prospect of owning a strategy or business area without intruding on established roles can be a significant attraction. With lean teams and flat hierarchies, start-ups offer an environment that encourages individual ownership of strategies or processes.
Fewer Politics: With smaller teams and flatter hierarchies, start-ups offer a degree of freedom that many find attractive. Here, you can work on what you want, unhindered by layers of bureaucracy or extensive corporate politics. However, while the freedom can be exhilarating, it's important to self-assess: do you thrive in a more structured environment or one that gives you the freedom to explore?
Rapid Progression: Moreover, start-ups provide opportunities for rapid career progression. You might climb the ranks to become a 'big boss' far quicker than in larger, established companies. However, this accelerated career growth is often accompanied by challenges, including learning from failures rather than a trusted mentor.
Payday: Regarding financial rewards, the potential for a significant payday exists, especially if the start-up excels. Over the long run, if the start-up is successful, it should be the most lucrative option as you share in the fund's success. Benefiting directly from the performance but also rising with the tide. If you can secure equity or partnership, it can be hard to beat if all goes well. However, it's vital to remember that the road to such success can be long and instant windfalls are rare, with most profits typically reinvested into the business.

Start-ups – disadvantages

Despite the potential rewards, joining a start-up also comes with numerous risks.

Most New Funds Fail: The reality is that most start-ups, especially in the hedge fund industry, face a high risk of failure. Since 2015, over 3000 funds have shut down, the majority under three years old and managing under $500 million in assets. According to Hedge Fund Research, the average age of a liquidated hedge fund in 2022 was 4.5 years. But this data is skewed as some 15 years plus funds were shut down, so the majority were under a year.
Raising AUM is hard: Start-ups face significant hurdles, with asset-raising being a major one. The ability to attract and maintain investors is crucial to the sustainability of any hedge fund. However, this task is highly challenging, particularly in the current economic climate, where most assets flow towards established market players. One needs to consider the asset-raising aspect seriously. If you're a PM and need to help raise assets, this can seriously distract you from trading. Now, you need to succeed on two fronts.
AUM Stability: Understanding the origin of a start-up's assets is advisable. Who are the investors? How securely is their money tied to the venture? Could they withdraw their funding quickly, and what is their risk tolerance? Understanding these factors can give you insights into the stability and long-term prospects of the start-up. There have been instances where Portfolio Managers joined groups only to discover six months later that the owner or investor had changed their mind and withdrew funding. This happens with established groups also - most will know of a discretionary group wanting to build systematic, only to reverse course 12 months later.
Low Budget : Start-ups often face significant budgetary constraints, affecting their competitive edge. These limitations mean they may be unable to compete on salary, offer guarantees, or invest in sophisticated trading platforms and high-priced data sets.
Tech not up to scratch: Given their limited budget, start-ups may not have state-of-the-art trade infrastructure. Instead, they tend to operate on more basic technological infrastructure. While cheaper and more accessible technology has lowered the entry barrier for start-ups, these ready-made platforms may not be sufficient for specific quant strategies that demand higher processing power and speed.
Multiple Roles : Working in a start-up also means wearing many hats, which, while initially appealing for its exposure, can become burdensome over time. The operational aspects can overshadow one's core passion for trading, adding to the start-up's challenges. Shockingly, research suggests that 50% of hedge fund start-up failures are due to operational errors in trade processing, administration, accounting, and reporting.
Bigger fallout from politics: Of course, like any venture, there are two sides to the "less politics" coin. Start-ups might be free of corporate politics, but the fallout from disagreements or changes in direction is usually far higher and more of an existential risk.
Low Comp: While the upside can be significant, it is not the average experience. Start-ups must run lean, meaning no high base salaries or guaranteed bonuses. Taking money out in the first couple of years might also not be possible. Often, start-ups prioritise reinvestment of earnings over payouts. This practice aids growth but might not be ideal for individuals seeking immediate financial rewards.
Key Man Risk: A start-up often has 'key man risk.' If a crucial person leaves, the entire organisation could suffer dramatically. This individual might be the primary driver of the fund's success, hold the team together, or be the main attraction for AUM. Their departure can profoundly impact the start-up's survival and success.

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In summary, the allure of start-ups, characterised by high rewards and growth opportunities, is compelling. Start-ups offer unique options such as a fresh slate for innovation, significant ownership over strategies, less bureaucracy, rapid career progression, and the potential for substantial financial rewards.

However, it comes hand in hand with significant challenges that require careful evaluation. Factors such as capital acquisition, technology access, operational responsibilities, and understanding the investor base and potential 'key man risk' demand thorough consideration. It's crucial to approach these exciting but risky ventures with a balanced perspective, accounting for potential rewards and inherent risks. With an optimal blend of skills, timing, and a touch of fortune, a start-up may be the perfect platform for professional advancement and financial success.

The Established - Mid-size & Big Funds

First, I will outline the pros and cons of both mid-sized and large funds compared to start-ups, as there are attributes that apply to both. Subsequently, I will delve into the distinctions between mid-size and large established funds, highlighting their strengths and drawbacks.

General - Established - Advantages

Established groups, generally those with over $1 billion in assets, have several advantages and positives that can be generalised. Such as:

Stability: Established organisations offer a stable platform, primarily due to their size and reputation. Staff turnover tends to be low, and the long-term and secure capital base makes them less susceptible to redemption-related hits. Although the financial industry offers no guarantees of stability, established organisations provide a higher chance of stability. This stems from their successful track record, attracting a steady influx of capital.
Expertise: While a start-up might have one renowned expert at the helm, established organisations could be home to dozens or even hundreds of top industry professionals. These experts, who have decades of experience, contribute to the collective knowledge and proficiency of the organisation. This creates a conducive learning environment for more junior members, fostering a self-perpetuating proficiency machine that can outperform, outsmart, and outmanoeuvre smaller start-ups.
Infrastructure: Established organisations typically have superior trading platforms, technology, and data management systems. They can afford to invest millions in unique data sets, cutting-edge infrastructure, and innovative technologies, giving them a significant advantage over start-ups. In a world where generating alpha is paramount, the top established firms provide their quants with the best tools for the job. For instance, a quant hedge fund within an established organisation may have access to over 2000 different data sets, a luxury beyond the reach of most start-ups.
Budget: The budgets of large organisations dwarf those of start-ups, enabling them to invest heavily in the newest technologies and data. The larger budgets also allow more substantial investment in personnel development, superior research capabilities, and advanced risk management systems. It also extends to the war for talent, where they can hire the best. This financial capability can translate into a competitive edge that is hard to beat.

General - Established - Disadvantages

Established entities in any industry invariably have both positives and negatives. Identifying and assessing these factors is crucial before making a career move. The impact of these downsides may vary based on your characteristics, career aspirations, and where you are in your professional journey.

Over-specialisation: One potential pitfall is the highly specialised, narrow roles often found in established organisations. The big groups, especially the collaborative research style groups, will break down the trade life cycle process into smaller and even smaller parts. Meaning you hyper-focus on one piece of the puzzle. It could be data cleaning or execution. This over-specialisation can mean you lack general and well-rounded skills, limiting your exposure and career progression.
Limited Scope: In many established firms, roles can be restrictive, curbing your innovation ability. Strict internal policies often govern team tasks, leading to potential frustration when trying to extend your research or portfolio. In some quant trading firms, you might face restrictions on what datasets, assets, and markets you can research. The bureaucratic structure often found in larger entities can quell creativity and motivation.
Politics: Internal politics are typical in most large, established organisations. Though subtle, this factor can significantly influence the work environment, sometimes leading to rifts and divisions within the organisation.
Career Progression: Ironically, a disadvantage of established organisations can be the lack of career progression. It might seem counterintuitive as larger organisations have more roles and positions, but these are occupied, and two, more people are fighting for promotions. The opportunities for upward movement are often only available if someone vacates a position. This situation can lead to a sense of hitting a glass ceiling, especially for mid-level professionals. For example, gaining independence from your PM is near impossible if you're a sub-PM with an established multi-manager. The fact is the PM will not want to lose your PnL stream. So a sub-PM's only option is to seek independent status externally.
Cut-throat: Within established organisations, roles that directly contribute to alpha generation can be unyieldingly cut-throat. This relentless pursuit of excellence keeps everyone under intense scrutiny. Consequently, these prestigious entities are known for their selective hiring practices and readiness to part ways with staff who do not meet their high-performance standards.

Lastly, it's worth mentioning the unique case of multi-managers. These entities blend established infrastructure and a start-up's vibrancy with small trading units or 'pods'. However, these pods' experience can range widely, from long-established to essentially start-ups. These pods offer some freedom similar to a start-up environment but could also come with challenges akin to those in larger organisations.

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In summary, established funds offer significant benefits such as stability due to their size, reputation, and secure capital base. They also house a wealth of industry expertise, superior infrastructure, and larger technology, data, and personnel development budgets.

However, these come with certain drawbacks. The potential for over-specialisation can limit broader exposure, potentially restricting career growth. Roles within these entities may also limit innovation due to their bureaucratic nature and stringent internal policies. Other challenges include potential internal politics and limited opportunities for career advancement due to the competitive nature of the environment. The high-performance culture can also make these roles highly pressurised and cut-throat.

Mid-sized - Advantages

A simplistic comparison between start-ups and the rest fails to capture the full breadth and depth of the business landscape. Comparing a $2bn organisation to a $22bn one wouldn't be accurate or fair. A more nuanced approach would divide these entities into mid-sized and large firms. This division facilitates a more precise understanding of opportunities within established organisations.

As introduced earlier, I'm classing mid-sized funds as having AUM $1bn to $10bn and big funds as anyone over $10bn in AUM. It is crude, could easily be argued with, and doesn't really factor in age. But the lines are already blurry in a space where you can have an $8bn start-up or a start-up pod in a $50bn group. On average, certain characteristics and traits can be seen more in one group than another.

Let's dive into some of the advantages of mid-sized firms.

Specialised focus: Mid-sized organisations often distinguish themselves with a specialised focus, making them ideal environments for professionals in their careers' early or intermediate stages to develop and expand their skills. They often hone in on specific areas like macro, index arbitrage, or statistical arbitrage, creating a hotbed of expert knowledge. This niche specialisation is a defining characteristic of mid-sized firms, and their mastery in these areas can offer unparalleled opportunities to acquire deep expertise.
Capital To Compete: Mid-sized firms possess a significant financial advantage that allows them to contend effectively with larger entities. Their robust capital base permits them to attract and retain top talent and invest in cutting-edge technology. PMs at these firms can handle substantial portfolios and match larger competitors regarding infrastructure and data resources. Consequently, PMs can confidently trade, assemble a competent team, provide competitive remuneration, and draw additional capital.
More Visible Impact: The smaller scale of mid-sized funds can give your work a more immediate and visible impact, given that 50 to 250 people are running the fund. Your role might be broader, and your contributions might be more directly linked to the fund's success. In a larger fund with thousands of employees, your work may be one small piece of a giant puzzle, and its direct impact may be more difficult to discern.
Cultural Differences: Mid-sized funds often offer a different work culture than large firms. The environment might be more entrepreneurial and less formal, with more direct interaction with senior management and colleagues. The leaner management structure of mid-sized firms fosters a more flexible environment, reducing internal politics and enabling quicker decision-making. Larger funds, on the other hand, may have a more corporate culture with established hierarchies.
Innovation Prospects: What distinguishes mid-sized firms further is their innovation potential. They typically have the balance between having the capital to invest in new areas and new technologies while not having the overhang of established practices and legacy systems.
Career Advancement Opportunities: Mid-sized firms tend to have streamlined, flattened structures, which lend themselves to faster career progression. This contrasts larger funds or asset managers, where the hierarchical ladder can be daunting, and leadership roles are often long-occupied. In larger firms, climbing the ranks can be more competitive and time-consuming due to more employees.
Growth Opportunities: Mid-sized firms possess a scale that supports substantial growth and diversification. Generally, these organisations have achieved high proficiency in a particular field and are looking for diversification opportunities to spur growth. This situation opens up many exciting possibilities, evoking the vibrancy and exploratory nature of start-up environments. This becomes an appealing prospect for professionals in the mid to late stages of their careers who desire growth without high risk.
Closer to Decision-Making Process: In mid-sized funds, individuals are typically closer to decision-making. You have more opportunities to understand the fund's strategy and management and contribute to these decisions directly. Due to their size and complexity, larger funds can sometimes be more bureaucratic and may involve slower decision-making processes, where you can be a few steps away from the process.

Mid-sized - Disadvantages

Along with the advantages, there are potential disadvantages of joining a mid-sized fund ($1bn to $10bn). Here are a few to consider:

Risk of Stagnation: There is a risk that a mid-sized fund may grow differently than expected or could even shrink. Unlike larger funds, which often have diversified portfolios to mitigate risk, mid-sized funds may be more susceptible to market volatility and poor strategic decisions.
Diversification Killer: We mentioned an advantage is the desire to diversify to spur growth in mid-sized funds. This diversified growth approach does come with its challenges. As evidenced by some firms, diversification can pose severe risks if not executed correctly. A new business unit, or new strategies, swallow up capital and resources, taking them away from other teams. There are many examples of funds branching out into a new space in the name of diversification, only for the new venture to tank and bring the whole business down, or best, set the business back. Also, introducing a new department or role can face considerable resistance due to established conventions, company culture, and management dynamics.
L ess Brand Recognition: Larger funds typically enjoy more prominent reputations and can provide better name recognition on your CV. Mid-sized funds may not offer the same level of brand recognition, which could be a disadvantage if you plan to move in the future.
Potential for Overwork: While your work's impact may be more visible at a mid-sized fund, there could also be more pressure and expectation for you to deliver results. The workload may be heavier, with a broader scope of responsibilities than a more specialised team-centric role at a larger fund.
Less Structured Training and Development: While big funds often have formalised training and mentorship programs, mid-sized funds may provide a different level of structured professional development. This lack of formal training could be a disadvantage, especially early in your career.

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In conclusion, mid-sized firms offer an appealing "Goldilocks" scenario — not too large, not too small, just right. They successfully combine the advantages of start-ups and large firms, offering an entrepreneurial spirit, quicker career progression, financial stability, and niche expertise.

However, it's important to note that the advantages can vary greatly depending on the specific fund. While these generalisations can provide a starting point, quants should thoroughly research and consider the unique characteristics of each fund when making their decision.

Large Funds - Advantages

We defined big groups as any fund managing over $10bn.

If your ambition is to handle gigantic portfolios exceeding $5bn, $10bn, or even $15bn, established organisations are the only viable choice.

Brand recognition: One of the most striking advantages of joining a prominent big hedge fund is the brand value it brings to your resume. Like it or not, the industry, and indeed the world at large, respects and recognises brand power. Having the name of a well-established, reputable hedge fund on your CV could significantly elevate your professional standing.
Broad Exposure: When you join a big hedge fund, one possible benefit is wide-ranging exposure. These finance behemoths operate across a diverse portfolio and have a broad investment scope. As a quant, you are not necessarily confined to a niche area, as internal moves are possible. However, ensure you check on the research structure. Some collaborative shops have a relaxed approach to the whole team's work. A large 30-person group can cover multiple aspects of the trade lifecycle, owning the strategies for a given asset class. Within these teams, you can move between projects and gain valuable experience.
Be wary of the other approach, the factory-style research approach. The team is split to focus on one part of the trade lifecycle. You might be focused purely on data cleaning and indigestion with some preliminary analysis, or you might be focused purely on portfolio construction and optimisation. The spots for alpha research are already filled, and you likely have a few people ahead of you in the queue. These can be a great place to specialise, but fair warning, you will get pigeonholed very quickly and may struggle to move to a different set-up that wants a more well-rounded skill set later in your career.
Commitment to professional development: These larger entities tend to have deep pockets and a vested interest in their employees' growth. So giving them access to state-of-the-art training programs, conferences, workshops, and opportunities for further education. Big hedge funds are fertile ground for quants hungry to learn, grow, and climb the career ladder.
Track Record: Remember, these organisations didn't become large by accident. Their size is often a testament to their success and effectiveness. They have demonstrated a capacity to thrive and evolve in an often volatile market environment, which offers a wealth of lessons. Or they have a great IR team with long lock-up periods…
Stability: Larger hedge funds are typically better equipped to withstand economic storms due to their diversified portfolios. While the financial industry is innately unpredictable, a role in a large firm can bring a certain degree of job security - a comfort not to be dismissed.
Tools for the job: A quant's success heavily depends on the quality of their tools. With their size comes the ability to invest in cutting-edge technology and computational resources. From high-performance computing resources to advanced software tools, to access to premium data sources, to building air hockey robots during your lunchtime.
Compensation: Due to their resources, big funds typically offer competitive salaries and benefits, including lucrative bonuses and other financial incentives. While money shouldn't be your only motivator, it's undeniably an essential factor in any career decision. The baseline is typically higher in big established funds versus start-ups and mid-sized. The median upside is likely higher as more significant amounts of AUM result in larger bonus pools. However, the total potential upside is more capped than the mid-size groups and start-ups, whose potential limits are far higher.
Economies of scale: As mentioned, big funds typically have the most advanced trading infrastructure, access to the best data, and employ the most talented people. However, it also transfers to other areas. Their size means they can drive hard bargains resulting in various advantages, such as better execution, better prices from brokers, wider margin amounts, greater book size, broader market access, and more.
Community: Large hedge funds offer a vibrant community with ample networking opportunities. Collaboration with industry leaders, attendance at significant events, and interaction with diverse colleagues contribute to a quant's career growth and prospects. Such environments bring you into contact with the industry's brightest minds, which can serve as invaluable resources in your career journey. Notably, many leaders of start-ups and mid-sized groups often began their careers in large firms. However, these advantages may not hold true in a pod structure where cross-team collaboration is also non-existent.

Big Funds - Disadvantages

Joining a large hedge fund ($10bn+) has potential disadvantages. Here are a few you may want to consider:

Limited Autonomy: In larger firms, your role will likely be highly specialised and a vital team function. Leaving less room for creative decision-making or the freedom to explore different areas of interest. Bigger firms often have established processes and a more hierarchical structure, which can limit your autonomy.
Less Visibility: Given the sheer size of a large fund, it may take more work to stand out and make significant contributions that get noticed. The impact of your work might be less visible than what you could accomplish in a smaller, mid-sized fund or start-up.
Bad Tech: It was mentioned big funds have an advantage on tech, mostly given their increased budget. But this is not automatically the case. Huge established funds, especially those with a discretionary trading DNA, can have shockingly poor platforms for quant trading. In a pod structure, you're given the basics and expected to build everything else; this can be a blessing or curse. When joining a big fund, you must double-check whether the platform is advantageous or disadvantageous.
Bureaucracy and Red Tape: Larger firms often have more levels of management, which can mean a slower decision-making process and more red tape. As a result, it may take more work to innovate or make significant changes promptly.
Impersonal Environment: In a bigger firm, it's easier to feel like a small cog in a giant machine. You may have less interaction with senior leadership, and the corporate culture might feel more impersonal.
Less Flexible Work Environment: Bigger firms are more likely to have established cultures and ways of doing things. While some large firms are working to become more flexible, smaller firms are more agile and adaptable. The banks and likes of Citadel, as well as big tech, require people back five days a week. While the small funds I talk with are far more flexible, offering hybrid set-ups in nearly all instances.
Competitive Atmosphere: With many talented individuals vying for recognition, promotions, and bonuses, the environment at large funds can often be highly competitive. This might only suit some, especially those who prefer a more collaborative and less cut-throat environment.

In summary, joining a large hedge fund can offer many advantages: enhancing your resume with a respected brand name, providing a front-row seat to best-in-class operations and strategies, and offering the chance to work and network with some of the industry's brightest minds. While every career decision should be based on your aspirations and circumstances, these benefits are worth considering when contemplating a role in a big hedge fund.

However, as with most things in life, there are two sides to the coin. Large hedge funds, particularly those over $10 billion, can present several drawbacks. Among the challenges prospective employees might face are limited autonomy, less individual visibility, outdated technology platforms, bureaucratic decision-making, impersonal environments, inflexible work arrangements, and a highly competitive atmosphere. These factors highlight the importance of considering the potential downsides before joining a large hedge fund.

Conclusion

Navigating the hedge fund industry presents a spectrum of choices that span from joining start-ups, and mid-sized firms, to big hedge funds. Each option has unique advantages and challenges, and their relevance can be highly individual, varying wildly depending on personal aspirations, career stage, and work style.

Choosing between an established entity and a start-up is influenced by your personality, career stage, and long-term aspirations. An established organisation might be perfect if stability, structured learning, and a well-defined role align with your career goals. Conversely, a start-up might be a more suitable environment if you're seeking to stretch your boundaries, crave autonomy, and are comfortable with risk.

Early and mid-career professionals may find that larger, established groups offer an advantageous springboard for learning, growth, and building a solid resume. The stability, structure, and the chance to work alongside some of the industry's brightest minds can provide an enriching environment that fuels professional development.

However, these advantages can gradually fade for those feeling stagnated or hitting perceived glass ceilings in larger organisations. In these cases, start-ups' innovative drive, autonomy, and agility might present a refreshing alternative. Here, the opportunity to be part of something built from the ground up can reignite passion and provide a meaningful career shift.

Start-ups can provide an exhilarating, hands-on environment for those seeking to learn quickly and make significant contributions from day one. They offer a chance to be part of something from the ground up and see first-hand the result of one's efforts. However, these opportunities come with a higher risk due to the volatility and instability of early-stage ventures. Consider the commitment required, the pressure of high expectations, and potentially lower starting compensation.

Don't join a start-up based solely on the "what-if-everything-went-well" scenario. Aim for the best outcome, but be aware of the inherent risks and challenges that come with the start-up territory. Similarly, consider the potential drawbacks, like bureaucracy, limited autonomy, and potentially slower innovation to join a big fund.

Mid-sized firms bridge the gap between start-ups and large firms, offering a balanced blend of stability and entrepreneurial opportunity. They provide an ideal platform for acquiring deep, specialised expertise and getting closer to decision-making processes. At the same time, they pose challenges such as potential stagnation, difficulties associated with diversification, and less brand recognition.

With their brand value, broad exposure, commitment to professional development, and stability, big hedge funds can provide a robust launchpad for an ambitious career. They offer sophisticated tools and resources, competitive compensation, and opportunities to network with industry leaders. However, they may limit your autonomy, offer less visibility for individual contributions, and exhibit higher levels of bureaucracy. The larger the organisation, the higher the possibility of feeling like a cog in the machine.

