Hedge Funds interview preparation

Questions

100

Traced to a firm

39

Firms

16

Updated

September 2026

Asked at

All firmsMan Group10Balyasny Asset Management7Bridgewater Associates3DED.E. Shaw3Apollo Global Management2KKR2Oaktree Capital Management2Point722SCSquarepoint Capital2ACAQR Capital Management1BGBaupost Group1Coatue Management1HPS Investment Partners1Northern Trust1Viking Global Investors1Wolverine Trading1

Topic

All topicsStrategy taxonomy8Stock pitch10Short selling6Portfolio construction8Risk and drawdown8Performance and alpha7Event-driven and merger arb8Distressed and credit5Fund structure and economics7Financing, NAV and operations6Compliance and research process5Quant and systematic6India and Category III AIFs5Career and fit11

Level

AnyCoreIntermediateHard

Type

AnyTechnicalMarket viewBrainteaserCaseFit

1. 034What is a drawdown, and why do funds care more about it than volatility?Risk and drawdownCoretechnicalMulti-manager platforms

   Say this

   A drawdown is the peak-to-trough fall in NAV, measured from the highest point reached. Funds care about it more than volatility because it is what triggers redemptions, stop-outs and the high water mark problem, all of which are path dependent in a way volatility is not.

   Then walk it

   1. The arithmetic is asymmetric and that is the whole point. Down 50 percent requires plus 100 percent to recover. Down 20 percent requires plus 25. Compounding punishes the depth of the hole, not the wiggle.
   2. Volatility is path independent and drawdown is not. Two funds with identical monthly volatility can have very different worst drawdowns depending on whether the bad months clustered.
   3. The commercial reason is redemptions. Investors leave near the trough, so a deep drawdown permanently shrinks the capital base and the manager never gets to earn the recovery on the original amount.
   4. Then the fee mechanics: below the high water mark the manager earns no performance fee until the loss is recovered, so a deep drawdown can make a business unviable even if the strategy eventually works. That is why funds sometimes close after a bad year rather than grind back.
   5. On a platform it is even more direct. The drawdown limit is a contractual stop, so a path that touches minus 8 percent and recovers is worse than a path that grinds to minus 5 and stays, because the first one ends your seat.
   6. Useful additional measures to name: time to recovery, the Calmar ratio which is return over maximum drawdown, and the Sortino ratio which only penalises downside deviation. Maximum drawdown alone is a single historical observation and therefore a fragile statistic.

   Where candidates lose it

   Defining drawdown correctly and then giving a purely statistical reason for caring. The reasons are commercial and structural: redemptions, the high water mark and the platform stop. Also do not present maximum drawdown as a robust risk measure. It is one realised path, and the next one will be different.

   Expect next

   • How long does it take to recover a 25 percent drawdown at a 10 percent return?
   • What is the Calmar ratio?
   • Why would a fund shut down rather than trade back to its high water mark?
2. 036How does a stop-out work on a multi-manager platform?Risk and drawdownIntermediatetechnicalMulti-manager platforms

   Say this

   Each PM gets an allocation with a drawdown limit attached, usually a soft level where risk gets cut and a hard level where the book is liquidated and the seat ends. It is measured on the PM's own P&L against allocated capital, from the high water mark, and it is enforced by the risk team rather than negotiated.

   Then walk it

   1. Typical structure: a soft limit around 3 to 5 percent of allocated capital where gross is halved, and a hard stop around 7 to 10 percent where positions are flattened by the central desk.
   2. It is measured from the peak, so a PM up 6 percent has effectively earned a larger cushion for the year. That is why the first quarter of a new allocation is the most dangerous period in these seats.
   3. The mechanism is also a capital allocation tool. Risk budget is reallocated from PMs in drawdown to PMs performing, which is why the platform's own return stream is smoother than the average pod's.
   4. It is enforced centrally with real-time position and P&L monitoring, plus factor limits, single-name limits, liquidity limits and sometimes overnight and earnings-event restrictions.
   5. The behavioural effect is significant and worth being honest about. Hard stops truncate the left tail for the firm, but they also force selling into weakness, and they push PMs to run shorter horizons and narrower theses than they might otherwise. A structurally correct three-year view is not investable in a seat with a 6 percent annual stop.
   6. Second-order consequence: because platforms hire similar people and impose similar limits, a factor shock can force simultaneous deleveraging across many pods. The stop-out protects each fund and can amplify the market move.

