The Full Guide to Quant Funds: Careers, Salaries, Recruiting, Exits, and More
Rather than relying on antiquated concepts like fundamental analysis and human discretion, quant funds use data, statistical models, algorithms, and automated systems to trade.
Some quant funds, such as Renaissance Technologies, have performed amazingly well, with its Medallion Fund generating 66% annualized returns between 1988 and 2018.
But pointing to Renaissance as evidence of quant investing being “the best” is a bit like pointing to Warren Buffett as evidence of value investing being “the best.”
Yes, either strategy can perform well, but there’s only one Warren Buffett – and only one Renaissance.
Industry-wide performance is a mixed bag, and while quant funds offer plenty of benefits, such as extremely high starting compensation, I believe they’re a bit overhyped.
But before digging into the performance and salary levels, let’s start with the fundamentals of quant funds:
What is a “Quant Fund”?
Quant Fund Definition: A quant fund is a hedge fund that uses statistical techniques, mathematical modeling, and automated algorithms, rather than fundamental analysis and human judgment, to make investment decisions and execute trades.
Like all hedge funds, quant funds raise capital from institutional and accredited investors and invest it in liquid, publicly-traded assets to outperform the overall market.
Quant funds can be single-manager or multi-manager, and the trade-offs are similar: SM funds are more like “practices,” while MM funds operate more like corporations.
So, the key distinction is the investment analysis.
A hedge fund Analyst at a long/short equity fund might spend a lot of time reading annual and interim reports, speaking with management teams, conducting due diligence, and building traditional financial models.
But at a quant fund, Analysts spend their time devising statistical criteria to make investment decisions and testing those criteria with backtests before using them in live markets.
For example, a Quant Researcher might spend the day thinking about this type of question:
“Here are 200 possible conditions that might result in the stock price of Company X rising. Let’s test the statistical significance of these conditions and use them to predict the direction of Company X’s stock price.”
The researcher would then brainstorm a list of these conditions based on other strategies, academic papers, and analysis of internal data:
- Over the past 10 years, whenever Company Y’s stock rose by 10%, Company X declined by 5%; there’s a 65% correlation between the two.
- Over the past 5 years, whenever the trading volume of Company X exceeded A, its stock price increased by X% on average; there’s a 50% correlation between the two.
- In the past year, whenever oil exceeded $Z per barrel, Company X declined by Y% on average; there’s a 70% correlation between the two.
The Quant Researcher would then use these findings to develop a mathematical model for Company X’s stock price.
The Trader would develop strategies to get the best price on each trade, and the Developer would implement the investment strategy and trading execution in code.
Wait, But What is a “Quant”?
Banks and other finance firms have started labeling every position “quant,” so it has become a nebulous term.
It could mean anything from a middle-office role at a bank to an algorithmic trader at a large bank to a hedge fund professional who owns their P&L.
To make things even more confusing, many hedge funds also employ “quants” for risk management or price modeling.
If you work in risk management, you are doing something “quantitative,” but it’s not quite the same as coming up with ideas that could generate trading profits.
This article will focus on quant research roles at hedge funds, where the main job is to devise investment strategies.
The Quant Fund Team: Researchers, Traders, Developers, and Everyone Else
At most quant funds, the main categories of junior-level employees are:
- Quant Researchers or Quant Analysts: They create statistical models by reviewing academic research, brainstorming ideas, and backtesting new strategies. You’ll see many statisticians, physicists, and mathematicians in these roles, and quite a few have Ph.D.’s – though an advanced degree is not necessarily required.
- Quant Traders: Traders execute the researchers’ ideas, but at quant funds, they also code and create automated systems to make trades. The difference is that they focus on the efficiency of this trading activity rather than creating strategies in the first place.
- Quant Developers or Software Engineers: They develop data access and analytical tools, and they have to understand not the software and hardware but also markets and trading strategies. They must write clean, extensible, and robust code because the algorithms change all the time.
- Business Development / Operations / Compliance: Professionals in these areas work on non-investment tasks for the fund, such as complying with regulatory frameworks, figuring out taxes and fees, and improving efficiency.
Above these levels, there are the Portfolio Managers (PMs) and senior executives (if the firm is big enough).