Keep in mind; there's no one-size-fits-all answer. This decision should be a reflection of your long-term career goals and preferences. Consider where you envision your career trajectory in the next 10 to 20 years, and choose the path that best facilitates this journey. Reflect on the aspects of the job you enjoy the most and ensure your selected environment supports these passions.

In conclusion, whether you're considering a start-up, a mid-sized firm, or a big hedge fund, remember the grass isn't always greener on the other side. Each choice presents unique advantages and potential challenges. The right fit will be the one that aligns with your career goals, personal development objectives, and work-life balance needs.

Understanding the potential advantages and disadvantages of each setting can help you make a more informed decision that resonates with your unique aspirations. Remember, it's not just about choosing the right fit for the current stage of your career but also about positioning yourself for the opportunities that may arise in the future.

Quant Trading Careers Unveiled

Sat, 11 Nov 2023 12:05:02 GMT

Quant Careers Unveiled

Quantitative trading combines mathematics, technology, and financial acumen to create sophisticated trading strategies. Understanding the full scope of career options can be challenging for quant because no two groups or roles are the same.

This article aims to clear the fog around the roles and skills needed within quant trading. We'll cover responsibilities, organisation fit, challenges evolution, and compensation of each role. It's a guide to help quants see where they sit and what it takes to excel in each position, providing transparency in an often opaque field.

An important caveat is that this does not capture everything; there will be many outliers. Especially around the compensation numbers, I aim to cover 80% of the market.

Understanding the Quant Ecosystem

In quant trading, the organisational landscape can be confusing. There are macro funds, Commodity Trading Advisors (CTAs), multi-strategy, asset managers, credit funds, pod funds and more. You can divide the space into the research style approach or the pod structure approach.

Research Groups: These teams are generally larger and highly collaborative, focusing on a single primary strategy that may have various sub-components. For instance, a group might trade CTA strategies encompassing trend-following, fixed-income arbitrage, and relative value. Teams are often divided based on asset classes or specific strategy styles; their mandate is to develop strategies within that focus. These funds generally employ 100 to 1000 people, the biggest being 1500 to 2500 people.
Examples include MAN AHL, Two Sigma, and DE Shaw.

Pod Structures: Prevalent in multi-strategy funds. Each pod operates like a mini-business, in competition with the market and other pods within the fund. The idea is to generate positive performance through meticulous risk management, irrespective of market conditions. While the teams in each pod are smaller, the number of pods can be substantial—ranging from 100 to over 300 in the largest funds. Overall, the funds typically employ 1000 up to 5,000 people.
Examples include Millennium, Point72/Cubist, and Baylasny.

Deep Dive into Each Role

This section will dive into critical roles in the quant world, covering responsibilities, skills, career paths and more.

Quantitative Researchers

Responsibilities : As the architects of trading signals and strategies, they are immersed in data analysis, model development, and backtesting. Their highly analytical work involves sifting through vast datasets to identify trading opportunities. A Quant Researcher (QR) typically focuses on creating automated signals or strategies. They analyse data to make predictions and position accordingly to generate alpha.
Required Skills : Strong mathematical background. Programming in Python is best. R & Matlab is okay but not widely used. SQL for handling data is needed. Knowledge of Python libraries, such as pandas, NumPy, TensorFlow, or PyTorch. Java or C++ is not required, but it is an advantage. Statistical analysis and machine learning knowledge, anything from linear regression to deep learning, is good.
Organisational Fit : Found in research groups and also found under Portfolio Managers in multi-strategy funds.
Collaboration : Work with data scientists for data preparation and quantitative developers to implement their models. Also, work with the portfolio manager or head of the desk, who sets the research direction and discusses the weight for each signal.
Sub-Categories: While alpha research remains a core focus, there are specialisations in risk modelling, portfolio construction, optimisation and signal combination. QR's tend to focus on alpha generation in pod structures and require complete end-to-end skills - from data gathering to analysis and signal creation to portfolio construction and execution. Whereas QR's in research shops are typically far more specialised, focusing on just one part of the trade life cycle.
Challenges : Quantitative Researchers face issues such as model overfitting, where strategies that perform well on historical data fail to work in the real world. They also tackle the high expectations of developing consistently profitable strategies amidst market noise and data anomalies.
Evolution : The role has evolved with the advent of machine learning and big data. Researchers need to be proficient in machine learning algorithms and natural language processing.
Career Path : Junior Researcher -> Quant Researcher -> Senior Researcher -> Head of Research / Head of a Desk, or transition to Portfolio Manager.
Typical Base Salary: £70,000 - £150,000+ in the UK; in the US, $100,000 - $200,000+
Bonus Potential: This can be up to 100 to 200% or more of the base salary, depending on personal performance and the profitability of the strategies developed. The very top researchers can hit seven figures.

Quantitative Analysts

A Quant Analyst (QA) differs from a Quant Researcher (QR). There is no specific definition of either; it depends on the firms, teams, etc. While there can be a lot of crossover in skill sets, there are differences in their work.

QR's tend to be focused on fully systematic trading and create buy or sell signals based on their pattern analysis. A QA, in contrast, is focused on financial modelling, risk management and trade support.

A QA is commonly found on the sell side at banks, in discretionary trading shops, or macro-style trading groups. QR's can be found on electronic and central risk desks on the sell side or within quant trading funds.

Responsibilities : Perform tasks like financial modelling, model validation, pricing, risk assessment, and trade support. They can build models around the yield curve so a PM can use them to make decisions. Or pricing of options for traders.
Required Skills: Mathematical modelling, deep financial acumen, data analysis, risk management. Python and C++ are needed, less so Excel and VBA these days.
Organisational Fit: Common in larger organisations and research groups, but also in pods under discretionary or macro PMs. They are also standard on the sell side.
Collaboration: Provide a critical link between models and traders.
Sub-Categories: Beyond model validation and risk assessment, Quantitative Analysts can cover market microstructure analysis and liquidity modelling, sometimes called Algo Quants.
Challenges: QAs must navigate the complexities of financial modelling against the backdrop of ever-changing market conditions. They also face the technical challenge of ensuring the accuracy and robustness of risk models.
Evolution: The role is evolving to require a deeper understanding of machine learning and data science to create more sophisticated models for understanding market behaviour.
Career Path: Analyst -> Senior Analyst -> Quant Researcher or Risk Manager, potentially PM.
Typical Base Salary: £60,000 - £150,000 in the UK; in the US, $90,000 - $200,000
Bonus Potential: Bonuses may range from 20% to 100% or more of the base salary, with higher bonuses typically awarded to those on the buy side or with a significant impact on profits.

Portfolio Managers

Responsibilities: Decision-makers responsible for strategy implementation, market analysis, risk assessment, and overall portfolio performance.
Required Skills: Strong financial acumen, risk management, and managerial skills. The mindset to handle risk. Proven ability to generate alpha.
Organisational Fit: Mainly found in pod structures within multi-strategy funds.
Collaboration: Work closely with everyone in the pod for effective strategy implementation.
Sub-Categories: Portfolio Managers are now often specialised by trading frequency (HFT, MFT) or strategy type (e.g., arbitrage, market-making). Some even focus exclusively on overseeing machine learning-based strategies.
Challenges: PMs deal with the pressure of making real-time decisions that can have significant financial consequences. They must balance the search for alpha with risk management, often within the constraints of a rapidly changing global market landscape.
Evolution: The role is becoming more data-driven, requiring a deeper understanding of data science and programming to make informed decisions.
Career Path: Sub PM -> PM -> Snr PM -> Head of Trading Desk or CIO.
Typical Base Salary: £100,000 - £250,000+ in the UK; in the US, $120,000 - $250,000+
Bonus Potential: Extremely variable; can exceed several times the base salary. Good performers may earn millions in bonuses, with the elite, the very top, in the tens of millions. Typically, the payouts are 15-20% for hedge fund managers, up to a max of 25%. While prop quant traders can expect payouts of 35% to 50%.

Quantitative Developers

Responsibilities: Their day involves coding, debugging, and deploying algorithms that execute trades based on models developed by researchers. They can be responsible for bringing and distributing data among the team. They can also include building risk reporting tools, trading tools, or dashboards updating a PM. A quant dev could also build research and machine learning platforms, back testers, simulators, etc.
Required Skills: Software engineering, programming in Python, then one of C++ or Java, and depending on the role, usually either KDB+/q or SQL. All with a familiarity with financial markets. Big data technologies such as Kafka, Spark, Hadoop, etc. Knowledge of Python libraries that a QR uses is good.
Organisational Fit: Present in research groups and pod structures, working closely with researchers or under a Portfolio Manager.
Collaboration: Liaise with Software Engineers for seamless algorithm integration. Work with researchers to understand their needs and deliver data.
Sub-Categories: Quant Devs can specialise in many ways. Some examples focus on execution algorithm development, specialising in creating and refining the algorithms that directly interact with the market. Or concentrate on research infrastructure by building the tools and systems that facilitate the development and testing of trading models.
Evolution: Quant Devs need to adapt to the use of big technologies, the need for speed, and machine learning integrations. The growing trend is that they need to be more cross-disciplinary, merging software engineering with quantitative finance to meet the demands of modern trading platforms.
Challenges: For Quant Developers, challenges include writing efficient code that can process large volumes of data with minimal latency. They must constantly refine algorithms to adapt to market conditions and maintain an edge in execution speed.
Career Path: Quant Developer -> Senior Quant Dev -> Lead Quant Dev or transition to Quant Analyst/Researcher.
Typical Base Salary: £60,000 - £150,000+ in the UK; in the US, $90,000 - $175,000+
Bonus Potential: Generally, between 10% - 50% or more of base salary, but higher for developers in more profitable trading groups.

Software Engineers

Software Engineers are the hardest to define as it's a blanket job title covering many functions. Each will specialise and work on different projects in different parts of the trading process. Some can be front office, and some are back office or technology. Some focus on risk management, some on the data infrastructure, and others on market connectivity and speed. Software Engineers can be Quant Developers, and Quant Developers can be Software Engineers in their responsibilities.

Trying to cover all this:

Responsibilities: Software Engineers focus on developing the software components that make up the trading system. It can include creating algorithms and user interfaces to ensure seamless integration with existing systems. Their responsibilities can also be exchange connectivity, market data feeds, and order management systems if in a more short-term trading shop. Or they can be focused on risk management software or data infrastructure.
Required Skills: Strong programming skills are essential in languages like C++, Java, or Python. The skill set can vary depending on the trading frequency of the firm. For example, in High-Frequency Trading (HFT) environments, there's a greater emphasis on low-latency C++, STL, parallel programming, and even specialised skills like FPGA programming or ASIC cards. Good knowledge of network protocols, like TCP/IP, is valuable.
Organisational Fit: Found in both research groups and pod structures, they often collaborate closely with quantitative developers and quants to tailor the software to trading strategies. Sometimes, they can be classed as Front Office, alongside Researchers and Traders, or sitting within technology supporting the front office.
Collaboration: They work with PMs and researchers to understand their needs, including quantitative developers for algorithm integration and infrastructure developers for system compatibility.
Sub-Categories: The role varies significantly depending on a firm's trading frequency and set-up. HFT firms emphasise low-latency programming and parallel computing, while MFT firms may focus more on algorithmic complexity.
Challenges: Developing and maintaining robust trading platforms that can handle the high-speed requirements of quant trading. They must ensure system integrity and prevent downtime, which can be costly in high-stakes trading environments.
Evolution: Software Engineers are increasingly critical for quant trading and being seen as such. Many HFTs prioritise them over quants as they are locked in a technological battle. Funds have realised their importance, no longer labelling them as "back-office". As the world drives more towards AI-based systems, developers can find themselves building machine learning platforms. The rise of generative AI like ChatGPT will be an exciting evolution as it can perform more coding, increasing developers' productivity.
Career Path: Software Engineer -> Senior Software Engineer -> Lead Software Engineer or transition to a specialised role like Quant Developer.
Typical Base Salary: £60,000 - £150,000+ in the UK; in the US, $70,000 - $200,000++
Bonus Potential: Typically ranges from 10% - 50% or more of the base salary, with potential for more at firms where technology is a critical competitive advantage.

Risk Managers

Responsibilities: Focus on identifying, assessing, and mitigating risks through constant monitoring and stress-testing of trading strategies.
Required Skills: Risk modelling, portfolio construction and optimisation, market risk expertise, credit risk skills, compliance knowledge, strong communication skills, financial acumen and in-depth market knowledge. Some programming experience in R, Python, SAS, and SQL for quant risk roles.
Organisational Fit: Found in both research groups and pod structures, often as a separate unit.
Collaboration: Work with Portfolio Managers and researchers to assess risks.
Sub-Categories: Risk Managers are evolving into specialists in regulatory compliance, operational risk, and even cybersecurity, given the increasing threats to trading infrastructure.
Challenges: Risk Managers are on the front line of defence against financial loss. They must be adept at predicting and mitigating potential market risks and ensuring compliance with an increasingly complex regulatory landscape.
Evolution : The role now often requires a blend of traditional risk management skills and newer competencies like data science and programming.
Career Path: Risk Analyst -> Risk Manager -> Chief Risk Officer (CRO).
Typical Base Salary: £60,000 - £130,000 in the UK; in the US, $95,000 - $180,000
Bonus Potential: Generally, up to 40% - 60% of base salary, with variation based on the impact of the risk management strategies on saving the firm from potential losses.

Data Scientists

Responsibilities: Specialised in handling and interpreting vast datasets, they clean, prepare, and sometimes source the data for researchers.
Required Skills: Strong programming in Python, data manipulation skills in SQL, machine learning, and statistical analysis abilities. In-depth knowledge of machine learning algorithms, such as Deep Learning and Neural Nets, Support Vector Machines, ensemble methods like Random Forests and Gradient Boosting Machines, and reinforcement learning algorithms. Knowledge of pandas, NumPy, SciPy, TensorFlow, Keras, PyTorch, NLTK, and big data like Spark, Hadoop, or Hive.
Organisational Fit: Usually, part of research groups but can also be in larger, more diversified pod structures.
Collaboration: Work closely with quantitative researchers for data provision and analysis.
Sub-Categories: Data Scientists in quant trading specialise in alternative data analysis, sentiment analysis, or behavioural economics. Data Scientists can also be found in fundamental pods to offer data analytical insight into the trading the discretionary PM does.
Challenges: The primary challenge for Data Scientists in quant trading is managing and interpreting massive, often unstructured data sets. They must extract meaningful insights for strategy development while avoiding the pitfalls of spurious correlations.
Evolution: As the volume and variety of tradable data grow, Data Scientists must be proficient in advanced machine learning algorithms and data engineering skills. Some must have in-depth financial market knowledge, especially those close to the alpha generation. In contrast, others need business acumen to apply their skills across a firm.
Career Path: Data Analyst -> Data Scientist -> Lead Data Scientist or transition to Quant Researcher.
Typical Base Salary: £60,000 - £130,000+ in the UK; in the US, $90,000 - $200,000+
Bonus Potential: Bonuses for Data Scientists can vary widely but often fall in the 20% - 100% range, depending on the direct impact of their work on trading gains.

Execution Traders

Responsibilities: Execute the trades, handling orders that require manual intervention.
Required Skills: Quick decision-making, attention to detail, and understanding of market microstructure.
Organisational Fit: Less common in pod structures, where a Portfolio Manager is responsible for the trading execution. Typically, research-style funds will have an execution team to monitor the trading and handle the execution.
Collaboration: Work closely with Portfolio Managers or Heads of the desk, sometimes researchers.
Sub-Categories: With the rise of automated trading, Execution Traders oversee algorithmic strategies and intervene only when manual oversight is required. Some can be specialised with the quant skills to alter algos when needed.
Challenges : With more trading being automated, Execution Traders' roles have shifted towards monitoring these systems, requiring them to understand algorithmic trading and the ability to intervene effectively when automated processes falter.
Evolution: The role is becoming more analytical, requiring a deeper understanding of algorithms and even some coding skills to tweak trading algorithms.
Career Path: Trader -> Senior Trader -> Head of Execution
Typical Base Salary: £50,000 - £100,000 in the UK; in the US, $75,000 - $150,000
Bonus Potential: Bonuses can range widely from 10% to 50% of base salary, reflecting the importance and performance of their executed trades.

Conclusion

In the fast-paced world of quant trading, careers are as diverse as they are rewarding, with each role-playing a pivotal part in the tapestry of the financial market. Quantitative Researchers, Analysts, and Portfolio Managers forge the strategies that drive the industry forward, while Quantitative Developers and Software Engineers create the technological backbone that enables success. Risk Managers guard against uncertainty, and Data Scientists transform data into opportunity.

As this field grows ever more complex and intertwined with technological advancements and big data, the demand for sharp, innovative minds is unrelenting. The path for aspirants is clear: cultivate a deep, versatile skill set, remain agile, and you could be shaping the future of finance. This is a world where excellence and creativity are not just welcomed but required, offering high stakes and high rewards for those who rise to the challenge.

ChatGPT: Hedge Fund’s New Edge

Sat, 11 Nov 2023 12:00:49 GMT

ChatGPT: Hedge Fund’s New Edge

Artificial Intelligence (AI) is revolutionising industries, from healthcare to automotive. ChatGPT, a standout in AI, is known for its task automation and human-like text generation.

In finance, hedge funds are particularly keen on leveraging this technology. This article delves into how hedge funds are adopting and adapting ChatGPT to gain a competitive edge while highlighting the more cautious approach banks take.

The Hedge Fund Landscape

Adoption Rates

The finance sector has always been a hotbed for innovation, and hedge funds are no exception. According to a recent survey conducted by BNP Paribas' Capital Introduction team, 44% of money managers use ChatGPT professionally, indicating a significant shift towards AI adoption in the sector.

Another 10% are considering its use, showing strong interest within the community. The respondents, hailing from firms with a combined AUM of $250.5 billion, are primarily based in America, followed by EMEA. Interestingly, most managers using ChatGPT come from fundamental firms, while quant firms prefer their machine-learning programs.

This high adoption rate among hedge funds is not merely a trend but a reflection of the sector's constant pursuit of efficiency and competitive advantage. While the BNP Paribas survey provides a snapshot, it's worth noting that the landscape is dynamic. Hedge funds are not just adopting ChatGPT; they are actively experimenting with it to find new use cases that can add value to their operations.

Use Cases

ChatGPT adoption in hedge funds is about solving real-world challenges and enhancing various aspects of the business.

According to the BNP Paribas survey, those using ChatGPT professionally use it in many areas:

Document Summarisation: 36% use it to summarise documents such as regulatory findings, broker reports, and academic papers.
Marketing: 35% employ ChatGPT in their marketing efforts.
Coding: 6% use it for coding tasks.
Email Drafting: 6% utilise it for drafting emails.
Legal Analysis: 6% use it for preliminary analysis of legal documents.

The 6% for coding may seem low, but it's worth noting that most respondents were from fundamentally driven hedge funds.

Leading hedge funds like Man Group and Citadel are at the forefront of this technological revolution. Man Group employs ChatGPT to review stacks of academic papers and for the preliminary analysis of data sets. Bloomberg reported Citadel was negotiating an enterprise-wide license for ChatGPT, seeing potential in tasks like translating code between languages. Another quant fund, Campbell & Co, uses ChatGPT to summarise internal research and write boilerplate code, demonstrating the technology's versatility.

Coding

Coding is an obvious use case. ChatGPT has already had and will continue to affect coding significantly. Ignoring this trend would be a mistake.

Over 40% of the code on Github is already AI-generated. In a controlled experiment, a group of coders using GitHub Copilot completed tasks 55% faster than those without. Copilot is a specialised version of GPT-3 trained on gigabytes of software code to autocomplete instructions, generate entire functions, and automate other parts of writing source code.

Code Generation: ChatGPT excels in generating initial code drafts, serving as a valuable assistant to skilled programmers. While it can't yet produce fully functional code for a non-specialist, it's a powerful tool for getting the first draft down.

Bug Identification: One of the most time-consuming tasks for developers is debugging. ChatGPT can swiftly identify simple bugs like extra spaces or missing semicolons, freeing developers to tackle more complex, structural issues.

Error Understanding: Developers often venture outside their areas of expertise, whether it's a new programming language, hardware or an unfamiliar API. ChatGPT can demystify errors, offering guidance on resolving them without needing external help, thus streamlining the development process.

Documentation : Inadequate documentation can be a significant bottleneck, especially when developers are in a flow state. ChatGPT can auto-generate documentation as code is written, mitigating future headaches and ensuring smoother handoffs between team members.

Collaborative Intelligence: One super-size fund is taking ChatGPT's capabilities further. They're using it to make intra-team recommendations, effectively learning from each coder's successes and challenges to offer timely advice to others facing similar issues.

Well-Being

Interestingly, a side effect of Copilot, as reported in the survey, was the effect on coders' well-being. According to the study, 60–75% of developers reported a heightened sense of job satisfaction, reduced levels of frustration, and an increased ability to concentrate on tasks that they find genuinely fulfilling when using GitHub Copilot.

Another intriguing facet of GitHub Copilot's impact is its role in mental energy conservation—a critical factor in a developer's daily grind. The research indicates that 73% of developers found it easier to maintain their workflow, commonly called 'staying in the flow,' when using this AI-powered tool. Even more striking is that 87% of developers noted that GitHub Copilot significantly reduced mental exertion during monotonous, repetitive coding tasks.

These are encouraging results in an era where well-being and work/life balance are at the forefront of most minds.

Automating Routine Tasks

As reported by Bloomberg, hedge funds like Man Group and Citadel are looking to use ChatGPT to handle routine and mundane tasks, the "grunt work". This could include data scraping, preliminary data analysis, and even initial stages of research. By automating these tasks, hedge funds free portfolio managers and researchers to focus on more strategic activities.

Marketing

According to a BNP Paribas survey, 70% of hedge fund managers who have adopted ChatGPT use it for marketing purposes. They're leveraging the technology to generate persuasive text for investor presentations, newsletters, and social media campaigns. This becomes particularly advantageous for small to mid-sized funds that may not have the luxury of a dedicated marketing or research team, effectively levelling the playing field with their larger counterparts.