   Where candidates lose it

   Describing it as a risk limit without the career consequence, or pretending you would simply never hit it. Say the numbers, say it is measured from the high water mark, and name the cost: hard stops force selling at the worst time and shorten every PM's investment horizon. Interviewers at platforms respect candidates who see both sides.

   Expect next

   • How would that change how you invest compared with a long-only seat?
   • What happens to your capital if you make money?
   • Is there a systemic cost to everyone having the same stop?
3. 037What is VaR, and what does it miss?Risk and drawdownIntermediatetechnicalRisk management

   Say this

   Value at Risk is the loss you would not expect to exceed on a given percentage of days. A one-day 99 percent VaR of 2 percent means that on 99 days out of 100 you lose less than 2 percent. What it misses is everything on the hundredth day, which is the day that matters.

   Then walk it

   1. Three ways to compute it: parametric from a covariance matrix, historical simulation from actual past returns, and Monte Carlo. Historical is the most common on an equity book because it makes no distributional assumption, but it can only show you crashes that already happened.
   2. The first big miss is the shape of the tail. VaR says nothing about how bad the bad day is. Expected shortfall, the average loss given you breached VaR, is the fix, and it is what Basel moved to for exactly this reason.
   3. The second is that it is not sub-additive in general. You can combine two books and get a VaR higher than the sum, which makes it a poor tool for allocating risk between pods.
   4. The third is estimation from a calm window. Volatility clusters, so a VaR calibrated on a quiet period understates risk precisely when positions have been built up on the basis of that calm.
   5. The fourth is what it cannot see: correlations going to one, liquidity vanishing, a borrow recall, gap risk over a weekend, and any non-linear payoff if you hold options and only use a linear approximation.
   6. So I would use it as a daily monitoring number and never as the risk decision. The decisions come from stress tests, liquidity analysis and position limits. As a one-line summary: VaR tells you how much you lose on a normal bad day, not on a bad day.

   Where candidates lose it

   Defining VaR and stopping, or getting the direction of the confidence level muddled. The question is explicitly about the limitations, so the second half of your answer is the answer. Name expected shortfall, name the calm-window calibration problem, and say what you would use instead.

   Expect next

   • What is expected shortfall and why is it better?
   • How would you stress test instead?
   • What is the danger of managing a book to a VaR limit?
4. 038Do you use stop losses on a fundamental book?Risk and drawdownIntermediatetechnicalLong-short equityMulti-manager platforms

   Say this

   Yes as a risk overlay, no as the investment decision. A price stop protects the fund from a thesis that is wrong in a way you have not yet understood. But on a fundamental book the primary sell trigger has to be the thesis breaking, and the two should be tracked separately.

   Then walk it

   1. The case for stops: a large adverse move is information. The market may know something you do not, and the discipline of cutting at a pre-set level protects you from the two biases that actually destroy books, anchoring and averaging down on conviction.
   2. The case against: fundamental theses need time, and a mechanical stop guarantees you sell your best ideas at the worst prices during factor rotations that have nothing to do with the company.
   3. So the workable version is an alert rather than an automatic sale. Down 15 percent triggers a mandatory re-underwrite from scratch, where I have to justify the position as a new purchase at today's price. If I cannot, it goes.
   4. Volatility-scaled levels are better than fixed percentages. A 15 percent stop on a 20 percent volatility utility and on a 70 percent volatility biotech are completely different statements about conviction.
   5. In a platform seat it is not a choice anyway. The drawdown limit at the book level effectively imposes stops at position level, and the honest framing is that the risk system owns the stop and the analyst owns the thesis.
   6. One number worth having: if your average winner is up 30 and your average loser is cut at 12, you can be right less than half the time and still make money. Say that and the question becomes about expectancy rather than about pride.