At quant funds, PMs are also in charge of final investment decisions, risk management, investment logistics, marketing, and fundraising.
The difference is that rather than judging individual trade ideas generated by the Analysts, they’ll review automated strategies and decide which ones to implement based on backtest results and smaller tests run in the live markets.
For example, if the PM manages a $100 million portfolio, they might allocate $20 million to Strategy #1 and $10 million to Strategy #2 because of concerns that Strategy #2 might create too much portfolio-wide risk.
Why Do Quant Funds Need So Many People? Isn’t the Trading Automated?
The short answer is that no strategy works forever.
Market conditions change over time, and strategies become less effective as more firms use them.
Back in 1990 or 2000, you might have been able to build a trading strategy and “set it and forget it,” but the past few decades have turned that into a pipe dream.
So, the Researchers, Traders, and Developers constantly tweak the models and code to adjust the firm’s strategies.
And after a certain point, simple tweaks stop working, so they’ll need to generate completely new ideas.
As the “half-lives” for these new ideas decrease, even larger teams and more work are required.
The Path to Portfolio Manager (PM)
Once you pass the 4-5-year mark, your compensation flattens out, so you need to win a promotion to Portfolio Manager if you want to earn more.
As with all hedge funds, you need to show strong P&L results that you can claim directly.
But if you’re researching strategies and building statistical models, it isn’t easy to take 100% credit for anything that happens – especially if you’re in a large team.
There’s nothing “wrong” with staying in a Quant Researcher position rather than advancing to PM, as you’ll still earn a ton of money if you perform well.
But your career will be less stable, and your compensation will reach an admittedly high ceiling.
The best way to advance to a PM position is:
- Join a multi-manager hedge fund that has clearly defined career paths and promotion criteria.
- Move “as close to the alpha as possible” by working in a smaller, siloed team where you can take more credit for the results.
- Assume more of a managerial role and develop direct relationships with the LPs so that you can point to results such as AUM growth.
The Top Quant Funds
There are almost too many “top quant funds” to list, but I’ll give it a shot.
Some of the top funds include D.E. Shaw, Renaissance, Two Sigma, Citadel, AQR, Point72 (formerly SAC), Quantitative Management Associates, AlphaSimplex, Capula, PanAgora, Acadian, Man Group, Millennium, and Bridgewater.
Some of the larger ones, such as Bridgewater and Citadel, do a lot more than just “quantitative investing,” so they’re not dedicated quant funds.
Then there are firms that are more on the market-making or prop trading side, but which could also be labeled “quant”: Jane Street, Hudson River Trading (HRT), IMC, Optiver, DRW, and SIG are all examples.
Some of these firms, such as Jane Street and Two Sigma, like to hire undergrads, while others prefer Ph.D.’s and other advanced degree holders.
Quant Salary and Bonus Levels
Base salaries for entry-level Quant Researchers at hedge funds in New York are around $125K to $150K, with bonuses worth 50-100% of that.
So, you could potentially earn between $200K and $300K USD in entry-level roles in this field.
Some quant funds have paid even more than $300K to new hires, with signing bonuses that take total compensation closer to $400K.
After you’ve been working for several years, total compensation moves toward the $500K+ level.
To go beyond that, you usually need to become a Portfolio Manager or win a more senior position at the firm.
At those levels, compensation could go beyond $1 million per year – depending on your results and the firm’s overall performance.
If you’re a Quant Developer or Quant Trader, entry-level compensation is similar, but the salary vs. bonus split may differ.
If you remain a Quant Developer, your compensation will move toward what Big Tech companies pay their mid-level engineers – but it’s unlikely to go much beyond that.
Yes, good code adds value, but if you don’t own a P&L or attract clients’ assets, your compensation will always have a ceiling.
If you’re a Quant Trader, your compensation could move up closer to what Quant Researchers earn or even go beyond it (completely dependent on your performance and how the team splits its P&L).
Quant Hours, Lifestyle, and Culture
Yes, the entry-level compensation in quant fund roles is amazing… but there are some downsides.
For one, it is a stressful job where the weekly hours can extend up to 60-70+ per week, depending on market conditions.
The average is closer to 50-60 per week, and like sales & trading roles, you’re busy during the entire day with little downtime.