Sentiment Analysis

Sentiment analysis is another promising use case. ChatGPT can process vast amounts of news articles, social media posts, and financial reports to gauge market sentiment. Then, trade on market sentiment or news events. ChatGPT is much better than previous natural language processing models (NLP).

A recent research paper proves “ that GPT models deliver a considerable improvement in classification performance over other commonly used methods. We then demonstrate how the GPT-4 model can explain its classifications that are on par with human reasoning."

MAN AHL recently backed this up, publishing results where ChatGPT outperformed sentiment-based word counting. The article also points out a significant cause for difference. Classic sentiment models are trained under supervision to label words. ChatGPT shifts to a generative model using deep learning neural networks, allowing a deeper understanding of the text. It is better able to appreciate other words in the sentence and the context of a sentence to glean sentiment better.

However, a limitation is that ChatGPT is trained on a broad range of internet data. Its performance could be improved if trained in a specific niche manner—for example, training on the fed meetings or considering only specific financial news. Additionally, the AHL article points to the importance of prompts, as the prompts written affected the outcome.

Although… if everyone starts using ChatGPT for sentiment analysis, does that mean the alpha will be arbitraged away? Could sentiment analysis become a crowded trade like index rebalance?

Risk Assessment

ChatGPT can be programmed to monitor multiple data sources continuously for potential market risks and opportunities. It can analyse market news, economic indicators, and social media sentiment to provide real-time alerts. This enables portfolio managers to make informed decisions quickly, a crucial advantage in volatile markets.

Back-Testing Strategies

ChatGPT can also be invaluable in the back-testing phase of strategy development. Automating the back-test coding can significantly speed up the validation process for new trading algorithms. This allows quants to iterate through potential strategies more efficiently, discarding the ineffective ones and refining the promising ones.

Strategy Creation

It cannot create an entire strategy. However, ChatGPT can assist in the initial stages of strategy creation. By analysing vast datasets, it can identify potential patterns or anomalies that human analysts might overlook. This can serve as the foundation for new trading strategies, which can be further refined and back-tested.

By embracing ChatGPT, hedge funds are not just streamlining their operations but are also opening up new avenues for innovation and efficiency. As the technology evolves, we'll see even more creative and impactful use cases emerge in this sector.

The Broader Context

The adoption of ChatGPT in hedge funds is part of a larger technological wave sweeping the financial sector. While hedge funds quickly experiment with ChatGPT, banks like Goldman Sachs and JPMorgan are more cautious, citing regulatory issues.

However, banks aren't entirely avoiding generative AI. Goldman Sachs uses generative AI tools to assist its software developers in writing and testing code. It has initiated a "proof of concept" using generative AI to assist in coding tasks. While the bank aims to make human coders "more productive" rather than replace them, it's a sign that banks are open to controlled experimentation with this technology.

As reported by eFinancial Careers, Vacslav Glukhov, head of EMEA quant research for e-trading at JPMorgan, discusses the potential impact of ChatGPT on various roles within banks. He believes that ChatGPT will mostly automate jobs that involve commentary on figures and rehashing existing ideas. He suggests that while many jobs could be automated, roles that require human intelligence and the ability to predict unusual situations will still be crucial.

David Siegel of hedge fund Two Sigma and Marty Chavez of investment management firm Sixth Street offer a sceptical view. Siegel mentions that "AI has been having an impact for decades, this stuff isn't brand new," and that "people are reading too much into it". The top quant hedge funds have used machine learning and artificial intelligence for many years.

Chavez adds that ChatGPT and similar technologies will never achieve the "holy grail" of predicting the stock market because their strengths lie in analysing stable datasets, unlike the stock market. Additionally, Vacslav Glukhov emphasises that while ChatGPT can handle routine tasks, it can't replace human creativity and originality.

AI has made significant strides in various sectors, but its effectiveness in predicting stock market movements remains complex and unresolved. The "Holy Grail" for financial markets is an AI that can predict stock prices more accurately than humans, a challenge that remains unmet.

Future Trends

As we've seen, the adoption of ChatGPT in hedge funds is already quite extensive, but what does the future hold?

Based on current trends and the evolving needs of the industry, here are some directions we can expect:

As the technology matures, we can anticipate more robust compliance features that make it easier for hedge funds to navigate the regulatory landscape. This could make banks more comfortable adopting ChatGPT, as seen in their cautious approach. Glukhov is less convinced that ChatGPT will replace humans in complex risk and compliance roles. He argues that the technology is not numerically oriented enough to replace model validation quants.

The adoption of ChatGPT is poised to have far-reaching implications. Over the next 12 months, the technology could shrink workforces and disrupt the quant and coding market, lowering the bar for smaller funds to enter the space.

Human + Machine Collaboration

The conversation around ChatGPT often centres on its capabilities for task automation and efficiency. However, its more nuanced role is in augmenting human capabilities. Marco Argenti from Goldman Sachs notes that the technology's goal is to make human coders "more productive," not to replace them.

The real potential of ChatGPT may not lie solely in its standalone capabilities but in how it can be guided by human insight. Consider this: What if the key advantage is not what ChatGPT can do autonomously but what it can achieve when directed by thoughtful human questioning?

Ray Dalio's perspective from "Principles: Life and Work" is apt: “Smart people are the ones who ask the most thoughtful questions, as opposed to thinking they have all the answers. Great questions are a much better indicator of future success than great answers.”

In this context, you don't need to be an expert coder or data scientist to extract valuable insights. If you know the right questions to ask, ChatGPT can provide the answers. This is less about automation and more about broadening the scope of who can participate in complex decision-making.

This form of human-machine collaboration could be a significant asset. It's not merely about speed or efficiency; it's about enabling more people to engage in tasks that previously required specialised skills. The edge may go to those who can ask the right questions and, with tools like ChatGPT, find the answers they need.

Expanding Use Cases

The BNP Paribas survey indicates that hedge funds are looking to expand the use of ChatGPT in areas like marketing and summarising documents. Given the technology's versatility, we can see it applied in even more innovative ways, such as advanced data analytics or predictive modelling.

Integration with Other Technologies

ChatGPT will integrate more with other AI and machine learning technologies, creating more comprehensive solutions. For example, combining ChatGPT's natural language capabilities with predictive analytics tools could offer more nuanced trading strategies.

Democratisation of Technology

As AI tools become more accessible and affordable, smaller hedge funds may adopt ChatGPT to level the playing field with larger competitors. This could be a game-changer in an industry where scale often dictates success.

Talent Dynamics

Using advanced technologies like ChatGPT could shift the talent dynamics in the industry. While the need for human expertise will never be entirely replaced, the roles and skills required may evolve, placing a higher premium on adaptability and tech-savviness.

Moreover, adopting ChatGPT and similar technologies is a strategic move to attract top talent. In an industry where the war for talent is fierce, especially among quantitative researchers and portfolio managers, cutting-edge technology can be a differentiator. It signals prospective employees that the firm is forward-thinking and open to leveraging technology for better decision-making and operational efficiency.

Based on the GitHub survey and the massive reduction in work-related stress, it is plausible to see developers, coders, and quant move towards platforms and work environments that make their jobs easier. It will get to a point where, if you aren't running Copilot or similar, developers and quant won't join.

Plus, it removes the need to hire more staff if you can turn your team into 10x coders!

Conclusion

The financial sector stands at a crossroads with the advent of ChatGPT. Hedge funds, ever agile and innovative, are capitalising on this technology to sharpen their competitive edge. In contrast, banks are treading cautiously, weighed down by regulatory considerations.

To be clear, ChatGPT isn’t going to create an edge directly, a.k .a. alpha. (Unless you’re sentiment-based). It will create an edge away from pure alpha. It will give you an edge in helping you raise assets better than the competition with more persuasive marketing material. An edge by allowing coders to code quicker. Or an edge by improving staff well-being, productivity and turnover, attracting more coders. Or an edge by iterating quicker and homing in on the right solution quicker than your competitors.

For hedge funds, the future with ChatGPT looks promising. Those who adapt and evolve with this technology stand to gain significantly, not just in operational efficiency but also in talent acquisition. In a fiercely competitive talent market, not leveraging ChatGPT could become a deal-breaker for prospective employees.

Moreover, the technology's potential to boost staff productivity and well-being could soon make it indispensable in the workplace.

As we peer into the future, one thing is unmistakable: ChatGPT and similar large language models will redefine the contours of the financial industry. Firms that successfully navigate this intricate landscape will set the pace in the coming years.

In a zero-sum game like trading, every edge counts. ChatGPT offers that edge—be it in coding, analysis, or staff productivity. If you're not already exploring this technology, you're risking more than just falling behind—you're risking obsolescence.

So, the question remains: Will your fund lead the charge in embracing ChatGPT, or will it watch from the sidelines?

References

"The Rise of AI Assistants, Hedge Fund Managers, and ChatGPT" - BNP Paribas
https://globalmarkets.cib.bnpparibas/the-rise-of-ai-assistants-hedge-fund-managers-and-chatgpt/
"Goldman Is Reportedly Using AI to Write Code as Banks Crack Down on ChatGPT Use" - Forbes
https://www.forbes.com/sites/siladityaray/2023/03/22/goldman-is-reportedly-using-ai-to-write-code-as-banks-crack-down-on-chatgpt-use/
"Hedge Funds Are Deploying ChatGPT to Handle All the Grunt Work" - Bloomberg
https://www.bloomberg.com/news/articles/2023-05-31/hedge-funds-are-deploying-chatgpt-to-handle-all-the-grunt-work
"JPMorgan's Push Into Finance AI Has Wall Street Rushing to Catch Up" - Bloomberg
https://www.bloomberg.com/news/features/2023-05-31/jpmorgan-s-push-into-finance-ai-has-wall-street-rushing-to-catch-up
"Bridgewater's Greg Jensen Explains How the World's Biggest Hedge Fund Is Investing in AI" - Bloomberg
https://www.bloomberg.com/news/articles/2023-07-03/bridgewater-s-greg-jensen-explains-how-the-world-s-biggest-hedge-fund-is-investing-in-ai
"AI Can Write, But Is It Any Good at Picking Stocks? QuickTake" - Bloomberg
https://www.bloomberg.com/news/articles/2023-07-17/ai-can-write-but-is-it-any-good-at-picking-stocks-quicktake?leadSource=uverify%20wall
"Hedge Fund Survey Reveals How Money Managers Use ChatGPT" - Bloomberg

https://www.bloomberg.com/news/articles/2023-07-31/hedge-fund-survey-reveals-how-money-managers-use--chatgpt

"ChatGPT, Coding, and the Software Crisis" - Wired
https://www.wired.com/story/chatgpt-coding-software-crisis/#intcid=_wired-verso-hp-trending_985a6d83-30d1-43af-98d5-de57fa80078d_popular4-1
"ChatGPT and Its Impact on Jobs in Banks" - eFinancialCareers

https://www.efinancialcareers.co.uk/news/2023/03/chat-gpt-jobs-banks

"Can ChatGPT Beat Word-Counting Humans?" - Man Institute

https://www.man.com/maninstitute/can-chatgpt-beat-word-counting-humans

"Understanding and Improving US Bank Sentiment Analysis with ChatGPT" - SSRN

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4399406

"GitHub Copilot is Generally Available to All Developers" - GitHub Blog

https://github.blog/2022-06-21-github-copilot-is-generally-available-to-all-developers/

"Research: Quantifying GitHub Copilot's Impact on Developer Productivity and Happiness" - GitHub Blog

https://github.blog/2022-09-07-research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/

"How to Backtest a Trading Strategy Using ChatGPT" - Quantified Strategies

https://www.quantifiedstrategies.com/how-to-backtest-a-trading-strategy-using-chatgpt/

A Guide to Quant Portfolio Manager Interviews

Sat, 11 Nov 2023 11:49:14 GMT

Navigating the Maze: A Guide to Quant Portfolio Manager Interviews

Quantitative trading can be compared to navigating a complex maze, where the pathways shift unpredictably, and participants must adapt to these changes. The job interview is the first obstacle to overcome in this stimulating field, much like the entrance to a maze.

Job interviews resemble intricate puzzles for Portfolio Managers (PMs) in the quant trading sector. The objective isn't merely to demonstrate technical prowess and industry knowledge but also to illustrate adaptability, resilience, and a deep comprehension of the field's subtleties.

Having guided numerous PMs through this maze-like process as a recruiter in this specialised sector, I can help uncover the keys to success in these interviews and secure a rewarding role in this dynamic industry.

In this comprehensive guide, we will delve into the following crucial topics:

Understanding the Role and Expectations
Approaching Technical Questions and Coding Tests
Demonstrating Your Track Record
Grappling with Intellectual Property Concerns
Highlighting Soft Skills
Assessing Cultural and Trading Style Compatibility
Justifying Past Job Moves and Tenures
Questions a PM should ask
Example questions a PM might face

Understanding the Role and Expectations

The role of a Quantitative Portfolio Manager in the quant trading space is a blend of multiple disciplines - part mathematician, part statistician, part coder, and fully immersed in the world of financial markets. A solid foundation in quantitative techniques, an understanding of financial markets, and proficiency in programming are vital. As a PM, your ability to leverage these skills to develop and implement advanced trading strategies sets the baseline for your potential success in this role.

However, technical knowledge alone isn't enough. The finance industry, particularly quant trading, is ever-evolving. The advent of new technologies and methodologies means that staying updated isn't just an option; it's an absolute necessity. Whether it's the application of machine learning in trading or developing new algorithmic strategies, the ability to incorporate these advancements in your work can significantly differentiate you from others.

But it's not just about hard skills and staying abreast of industry trends. As a PM, you're expected to possess critical soft skills like clear communication, adaptability, leadership, and the ability to handle high-pressure situations. These skills are paramount in steering your team towards success, making quick yet informed decisions, and effectively communicating complex quantitative ideas to various stakeholders.

Depending on the group and role you aim for, these factors will change in weight. A silo PM on a multi-manager platform doesn’t need to worry much about communication skills. While a PM expected to lead a team or be part of the fundraising efforts will be judged on their communication skills.

Navigating Technical Questions and Coding Tests

Within the realm of quantitative trading, the expectation for portfolio managers is to hold a robust foundation in mathematics, statistics, programming, and finance. The industry demands high technical proficiency, with interview questions often delving deep into complex mathematical and statistical concepts and programming languages like Python or C++.

Each firm carries its unique approach to evaluating PMs and researchers. Researchers undergo rigorous testing, with online tests, take-home assignments, on-the-spot coding and more.

PMs face a more straightforward process, with rarely any take-home assignments or online coding challenges, mainly owing to their successful track records. This track record is evidence of their fulfilment of the basic requirements of a Quant PM.

However, PMs are not exempt from having their technical skills quizzed during interviews. This evaluation helps determine their potential success on the client's platform and the level of support they might need - both vital factors in assessing a PM's potential as a successful hire.

Practising coding exercises and technical problem-solving can help refine skills and bolster confidence. Being well-prepared for technical questions and coding tests demonstrates proficiency and readiness to meet the industry's demands.

Anyone who does not have a track record should 100% do practice tests. PMs are the exception to the rule. However, PMs must expect deep queries into their technical abilities.

I advise PMs to review and brush up on the technical side. Someone else may have built an optimiser or back tester years ago. At a minimum, talk confidently about any programming done in the build-out of your system and how you could do it again if needed.

There is typically one of two reactions (guess which is more popular?)

Either you go the way of the athlete, believing practice makes perfect, investing time in brushing up on technical skills.
Or, who cares about my coding if I’m generating $30m a year…

Hard to argue with that…

Demonstrating Your Track Record

During the interview process, an inevitable hurdle that PMs encounter is demonstrating their performance record and adeptness at handling various market conditions. Hiring firms typically seek a formidable track record of performance and an understanding of manoeuvring through ever-changing market conditions.

Your track record is one of the most intensely scrutinised aspects by potential employers during the interview process. A robust, verifiable track record is undeniable evidence that cuts through the ambiguity, affirming your skills and expertise. It is a tangible demonstration of your ability to conceive, design, and execute successful trading strategies.

However, substantiating your track record is more complex. Non-disclosure agreements (NDA) and proprietary knowledge restrictions mean you may have difficulty articulating a comprehensive account of your past work. This is where your ability in effective communication becomes pivotal. Without disclosing proprietary information, you would need to communicate your work's nature carefully, your role in the team, the strategies you crafted, and their respective outcomes.

Navigating Intellectual Property and Legal Challenges

One particularly thorny issue is negotiating intellectual property (IP) matters. Firms in the quant trading industry heavily rely on proprietary strategies and methodologies, creating a tricky dynamic for candidates. They need to communicate their skills and experiences effectively, yet they must do so without violating confidentiality clauses or revealing proprietary details.

Candidates must strike the right balance in sharing information during interviews. They should draw attention to their relevant skills and experiences yet remain mindful of the boundaries of confidentiality. This often means discussing experiences in more general terms and not disclosing specific details that could infringe upon intellectual property agreements.

In scenarios where you own the intellectual property, you have more freedom to discuss your strategies in detail. However, it would be best to remain cautious about revealing too much. Regrettably, some entities may exploit the interview process to 'fish' for strategic insights. To navigate this, share enough information to generate interest, but avoid giving away all your trade secrets. Additionally, ask which firms are typically fishers - ask friends, your network and experienced recruiters.

The situation is more complex if you don't possess the IP or have strict NDA's. During interviews, you might be asked about your models and strategies. This requires careful navigation. You should illustrate your experience and skills without disclosing specific proprietary information. Instead, focus on discussing your role, the skills you employed, and the outcomes you achieved. If replicating your previous strategies isn't permissible, shift the focus onto the new ideas you can bring.

Another challenge is the non-compete clauses often embedded in employment contracts within our industry. It's essential to understand the nuances of these clauses and ensure that your career moves do not violate them. While discussing new roles with potential employers, being transparent about any existing non-compete constraints is crucial.

Understanding legal and ethical constraints can sometimes feel like navigating a minefield, especially if you're in transition and eager to share your expertise. It's strongly advisable to seek professional legal counsel. An experienced attorney can guide how to discuss your experience within the bounds of your agreements without selling yourself short.

The Importance of Soft Skills for Portfolio Managers

In the quant trading space, technical prowess is undeniably crucial. But it's only one part of the equation. Soft skills - the interpersonal attributes that enable you to interact effectively and harmoniously with others - are significant.

As a Portfolio Manager, your role extends beyond developing trading strategies and managing portfolios. You often lead teams, communicate complex ideas, make crucial decisions under pressure, and constantly adapt to market changes.

Communication is a crucial soft skill for a PM. You need to articulate complex quantitative concepts and strategies to stakeholders who may not have a quantitative background. During your interviews, demonstrate your ability to explain complex ideas clearly and concisely.

Leadership is critical, especially for PMs managing a team of quants. Talk about your leadership style, how you motivate and guide your team, handle conflicts, and foster a collaborative environment. If you have examples of when your leadership contributed to the successful execution of a strategy or project, be sure to share those.

Adaptability and adjusting to new situations and changes are crucial in a rapidly evolving industry like quant trading. Demonstrate your adaptability by discussing instances where you had to adapt your strategies to market changes, new technologies, or regulatory shifts.

Handling pressure is an inherent part of a PM's role. The financial implications of your decisions and the volatile nature of markets can create high-stress situations. Discuss how you manage stress, make sound decisions under pressure, and maintain a level-headed approach even in challenging circumstances.

Evaluating Cultural Fit

Every quant trading firm boasts unique culture and trading style. Your success and job satisfaction as a Portfolio Manager hinges on aligning these factors with your values and trading philosophy.

A firm's culture combines values, working environment, and business practices. During interviews, assess this by asking about the work atmosphere, work-life balance, and the firm's handling of successes and failures. Talking with current or past employees can also provide valuable insights.

Cultural fit also delves into behavioural aspects. Prospective employers focus on traits like resilience, openness to feedback, and flexibility, which are vital in a fast-paced environment like quant trading. Firms also value candidates sharing their approach to risk, problem-solving, and ethics, which are crucial to shaping the firm's trading strategies.

Interviewers may pose behavioural questions or hypothetical situations to understand your handling of challenges and decision-making. They might explore your past experiences, looking for insights into collaboration, leadership, and problem-solving. This, combined with your company research, can help show your alignment.

Trading Style Compatibility

Trading style alignment with your expertise and the broader business context is also vital. Companies vary significantly in their trading approaches, from high-frequency firms to long-term trend-following funds. Understand the firm's trading style and ensure it aligns with your skills. Interviews also offer the chance to ask thoughtful questions about their trading approach.

Remember, a clash of trading styles can lead to conflicts, misalignment, and failure. For example, leading the systematic trading business in a traditionally discretionary firm can bring resistance from stakeholders. Likewise, attempting high-frequency trading in a firm lacking the necessary technology can cause friction.

When exploring new opportunities, scrutinise all aspects that could affect your success, and discuss potential obstacles openly.

Navigating and Addressing Career Transitions

A vital aspect of any Portfolio Manager's interview process is the ability to communicate and justify past career moves effectively. Each transition forms a distinct chapter in your professional narrative, and how you present these shifts can significantly influence potential employers' perceptions of you.

In your interview, please be ready to discuss your job history and career trajectory, including the reasons behind leaving previous positions or short tenures at certain firms. It's essential to convey these transitions candidly and positively, emphasising what each role taught you and how it contributed to your professional development.

Frequent job changes can often label candidates as 'job hoppers', potentially raising questions about their commitment and loyalty. PMs with several short-term positions must be ready to provide compelling justifications for these brief stints. Often, it's not the short tenure but the lack of a clear and sensible explanation that concerns potential employers.

Negative experiences can happen. Whether these experiences stem from clashes with management, poor cultural fit, or underperformance, it's vital to navigate these discussions tactfully. Strike a balance between honesty and presenting yourself positively, avoiding blame-shifting or negativity.

Candidates should also discuss any steps they have taken to address or learn from these experiences. This showcases your resilience and adaptability and conveys your willingness to grow from past experiences.

The Two-Way Interview: Evaluating Your Potential Employer

While preparing for a Portfolio Manager interview, it's easy to forget that the process isn't one-sided. As much as the firm is evaluating your candidacy, you, too, should assess the potential employer to ensure it's the right fit for your career goals, working style, and trading philosophy. The significance of this mutual assessment can't be overstated in the quant trading world - understanding whether you can succeed and thrive in a prospective role is of utmost importance.