   Where candidates lose it

   Answering with a flat no because you are a fundamental investor, or a flat yes because it sounds disciplined. Both are one-sided. The credible answer separates the risk overlay from the investment decision and mentions the forced re-underwrite, which is what actually happens on good books.

   Expect next

   • Would you ever average down?
   • How would you set the level?
   • What is the difference between trimming and selling?
5. 039How do you think about transaction cost?Risk and drawdownIntermediatetechnicalMan GroupInvestment Management · Boston · 2022

   Say this

   In three buckets: the explicit costs, the spread, and market impact. The first two are small and easy to measure; impact is the big one and it scales with size, so for any strategy with turnover, cost is not a friction on the return, it is a constraint on the strategy.

   Then walk it

   1. Explicit: commissions, exchange fees, taxes such as stamp duty in the UK or STT in India. A few basis points, predictable, and in India securities transaction tax genuinely changes which strategies are viable.
   2. Spread: you cross half the bid-ask to trade immediately. In a liquid large cap that is one or two basis points; in a mid cap it can be 30.
   3. Impact: your own order moves the price. The standard working model is that impact grows roughly with the square root of the order size as a fraction of daily volume, so trading 10 percent of ADV costs far more than twice trading 5 percent.
   4. Then the cost nobody puts on the invoice: opportunity cost and delay. Trading slowly reduces impact and increases the risk the price runs away from you. That trade-off is exactly what an execution algorithm is solving, and implementation shortfall against the arrival price is the right way to measure the whole thing.
   5. The practical consequence is that cost has to be inside the signal, not after it. If a signal has 20 basis points of gross edge and 15 of round-trip cost, it should be traded slowly, netted against other signals, or not traded at all.
   6. One number to make it real: a book turning over 200 percent a year at 15 basis points round trip pays 60 basis points annually. That is the difference between a good year and an average one, and it is why netting flows across pods is a genuine advantage of a large platform.

   Where candidates lose it

   Answering with commissions and the spread and missing impact. Impact is the entire subject for anyone running real size, and the square-root rule plus implementation shortfall is the vocabulary that shows you have looked at execution data rather than a textbook. Also mention that cost belongs inside the signal, not subtracted afterwards.

   Expect next

   • How would you measure your own market impact?
   • How does cost change the optimal holding period?
   • What is implementation shortfall?

   Reported by candidates at Man Group (Investment Management, Boston, 2022). Source: Wall Street Oasis.

6. 041What is the difference between alpha and beta, and how do you actually separate them?Performance and alphaIntermediatetechnicalMulti-manager platformsAsset management

   Say this

   Beta is the return you get for taking market risk, which anyone can buy for a few basis points. Alpha is what is left after you strip out every systematic exposure you were paid to take. You separate them by regressing the return stream on the factors and looking at the intercept.

   Then walk it

   1. The single-factor version: regress fund returns on market returns. The slope is beta, the intercept is alpha, the residual is unexplained. A fund with 0.6 beta in a market up 20 percent earned 12 points from beta before any skill.
   2. The problem is that a single factor flatters almost everyone. Add size, value, momentum, quality and low volatility and a lot of claimed alpha becomes a known factor tilt. That is the entire point of the Fama-French and Carhart extensions.
   3. For hedge funds you go further and add the exposures specific to the strategy: credit spreads, volatility, term premium, and for merger arb a factor that is essentially short the market's tail. Fung and Hsieh style models exist precisely because hedge fund returns are non-linear.
   4. The practical version inside a fund is attribution rather than regression. Decompose today's P&L into market, sector, style and residual, and only the residual counts as stock picking.
   5. The subtlety worth saying: alpha is model dependent. It is defined as what your factor model cannot explain, so a better model shrinks it. Alpha today is often just a factor nobody has named yet, which is roughly the history of quantitative finance.
   6. And beware short samples. With three years of monthly data the standard error on alpha is large enough that a 3 percent annual alpha is statistically indistinguishable from luck. Saying that is what separates a candidate who has run the regression from one who has read about it.

   Where candidates lose it

   Defining alpha as 'outperformance versus the index'. That is beta plus a benchmark choice. Alpha is residual to a factor model, which means it depends on which factors you include, and any honest answer says so. Also mention the sample-size problem: it is the fastest way to sound like you have handled real return data.