If you’re comparing quant jobs to software engineering at Big Tech companies, the hours are longer, and the stress is greater on the quant side.
Also, tech culture tends to be relaxed and collaborative; everyone is working to build better products, but they’re not directly competing with each other.
By contrast, quant funds tend to be more cutthroat and competitive, and there may be a lot of “siloing,” where teams do not share ideas or insights.
Not every firm is like this, but in general, the atmosphere is quite different from the one in tech.
You are there to make as much money as possible – not to sit around playing games or chatting with your co-workers while doing a few hours of “real work.”
Quant Fund Recruiting: Who Gets In?
The biggest myth about quant fund recruiting is that you “need” a Ph.D. in math, physics, statistics, or computer science to break in.
There are plenty of Ph.D. holders in the field, and some funds do prefer to hire Ph.D.’s, but the degree is not necessary to win offers at all funds.
If you don’t believe me, look at the way Citadel describes its “Quantitative Researcher” position:
“We are seeking top undergraduate, master’s, and Ph.D. students who are entrepreneurial self-starters and enjoy being in a fast-paced and dynamic environment for exciting opportunities in our automated quantitative trading businesses.”
The reason for this is simple: most of the math used at quant funds is not that advanced.
It’s closer to what undergrads in technical fields need to know (multivariable calculus and differential equations) than it is to the level required to understand Andrew Wiles’ proof of Fermat’s Last Theorem (for example).
Oh, and if you’re interviewing for Trader or Developer roles, your coding skills matter more than your knowledge of advanced math.
To have the best chance of breaking in as a Quant Researcher:
- Be a fresh graduate from a solid-to-top-tier university. Pedigree still matters at quant funds, but less so than in fields like IB/PE.
- Intern at quant funds, prop trading firms, or tech companies, or complete other math/statistics/coding-related roles.
- Complete a “mixed” technical degree, such as something that combines elements of math, stats, and computer science. A pure math or physics degree will be much more difficult and won’t necessarily provide a big benefit.
- Do your own side projects, such as automated trading strategies based on academic papers, and produce results and backtests.
- Compete in math competitions, such as the Putnam, that require creative solutions to tricky problems based on high school and undergrad-level math.
Hobbies, activities, and leadership experience won’t necessarily help with quant fund recruiting because they only care about your problem-solving skills.
Some of the larger quant funds conduct on-campus recruiting and offer online applications; for the ones that do not, old-fashioned networking via LinkedIn and email is your best bet.
If you have completed a Ph.D. or you’re working toward one, you’ll almost certainly interview for Quant Researcher roles.
In this case, you need a demonstrated interest in the financial markets to succeed.
There are plenty of academics who are “good at math,” but unless they have a serious interest in the markets, trading, and investing, they tend to perform poorly at quant funds.
You can prove your interest with relevant side projects you release on GitHub or closely related internships.
If you interview for Quant Developer roles, you’ll have to complete coding exercises and whiteboard problems, similar to tech interviews.
For Trader roles, take a look at the article on prop trading careers to get an idea of what firms want in candidates.
That same article also has suggestions and resources for practice coding exercises.
The Quant Fund Recruiting Process and Interview Questions
Firms that use on-campus recruiting follow the standard process: they’ll give you an online test, move to HireVue or phone interviews, and then do back-to-back interviews with several full-timers.
If you’re an experienced candidate, it’s common to go around in circles interviewing with funds indefinitely.
Many quant funds don’t necessarily want to hire anyone, but they always want to get new strategies that have worked for others.
So, if you keep going through interviews where they ask increasingly detailed questions about your strategies but don’t give you a decision time frame, you may want to reconsider the fund.
Interview questions at quant funds are beyond this site’s scope, as we focus on accounting, valuation, and corporate finance topics.
But you can expect a wide range of math, probability, and statistics questions, so you should prepare accordingly.
A few recommended resources include:
- Quant Trading Primer by Max Dama for an overview of the key topics.
- Heard on the Street for interview questions.
- Cracking the Coding Interview for practice programming exercises.
- Elements of Statistical Learning
- Advances in Financial Machine Learning – Maybe a bit too advanced for an internship; more of an on-the-job reference.
- High-Frequency Trading – This one covers common quant strategies.