So, how do you flip the script and interview your potential employer effectively? Here are some strategies and example questions you might consider:

Understanding the Firm's Culture and Values: Your alignment with the company's culture is essential for your job satisfaction and long-term success. Ask about the company's values, their approach to work-life balance, how they handle successes and failures, and what they do to foster a positive work environment.

"Can you describe the company culture here, and what specific attributes make it unique?"
"How does the firm handle successes and failures? Can you provide an example of each?"

Assessing the Trading Style and Approach: As discussed earlier, the firm's trading style should resonate with your skills, experiences, and preferences. Ask about the company's trading philosophy, technology utilisation, and risk management practices.

"Could you describe the firm's trading style and philosophy? How does it differentiate from your competitors?"
"What kind of technology stack does the firm use, and how open is it to innovation in this area?"
"Can you describe the firm's approach to risk management?"

Understanding Your Role and Responsibilities: A clear picture of your expectations can help determine if the role aligns with your skills and interests. Ask about the day-to-day responsibilities, expected performance, and the resources provided to achieve these goals.

"What will be the size of the book I'll be managing? How and when does scaling occur?"
"Can you clarify the performance expectations for this role? What key performance indicators (KPIs) would be used to assess my performance?"

Inquiring About Risk Limits and Constraints: Understanding the firm's risk tolerance is crucial, as it will dictate your trading strategy and your freedom in managing your book.

"Can you elaborate on the firm's risk limits and how they are set?"
"What risk limits do you have in place? Are they contractually agreed or applied on a discretionary basis?"
"How frequently are these risk limits reviewed and potentially adjusted?"

Investigating the Resources and Support Provided: The resources and support available can significantly impact your ability to perform your role effectively.

"What resources and support are provided by the firm? What's the firm's technological infrastructure like? Will there be support for research and strategy development?"
"Are there resources I would need to bring or arrange myself?"

Clarifying Compensation Structure: While compensation can be a sensitive topic, it's an essential factor to consider.

"Could you provide some insight into the compensation structure for this role? How is it aligned with my performance and the performance of my book?"
“How are costs calculated and from where are they taken - Gross, net, top, bottom?”

Gauging Leadership and Decision-Making Processes: The leadership style of the firm and how decisions are made can greatly impact your job performance and satisfaction. Inquire about the decision-making process, how innovation is encouraged, and how conflicts are resolved.

"Can you describe the leadership style within the firm?"
"How does the firm encourage innovation? Can you provide an example of a recent innovation implemented?"

Remember, these conversations provide vital information and signal your preparedness and seriousness about the role to the potential employer. Each question offers a unique lens to view and assess the firm and the position you are considering.

It will likely be hard to ask all these questions in all interviews. The first couple rounds of interviews will focus predominantly on the PM. In later stages, the process becomes more two-way communication as both parties assess the potential for a successful partnership. Tagging a couple of these questions at the end of each round is a good tip. Also, don’t be afraid to ask for more conversations with senior management, CIO, CTO or CRO to improve your understanding. Any group should accommodate this in the pre-offer/offer stages - if not, it’s a potential red flag…

By asking insightful questions and critically evaluating the responses, you'll be in a stronger position to make an informed decision about your potential fit with the company. Remember, an interview is an opportunity to find a role where you can succeed and grow.

As an alpha generator, the role is less of an employee and more of a partnership. It is an excellent sign if you feel like this at the end of the interview process.

Conclusion

Stepping into the world of quant trading as a Portfolio Manager is a thrilling journey, laden with unique challenges, learning opportunities, and potential growth. Your unique blend of skills, experiences, and insights will be key in navigating this dynamic field.

As a seasoned recruiter in the quant trading sector, I've observed the transformative impact of the right alignment between a PM and a firm. It extends beyond merely filling a role - it's about cultivating synergies that fuel innovation, growth, and success.

However, the journey towards this alignment, through the job market and interview process, can be complex. With careful preparation, self-awareness, and the right guidance, you can successfully showcase your potential and seize the perfect opportunity.

Furthermore, we've highlighted the importance of assessing your potential employer. The article provides a set of questions that can help gauge your chances of success and understand your role better. Remember, preparedness is key, and it works both ways - knowing what questions to ask and anticipating what might be asked of you.

By diligently addressing these challenges and continuously honing your skills, you can highlight your ability to thrive in the quant trading industry. It's a commitment to growth, hard work, and ongoing learning that paves the way to success.

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Questions a PM might face:

Background and Timeline
Provide the timeline of strategy development – where/how was it developed, who all were involved?
Is the strategy fully systematic or are there discretionary elements?
Would the strategy still be running at your previous firm or any other place? If yes, then what is the impact of this?

Strategy description

Provide a brief summary of each underlying trading model and it’s source of alpha? (why do you think the model works, and it’s edge vs other similar strategies)
How are the different models combined into a portfolio?
Are there any aspects of your models or portfolio construction that differentiate your approach vs. other similar strategies?
What instruments does the strategy trade? Please provide a detailed list
During which hours does the strategy trade?
What is the average and range of holding periods for each trade?
How is the strategy currently implemented? (coding environment, trading systems etc.)
What kind of market conditions does the strategy do well in? When does it perform poorly?
How does the model do when there is a volatility spike (VIX sharply higher)? How does it do during period of equity market drawdowns?

Execution

What is the trading setup – venues that you trade on, and execution algorithms used?
How sensitive is the strategy to execution and latency?
Is the execution passive or aggressive?
What is the trading volume per day relative to the risk allocation?
Do you need an EMS or OMS? If yes, then please outline the key requirements of the system. If you’ve used a 3rd party system in the past, which one was it?
Do you have ideas on how to improve upon your existing execution setup?

Performance track record

Provide a live trading performance, daily resolution if available - $ and % PNLs
How can the track record be verified?
Provide backtest/simulation performance for the longest period available, daily resolution. Detail all assumption included in the simulations – spreads, fees, clearing and financing costs etc.
Any reasons and issues that have caused differences between live and simulation performance?
Provide the following statistics for Live and Simulated performance separately:
Sharpe ratio
Sortino ratio
Best/worst 1-day, 5-day, 20-day period
Average up day return, Average down day return
Ratio of up-days to down-days
Max drawdown peak to trough
Longest drawdown (peak to trough to full recovery)

Data and Technology

What are the underlying data sets that the model relies on?
How have you accessed this data in the past?
What technology needs are there for the strategy to go live? Do you need developer help to connect to databases, execution systems etc.?

Risk Allocation and Management

What are the internal exposure/risk limits and controls? How large can single exposures become? Are there concentration limits?
How do you monitor the risk of the strategy? Any metrics used? How often are these reviewed?
Explain any risk management features of the positions, models or strategy not covered above
What size has the strategy been traded on previously?
What is maximum projected capacity for the strategy? How is this estimated?
How do you monitor production PNL vs. simulation results?
What are the key factors and metrics to consider if thinking about increasing or decreasing allocation to the strategy?
Do you use any stress scenarios to test the risk management and performance?
How does the strategy/risk framework handle major known and unknown events?
Does the strategy have a max drawdown limit and at what point would you believe the strategy is no longer working?

The Evolution of Statistical Arbitrage: Rise of Alternative Data and Shorter Holding Periods

Mon, 18 Sep 2023 09:16:09 GMT

The Evolution of Statistical Arbitrage: Rise of Alternative Data and Shorter Holding Periods

Quantitative trading has long relied on statistical arbitrage, which uses complex mathematical models to spot and exploit fleeting price differences between related financial assets. Over the last decade, as a headhunter specialising in this field, I've observed significant shifts in the stat-arb trading landscape, with two notable trends coming to the forefront: the growing use of alternative data and the trend towards shorter holding periods.

In this LinkedIn blog post, we explore the evolution of statistical arbitrage, from its inception featuring straightforward pairs trading and mean reversion strategies primarily based on technical data to today's sophisticated methodologies. Our discussion will focus on the rising significance of alternative data sources and the transition from holding periods of one to two weeks to predominantly intraday operations in the context of stat-arb strategies.

The Early Days of Statistical Arbitrage

What is Statistical Arbitrage?

Statistical arbitrage, commonly abbreviated as Stat Arb, is a quantitative investment approach typically utilised by hedge funds. This strategy involves using intricate mathematical models to detect trading prospects originating from market inefficiencies. It operates on the concept that when the price of interrelated securities strays from its usual correlation, there's a high probability it will revert to its average over time. Factors such as mispricing, market sentiment, or temporary supply-demand imbalances could cause this divergence based on any statistical measure like correlation or cointegration.

Statistical arbitrage trading techniques primarily focus on the evaluation of technical data, including historical price and trading volume information. Quantitative models sift through a massive amount of historical data to spot patterns and relationships, primarily identifying short-term mispricings and inefficiencies, which are subsequently leveraged for profit.

Traders practising statistical arbitrage adopt a long position in the undervalued security and a short one in the overvalued counterpart, betting on the convergence of prices. This technique results in a market-neutral strategy that depends less on overall market movements and more on the relative price fluctuations of the involved securities.

Various forms and timelines can accommodate statistical arbitrage, from high-frequency trading (HFT), where positions are held for exceedingly short periods, to medium-frequency trading (MFT) strategies, where positions could be held for days or even weeks. The strategy is commonly used in MFT, where patterns are considered more reliable than HFT due to potential noise. The adoption of HFT, in turn, assists MFT by enabling the swift execution of trades, usually within milliseconds or microseconds. This is crucial, considering the targeted price discrepancies are typically minimal and fleeting.

Statistical arbitrage, while theoretically a low-risk strategy due to its market-neutral aspect, has risks. These can emanate from model overfitting, where predictions based on historical data may not sustain in the future, and from major market shocks that can disturb statistical relationships. Crucial updates hitting the market, such as earnings reports, unique dividends, or legal proceedings, are instances of stock market fluctuations that can disrupt short-term statistical correlations.

The strategy requires three critical factors: predictability, as success is unlikely without the ability to foresee price movements; volatility, since statistical arbitrage is ineffective in low volatility scenarios such as those observed in 2016 and requires price movement for success; and dispersion, dispersion in price and variance of ideas and viewpoints and dispersion of price movements. The strategy thrives on differing opinions; it needs some to believe that prices will continue their trend while others expect them to revert. Meaning price movements need to be different in relation to others. They can’t all move up or all down. Stat-arb requires one up and one down!

How quick

The definition of HFT or MFT and their respective typical holding periods is not set in stone. Over a decade ago, HFT was associated with intraday or quicker holding periods—seconds, minutes, and sub-second durations. In contrast, medium frequency was anything from days to weeks, typically with an average holding period of one or two weeks. This categorisation has evolved, which now includes HFT, Intraday, MFT, and Low-Frequency Trading (LFT).

HFT is ultra-fast trading that is measured in seconds, milliseconds and microseconds, the majority being sub-second. Intraday is anything from a minute upwards, 15 minutes, an hour, holding up to 6/8 hours to the end of the day. Nothing overnight, as the name suggests. MFT is days to weeks but can and does include intraday. Some strategies hold for minutes and hours out to a few days. The pure traditional stat-arb average holding period is within 1 to 3 weeks. LFT is, for me, anything holding longer than a month.

Capacity

Speed can’t be discussed without also linking to capacity and the two are intertwined. Trading strategies differ in their capacities based on their holding periods and execution speed.

Capacity in trading is the maximum volume of stocks, securities, or commodities a system can effectively handle without notably impacting the market price. This is affected by the market's liquidity, the size of a trader's orders, their risk tolerance, and capital base.

HFT and intraday strategies, operating at high speeds with short holding periods, typically have constrained capacities. This is due to their immediate impact on market prices. If you were to order $1bn worth of Apple stock suddenly, the market would move against you so fast that any alpha you’d predicted would disappear with the slippage and execution costs. HFT is more light-footed, in and out quickly in small amounts.

Conversely, MFT and LFT strategies have higher capacities. MFT allows larger orders to be gradually executed, reducing immediate market impact. LFT strategies, spanning a month or longer, can accommodate substantial order sizes as trades are distributed over longer periods, thus reducing market impact and increasing capacity even further.

Traditional stat-arb techniques

Traditional techniques of stat arb encompass a variety of strategies. For instance, pairs trading, a widely used and straightforward method during the early days of statistical arbitrage, involves identifying pairs of highly correlated assets, such as stocks of companies within the same industry, like Coca-Cola and Pepsi. Traders would closely watch these pairs, waiting for their price relationship to diverge from the historical norm. The corresponding strategy would involve purchasing the underperforming asset while short-selling the overperforming one in anticipation of their prices eventually reverting to the historical average.

Mean reversion is another prevalent stat-arb strategy. It operates on the premise that price movements of financial instruments are typically mean-reverting. This implies that when prices significantly stray from their historical averages, they are likely to revert to these averages over time. Traders who apply mean reversion strategies seek assets experiencing temporary price deviations and place trades on the expectation of these prices eventually returning to their historical levels. Mean reversion can be an independent strategy or can underpin pairs trading strategies.

Index arbitrage is another tactic to leverage price discrepancies between index futures and their underlying stocks. Suppose futures are priced higher or lower than the index. In that case, traders may engage in simultaneous long and short positions in the futures and the underlying stocks, profiting from the anticipated price convergence. Speed is crucial for this strategy.

Exchange-Traded Fund (ETF) arbitrage revolves around exploiting the price differences between an ETF and the underlying assets it represents. Traders can create or redeem ETF shares to benefit from the price difference and earn a risk-free profit.

It's worth noting that the definition of statistical arbitrage and its included strategies aren't universally standardised. Every quantitative portfolio manager contributes their unique interpretation, skills, and market perspectives. Some may adopt a two-week holding period, while others opt for just a few days. While some focus on cash US equities, others diversify their portfolio with equities and futures. This diversity is the essence of market dynamics and its zero-sum nature. The strategies above serve as a simple starting point for understanding the complexities of statistical arbitrage.

Having established a foundational understanding of statistical arbitrage and its historical context, we are now poised to delve into two prominent trends I've observed: the shortening of holding periods and the ascent of alternative data. We will explore the possible causes behind these trends and discuss their potential implications in finance.

Shortening of Holding Periods

Stat Arb strategies have witnessed a significant change in their holding periods over the years, transitioning from a typical duration of one to two weeks to predominantly intraday to few days timeframes. A combination of factors such as heightened competition, technological advancements, and the growing demand for rapid execution has influenced this shift.

With more market participants stepping into this field and alpha signal decay setting in, strategies have progressively adapted to shorter holding periods. It is rare to encounter a pure stat-arb strategy maintaining positions beyond two weeks. Most operate within one to five days, but an increasing proportion of these strategies gravitate towards intraday trading with minimal to no overnight holding.

This change has sparked a convergence of styles among different trading groups. Traditionally, HFT groups like Tower and Jump mainly focused on ultra-HFT strategies such as market making and index arbitrage. Their primary edge was speed, though they undoubtedly incorporated some form of statistical arbitrage. On the other hand, quant firms like WorldQuant and Cubist typically covered horizons of one to two weeks. Over time, these distinct approaches have melded. HFT groups have ventured into intraday and short-term strategies of a few days, while medium-frequency firms have also infiltrated the shorter-term intraday domain in their search for alpha.

As more participants occupy the stat-arb landscape, the alpha diminishes as it gets arbitraged away, migrating further towards shorter-term strategies. It's important to note that statistical arbitrage doesn't lend itself to longer-term holding periods. As it primarily relies on technical data like price and volume, the relevance of these factors tends to diminish as the investment horizon extends beyond a month. Beyond this timeframe, price movements become more like noise as fundamental data such as earnings reports, financial statements, and economic indicators impact prices more than statistical anomalies. It is in the MFT to LFT space where both fundamental and alternative data become more useful, over technical data.

Factors driving this trend

The financial sector faces the twin challenges of escalating competition and the need for accelerated trade execution. Quantitative trading, in particular, is witnessing increased rivalry, with a burgeoning number of players applying quantitative techniques to exploit mispricings and inefficiencies. This surge in competition has led to temporary price discrepancies, once available over extended periods, becoming increasingly short-lived, necessitating rapid identification and execution of trades to take advantage of fleeting opportunities.

In the face of technological progress, high-frequency trading has asserted itself as a leading approach. Enhanced computing power and upgraded trading infrastructure have enabled market participants to process and analyse immense volumes of data and execute trades at unmatched velocities. HFT's capacity to pinpoint minor price differences within fractions of seconds has significantly impacted financial markets, further diminishing holding periods. This ongoing technological evolution empowers quantitative portfolio managers and researchers to develop intricate statistical arbitrage strategies characterised by increasingly shorter holding periods.

Implications of shorter holding periods for quant PMs and researchers:

The transition towards shorter holding periods in stat arb carries several implications for quantitative portfolio managers and researchers. Foremost, the growing emphasis on speed and real-time decision-making mandates that traders remain updated with the newest technology and retain a state-of-the-art trading infrastructure. The arms race for alpha starts with the very technology you go into it with.

Secondly, as holding periods contract, research endeavours increasingly concentrate on discovering and capitalising on more detailed market patterns and inefficiencies. Techniques using Ai and deep learning are pushing pattern recognition to new heights.

Finally, the need for more sophisticated risk management and execution algorithms becomes critical to successfully negotiate the challenges tied to intraday trading and mitigate the impact of transaction costs on returns. There is no point in building the world's best prediction machine if the market slips away by the time you react and execute, and your execution costs eat your alpha.

The shortening of holding periods was always evident in HFT trading. Groups battled to get quicker and quicker. They'd dig 1000km ditches straighter just to shave seconds off their execution between NYC and Chicago. They pay big money to co-locate their servers next to the exchange. They went from Java to C++, then to FPGA, and even microwave technology, all to be quicker. Now, the speed race is basically won by a couple of big HFT prop firms; as the cost of entry becomes far too great, a similar game is playing out in the MFT stat arb world with shortening holding periods.

However, unlike HFT, which primarily focuses on increasing speed, MFT is all about improving speed and predictive accuracy. While HFT hinges on speed and technical data, MFT leans on prediction and technical data. As competition increases in the MFT arena, it paves the way for the next significant trend - the surge of alternative data!

The Rise of Alternative Data

Alternative data utilisation has seen a rapid surge recently. The industry's projected expenditure hit $1.7 billion in 2020, indicating a sevenfold jump from just five years before.

The proliferation of internet usage, the growth of social media, the advent of the Internet of Things, and technological advances facilitating data creation and storage are key drivers behind the exponential increase in alternative data. By the end of 2025, The World Economic Forum believes we will create 400 times more data per day than in 2012…

Alternative data refers to information not readily available through conventional financial sources like financial statements, analyst reports, or market price data. These non-traditional data sources provide additional insights into market behaviour, enabling traders to identify unique trading opportunities and gain a competitive edge.

Examples of alternative data sources:
Social media sentiment: The advent of social media platforms like Twitter, Facebook, and Reddit has opened up a vast repository of user-generated content reflecting public sentiment towards companies, products, and market trends. By analysing social media sentiment, traders can gauge investor sentiment and anticipate potential market movements, allowing them to make more informed trading decisions. Or they ignore it and get squeezed out, like in Gamestop.
Satellite imagery: Satellite imagery provides valuable information about various economic activities, such as the level of construction, traffic patterns, and even the number of cars in a retailer's parking lot. By analysing this data, traders can gain insights into a company's performance, sales, or supply chain dynamics, which can, in turn, help inform their trading strategies.
Credit card transactions: Aggregated credit card transaction data offers insights into consumer spending habits, allowing traders to monitor trends and assess the health of specific companies, sectors, or the broader economy. This information can be especially valuable in predicting earnings announcements or understanding the competitive dynamics within a particular industry.
Web Traffic and App Usage Data: Data from website traffic, mobile application usage, and online platforms can offer insights into consumer behaviour, brand popularity, and potential sales trends. For example, increased visits to a retailer's website or an uptick in app downloads could signal stronger-than-expected quarterly results. However, be wary of betting on page views to avoid having the next pets.com on your book!
News Sentiment Analysis: Natural language processing (NLP) techniques can be used to analyse news articles and press releases to extract sentiment about a particular company or sector. Changes in sentiment can potentially be used to predict future price movements.
Geolocation Data: Data from smartphones and GPS devices can reveal patterns in consumer behaviour, such as foot traffic to a retail store or visits to a particular location, which can indicate a business's popularity or potential sales. I know one strategy analysed the footfall into every Starbucks in the USA. Over the quarters, it could see in real-time whether there were more customers or fewer than the previous quarter, and so predict an earnings miss or beat.
Weather Data: Weather patterns can influence consumer behaviour and impact operations in the agriculture, retail, and energy industries. For example, hot weather could boost sales for a clothing retailer or impact crop yields for a farming company. An area Citadel supposedly excels in with a team of weather scientists predicting weather patterns.
Others include; E-commerce Data, Supply Chain Data, Public Records, Healthcare Data and more.

Alternative Data Usage

As the landscape of statistical arbitrage evolved, the focus expanded beyond traditional technical data, and market participants began exploring alternative data to enhance their trading strategies.

Alternative data has been used in the LFT and multi-factor trading space for many years. Here, alternative data nicely dovetails with fundamental data to enhance insights. But there is a growing trend of combining alternative and technical data in the MFT stat-arb world. Alternative data isn’t used in HFT. Knowing how many people walked into a Starbucks is ultimately pointless in ultra-fast trading.

Incorporating alternative data into statistical arbitrage has significantly diversified the strategies and techniques available to quant PMs and researchers in MFT. By tapping into these new data sources, traders can uncover new signals, develop more robust models, and improve their ability to generate alpha.

The use of alternative data has led to the creation of entirely new trading strategies and enhanced existing ones, allowing for the identification of more subtle and complex relationships between financial instruments and providing additional risk management opportunities.

However, acquiring alternative data doesn't automatically generate an overflow of returns. As per Bloomberg, some data sets may be less immediately valuable. As Chris Longworth, a senior scientist at GAM Systematic in the U.K, notes, accessing better data is just part of the picture. Equally important is how this data is incorporated into models and how the resulting uncertainties are handled.

Factors driving this trend

While the surge can be attributed to enhanced data availability, advancements in analytics, and the pursuit of superior informational edge, I have an underlying personal theory linking this rise with the evolving dynamics within the teams implementing these strategies.