   Expect next

   • How many years of data do you need to prove an alpha is real?
   • Is a small-cap tilt alpha or beta?
   • How would you decompose a long-short fund's returns?
7. 042What is the Sharpe ratio, and what are its limitations?Performance and alphaCorephone / first roundAsset managementMulti-manager platforms

   Say this

   Excess return over the risk-free rate, divided by the volatility of that excess return. It is return per unit of risk, and the limitation is that it defines risk as volatility, which is the wrong definition for anything with a skewed or illiquid payoff.

   Then walk it

   1. Rough benchmarks to have in your head: a long-only equity index sits around 0.4 to 0.5 over the long run, a decent hedge fund 0.8 to 1.2, a platform at the fund level 2 or more because of diversification across pods, and anything claiming 4 over a long period needs explaining.
   2. Annualisation matters and gets fumbled. Multiply the monthly mean by 12 and the monthly standard deviation by the square root of 12. That scaling assumes independent returns, which is exactly what fails for illiquid books.
   3. Limitation one, symmetry. Volatility punishes upside surprise as much as downside. A fund whose good months are huge looks worse than a fund grinding out the same return, which is backwards for an investor.
   4. Limitation two, and this is the big one: a strategy that sells tail risk has a beautiful Sharpe until the tail arrives. Writing out-of-the-money options or running a levered convergence trade manufactures a high Sharpe by hiding the risk in the third and fourth moments.
   5. Limitation three, smoothing. Illiquid positions marked on stale prices have artificially low measured volatility, which inflates the ratio. The tell is high autocorrelation in monthly returns, and I would check that before believing any private-credit or distressed Sharpe.
   6. So I would look at Sharpe alongside skew, kurtosis, worst drawdown, time to recover and return autocorrelation. Sortino and Calmar cover part of the gap, and neither fixes the fundamental point that one number cannot describe a return distribution.

   Where candidates lose it

   Forgetting the risk-free rate in the numerator, or annualising by multiplying volatility by 12. Then, on limitations, giving only the symmetry point. The tail-selling and the stale-marks problems are what an allocator actually worries about, and naming autocorrelation as the diagnostic is the detail that lands.

   Expect next

   • How would you detect a fund that is selling tail risk?
   • What does high autocorrelation in monthly returns tell you?
   • What is the difference between Sharpe and Sortino?
8. 043What is the difference between the Sharpe ratio and the information ratio?Performance and alphaIntermediatetechnicalAsset managementQuantitative hedge funds

   Say this

   Sharpe measures excess return over cash divided by total volatility. Information ratio measures excess return over a benchmark divided by tracking error. Sharpe asks whether the whole portfolio was worth owning; information ratio asks whether the active decisions added value.

   Then walk it

   1. The numerator differs in what you subtract. Sharpe subtracts the risk-free rate. IR subtracts the benchmark, so all the market return is stripped out and only the active bet remains.
   2. The denominator differs too. Sharpe uses the volatility of total excess return; IR uses tracking error, the volatility of the difference from the benchmark.
   3. For a genuinely market-neutral hedge fund the two converge, because cash is effectively the benchmark. For a long-only manager they are very different: a fund can have a strong Sharpe from owning equities and a terrible IR from poor stock selection.
   4. The fundamental law of active management lives here: IR is roughly the information coefficient times the square root of breadth. It tells you a manager can raise IR either by being more skilful per decision or by making more independent decisions, which is the entire argument for systematic investing.
   5. Practical benchmark: a long-only manager with an IR of 0.5 sustained over ten years is genuinely good. Most active managers do not clear it after fees, which is why the fee compression happened.
   6. The shared weakness: both use volatility as risk and both need long samples to say anything. And IR is hostage to the benchmark choice, so a manager can improve their IR by picking a benchmark they are structurally tilted away from. Always ask what the benchmark is before reading the number.

   Where candidates lose it

   Saying they are basically the same thing, or getting tracking error and volatility confused. The distinction is what you subtract and what you divide by, and the good answer adds the benchmark-gaming point. For a market-neutral fund saying they converge shows you understand the definitions rather than memorising two formulas.