Quant Fund Exit Opportunities
And now we arrive at the major downside of quant fund jobs: the exit opportunities aren’t so broad.
Your main options are:
- Stay in the role and keep performing well, even if your job title stays the same (not an exit opportunity).
- Win a promotion to PM or a managerial role where you’re leading a team but not necessarily doing day-to-day technical work (also not a true exit).
- Move into other trading/investing-related roles, such as prop trading, or win a quant trading role at a large bank (lower pay, but more job security).
- Join a tech or fintech company that’s seeking candidates with programming/statistical skills.
- If you have a Ph.D., return to academia.
And… that’s it.
Getting into fields like investment banking, private equity, equity research, corporate development, corporate finance, or venture capital from a quant fund is unlikely, especially if you’ve been in the role for 5+ years.
The skill sets are too different, and the important points in some of these roles – such as deal negotiations – are useless in quant roles.
It’s not impossible; we have covered stories of readers who left quant roles and broke into fields such as equity research before.
But it’s also not likely, and it gets more difficult the longer you stay in a quant role.
Turnover at quant funds also tends to be high, especially among Ph.D. and other advanced degree holders.
These degree holders are extremely smart, but many of them have no interest in the financial markets.
They want to solve math puzzles or work on proofs all day, which is not a recipe for success when you need to generate profits for your firm.
So, Are Quant Funds the Future?
As you can tell by this article’s tone, I am less bullish on quant funds than many other people.
The main problems are:
- It’s a mature/crowded industry – This was less true in 2000 or 2010, but as of 2020, it has become more difficult to find winning strategies that continue to perform well over time. Also, “quants” have become a bit commoditized, and many strategies are dependent on buying massive amounts of high-quality data.
- Performance has been mixed – Quant funds have performed poorly over the past few years, and so far in 2020, they’ve underperformed the average hedge fund and the average U.S. equity mutual fund. After some strong results in a few years of the past decade, fewer than 20% of quant mutual funds now outperform the broader market.
- You don’t develop a broad skill set – If you decide you want to make a total career change, you might need business school to make it happen (unless you’re starting your own company).
So, I’m not convinced that quant funds are going to take over the world.
If you’re thinking about pursuing quant fund jobs, it’s similar to the sales & trading vs. investment banking decision: S&T can be good, but it is a more specialized opportunity.
Quant funds offer an even more extreme version: they can be very, very lucrative for the right person, but they do not have the broad appeal of IB and related fields.
And since everyone likes pro and con lists, let’s close this article with such a list:
Quant Fund Pros
- Extremely high starting compensation, with the potential to earn $500K+ within a few years… if you perform well and you’re at the right firm.
- Recruiting is more accessible than IB/PE recruiting since you don’t necessarily need to start years in advance or attend an Ivy League school – you just need solid math/probability/coding skills, a technical degree, and some work experience.
- The work can be far more interesting than anything you do at large banks, at least if you enjoy math and coding challenges.
- The culture and lifestyle can be much better than what you’d get at a bank, as you’ll work less and have more time for outside activities.
Quant Fund Cons
- Exit opportunities are limited, especially if you want to move out of math/coding/data-oriented roles and do something broader.
- Promotion can be quite difficult because it’s tough to claim “ownership” of your P&L, and it is increasingly difficult to find strategies that perform well over long time frames.
- The culture at some firms can be cutthroat and competitive, so you should not expect the warm/fuzzy feelings you’d get at tech companies. Turnover can also be high, and many people don’t last beyond their first few years.
- The industry is far more crowded and mature, which means that quants have become more commoditized.
It’s not worth it to break into a quant fund and “stay for a few years” before figuring out what you want to do.
So, if you could see yourself working on math/stats/coding problems related to the financial markets for, say, 10+ years, quant funds could make a lot of sense.
Even if you get tired of the work, you’ll have earned so much money over that period that you can go off and do something else – as many professionals do.
You won’t have enough to buy a yacht, but you will have enough to relax and pursue personal interests for a while, do volunteer work, or take a lower-paying-but-more-fulfilling job.
On the other hand, if you want a broader skill set, you’re not sure what you want to do long-term, or you do not want to work on math and coding problems all day, quant funds make less sense.
That’s the best way to decide, even if it’s not exactly a “quantifiable” question.
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