In quantitative trading, senior PMs hold the reins of time-tested stat-arb models. These models, honed over years or even decades, are akin to a Formula One car—carefully engineered and relentlessly fine-tuned for maximum performance. However, in this race for alpha, PMs are wary about sharing the blueprints of their "Formula One cars." They understandably guard the intellectual property of their strategies, quite rightly limiting junior researchers' access to avoid the risk of their proprietary methods being taken and replicated or used competitively elsewhere.

While the senior PMs are engrossed in enhancing their high-performance trading models, an interesting shift is observed among junior researchers. They are progressively focusing on novel datasets, especially in cash equities, spurred by the seniors' justified protective stance over traditional stat-arb strategies. Keen to deliver alpha, junior researchers relish the opportunity to dive into the untapped potential of alternative data. Leveraging the latest modelling techniques and machine learning methodologies, they can extract valuable insights, creating a refreshing alternative to routine tasks like portfolio construction, data cleaning, risk analysis, or execution-type research work.

The trend is driven, in part, by the simple fact a senior PM doesn't want their junior too close to their original strategy, so rather than give them historical price data sets, they give them alternative data sets to see if any value can be found.

Another factor is that, initially, low-frequency and multi-factor strategies found the most utility in alternative data, allowing for more accurate long-term forecasts. For example, satellite data determining the frequency of cars in Walmart parking lots could predict earnings and guide investment strategy. Traditional stat-arb focused on exploiting short-term market price inefficiencies in technical data and previously considered such alternative data irrelevant. However, this viewpoint is evolving.

In recent years, stat-arb is increasingly blending with alternative data. It's like tweaking the Formula One car's engine to run on a novel fuel mixture, aiming for short-term price inefficiencies while keeping an eye on potential long-term shifts. This trend emerged particularly during the low-volatility environment of 2016-2018, where groups sought to incorporate alternative data to supplement their work.

The real breakthrough lies in machine learning's application in statistical arbitrage, particularly deep learning. These algorithms, capable of discerning complex patterns and relationships in data, facilitate the identification of temporary mispricings and market inefficiencies more efficiently. As deep learning algorithms' insatiable appetite for data grows, we can expect even greater use of alternative data, pushing the boundaries of quantitative trading towards exciting, uncharted territories.

Conclusion

"It is not the smartest or strongest that survive, but the ones most adaptable to change that survive."

The landscape of statistical arbitrage has evolved dramatically over the past decade, with the increased incorporation of alternative data sources and the shortening of holding periods as two key trends shaping the field in different ways. These changes have brought challenges and opportunities for quant PMs and researchers, requiring them to adapt and innovate to stay competitive.

Integrating alternative data into stat-arb strategies has expanded the range of techniques available to market participants, allowing them to uncover new trading opportunities and improve the overall effectiveness of their models. However, the shift towards shorter holding periods has also emphasised the need for speed, real-time decision-making, excellent execution and advanced risk management.

Statistical arbitrage, in its broadest sense, is here to stay. Traders will continuously seek statistical patterns that offer trading opportunities. However, the conventional understanding of stat-arb as a stand-alone strategy must be updated. Maintaining a competitive edge in today's market necessitates incorporating additional data, employing new techniques, shorter holding periods, and leveraging advancements in machine learning and other technologies.

As the world of statistical arbitrage continues to change, it is crucial for quant PMs and researchers to remain agile and embrace the opportunities presented by this evolving landscape. By staying at the forefront of technology and continually refining their skills, they can harness the full potential of alternative data, develop cutting-edge strategies, and ultimately succeed in this highly competitive and dynamic industry.

Strategies using alternative data might now be more aptly described as 'data arbitrage'.

What are you seeing?

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We're eager to hear your perspectives on the trends highlighted here and their influence on your quant trading and statistical arbitrage experiences. Any other trends or challenges in your view? Share your thoughts below.

If you found this discussion valuable and want to stay informed about the latest developments in quant trading, statistical arbitrage, and the broader finance industry, be sure to follow our page. We will continue to share insights, updates, and thought-provoking content that aims to inform, educate, and inspire. We also encourage you to engage with our future posts by sharing your thoughts, questions, and experiences as we strive to stay ahead in this ever-evolving field.

Quant Trading Secrets: How to Successfully Leap from Sell-Side to Buy-Side

Mon, 18 Sep 2023 08:47:06 GMT

How to move from the sell side to the buy side?

One of the most popular statements I've heard over the last decade as a headhunter is, "I want to move to the buy side".

It is a worthy goal and, for some, a great move. However, it's a difficult move to achieve. I've been fortunate to have helped numerous people realise this ambition, so I am compiling a short guide to help prime those thinking about making the switch.

What even is the buy side?

The buy-side is the groups that buy securities for their own account (prop) or investors' account (Hedge Fund) to generate a return. The opposite is the sell side, which provides the services required to facilitate the buying and selling of securities. Such as underwriting, prime brokerage, and execution, to name a few.

Buy-side groups are hedge funds, prop shops, asset managers, insurance firms etc. For this article, when we say the buy side, we're referring to prop shops & hedge funds.

The sell-side is a loosely collected bunch of groups that include investment banks, data vendors, research houses, and market makers (although some market makers are prop shops and have carved out their own niche these days as the HFT world has essentially taken this over). Practically anyone who provides a service and is not directly involved in taking risks with someone else's money. (Although, not taking risk is laughable given SVB, First Republic etc.)

Why move to the buy side?

This complex answer depends on the individuals and their motivations and career goals. While no two people are the same, we can generalise some main motivations and reasons.

The most apparent reason is trading-related, being able to generate alpha and take risk. This is very difficult to do on the sell side due to rule changes since the 2008 financial crisis and for reasons beyond this article.

Compensation is another obvious one, with bonuses essentially unrestricted. The comp structure is very different between the sell-side and buy-side. Hedge funds typically pay lower base salaries than investment banks. However, the bonuses swing the total compensation back in favour of the buy side. Especially if your bonus is contractually linked to PnL with a percentage deal, which many Portfolio Managers are on.

Culture is also a significant factor. Hedge funds can have a more tech or start-up feel, a more modern approach. Hedge Funds started on the outside; the outcasts originally started them. So they have never had this corporate feel. They have unique cultures, Bridgewater being the prime example . The sell-side is typically big institutions, giving them an older, corporate, hierarchical feel.

Hedge funds and prop shops have a far quicker turnaround time in their work. This creates arguably a more high-pressure environment, but one that is far more focused on actions and results. While banks can need multiple levels of sign-off before starting any project, and you will need permission to make the slightest alteration to an algo.

There is more hierarchy on the sell side, and people may want to move to a flatter structure with fewer office politics. Yet, that doesn't mean you can escape office politics! Some hedge funds have a reputation for being highly political and having an "inner circle".

Career advancement tends to be more meteorically based on the buy side as it's more focused on the bottom line and your PnL contribution. A large institution requires a little more manoeuvring, hand-shaking, coffee catch-ups, and bargaining included in that promotion.

The buy side is usually more exciting work. It is the tip of the spear type of work. They will have the latest data sets, the coolest new tech, computers & hardware, etc., needed to do your job. The work is on the bleeding edge, where the boundaries are pushed. Ultimately, the sell side facilitates the buy side, so it is a little reactionary in its work and evolution. Many complain about the old legacy systems at investment banks that make it hard to implement innovative solutions. They have old systems on top of old systems - turtles all the down, according to some devs!

What to consider?

When looking to switch to the buy side, the first consideration is which type of group you should go for. The buy side world is vast, and groups come in many shapes, sizes and styles. This should be a key consideration in which groups you target in your search. If your long-term aim is to become a PM, then targeting multi-manager platforms is best. If you prefer a collaborative research environment, targeting the larger research houses is best for you.

Another variable is trading style. Are you best suited to a high-frequency shop or a long-only shop? The two are immensely different in everything they do. And there are many different styles in between. If you're sitting at a market-making desk, there is little point in targeting a big asset manager.

As well as understanding and targeting specific groups, you should research and target particular roles. It's important to consider what roles are available on the buy side to target the types that best fit your skills. If you've been building algo execution strategies, they will unlikely hire you for an alpha research role. But, with the understanding of what roles suit your current skills and what skills are needed in your desired role, you can begin to take steps to close the gap. For example, if you're in algo execution, you should go for short-term price prediction roles instead of targeting medium-frequency stat-arb alpha research roles.

Don’t Overreach

When people move, they rightly expect an uptick in their job function & responsibilities as well as comp. Quant Developers want to become Quant Researchers; Researchers wish to to become portfolio managers; Data engineers want to become Data Scientists. This is what you should normally do. However, when you add in a switch from the sell-side to the buy-side, you need to acknowledge that this adds another layer of difficulty. The likelihood of getting an uptick in job functions is hard.

Career upgrade moves are 100% feasible; I focus on improving someone's career. However, the gap between these moves needs to be figuratively small. Such as moving from technology to front office, researcher to sub-PM, and sub-PM to PM. Quant dev to a quant researcher. VP to Director. Algo execution to a market maker. These are all achievable.

The point here is that these moves happen within the same vertical of the sell or buy sides and do not occur across the verticals. You won't be going from a quant developer at an investment bank to a quant researcher on the buy side. Quant dev reporting into a PM - 100% possible.

When you wish to cross from the sell side to the buy side and advance your role, it's almost impossible. The main reason is that competition is fierce. Why would a hedge fund hire someone with five years of experience doing market microstructure research to research alpha in medium-frequency stat-arb? There are plenty of quant researchers that would be a far better fit. But joining a fund to be their microstructure expert would be outstanding; then you start adding extra value, doing extra internal work to move towards alpha. Staff retention is an essential priority for many funds. So once you're in, funds will be far more accommodating to their current employees' growth plans than the strangers they are interviewing!

The other primary reason is to target roles where you can add value. Ultimately, the buy side is all about the bottom line. You can't expect an offer if you're not a value add. You have to put yourself in their shoes. What is your value add? Your value is your experience. A hedge fund likely wants to hire you because of what you've done for the last few years. Built the CDS portfolio trading business; great, you can help develop our credit trading desk. Have you created central risk internalisation strategies? Great, you can help internalise our flow.

Hedge funds hire on value add, rarely potential value - at least for mid to senior roles. Potential comes second. Even PhD and Master's students are hired based on their experience, schooling, pedigree, achievements, hard quant skills & programming ability, and the complex statistical calculations they made about black holes! How will you add value to a medium-frequency portfolio manager if you've been doing market making? Yes, you will have strong core skills in terms of coding, quantitative, and general finance knowledge. But you don't have direct day-one skills - someone else does!

I advise focusing on making the switch first, then continuing the journey to your career goal role.

In Practice

Ok, You’re thinking; “I should consider what groups to target and be specific about which roles I can add value to the most. But what does that actually look like?”

If you design and build execution algos and heavily research market microstructure, targeting groups trading higher frequency is a good option. Also, there are PMs with huge books where minimising slippage can contribute significantly to alpha generation or completely ruin any alpha made.

If you're less worried about alpha research and want the buy side as a whole, your options open further. Some hedge funds have dedicated teams building in-house algos for execution for the entire fund. Another option is to look at some of the central risk teams where internalisation of flow is essential. These started on the sell-side but have grown at the more significant hedge funds.

If you are a quant trader on a market-making desk doing pricing and auto-hedging research, then HFT prop shops would be an excellent option. Also, some hedge funds are doing more and more intraday trading, sub-30 minutes, so these would also be good for you to target.

If you're on the program trading side, your priority should be to start being able to take risks and do principal trading, not pure agency. Hedge funds have no interest in agency traders other than purely for execution work. With risk-taking and principal trading experience, you can target roles at big index rebalance funds & teams. (Although the rebal trade hasn’t worked for the last year or so)

Central risk desks are the closest match between the sell and buy sides. While prop trading is banned, alpha research still lives on here. Typically, you're looking at stat arb, mean reversion strategies. The big thing that is often overlooked by CRB quants is that your alpha may be based on banks' flow or unique proprietary data sets belonging only to the bank, things that are not present or portable to the buy side. Can you replicate your Sharpe 3 strategy without the bank's flow or proprietary data? If you are thinking about switching in the coming months & years, it might be prudent to check how portable your signals are and maybe come up with some that are.

If you're a Quantitative Developer, first, we need to consider what type of quant dev you are and where you sit. If you're focused on risk and PnL reporting systems, these are skills in demand on the buy side, but by central teams rather than portfolio managers. If you support traders or quants in getting data or building simulation and backtesting environments, then you are of more value to Portfolio Managers and front-office teams.

If you're a developer, I would advise you to get into the front office first. From there, experience is the key indicator of which direction to go. If you've been working on exchange connectivity or market data feeds, you are a value add to an HFT group or centralised technology team. If you've been implementing strategies and writing production code or building trading engines and systems, you would be of value to a Portfolio Manager building a pod.

How to make a move?

So now you've decided which groups are best to target and what types of roles will be the best match to apply for. But what if you're slightly short on the requirements or want to strengthen your case until you're ready to move?

What skills you need is determined by the group and role you target. The hard skills are mathematical/quantitative and coding. After that, it's role-specific, such as building a backtesting environment, researching new signals, using machine learning, portfolio construction & optimisation skills, idea generation, etc.

Quant Skills

Quantitative skills are obviously needed to move to the buy side. But there are various quant skills. Having Monti Carlo and pricing libraries experience means you should target the discretionary style shops instead of the fully systematic shops. Skills around optimisation and automation allow you to target systematic funds.

Data Skills

Data Science has always been around in quant trading; quants have always analysed data or created statistical strategies. The buy side is always interested in machine learning and AI techniques. It's not a prerequisite but would help you get a buy-side job if you have experience applying deep learning to parts of the trade life cycle, such as portfolio construction, alpha research, time series analysis, alternative data analysis, etc.

Trading Skills

Trading skills are important and highly valuable, depending on the role. Trade execution experience is less critical for pure alpha research roles or when joining a collaborative place that separates the function. Execution trading and risk management are part of the trade life cycle process. But in a research style fund, they may be irrelevant for the role of alpha researcher as they outsource execution to a dedicated team. Check out this paper by Marcos Lopez de Prado for more info on the research style factory shop . If your long-term ambition is to become a portfolio manager, trade execution experience would be excellent. It is of more use in a multi-manager group where you could help the PM manage the book.

Coding Skills

It's pretty simple for the buy side; do not expect a front office job if you can't code .

The requirements for front office roles have drastically evolved in recent years. Coding has become an essential skill, no longer optional but integral to success in these roles.

The necessity of coding skills for securing a front office role within quantitative finance cannot be overstressed. As the industry evolves, coding has become an essential tool, almost as important as numeracy and financial theory understanding. Without the ability to write and understand code, securing a front office quantitative role is becoming increasingly challenging. In fact, proficiency in programming languages like Python, R, and C++, among others, is now a prerequisite. These skills are vital for the development and implementation of complex models used in pricing, risk management, and strategic decision-making.

Even positions like traditional investment analysts, which were once heavily reliant on fundamental research, are now utilising data, particularly alternative big data, to augment their research capabilities. Basic coding skills have become needed in such scenarios.

In light of this, if you aspire to land a quantitative or systematic role in the front office, mastering coding is no longer a recommendation, but a requirement. Proficiency in programming will not only help you to navigate through complex data structures but also enable you to design and implement innovative trading strategies, perform risk management, and optimise portfolio construction. Thus, possessing coding skills is a crucial factor that can significantly influence your career trajectory in a quant role in the front office.

Python is the best to learn as nearly every fund uses Python, primarily for research. Some use R, and even rarer is Matlab – but you need comprehensive skill and experience in at least one of Python, R or Matlab. Knowledge of Python packages, such as Pandas, NumPy, and TensorFlow, is advantageous. These are all used by the buy side, and if you can show a project, even a personal project, where you used them, you are a step ahead of your competition.

SQL is handy to have for several hedge funds, and this is because it allows you to get and handle the data. You don't need in-depth knowledge, but you need to be at least able to run queries.

If you're targeting more HFT-style shops, you need object-oriented programming skills, C++ or Java. Python will be used for research, but C++ or Java will be for production code. These days languages like Rust are becoming increasingly popular, particularly in crypto,. Learning this language could be a journey that is less well travelled, giving you an advantage over others.

For developers, programming is another consideration needed in what groups to target. Essentially half of the buy side will be inaccessible to you. Some shops use Java, and some use C++. It's worth knowing this before thinking you can get a job there. The same rings true for the banks; some have their systems in C++, and others are Java based. Rarely are the two languages both used in the front office at the same institution.

Insider Tip : There is a growing trend across both the buy and sell side of having quants write better code. Groups are pushing research and production development closer together. Groups want the quants to be able to implement more of their code rather than just passing it off to the development team. Strategies that work great in Python backtesting can lose functionality or performance when entered into production-level code. A quant who can write production-level code, or close to it, has immense value!

Learning & Development

If you're serious about moving from the sell side to the buy side, first get your skills in order; otherwise, you will be found out in interviews and burn an approach. You only get one try; if that fails, you must wait a year or longer before applying again. And you'd need to improve during that time, so why not improve or practise before? There are tons of practice tests online that can be used to sharpen your skills. Examples in footnote.

Getting the right skills means using those skills. Saying you used C++ during your PhD will not cut it if you haven't used it in the four years of your career. A Python programming course certificate certainly looks good on your CV and LinkedIn profile but counts for a little if you've never used Python programming in your job.

Remember - your competition has used it!

Get the certificate, but then use it. You want to point to a project where you've used it. The project doesn't have to be in work, it helps, but I've seen people get jobs because a personal project they completed aided their application. In this case, the quant was an equities microstructure researcher but built an alpha prediction model for the crypto market in their spare time.

Get the right experience. Similar to getting the right skills, you need the right experience. First, experience using your skills - coding, data analysis, problem-solving, etc. But, get the right experience. If you are focused on algo execution strategies but want to do alpha research, don't expect a hedge fund to hire you for this. Instead, transfer internally to the CRB desk. Or ask your boss to increase the scope of your research to try to use microstructure data for price prediction. If you are an agency trader, get some principal risk-taking experience.

Compensation Considerations

Compensation is the last thing to consider when and how to move from the sell side to the buy side. As mentioned, money is a significant motivation for moving from the sell side to the buy side. Although, you do need to be pragmatic.

First, bases are different. Typically they are lower on the buy side. As a Director at a bank, you're likely getting £175k to £225k as a rough range. That isn't happening on the buy side, and it will be more around the £150k-175k type range for a front office role. Your total compensation should be higher, and you'll likely be getting some guarantee in bonus if the base is being lowered, so you are not taking a hit on total comp. But can you take that hit on your regular income?

A major reason bases are lower is the performance-related nature of the buy side. It is about how much money you generate and therefore is rewarded based on that. You're not there to pick up a salary. A portfolio manager knows their budget and what their bonus pool will likely be; they want to avoid a high fixed cost on their balance sheet. Instead, they are happy to share in the upside.

As said, you need to be pragmatic - expecting a jump from £175k to £200k+ and a buy-side move is not practical. That is wishful thinking, and you'll be waiting long for that offer to come around. It pays to be pragmatic.

A hard truth to hear is you're not likely to get your dream role and dream pay package in one move. Your best option is to target switching to the buy side and then progress to the role you want once you've made the switch. If your 5-year or 10-year plan involves being in a senior position on the buy side, should you jeopardise that by arguing over the fact that they are offering you £120k when you're a VP on £140k? Your total comp will be north of £200k on the buy-side, while at the bank, MAYBE it hits £180/200k total comp. On the sell side, there is a ceiling to what your bonus can be for the vast majority. On the buy side, the vast majority have essentially uncapped bonus potential. In 5 years, will you care about that 20k that is so important today, and after tax is like 12k? The point is don't focus on just the numbers today; instead, look at the bigger picture, the role, the growth potential, the group and its direction, and what you will earn in 3, 5, or 10 years. The offer isn't just what is written on the paper; it includes everything that isn't directly in the offer letter.

20 of London’s top hedge funds – and what they pay.

Conclusion

The quant trading world is ever-evolving, and as you progress in your career, continue to challenge yourself and explore new frontiers in the buy-side space. We hope this guide has provided valuable insights to help you navigate your transition and embark on a fulfilling and successful buy-side career.

I've moved dozens of people from the sell side to the buy side. They all had the above things in common. They didn't overreach, knew what their value add was, went for the roles that fit their current skills, added skills & experience where they were short and focused on the bigger career picture.

Competition for roles has been the highest I've seen in the last ten years. There are many quants with similar skills, data scientists in other industries, and developers in tech, all competing for buy-side roles in only a handful of groups. It would help if you thought about your competition. Rarely do people think about others when applying for a job. They think I am a great fit; they should hire me. But there is usually someone that is a better fit; hopefully, with this guide, that person can be you.

Patience is underrated, and these processes take time. Remembering that patience and persistence are key factors in this journey, with a willingness to adapt and learn new skills, and you’ll go far!

Lastly, the ideal roles that tick all the boxes above aren't waiting for you to decide to move.

They come up sporadically. A team member leaves. A team has a record quarter. A fund has a massive influx of AUM. There are many reasons why a group might or might not be hiring right now. So it pays to stay in touch with a headhunter and keep an ear to the ground for that ideal buy-side role so that when it comes, it's yours!

Join the conversation by sharing your experiences and tips on transitioning from sell-side to buy-side in the comments below. Your insights could make a difference in someone's career journey.

If you found this guide helpful, please 'Like' and share it with your network.

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Considering a buy-side move or need personalised guidance? Get in touch with us today to take the next step in your career journey.

Trade Secrets: Unveiling the Motivations Behind Portfolio Manager Career Moves

Mon, 18 Sep 2023 08:35:31 GMT

Why do Portfolio Managers move?

Introduction

Every year, a surprising number of successful portfolio managers and quant traders decide to change jobs. Despite having a sizable book, an efficient team, and a generous compensation package, they leave their current work environment. But why?

In this article, we delve into the key factors that drive these professionals to seek new opportunities. We’ll discuss book size, compensation and more.

I aim to inform any portfolio managers (PMs) thinking of a move of things they should consider. Also, it provides a framework for clients to retain and attract PMs based on what their competitors are doing.

While there are countless reasons for making a change, some are more evident than others. Some factors might be unique to an individual PM, while others are more widely applicable. And the importance of each factor can vary from person to person and can evolve. Unravelling these complexities is one of the most intriguing aspects of my profession.