   Expect next

   • For a market-neutral fund, which would you quote and why?
   • What is a good information ratio over ten years?
   • How can a manager game their information ratio?
9. 046What is a good hit rate, and why is that not the right question?Performance and alphaIntermediatetechnicalLong-short equityGlobal macro

   Say this

   Around 55 percent is genuinely good for a fundamental book, and many excellent macro managers run below 50. It is the wrong question on its own because P&L is hit rate times average win against miss rate times average loss. Expectancy, not accuracy, is what pays.

   Then walk it

   1. Write the identity out: expectancy equals p times average win minus one minus p times average loss. You can be right 40 percent of the time with a three to one win-loss ratio and run a very good book.
   2. That is exactly the trend-following and macro profile. Lots of small losses cut quickly, a few large winners held. The discipline is entirely in the size of the losses, not in the frequency of the wins.
   3. Fundamental long-short is the other shape: higher hit rate, smaller average win, because positions are researched and sized before entry rather than added to as they work.
   4. So the diagnostic questions are better than the hit rate. What is my average winner versus average loser, do I cut losers faster than winners, and am I adding to winners? A high hit rate with a losing book means you take profits early and let losses run, which is the most common retail-shaped mistake in a professional seat.
   5. Sample size again. Twenty positions tells you nothing about hit rate. You need hundreds of independent decisions before the number means anything, which is another reason systematic books can measure themselves and discretionary books mostly cannot.
   6. The honest limitation: hit rate is easy to game by how you define a decision. Was a position you added to three times one call or four? Without a consistent definition, the number is a story rather than a statistic.

   Where candidates lose it

   Quoting a high hit rate as though it were the goal. Interviewers use this to see whether you think in expectancy. Also watch the trap in the other direction: saying hit rate does not matter at all. It matters, it just has to be read with the win-loss ratio alongside it.

   Expect next

   • What is your win-loss ratio on your own investing?
   • Why do macro managers tolerate a sub-50 percent hit rate?
   • How would you know if you were cutting winners too early?
10. 048What is event-driven investing?Event-driven and merger arbCorephone / first roundEvent-drivenMulti-manager platforms

    Say this

    Investing where the return depends on a specific corporate event happening rather than on the business getting better. Mergers, spin-offs, restructurings, index changes, rights issues, activist campaigns. Because the payoff is tied to an event with a date, the main risk is completion and timing rather than valuation.

    Then walk it

    1. The sub-strategies: merger arbitrage, which is the biggest; spin-offs and stub trades; distressed and bankruptcy; capital structure arbitrage; index and technical events; activist and special situations.
    2. What unites them is a defined catalyst with a legal or contractual structure. You are underwriting a process, so the work is documents, regulators and counterparties rather than modelling ten years of cash flow.
    3. The payoff shape is characteristic: a high probability of a small gain and a small probability of a large loss. That is short optionality, and it means the return series looks smooth until it does not.
    4. Which has an important consequence for measurement. Event-driven strategies show attractive Sharpe ratios in benign markets because they are structurally short a tail. Any evaluation has to price that explicitly.
    5. Skill sits in unusual places: reading merger agreements, judging antitrust outcomes, understanding creditor classes and voting mechanics, and knowing the arbitrage community's positioning.
    6. The environmental dependency is worth naming. Deal flow is the raw material, so event-driven returns depend on corporate activity and on the regulatory climate. A hostile antitrust regime widens spreads, which raises returns and raises break risk at the same time.

    Where candidates lose it

    Describing it as 'investing in companies with catalysts', which is just fundamental investing with better timing. The defining feature is that the payoff comes from a structured event with legal mechanics, and the defining risk is that the event does not complete. Say the short-optionality point and you sound like someone who has looked at the return distribution.

    Expect next

    • Which event-driven strategy has the most capacity?
    • Why do these strategies show high Sharpe ratios?
    • What happens to event-driven returns when antitrust enforcement tightens?

Firm tags come from public, anonymous candidate reports on Wall Street Oasis: strong signal, not sworn testimony. Firms are named as the places a question was reported, not as partners of Fin Maverick. Answers are written for this page to show how to think out loud; they are not scripts to recite.