So, what drives a portfolio manager to make a move?
Are there any recurring themes or universally applicable reasons?
Are the push and pull factors the same for everyone?
How much do they trade?
What are they paid?

Reasons for Career Movement

Personal Reasons

There are numerous reasons for considering a job change, with many being highly personal. In this discussion, we won't delve into specific personal reasons but instead focus on some of the broader underlying motivations.

Common personal reasons might involve:

Dissatisfaction with their role.
Challenges with a demanding boss.
Shifts in company management or direction.
A badly evolving company culture.
Personal or family circumstances.
A need to relocate.

Broader Motivations

Beyond purely personal reasons, there are numerous fundamental motivations that, while not exclusive to an individual, still hold personal relevance. We'll examine the similarities between the push and pull factors in these situations. Such factors may involve:

Seeking higher compensation.
A larger percentage share.
A desire for career advancement.
Escaping unfavourable compensation structures.
Obtaining a more substantial book.
Gaining access to a superior platform.

The Role of Compensation

The first and most apparent reason is the desire for higher compensation, often expressed as “I will join you if you pay me more.”

On the face of it, it is a regular request; however, it is not so simple.

Before exploring the pull factors related to higher compensation, let's examine the different components of a PM's pay. A PM typically receives a fixed base salary and a bonus, constituting their total compensation.

Base Salary Considerations

Regarding base salaries, the industry has a standardised approach for PMs. A base of $150k to $250k is standard across most groups, although inflation has made $150k bases increasingly rare. There are outliers, of course, as with any bell curve, but those at the ends tend to be heads of a large team or division, maybe have come from the sell side where bases are higher, or perhaps they are willing to sacrifice bonus for the base, or through tenure have had it steadily increased yearly,

While the base salary is important, there are some truths that clients making an offer and candidates expecting a higher base salary need to acknowledge and understand.

A PM will not move simply because of being offered a higher base salary. Any that would, I would have serious question marks as to how good they are. A PM is paid on total compensation, so base is a significant aspect, but it only makes up a fraction of their total comp. The bonus is derived from the profit they make. So, a PM wanting a higher base, a higher fixed amount regardless of their performance, would suggest they do not believe in their ability to deliver on the promised performance and so want a backstop; a high base = a wide stop/loss!

Baffling Bases

A natural objection can be, "I have fixed costs to pay", when told the base is what it is. This only makes sense if they offer you a lower base - which they shouldn't. You would only consider a lower base if huge incentives elsewhere make sense.

I have had a client offer a partner-level PM on a £250k base, a £40k base salary. Yes, you read that right. I thought they were joking. I refused even to present the offer and let them do it. It came with a 40% PnL cut rather than a 20% cut, but still, it was incredible they believed that was acceptable. Eventually, the base reached £150k, but needless to say, the PM didn’t take it, and I’ve chosen not to work with that clown (client) since...

Another funny story: a PM argued he needed a bigger base salary as his kids were about to enter secondary school, and so the increase in school fees meant he needed a higher fixed amount due to higher fixed costs. The audacity was commendable.

Stating your lifestyle costs are increasing out of choice and using that as the reason you want an increase is naïve. Yes, it is a motivation to move. But do not say that to your future boss!

Yes, there are fixed costs to be concerned with – such as a mortgage, school fees, etc. but a PM who can't run a personal budget would set off red flags. Where’s your bonus from last year? Can that not cover it? Is your belief in your strategy that weak you need a base over bonus?

Another reason PMs may want an increase in the base is an entitlement – "I am moving, so I should get an increased base". This applies to most other roles, researchers, technologists, and for most people who move jobs. PMs, though, are alpha generators. They are there to generate money and get paid a percentage of that PnL. They are not there to provide a service and exchange time for money in a paid salary. If they generate profit, their pay should reflect that fact. If they don't generate profit, their income should reflect that. A modest base increase is reasonable - but a PM’s offer is much more than base. A PM fixated on base is a red flag.

So, the base isn't and shouldn't be a motivating factor. But it can make or break a deal. Bonus is where the real motivation lies.

The Importance of Bonuses

When it comes to bonuses, there's a considerable amount of variation.

Bonuses can be discretionary, determined by management based on various factors, or contractual, a fixed percentage of the PM's generated profits.

A discretionary bonus is calculated at the end of the year; it is based on a range of factors, some known, such as the returns you make; some unknown, such as other PM's performance; or more invisible, such as what a manager thinks of you. As the name suggests, it is not written in your contract; it is at the discretion of management, so it is not guaranteed come the end of the year. You still need to walk into the boss's office and hope they recognise your excellent work and give you a share you would be happy with. This uncertainty is not conducive to a good job in the preceding year as it creates games and politics. If it is a fixed percentage, you can get on with affecting the whole rather than the whole AND your slice of the whole. Reducing to a more straightforward format is always better and more motivating.

Naturally, you may say if I have generated millions in PnL it shouldn't be a long shot, as you can point to the PnL generated, and should management not pay, they know you're likely to walk. However, what if, despite your performance, the fund has a challenging year, or another PM crashed and burned, losing a few million?

Then at most discretionary bonus groups, netting will come into play. Your profit will be needed to cover the other PM's losses, immediately reducing your net PnL, so you are now getting a share of a smaller pie. Management has a discretionary pay-out because they can adjust to business needs. Why would you remain on a platform where your strategies are at risk to an unknown and uncontrollable aspect, such as another Portfolio Manager? More on netting later.

Discretionary bonuses are lower

Not only is there the risk of netting and the fact it is not written in your contract, but also the plain fact that most discretionary bonuses are lower than the written form.

A discretionary bonus would work out at 8-12%. Indeed, nothing to be disgruntled with, and it would represent a healthy return on your work. But, contract payouts at the low end are 12 to 15% on average, reaching 18% to 20% for top PMs.

25% is rare but not unheard of. In the highly competitive world of multi-strategy funds, the industry is showing signs of standardising towards 20% payouts. Prop groups and family offices have even more flexibility and can go as high as 40/50% - even 60% in rare cases.

Some may think 12% is decent. However, remember, it's not guaranteed. It's not written. This means it fluctuates widely within groups across the years. Some years it's 8%, another 12%.

From the PMs I speak to, uncertainty is the killer, not the "low" percentage.

Oooooh an extra 3%

Moving from 12 to 15% is not a motivator. Why would a PM making 10% on his book and getting 12% move to get 15%? An extra 3%?

The PM knows the strategy works, they have the data they require, the risk systems are in place, and, more importantly, risk & management understand the risk profile of the strategy. To then move and pull out the strategies if they own the IP, leave, and implement is a massive risk - for many reasons, the strategy might not perform as well elsewhere. All that for 3% extra will not move the needle enough for a PM to leave and join you. Expecting someone to do that is unrealistic; it is not a huge motivator. There needs to be more in the offer than a simple 3% increase in payout.

All of this refers to if they own the IP. If they don't own the IP, the risk is even more significant! They are not allowed to copy and paste the strategy. They have to come up with something new. So would they risk leaving a working strategy to go and design a new one - even if they firmly believe the new one will be better, for a 3% increase in payout?

No disco

The overarching motivation is to move from discretionary to contractual payout. A PM on a discretionary deal will naturally be motivated to join a fund that offers a percentage deal.

If you pay discretionary, consider offering your top PMs a contractual payout to help retain them. If you provide contractual ones as standard, targeting groups with discretionary payouts can give you the advantage in a talent-scarce and highly competitive market.

If a PM is already on a contractual payout, give them an increase in the percentage of PnL cut. But also prepare the offer around all your firm's advantages, whatever they may be - a larger book, better execution to harvest more alpha, increased volatility, wider/less strict or transparent risk limits, etc.

The Desire for Larger

Show Me The Money

Another significant motivator for PMs is the prospect of managing a more extensive portfolio. Multiple factors contribute to this desire, including the potential for increased profits and bonuses, greater responsibility, and enhanced internal respect.

PMs managing portfolios of $50-250 million, especially when leveraged, already have substantial assets under management. However, when their strategies are scalable and can handle more risk, missing out on additional profits can be frustrating. PMs may feel that their models are not being fully utilised, leaving room for growth that remains untapped. This can be particularly annoying for strategies like index rebalance, where a PM will want to concentrate their bets.

In addition to the missed profit potential, a larger portfolio can generate more substantial returns in dollar terms.

It doesn't take a quant to work out; a PM would naturally prefer to earn 10% on $500 million rather than 10% on $100 million.

Large funds, with AUM ranging from $10 billion to $50 billion and beyond, can readily offer $100 million, $250 million, or $500 million portfolio sizes to trade. Sometimes, the right PM with a scalable strategy can rapidly scale up to $4-5 billion GMV within a year. PMs with scalable strategies should prioritise finding a platform to support their growth rather than merely pursuing a higher percentage payout.

While increasing portfolio size is an attractive motivator, especially for junior PMs, it shouldn't be the sole focus and must be cautiously approached. Rapidly scaling a portfolio without considering the strategy's capacity can lead to failure. PMs should scale their strategies carefully and thoughtfully to avoid potential pitfalls.

Expanding Universe

Expanding one's tradable universe is attractive to PMs. Typically, less so to those who are already established with 3/4 different strategies. Portfolio managers can trade what they want within the scope of what they were hired for.

But for those less well-established, it can be a pain.

PMs can run into trouble when expanding their portfolio outside the main focus. Hiring researchers or sub-PMs to build a futures strategy while you're a cash equity PM will likely be problematic on some platforms. Management wants to avoid too much overlap, or some funds deny access to specific data sets to prevent you from trading them. This problem is particularly acute for quant traders (not to be confused with PMs in a pod structure). If you face these challenges, it might be time to look.

Groups wanting to hire top PMs should look here for soft but tangible advantages to add to their offer. Hiring a PM to build a strategy based on their track record of success, but also giving them the freedom to explore and expand into new strategies, can be highly motivating.

Diversify, Diversify, Diversify

Related to this, most groups will have a particular style and focus as their niche. Funds constantly seek PMs that can bring new value, always seeking "diversification".

However, this new value is usually new strategies, & new styles and, so a word of caution as PMs should consider the compatibility of their strategies and techniques with potential new platforms.

Is their tech set up for your strategy?
Do they have a suitable client base?
The right prime brokers?
The right execution? etc., that will allow your strategy to succeed.

There are so many cases of funds hiring a bunch of quantitative PMs only to change direction again a year or two later.

Larger Team

Growing a team is rarely a reason to move. In most places, the cost is passed on to the PM. So, hiring would be part of that budget. If you're generating enough PnL, you can afford to bring someone on.

However, when the big boy multi-strategy funds throw money at the PM, they will give them a budget to expand and hire separately from their PnL. This acts as a deal sweetener even if it wasn't the underlying reason to move. Therefore, it can be an attractive add-on to an offer you’re making to senior PMs.

The Impact of the Trading Platform

The quality of a trading platform is a strong motivator for PMs to change jobs or to remain where they are.

While what constitutes a good platform may vary, it depends on the PM's strategy and style, the fund's overall strategy, and other variables. A good platform should provide the PM with freedom, substantial AUM, flexible risk limits, and minimal overlap with other PMs' strategies. It also includes technical aspects, such as technology, data, risk management, and execution.

A funds technology stack is one of the most important considerations for a PM. They will always ask about it because it is fundamental to their success. PMs want to know if there will be structural advantages to joining your fund. Micro or milli-second execution? Are endless alternative data sets available? Tick data? Co-location? All this depends on the strategy you want - but these are the most important aspects of an offer. If your tech stack isn't fit for purpose, don't expect PMs to sign up.

The Importance of Execution Quality

When choosing a platform, it's crucial to consider the quality of execution. While often overlooked, better execution can be found due to a platform's size and volume, leading to noticeable gains in PnL. On the other hand, poor execution can be detrimental to performance. PMs should be mindful of potential issues when joining a platform expanding into new areas or lacking expertise in specific asset classes. Geographic location can also impact the quality of execution and access to prime broker services.

Platform attractiveness can be a significant initial draw, though its importance may fade as the decision-making process progresses and the focus shifts to specific roles and compensation. Nonetheless, the platform's quality should not be underestimated.

Risk Limits and Their Influence

The fear of strict risk limits can deter PMs from joining specific platforms. However, a wide range of risk limits are available, and PMs should engage in open conversations to find a platform that matches their style. Clear communication and agreement on risk limits before joining can alleviate concerns and prevent surprises.

The reality is there are many groups with a range of limits. There is a group out that is suited to your style. It's about being open-minded to have a conversation. Then, be clear-minded and resolute in what you will and will not accept. Most groups' risk limits are not an arbitrary number imposed by management. Rather, a carefully considered approach to the market and the strategy. After discussing with the portfolio manager, risk department, CIO, etc., you may have a 5&10 limit. 5% drawdown and your book is halved. 10% drawdown, and you're out. There is a range 3/6, 4/8, 5/10, but it can be pre-agreed and known. So, there is no surprise. There is no fear that you could be cut when you have a 3% DD. If all this is known before joining, it is no longer a hindrance or de-motivating factor.

Only join a group where the risk limits are known. If you're joining one where they can be changed in a heartbeat, you are not controlling everything you can control and, instead, opening yourself up to some uncontrollable risk.

In summary, PMs should prioritise finding a platform that aligns with their strategies and styles, offers the appropriate level of risk and reward, and provides high-quality execution. Keeping an open mind and evaluating each platform's merits will help ensure a successful transition.

Reputations

A PM can rule out a whole group in the opening stages simply upon hearing the name. I fully appreciate they may have had previous bad experiences or previously spoken to them. However, in most cases, it is hearsay, "oh, it's a revolving door", "oh, their risk limits are strict", and "hire and fire mentality". It amuses me how quant PMs look at data all day and then bases a career move on qualitative gossip. The simple fact is a PM should be unique – the best ones have a unique approach, unique style, and unique personality. So they are not the same as the previous ones that left. Things change, and what wasn't right for them doesn't mean it's not right for you.

Of course, there are groups with bad reputations, some with good reps, and many in the middle. What is essential is finding one that best fits yourself, your strategies, and your aspirations. Sometimes, they will represent high-risk options; other times, it might be a steady eddy. The important thing is to have an open mind. There are always trade-offs. You can have an excellent collaborative feel, with everyone pulling together and sharing ideas. However, bonuses are shared more equally; some people may not pull their weight, and there can be little individual recognition for your work.

Or you can go to a silo PM platform. Yes, if you have a significant drawdown, you are out. Yes, everyone is in individual teams, with little discussion occurring across teams. However, you're paid concerning what you generate, there is no netting, performance is usually higher, progression can be clearer, and success is far more based on you.

Addressing Netting Risks

A significant motivation to move is to remove the principle of netting across portfolios run by an individual PM.

Netting is when PM One makes $20m, and PM Two loses $10m. The fund's net PnL is $10m, and the PM that made $20m is paid on the net $10m - not the $20m they made!

Netting used to be popular but has died away/gone underground. If you are on a discretionary bonus - you can be sure some netting comes into it. If you’ve earned more PnL but your percentage went down, you may be thinking why. Well, chances are a PM elsewhere in the fund blew up, and your PnL is being used to cover them.

The only way to remove this risk is to get a contractual pay-out.

To retain top talent, funds can offer to remove netting risk for their PMs. PMs currently exposed to netting risk should consider seeking opportunities with firms that don't practise netting or negotiate the removal of netting risk from their existing arrangements.

There is room for improvement for PMs who have already addressed netting risk across their portfolios. Some firms may be willing to remove netting risk within a PM's individual portfolio, allowing them to benefit from the success of each individual strategy without offsetting gains and losses. Very handy when growing a team.

Seeding and Spin-Outs: Opportunities for Growth

For PMs with a maxed-out book size and a strong track record, seeking seeding or planning to spin out and launch their fund can be a viable next step. However, the current environment, with a high rate of hedge fund closures and startup failures, makes it challenging for new funds to succeed.

Seeding and spin-outs are two distinct opportunities for PMs:

Seeding: Some hedge funds or investors offer capital to PMs for trading, allowing them to launch their own fund. The relationship can vary from merely introducing capital to providing office space, IT and trade infrastructure, and back-office systems. Seeding typically requires the PM to have an excellent and long track record to secure the necessary capital.

Spin-outs: This option suits PMs not ready for independent seeding. PMs join a hedge fund as a standard in-house PM, building their track record and trust within the organisation. At a pre-agreed point in time and performance, the PM can spin out and become a separate fund. This arrangement allows PMs to gain more experience and establish a robust track record before launching their fund.

Launching a fund independently can be challenging, as PMs must effectively trade, market themselves, and raise enough assets to sustain the fund. The most common reason a hedge fund fails is for administrative reasons. A PM is distracted trying to raise capital or needs to learn how to handle compliance. Or is it trying to do so many different things they let the strategy slip.

The alternative routes of seeding and spin-outs provide PMs with the support and resources necessary to launch their fund successfully.

Conclusion

In conclusion, the decision for a Portfolio Manager to transition or seek growth is multi-faceted, influenced by a blend of personal and professional factors. The allure of better compensation, the opportunity to manage a larger book, the quality of the trading platform, and the potential to seed or spin out their own funds all play crucial roles in this decision-making process.

However, it's not just about the tangible benefits. It's also about aligning with a platform that resonates with their unique strategies and long-term career aspirations. It's about finding a place where they can leverage their skills, grow their portfolio, and, ultimately, make a significant impact.

While the landscape can be complex and filled with trade-offs, being open-minded, diligent, and strategic can guide Portfolio Managers towards the best possible decision for their career trajectory. Whether you're a Portfolio Manager contemplating a move or a firm seeking to attract or retain top talent, understanding these motivations can provide valuable insights and shape successful outcomes.

If you found this article insightful, please like it and share your thoughts in the comments section. Your feedback is greatly appreciated.

If you're a Portfolio Manager considering a career move or a firm looking to attract or retain top talent and would like to discuss these topics further, don't hesitate to reach out. I'd be more than happy to have a conversation.

You can reach me at henry.booth@quantlink.co.uk or message me on LinkedIn.

Let's navigate this complex landscape together.

Quant Trading: What is it? Who does it? What are the challenges?

Tue, 16 May 2023 13:48:10 GMT

Quant Trading: What is it? Who does it? What are the challenges? (12 min read)

The idea of this article is to provide an overview of what quantitative trading is, what trading styles use it, what types of groups use it, the types of roles available with quantitative trading and where it is all heading.

What is Quant Trading?

Quantitative trading, or quant trading, is a trading strategy that uses mathematical and statistical models to analyse financial data and make investment decisions. It involves using algorithms and computer programs to identify patterns and trends in market data and execute trades based on those patterns.

Quantitative traders use large data sets to develop and test their trading algorithms. They often rely on high-speed computers and other advanced technologies to execute trades quickly and efficiently. They may also use a variety of financial instruments, including stocks, options, futures, currencies, and digital assets, to build and manage their portfolios.

Hedge funds, investment banks, and other institutional investors use quantitative trading. Still, it is also increasingly being used by individual traders and investors with access to sophisticated trading platforms and tools. Quant trading aims to generate consistent profits over time by identifying and exploiting market inefficiencies and anomalies.

Quant Trading History

Quantitative trading has grown significantly in usage over the last 50 years. In the 1970s, quantitative trading was in its infancy, and most trading was done manually using fundamental analysis and technical analysis. However, the introduction of computers and the development of quantitative models in the 1980s led to an explosion in quantitative trading strategies.

In the 1990s, the rise of high-frequency trading and the development of algorithmic trading systems further accelerated the adoption of quantitative trading. By the early 2000s, quantitative trading had become dominant in financial markets, accounting for a significant portion of trading volume in many asset classes. It has only grown since.

"According to data compiled by Goldman Sachs and shared by Analysing Alpha, about 60%-70% of trading in equities in 2016 was via algorithmic trading, while about 40%-50% of futures trading was contributed by algorithmic trading. About 35%-50% of the commodity trading volume is generated by algorithmic trading, and similarly, nearly 40% of options trading was via trading algorithms. During that period, Forex recorded about 20%-30% of algorithmic trading, while fixed-income trading had about 10% of algorithmic trading. see the chart below:" [1] [2]

Overall, the increasing availability of data, advances in technology and computing power, and the development of sophisticated quantitative models and algorithms have fuelled the growth of quantitative trading over the last 50 years.

Example Quant Trading Strategies

Here are some examples of quantitative trading strategies:

Statistical arbitrage: This strategy involves identifying mispricings in securities that highly correlate with each other and then profiting from the price differences. Statistical arbitrage often consists of high-frequency trading and requires sophisticated statistical models to identify the mispricings. It also comes in the form of pairs trading. Some argue they are the same, while others feel they have inherent differences.
Momentum trading: This strategy involves buying securities that have shown strong price momentum in the past and selling those that have demonstrated weak momentum. Momentum traders may use technical analysis and machine learning techniques to identify trends and patterns in market data. Also known as trend following - but again, some will argue trend is slightly different from momentum! Momentum tends to focus on breakouts. In contrast, trend / CTA strategies are looking at longer-term tailwinds.
Mean reversion trading : This strategy involves buying undervalued securities and selling those overvalued, expecting prices to revert to their long-term averages eventually. Mean reversion traders may use statistical models and other quantitative techniques to identify undervalued and overvalued securities. Some argue mean reversion and stat-arb are the same things, which they can be, but they can look at things differently, so some will argue MR is a form of stat-arb but not all that stat-arb is pure MR. Pairs trading could also be classified as MR for some and not for others.
High-frequency trading: This strategy uses sophisticated algorithms and high-speed computers to execute trades quickly and efficiently. High-frequency traders often use quantitative models to identify market data patterns and make trades based on those patterns. HFT could be market-making, stat-arb or some other form of arbitrage. The key characteristics are its speed and its prediction engine for short-price prediction.
Multi-factor trading: Multi-factor trading is quantitative trading that involves using multiple factors or variables to determine trading decisions. It aims to reduce risk and improve returns by capturing a broader range of information. It incorporates fundamental, technical, and macroeconomic factors.

These are just a few examples of the many quantitative trading strategies. The specific approach used by quantitative traders will depend on their investment objectives, risk tolerance, and the markets they are trading. It will likely be a blend of these mentioned. Some will blend two or three of the above or build new strategies based on that market - such as index arbitrage, global macro with RV & Carry types strategies etc.

Critical features of quant trading

Here are some of the critical features of quant trading:

Data-driven : Relies on data, statistical analysis, and machine learning algorithms.
Objective: Removes subjective biases from decision-making.
Systematic : Adheres to defined rules and procedures for trades.
Automation: Employs automated trading systems for quick, efficient executions.
Risk management: Utilises rigorous techniques to limit losses.
Medium to High frequency: Involves large numbers of trades in short time frames with high-speed computers and algorithms.

Who does quant trading?

Various financial institutions use quantitative trading, including hedge funds, investment banks, asset management firms, and proprietary trading firms.

Hedge funds are some of the most significant users of quantitative trading strategies. Many of the most successful hedge funds in the world, such as Renaissance Technologies, Two Sigma Investments, and D. E. Shaw & Co., rely heavily on quantitative models and algorithms to make investment decisions. Their quant trading techniques differ based on investment philosophy, time horizons, and strategies.

Asset managers utilise quantitative trading across many areas. Some may use fully systematic trading strategies, while others use them more strictly for portfolio construction, optimisation, and risk management.

Prop trading houses have become increasingly quantitative in their approach. Some of the most famous prop trading shops, such as Tower Research and Jump Trading, focus predominantly on HFT trading, which would be impossible without quantitative-based trading.

Across all three hedge funds, prop shops and asset managers, different setups and styles influence the quantitative roles and strategies. While imperfect, you can divide groups into a pod or research setup. Typically the pod structure is found in multi-strategy groups with teams of portfolio managers. In contrast, the research-style shops have more singularly focused funds, where they run a large fund with multiple researchers adding value.

Investment banks use quant trading for algorithmic execution, electronic market making, and central risk trading strategies across various financial instruments.

Individual investors engage in quantitative trading through platforms and tools that offer data analysis, algorithms, and resources for data-driven decisions. This area is rising as data becomes more available and cheaper, such as with crypto exchanges providing their price feed data freely, lowering the entry bar. Websites like QuantConnect allow individuals to backtest strategies better. Some hedge funds have been capturing more of these people by allowing external contributors to provide signals, such as Qube Research & Technologies. [3]

Types of Roles at Hedge Funds

Quantitative trading involves a range of jobs that require different skills and expertise.

Quantitative Portfolio Manager: A Quant PM uses quantitative models and algorithms to manage investment portfolios. They are responsible for developing and executing trading strategies to generate profits for clients or their firm's accounts. A quant portfolio manager typically works closely with quantitative researchers to develop and refine trading models and algorithms. They may work alongside other professionals, such as data analysts, software developers, traders, and risk managers, to develop and execute trading strategies.

The role of a quant portfolio manager focuses on investment decision-making and portfolio management; depending on the size of the team, they may also focus on researching, developing, and implementing quantitative models and signals. They must understand quantitative techniques and communicate and explain complex models and investment strategies to clients and stakeholders. A quant portfolio manager is typically a more senior position that requires extensive experience and expertise in quantitative research, financial modelling, and investment management.

Quantitative Researchers or Quant Analysts (quants):

Quantitative researchers and quantitative analysts are professionals in quantitative finance, but they have different roles and responsibilities.

A quant researcher is responsible for developing new models and trading strategies by conducting research and analysing financial data, aiming to either generate alpha through an automated strategy or improve the risk management & execution of the automated strategy. They work on developing mathematical models and statistical algorithms to identify patterns in data and predict future market movements.

On the other hand, a quant analyst is responsible for researching and modelling securities. They analyse data, monitor trading systems, and identify potential market opportunities or risks. Quant analysts work closely with traders to help them make informed decisions based on the models and data analysis.

Overall, while both roles require strong analytical skills and a background in mathematics, quant researchers are more focused on models and strategies for systematic trading. In contrast, quant analyst models return profiles, yield curves, pricing of securities and more to provide alpha generation or risk management ideas to the portfolio manager, who trades in a non-automated manner.

Data Scientists or Data Analysts:

Data scientists at hedge funds develop and implement trading strategies by analysing large volumes of financial data. They handle data analysis, model development, testing, evaluation, and data management, contributing to data-driven trading strategies that generate alpha and improve risk management.

Data analysts gather and interpret data to inform business decisions. They use statistical techniques and visualisation tools to identify trends and patterns, focusing on descriptive analytics for insight and decision-making.

Data scientists, conversely, have a broader focus on developing data-driven models and algorithms to tackle complex problems. They possess strong mathematics, statistics, and computer science backgrounds, employing advanced techniques like machine learning and artificial intelligence for predictive and prescriptive analytics. Typically holding a PhD, data scientists apply advanced ML techniques like deep learning to create signals, setting them apart from data analysts.

Software Developers : Software developers build and maintain software and algorithms for quantitative trading. They may develop trading platforms, backtesting tools, and other supporting software. Specialisation in a programming language, such as C++, Java, or Python, is required. While in-depth financial knowledge is not essential, expertise in a specific market area, like market microstructure, is highly valued .

Traders: Traders execute trades using signals from quantitative models and algorithms, monitor markets, and adjust strategies in real time. Roles may vary, with some being execution traders or relying on algorithms. Top groups often have in-house algo execution quants.

Risk Managers: Risk managers monitor and manage risks linked to quantitative trading strategies. They use diversification, hedging, and stop-loss orders and have finance, economics, or risk management degrees. They are skilled in quantitative analysis, financial modelling, and regulatory compliance.

Quant trading involves a range of jobs that require a combination of skills in mathematics, computer science, finance, and business. Each job's roles and responsibilities depend on the institution and the trading strategy.

Potential Issues for Building a Quant Trading Business

Hedge funds face several challenges when building a quant trading business, including:

Data quality & availability: High-quality data requires investment in data infrastructure and analytics tools.
Talent acquisition & retention: Attracting specialised talent (PMs, data scientists, software engineers, quantitative analysts) is complex due to high demand and competition. Offering competitive compensation, professional development, and work-life balance is critical.
Technology and infrastructure: Quantitative trading needs sophisticated technology infrastructure for efficient trade execution. Hedge funds must invest in the right technology to execute trades quickly and efficiently.
Regulatory compliance: Hedge funds must comply with various regulations, including reporting requirements, risk management, and compliance with securities laws. This changed recently, with the SEC requiring big funds to report losses. [4]
Risk management: Robust systems are needed to manage market, credit, and operational risks.
Performance measurement & evaluation: Developing benchmarks and metrics helps measure quantitative trading strategies' performance and attract investors and employees.
Raising capital: Securing adequate funding from investors is essential to support the growth and operations of a quant trading business, which may require showcasing strong performance, risk management, and a competitive edge in the market.
Successfully building a quant trading business requires considerable talent, technology, infrastructure, and risk management investment to create effective strategies and generate returns for investors.

The Future of Quant Trading: Key Trends and Challenges

The future of quant trading is likely to be shaped by a range of technological, economic, and regulatory factors. Some potential trends and developments in quant trading include AI, data, new assets, and more. As we explore these trends, we must recognise how they will impact the industry and how quant traders operate. I wrote about the Future of Quant Trading here.

Ai & ML - Machine learning and artificial intelligence have been widely used in quant trading for many years and will only increase. Advances in these technologies are expected to lead to more sophisticated and adaptive trading models and more efficient and effective data analysis. This increase in technology may revolutionise how quant trading is approached and help traders gain a competitive edge in the market. I discussed it more here, Ai and Machine Learning in Quant Trading . [5]

It is just the beginning of the journey. Many firms were looking at Ai and ML in their trading strategies, how they analyse this data set, and how they make better predictions. However, the release of ChatGPT and OpenAi has flipped this. It is no longer only about how to incorporate Ai into the tip of the spear work, the alpha strategies. But how can we use Ai across our whole business? Ai for marketing to better raise capital, Ai for coders to increase their efficiency by 50% or more. Ai to ask finance-related questions by researchers or a CFO etc., with BloombergGPT. [6] Citadel is already looking at bringing it into its business. [7]

Alt Data - Quant traders are turning to alternative data sources as traditional data sources become saturated. These alternative data sources, such as satellite imagery, social media, and internet search data, can provide unique insights into market trends and consumer behaviour. By leveraging these new data sources, traders can identify opportunities and potential risks that may not be evident from traditional data sources alone.

New Assets - Quantitative trading is expanding into new asset classes and markets, such as cryptocurrencies, commodities, and emerging markets, as traders seek to diversify their portfolios and identify new sources of alpha. The growth of these new markets may lead to new trading opportunities and challenges. As the industry evolves, algorithmic trading will take up an increasingly larger share of total trading volume.

ESG - There is a growing focus on sustainability and ESG factors, which will likely impact how quant traders develop trading strategies. There may be an increased demand for strategies incorporating ESG factors and promoting sustainability, aligning with investors' values and interests. As quant trading becomes more popular and mainstream, it is essential for traders to consider the broader implications of their strategies on the environment and society.

Quantum computing - could significantly impact the field of quant trading. The increased processing power could be used to develop more complex and sophisticated trading algorithms and analyse larger and more complex datasets. [8] Quantum Computing for Finance (QCF) is a collaboration between the University of Oxford, JP Morgan, and IBM that aims to develop quantum algorithms for finance. The project is focused on applications such as portfolio optimisation, option pricing, and risk management. However, many technical and practical challenges must be addressed before quantum computing can be widely used in quant trading.

Conclusion

In conclusion, the future of quant trading is likely to be characterised by continued innovation and the adoption of new technologies and data sources. However, quant traders must also navigate a complex and rapidly changing regulatory environment and increased competition from other market participants. Staying ahead in this ever-changing field requires continuous learning about the latest technologies, machine learning advancements, data sources, and more. By doing so, quant traders can stay ahead of the competition and the alpha decay they face, ensuring ongoing success in the industry.

The best way for someone to succeed within quant trading would always be to continue learning in this ever-changing field. Those that stop learning thinking they have a winning strategy, are quickly caught and passed. One must stay in touch with the latest technologies, ML advancements, data sources and more to stay ahead of the competition and the alpha decay they face.

Please let me know what your thoughts are.

Do you think stat-arb and mean reversion are the same or different?

Where do you think Ai will go in Quant Trading?

Please like and comment below.

Thanks for reading, All the Best, Henry.

henry.booth@quantlink.co.uk

Founder of QuantLink, a specialist search firm in the quant trading space, aimed at providing unique opportunities and value to our network.

[1] https://www.quantifiedstrategies.com/what-percentage-of-trading-is-algorithmic/#:~:text=In%202021%2C%20nearly%2092%25%20of,the%20time%20of%20the%20report .

[2] https://www.quantifiedstrategies.com/what-percentage-of-trading-is-algorithmic/#:~:text=In%202021%2C%20nearly%2092%25%20of,the%20time%20of%20the%20report .

[3] https://www.qube-rt.com/external-contributors

[4] https://www.bloomberg.com/news/articles/2023-05-03/big-hedge-funds-face-new-72-hour-deadline-to-report-major-losses

[5] https://www.quantlink.co.uk/ai-and-machine-learning-in-quant-trading

[6] https://www.bloomberg.com/company/press/bloomberggpt-50-billion-parameter-llm-tuned-finance/

[7] https://www.bloomberg.com/news/articles/2023-03-07/griffin-says-trying-to-negotiate-enterprise-wide-chatgpt-license

[8] https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/exploring-quantum-financial

Overcoming Hiring Challenges in the Quant Trading Industry: Strategies for Attracting and Retaining Quant Talent

Tue, 16 May 2023 13:38:19 GMT

Strategies for Attracting and Retaining Quant Talent (11min read)

The quant trading industry is known for its highly competitive and fast-paced nature, where success relies heavily on recruiting and retaining top talent. However, the hiring process can be challenging, often taking time and resources. Moreover, a bad hire can be costly and detrimental to a firm’s overall performance, reputation, and team dynamics.

In this blog post, we will explore the primary challenges quant trading firms face when recruiting quantitative talent and offer practical solutions to overcome these obstacles. By implementing these strategies, firms can minimise the pain associated with lengthy hiring processes and avoid the pitfalls of poor hiring decisions while building a high-performing team that drives exceptional results.

Challenges in hiring quantitative talent

There are many challenges quantitative hiring firms face, the main ones being talent scarcity, competition from other firms & industries, and difficulty in assessing technical skills. Hedge funds, asset managers, and prop shops face challenges when hiring quantitatively skilled professionals due to the niche skill set and high demand for top talent in the industry. However, the specific problems may vary slightly between these groups, as their focus and objectives can differ.

Some common challenges all these firms face include:

Talent scarcity: Quantitative professionals with the necessary skills and expertise are limited, creating intense competition among firms to recruit the best candidates. This scarcity results from the specialised nature of the roles, competition from other industries, and a smaller pool of graduates from quantitative disciplines. This scarcity makes finding suitable candidates for open positions difficult, leading to extended periods with unfilled vacancies and the need to compromise on the desired skill set due to the limited candidate pool.

High demand for top talent: The constant battle to recruit the best candidates adds to the challenge, as firms compete not only with each other but also with financial institutions, technology companies, and startups for the same limited pool of talent. It is a trend that will continue and grow as Ai takes over the world and permeates every business. This increased competition for skilled candidates makes it difficult to identify and secure top-notch candidates, such as quantitative researchers and portfolio managers before rival firms snatch them up. It also drives up wages, with higher salary expectations from top candidates who can have two, three or more offers. This increased demand creates urgency with shorter time frames to offer before candidates accept other opportunities.

Competition from non-financial industries: Quantitative professionals are highly sought after by industries such as technology, e-commerce, and data analytics, which can provide alternative career options and competitive compensation packages, making it challenging for financial firms to attract and retain talent. Less of an issue for large funds, but many firms may struggle to match the compensation packages offered by tech companies and other industries, which can offer enormous stock-based compensation. During the process, firms need to be aware of the difficulty in showcasing the unique advantages of a career in finance over tech careers. Long working weeks of 70+ hours at hedge funds versus the roughly 50 at a tech firm need to be addressed and explained.

Failing that, getting a ping pong table in the office can be the tipping point for major career decisions, apparently!

Niche skill set requirements: Candidates for quant trading positions must possess a unique combination of skills, including expertise in advanced mathematics, statistics, programming languages, and finance. This specialisation narrows the pool of potential candidates and can make finding the perfect match for a given role challenging. It includes immense difficulty in identifying and accessing candidates’ skills needed in the day-to-day of the job. It can lead to a lengthy and complex recruitment process to evaluate specialised skills. Differences between groups also create challenges in training and onboarding candidates to fill skill gaps.

Cultural fit: Ensuring that candidates are a good cultural fit for a firm is essential, as collaboration and teamwork are critical in the high-pressure environment of quant trading. Company culture and getting hires wrong that do not fit into or add to company culture can result in clashes between employees. It can lead to increased turnover due to misaligned expectations or values and difficulty fostering a collaborative work environment. The need for behavioural and key competency assessments is ever-increasing as a result.

Ensuring diversity: The quant trading industry has historically been male-dominated, and ensuring a diverse workforce has become a priority for many firms. Recruitment efforts must actively seek out and encourage applications from candidates with diverse backgrounds, experiences, and perspectives to help firms achieve this goal. However, this can be challenging due to the limited pool of available talent and potential biases in the hiring process.

Bias in the hiring process: Unconscious bias can affect hiring and lead to missed opportunities in recruiting the best talent. Unconscious favouritism towards candidates with similar backgrounds or experiences as the hiring team is a classic example. Overlooking qualified candidates from underrepresented groups due to preconceived notions or stereotypes is a more insidious form of bias. Firms must actively work to eliminate biases to ensure a fair and diverse recruitment process.

Retention: Given the competitive nature of the industry, retaining top talent can be as challenging as recruiting them. Firms must invest in employee satisfaction, growth opportunities, and competitive compensation to keep their quantitatively skilled professionals engaged and committed to the organisation. If they don’t, they can expect high turnover rates and increased costs associated with frequent hiring and training. Worse, disruptions in workflow and team dynamics due to departures have a far higher cost that is much more difficult to assess.

Intellectual property concerns: Financial firms often rely on proprietary algorithms, strategies, and trading tools, making them highly protective of their intellectual property. Balancing the need to maintain confidentiality while discussing specific requirements and strategies with potential candidates can be challenging during the hiring process. It is imperative and difficult to assess someone’s ability to do a job when you do not have access to data that can prove or disprove the potential hire’s claims. Groups are now very vigilant and aware of this and want to avoid being caught off guard hiring the next Samarth Agrawal or Sergey Aleynikov .

Remote work and distributed teams: With the increasing trend of remote work, firms may face challenges in hiring and managing distributed teams. Effective communication, collaboration, and performance management in a remote setting can be difficult, especially in a high-pressure, fast-paced industry like quantitative trading. Firms that do not offer hybrid work, or at least the flexibility to work remotely, will likely suffer over the long term. The banks are pulling everyone back five days a week, with JP Morgan stating all MDs must be in five days a week . How long before those MDs insist on everyone else also being in, which will lead to a spike in people looking to leave?

A quick search on LinkedIn jobs and you can see it playing out. Some roles and industries are even more severe than these numbers, with hybrid and remote roles getting 7x to 10x more applicants versus entirely on-site roles.

As an example see the images below:

DRW had this role open for two weeks and is at 123 applications. Versus Point72’s job below, available for two months, only has 88. Both are top firms, but TWO MONTHS longer and only a little over half as many!
FYI — DRW requires five years vs three for Point72, so it should have a smaller pool. DRW is pricing research for delta one traders, whereas Point72 is end-to-end short-term alpha research in equities — so Point72 is arguably a more attractive role.

123 applicants for hybrid role in 2 weeks

88 applicants for an onsite role in 2 months

Yes, it is only a sample of one and massively overfitted. But this is what I am seeing. Consider it a discretionary POV rather than a quantitative POV.

Long hiring process: Another challenge is that searching for the perfect candidate can be time-consuming. The longer the hiring process, the higher the risk of losing top talent to competing offers. Groups that can streamline their processes without loss on evaluation can move quicker and are more likely to succeed in hiring when top-notch candidates become available. If you need ten-plus rounds, you will consistently lose to the groups that finish it in five rounds.

Cost of hiring top talent: Recruiting top quantitative professionals comes with a high price, including competitive salaries, sign-on bonuses, relocation expenses, and other perks, which may strain the resources of smaller or less-established firms. Smaller groups can and should be creative when attracting top-performing quants to relieve the difficulty of competing with larger firms with more resources.

There are still other costs and challenges in allocating resources for recruitment while maintaining operational efficiency. Time is the most precious resource. A senior portfolio manager or CIO who has to read and review dozens of resumes, arrange and conduct interviews, and more, means less time focused on critical revenue-generating tasks.

It is not just the cost to hire that you need to consider, but the cost of a bad hire. A bad hire can have substantial financial costs in dollar amounts but even more massive opportunity costs. The team could become dysfunctional, PnL suffers, projects could be delayed, and new system improvements fail — how much would that cost you?

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While these challenges are common across hedge funds, asset managers, and prop shops, the specific problems might vary slightly depending on the firm’s focus and objectives. For instance, hedge funds emphasise risk management and performance-driven compensation, while asset managers might prioritise long-term stability and risk-adjusted returns. Prop shops focus on developing proprietary trading strategies and tools. These differences could influence the precise skill set, experience, and cultural fit required for each type of firm, thereby affecting the hiring process. However, the overall challenges in recruiting quantitatively skilled professionals remain similar across the industry.

Hiring quantitative researchers, portfolio managers, and other quant professionals in this competitive industry can be daunting. By understanding the challenges faced during recruitment and adapting accordingly, firms can enhance their ability to secure the top talent needed to drive success in the quant trading space. Now, we will discuss strategies to overcome these challenges and how firms can position themselves as an attractive destination for the best and brightest minds in the industry.

Tactics for Overcoming Hiring Challenges in the Quant Trading Industry

In the fast-paced world of quant trading, firms must proactively address hiring challenges to secure the best talent. Let’s dive into some key tactics that can help your organisation overcome these obstacles and build a high-performing team.

Creating an Attractive Employer Brand

First and foremost, it’s essential to establish your firm as an employer of choice in the quant trading space. Offering competitive compensation packages, including salary, bonuses, and equity or profit-sharing options, sends a strong message to potential candidates about your commitment to their success.

Something as simple as including salary information in job descriptions is an absolute must. Including it gives your brand an edge, attracts more applications and typically results in fewer challenges during the offer stage. Filtering compensation earlier can help prevent pain from a rejected offer or too high expectations. If you are willing to pay the highest, for the best, great — but still give a range. A wide range is better than zero information.

But it’s not just about the money. A strong company culture emphasising innovation, continuous learning, and a healthy work-life balance can attract candidates who value a positive work environment. Provide professional development opportunities, such as training programs, mentorship, conference attendance, and access to cutting-edge research resources. It shows candidates that your firm is committed to their growth and success.

Building a Diverse Talent Pipeline

The inability to create a diverse and inclusive workforce may impact creativity, collaboration, and innovation. A lack of diversity will harm a group’s performance. If all the ideas are from the same men, who went to the same school, studied the same subjects and worked in the same ways, who will see things differently, in a new way? A potentially better way? This is why diversity matters, and I recommend reading Matthew Syed’s “Rebel Ideas” to understand this more.

To attract a broad range of candidates and increase diversity, actively engage with educational institutions that offer solid quantitative programs. Participating in campus recruiting events, guest lectures, and sponsoring student clubs or projects can help identify talented students and recent graduates from diverse backgrounds.

Additionally, attend and sponsor relevant industry events focused on quant trading, data science, and related fields. It helps you connect with potential candidates and increases your visibility within the industry. Collaborating with diversity-focused organisations like Women in Finance , the National Society of Black Engineers , or Women Who Code can further expand your talent pool and support your diversity initiatives.

Leveraging Employee Referrals

Encourage your existing employees to refer qualified candidates from their networks. This approach helps you find potential hires who may not be actively seeking new opportunities and increases the chances of an excellent cultural fit. Offering referral incentives, such as cash bonuses or recognition programs, can motivate employees to take an active role in hiring.

Focusing on Cultural Fit and Implementing Retention Strategies

Evaluating a candidate’s soft skills, values, and interpersonal abilities is crucial in determining their cultural fit within your organisation. Involving existing team members in the hiring process can provide valuable insights into how well a candidate might mesh with the team dynamics. Assessing cultural fit can be challenging, as it involves evaluating a candidate’s soft skills and interpersonal abilities. Here are some excellent assessments to help ensure the right hire: DISC profiling , Big 5 , McQuaig , Myers Briggs etc.

Finally, investing in strategies to retain your top talent is essential. Offering clear career growth opportunities, conducting regular reviews of compensation packages, and fostering a supportive work environment can contribute to employee satisfaction and retention. Providing flexibility to create work schedules that cater for better work-life balance and reducing commuting will set you apart and make you more attractive to high-calibre candidates.

By embracing these tactics, quant trading firms can overcome hiring challenges, build a talented and diverse team, and position themselves for long-term success in this competitive industry.

Conclusion

In the fiercely competitive quant trading industry, the ability to attract and retain exceptional quant talent is vital for success. Recognising the unique challenges associated with the hiring process and adopting targeted strategies can significantly enhance a firm’s capacity to bring on board and retain the best and brightest minds.

One powerful approach to overcome these challenges is partnering with a seasoned recruiter specialising in the quant trading sector. By leveraging their years of industry-specific experience, these recruiters offer invaluable insights into the market’s intricacies and grant access to an extensive network of potential candidates. Their proven track record in matching the right talent with the right opportunities provides a compelling case for their expertise.

Collaborating with a specialised recruiter offers numerous advantages to help your firm stand out in a competitive landscape. Their extensive network allows them to tap into a vast pool of quant professionals, including passive candidates who may not be actively searching for new opportunities. Their expertise in identifying candidates with the right technical skills, domain knowledge, and cultural fit for your organisation ensures the best match, saving you time and money.

Additionally, they can guide on crafting attractive job offers and competitive compensation packages that appeal to top-tier talent. By staying abreast of the latest industry trends, specialised recruiters can help your firm stay ahead of the curve when seeking candidates with cutting-edge skills and expertise. Ultimately beating your competition.

These recruiters’ streamlined hiring process enables more efficient application management, candidate screening, and interview coordination. It will save your firm time and resources while ensuring you secure the best talent.

By embracing these advantages and working with an experienced recruiter specialising in the quant trading space, your firm will be better positioned to overcome hiring challenges and build a talented, diverse, and dedicated team that drives exceptional performance and innovation.

If your firm is keen on surmounting hiring challenges in the quant trading industry and securing top talent, don’t hesitate to contact us. QuantLink is here to help you navigate the competitive landscape and find the best candidates for your organisation. Contact us today to discuss your hiring needs and discover how we can help you achieve your recruitment goals.

henry.booth@quantlink.co.uk

Future of Quant Trading

Fri, 17 Mar 2023 13:51:06 GMT

What opportunities are there for systematic trading? Where might it go next?

Originally created October 2021

There are a ton of opportunities for Quant Trading in the future, too many to list here. Some fraught with their own unique problems. Here are some of my thoughts based on my knowledge and understanding of space.

I’d love to hear from those in the sector what they think. Hopefully, this also gives juniors and those on the outside looking an understanding of where the sector is heading.

Opportunity One

Machine learning (ML) has created tremendous opportunities for systematic trading, a rules-based approach to trading. The trading system has improved since the 1920s when traders predicted moves based on charts, manual calculations, and hand-drawn graphs on glass. Now, trading systems use better computer processing and additional data. This new equipment has enabled traders to automate the process, allowing for quicker calculations and a larger scope of research. However, the process cannot be entirely machine-run. Humans are still needed during the research process to build models and use ML to predict signals. Logically, the next evolutionary step is teaching the system to perform its own research and to create its own signals.

Machine learning (ML) would improve all three key areas where algos are used: execution, prediction, and risk management. In execution, ML could effectively process order book data on a microstructure level. Machine learning could also more accurately predict alpha by incorporating more data into the process, particularly alternative data. It could also improve risk management by discovering correlations that enhance hedging. This benefit is essential to avoid past failures such as the bear market’s shut down in the 1970s when 170 out of 200 hedge funds were lost due to incorrect protection.

I wrote more on Machine Learning and its likely impact on quant trading over the next ten years here .

Despite its benefits, machine learning is not without issues. Successful systematic trading, especially machine learning, requires data. However, data and infrastructure can be tremendously expensive. According to AlternativeData.org, “Total spending on alternative data by buy-side firms will jump from $232 million in 2016 to $1.7 billion . . . in 2020.” [1] This prediction threatens high expenses to those involved in systematic trading. Even if one is not spending on data, they will be overtaken and beaten by those who are using it effectively.

Another issue involved in machine learning is uncertainty about how its models work. Machine learning models are more complex than quant models and more difficult to learn. With quant models, one can essentially “understand” how their model works. However, this is not always the case with machine learning. A ML model may perform a task for an unknown reason. Not being able to debug it is an even bigger problem than understanding. Beyond this, forming relationships with investors and effectively articulating quant functions are keys to successful fundraising. Explaining how ML models work will be crucial to investor confidence, capital raising, and more. Explainable AI is a hot topic for this reason.

Quants should be cautious when using ML because machine learning can find patterns that do not exist. A famous example is the Anne Hathaway Effect caused by news-reading algos. Many machine-learning algos were “reading” news articles and tweets that included the word “Hathaway” and deduced that Berkshire Hathaway stock was popular. However, the news was referring to the actress Anne Hathaway and her recent success. The mix-up caused the ML sentiment algos to make Berkshire Hathaway stock go up whenever Anne Hathaway was in the news! [2]

Another item one must watch for is biases. An advantage of systematic trading, and arguably its greatest strength, is the removal of human biases. However, a known concern is unwittingly incorporating biases into ML algos through the datasets used. [3] For example, the algorithm that Amazon used between 2014 and 2017 to screen job applicants reportedly penalized words such as “women” or the names of women’s colleges. [4] Ensuring one does not incorporate biases has always been hugely important, especially now in the current cultural climate. Including biases in algos that exploit biases would be ironic.

Opportunity Two

A second opportunity for quant trading is its ability to grow into new areas. The two hottest areas are fixed income and crypto. Systematic trading has always evolved into new markets and new strategies. Famous firms such as AHL started by following trends, and now it trades multiple assets in a multi-strategy approach. Jump Trading began as an HFT prop shop, but now it trades multiple assets in multiple frequencies and has a VC arm. Nearly all quant funds expand and seek new sources of alpha. The decay of signals has accelerated in modern times. Previously, a signal may have lasted for weeks, months, or sometimes longer. Now, the competition quickly finds them and arbitrages them away. Because of this struggle, finding new alpha is a constant battle for quants.

There are basically two ways to approach the problem: systematize an existing strategy or take one’s systematic model to a new asset. A quantitative, systematic, data-based approach is preferable to one that is purely discretionary. For example, an event-driven data analysis successfully predicted a merger arbitrage opportunity by looking at flight tracking data. [5] Quants saw a company jet flying to a particular airport in a seemingly random location and successfully deduced the company was flying there to meet with another about a proposed takeover.

Quantamental has been a buzzword for a few years now where quantitative and fundamental worlds collide. This space is growing by the day as more fundamental shops look to incorporate data and quantitative insights into their investment processes. Typically, this combination takes two forms. At the start, quants rank companies quickly and on a massive scale, allowing the analysts to focus on the best. Quants also work with the analysts to supplement their fundamental deep-dive research work with additional insight from alternative data sets, such as credit cards, foot-fall traffic, or perhaps satellite imagery.

Systematic trading could also expand into new asset classes in illiquid markets which have historically been reserved for manual execution, as eluded to above. Here, it would benefit from automation in algo execution. Fixed income, including credit (and particularly corporate credit), is being increasingly electronified. Years ago, equities experienced this process when the fixed income world had been out of reach. But, as opportunities become harder to find in equities and the sell side improves its ability to offer electronic trading, opportunities arise for systematic trading in the fixed income and credit worlds, especially while inefficiencies remain. There are approximately 41,000 stocks but millions of differing bonds.

Cryptocurrencies are (and will be) a big area for growth. The more markets that are traded systematically, the harder it will be to find inefficiency and make profits. One option to improve this method is to move on to inefficient markets such as newly electronified fixed income or volatile new crypto markets. Many groups are already operating new strategies and are actively hiring. Paul Tudor Jones of Tudor Capital began investing a while back. [6] BlackRock has approved the trading of bitcoin futures. [7] DRW has had a crypto business for a while now. Jump is heavily invested in the space. Steve Cohen, owner of Point72 has recently been convinced. [8] Just to name a few.

Systematic trading needs new spaces of opportunity because of a self-fulling prophecy and paradox. Systematic trading works by exploiting market inefficiencies. But, as it finds and exploits the inefficiency, it improves efficiency and reduces opportunities to exploit. The 2007 quant crash was the result of overcrowding and too many algos unwinding at the same time, showing that too many similar algos can harm the market. So, in essence and in irony, as systematic trading makes the market more efficient, it reduces its own ability to perform. Ergo, systematic trading needs to continue to move on and discover new inefficient markets to maintain its success, especially as more automated trading removes human biases.

The other solution to the systematic trading efficiency paradox and the third way Hedge Funds will grow in this area is to improve current processes. By having better data, better infrastructure, better execution, and better talent than their peers, businesses can aim to exploit inefficiencies by “seeing” them first either through speed or knowledge. Going forward, hedge funds must seek profit through weaker and more nuanced signals.

Where do you see opportunities for systematic trading to grow?

Please comment, like, or share this article. Thank you.

[1] https://www.marketwatch.com/story/the-explosion-of-alternative-data-gives-regular-investors-access-to-tools-previously-employed-only-by-hedge-funds-2019-09-05

[2] https://www.theatlantic.com/technology/archive/2011/03/does-anne-hathaway-news-drive-berkshire-hathaways-stock/72661/

[3] https://towardsdatascience.com/biases-in-machine-learning-61186da78591

[4] https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G

[5] https://www.marketwatch.com/story/the-explosion-of-alternative-data-gives-regular-investors-access-to-tools-previously-employed-only-by-hedge-funds-2019-09-05

[6] https://www.cnbc.com/2020/10/22/-paul-tudor-jones-says-he-likes-bitcoin-even-more-now-rally-still-in-the-first-inning.html

[7] https://www.bloomberg.com/news/articles/2021-01-20/blackrock-files-to-add-bitcoin-futures-to-two-of-its-funds

AI and Machine Learning in Quant Trading

Fri, 17 Mar 2023 13:43:38 GMT

AI and Machine Learning in Quant Trading... Is it all hype? Is it a revolution? What are some issues?

How AI and Machine Learning are used and how it will affect quantitative trading in the next 10 years.

Originally created May 2021

First, there is a difference between Artificial Intelligence and Machine Learning. AI is the whole collective conception of a computer being able to think like a human. Whereas, Machine Learning is a subset of AI, and is the ability of a machine to learn from data with no explicit programming.

AI and Machine Learning are hot topics in quant trading and can feel new areas. But, while perceived as magic to some, both are rooted in mathematics. Machine Learning techniques are statistically driven and have been used by quants for a long time.

Advances in computer processing power, availability of big data and media attention have created hype. Some say we’re right at the peak of inflated expectation according to the Gartner hype curve.

Machine Learning is most effective at improving parts of the trade life-cycle process, such as data processing & modelling, forecasting & signal research, risk management and execution.

Data processing & modelling have benefitted from Machine Learning. It has made the accumulation and exploration of data far easier. ML allows a quant to look at far more data in a shorter period.

Alternative data will grow over the next ten years, especially when you consider the quantity of data we create. The World Economic forum believes we will create 463 Exabyte’s per day by 2025 [1] ! The internet only created one Exabyte a day in 2012 [2] … An Exabyte is a 1 byte followed by 18 zeros!! One poll found 69% of funds are already using alternative data. [3] Machine Learning used is with alternative data to find new signals or enhance existing ones.

There are many examples of how alternative data is used. In one well-known example, a fund used flight tracking data to predict a merger [4] . In others, satellite imagery is being used to assess crop yields in commodity trading. Credit card data and footfall data are being used in equities. While sentiment analysis appears to be a productive predictor.

Alternative data is attractive, but for ML to be effective, data sets need to be very large with a long history. Any ML algo is only as good as the data we feed it, so it needs to be high-quality data. Many big data sets are only a couple years old and can be incomplete / inaccurate, so provide little predictive value.

Because of this, some argue whether the insights are valuable and the low signal-to-noise ratio makes it difficult to build a model. Credit card data will not show if there was a sale on, which caused the increased spending, and so unlikely to lead to an uptick in profits, for example.

Another concern is privacy, how the data was gathered and who has the rights to the data. This theme has been growing over the years and with Apple’s latest update even more prevalent.

Machine Learning has had and will continue to impact forecasting and finding new patterns. It could discover unknown factors. However, they would likely still need to be grounded in an underlying economic factor, which are well known, so chances here are slim. That said, Machine Learning increases the scale a quant can work at, like the scale of data they consider, as mentioned, or the scale of research they do. For example, Machine Learning could be better at combining non-linear signals or pooling many weak predictors.

Within forecasting, Deep learning, a form of Machine Learning, is having a big effect because it has excellent prediction power. But we struggle to understand how this predictive power is created, which can prove an issue for internal analysis. Being able to interpret and explain the model is key for compliance, investor confidence, and risk analysis.

In the future, we’ll see more Machine Learning algos taking actions, in particular in trade execution. Reinforcement learning, another type of ML, is being used to model a multi-agent approach in trade execution on a microstructure level, analysing the limit order book. In fact, reinforcement learning is trending nowadays for many aspects of quant trading, including portfolio construction & optimisation, as well as different clustering and prediction problems.

A major hurdle for ML is the complexity and scale of financial markets. Financial markets are a highly complex multi-agent system with billions of interactions between humans and algos. ML models have difficulty going beyond a dozen agents so far. This is the biggest reason why we don’t have a fully autonomous ML strategy. When you add in the non-static nature of markets, it makes it almost impossible.

Building good ML models for non-trivial problems in quant trading with the ever changing market dynamics is hard. You need to have large amounts of high quality data (which may not even exist given the changes in market dynamics with new financial products, new regulation, and new algos), a “good” model, and matching hyper parameters. It is very easy to go wrong, very hard to get right.

So, we’re a long way from having a fully automated ML based quant strategy that can do the entire investment process in a hands-off manner. If someone suggests they’ve done it, it’s likely too good to be true!

Nonetheless, it’s an exciting time for Machine Learning, it will continue to make a tremendous impact in quantitative trading over the next 10 years. Particularly, on individual parts of the investment process, like forecasting, modelling or execution.

How do you think Machine Learning will impact quant trading over the coming years?

If you've enjoyed this, please hit the like button or comment on your own thoughts!

[1] https://www.weforum.org/agenda/2019/04/how-much-data-is-generated-each-day-cf4bddf29f/

[2] https://www.zmescience.com/science/how-big-data-can-get/

[3] https://www.hedgeweek.com/2020/05/04/285283/hedge-funds-use-alternative-data-tipped-surge-new-industry-study-finds

[4] https://www.marketwatch.com/story/the-explosion-of-alternative-data-gives-regular-investors-access-to-tools-previously-employed-only-by-hedge-funds-2019-09-05

How To Become a Quant PM

Fri, 17 Mar 2023 13:30:47 GMT

How to become a quantitative portfolio manager?

When I ask candidates “What are your career aspirations?”, naturally there are a huge variety of answers, depending on a range of factors. That said, for quant researchers, one of the most popular answers is that they wish to become a Portfolio Manager.

To be hired as a Portfolio Manager, you need a track record. Some clients ask for ten years track record, others ask for three to five, some just need a one-year track on a deployable strategy. With no track record, no group is going to give you client or even prop money to make bets on the financial markets with zero prior experience.

So how do you get a track record? Well, by being a Portfolio Manager…. Therein lies the problem: To be hired as a Portfolio Manager, you need a track record. To have a track record you need to be a Portfolio Manager.

The set-ups

To understand the journey to becoming a Quantitative Portfolio Manager, we must first understand the different set ups. While each group is uniquely different in their approach and set up, we can generalise on a basic level for quant funds that they are either a multi-manager platform, or a collaborative research environment.

On a multi-manager platform, you have a Portfolio Manager, possibly with a sub-PM and then quantitative researchers beneath. A PM will have researched and created a portfolio of strategies and will be in charge of how to allocate the risk within that portfolio and setting the research agenda for the researchers.

A Quantitative Researcher works under a PM with the focus typically on alpha signal generation; commonly improving the current signals, or if more senior, coming up with new ideas & signals.

A Sub-PM is typically a researcher who has proven themselves to produce consistent new alpha, so has been given a small amount of risk to manage themselves, under the direct and strict supervision of the PM – essentially, they will create the signal and then “monitor” the trades.

Collaborative Research groups are different in that they have large teams of researchers reporting to a head of desk/research. The teams are usually asset class specific and will cover alpha research and portfolio construction. The team will have a discussion around the risk that is put on to each signal/model, yet the actual trading will usually fall under a separate team, an execution team.

There is a third type of group to briefly mention that have Quantitative Traders, these are heavily associated with fully systematic funds that were prop desks spun out of the banks. Whilst Quant Trader can be a board term, in this context, a Quant Trader is similar to a sub-PM, rather than PM. Senior Management will have total responsibility for the whole portfolio and then divide that into sub-portfolios specific to a strategy style and allocate risk to that person in a strict sense.

Original ideas!

Some researchers only focus on improving the current signals, which means at best you’ve inherited a strategy. Sadly, a group is not going to hire you to only replicate another person’s work. Maybe in years gone by would groups hire you to take a strategy and gain insight into the competition. Today, where trades are already so crowded, they want someone with more creativity. When the signals decay, as they inevitably do, there is no proof you can adjust to the changing market conditions if you haven’t created anything new. If you’ve been involved in idea generation, then you have extra value.

Once you have a track record of new ideas that have been proven to generate returns, you can start to acquire more of the skills a PM requires. The two major skills in my opinion are portfolio construction and risk management.

Portfolio construction appears to be a skill that is learnable relatively quickly, mastery is the difficulty. However, for something like a cash equity stat-arb strategy, mastery is not required. It is likely best achieved in your current role by working with team members or shadowing the PM.

Risk management experience is a bit trickier to acquire. Here I am talking specifically about running risk, money, PnL management. At a minimum, monitoring of execution is a handy first step. Realistically, it’s far bigger than that, and given the automated execution world we live in, not that simple. The real thing is to be involved in the discussion around risk allocation to your signals and the weightings each model is given.

Risk management is most important around drawdowns and how you handle them. Drawdowns are a natural part of the process, and you need to understand the mathematics & economics of why it occurred, as well as know what to do in theory. In the real world, none of that prepares you for actually telling management that you lost $100k, $500k, or a million today. That is more about human psychology, and can really only be mastered through experience. If you can’t explain why a drawdown has occurred, your tenor will likely be short lived.

Now you have original ideas, you know how to bring them into one strategy, and you have a foundation in PnL management, you can then ask to be given responsibility for managing the risk around your research and so become a sub-PM.

The Researcher Trap

Do not fall in to the trap that some researchers do, where they create a great signal, and think they deserve to become a PM.

Just because you’ve created a signal out of some credit card data doesn’t mean it will last forever!

It will not be long before it’s arbitraged away when everyone else finds it. Numerous quality ideas, coupled with abilities to manage risk is what is required to become a PM, sub-PM or a Quant Trader.

Stepping up

So you’ve stepped up from a researcher to become sub-PM or a Quant Trader, but to continue the journey, you need to be asking some different questions.

If you’ve been a sub-PM for a year, then you have the making of a track record. You’ll be able to point to a PnL history and claim it as yours. Once you have this, then your current firm should be looking to make you an independent PM.

Firstly, you should be asking for more autonomy to put your own trades on. I find sub-PM’s are strictly managed and everything needs to be signed off by the PM. Achieving autonomy is a step in the right direction to becoming an independent PM.

Additionally, you need to be asking to scale up. In the majority of cases, the amount of risk you can put on is strictly managed. The top groups are not going to hire you if you have a 3-year track record, but have only been trading $10m GMV… they are in a different league. At an absolute minimum, you need $20m capital/AUM, un-leveraged. Realistically, you need $50/$100m in book size for the top tier groups to consider you.

Scale isn’t just an issue of capital, it’s also about how diversified your own strategy is. A great Sub/PM or Quant Trader is looking into new data sets, new academic research, new market intel etc. in order to scale the portfolio in a different manner.

You get a better Sharpe ratio from 1000 decent signals, than you do off 10 great ones!

So you need to be asking about bringing new research in, which I find can be a sticking point, particularly in the Quant Trader set-ups I mentioned which seem to have more internal secrecy - potentially a hangover from their banking days where secrecy was king in a highly political environment. The Quant Trader style shops can limit access to particular data sets, prevent you from running different style strategies, restrict you to specific markets or even exchanges. All of which severely hamper your ability to scale ideas.

One thing to consider is that each group has its own trading mandate, so any new research would need to be in line with that or a natural progression for the group. You are not going to be able to go from stat-arb US equities to looking at index-rebalance in APAC.

If you’re running risk on strategy ideas designed or thought up by someone else, and not bringing your own, then you’re more of an execution trader. Signals decay and, as with the researcher, funds want to know they can count on you to continue to deliver returns when they do decay by creating new signals, as well improving old ones. Showing this skill is extremely important to being given independent status.

Reality

A PM will offer their best researcher a sub-PM role because he or she doesn’t want to lose the alpha generated by that researcher. However, once you’re a sub-PM you can hit the glass ceiling quite quickly. You can have a great run for 2/3 years as a sub-PM, but it can be difficult to go on to the next level and gain independent PM status. Management are unlikely to want to fix what is not apparently broken. Also, the PM will not want to lose the nice percentage you add to their portfolio and their back pocket!

Next Steps

There are steps to be taken at each stage of your career in order to achieve the career aspirations you desire. Starting with the big picture of what kind of PM you would want to be, and working backwards from there to understand the skills required at each stage. Then making concerted efforts to learn them, and put them into practice. The most obvious place to begin with is in your current role, the majority of groups and PM’s will certainly help you grow. It’s when you’re prevented from learning & growing, when you hit that glass ceiling, stopped from scaling or limited by imagined walls, that’s when you should consider if there is a better group that can facilitate your career aspirations.