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. 032What is capacity, and how do you know a strategy is crowded?Portfolio constructionIntermediatesuperdayQuantitative hedge fundsMulti-manager platforms

   Say this

   Capacity is the amount of capital a strategy can run before its own trading destroys the edge. Crowding is the same problem caused by other people: too many funds holding the same positions, so the exit is narrow. Both show up as rising cost and correlated drawdowns rather than as a signal that stops predicting.

   Then walk it

   1. Capacity is a cost problem first. As size grows, each rebalance moves the price more, so realised return falls even though the signal is unchanged. The practical test is to plot expected alpha net of modelled market impact against AUM and find where it crosses zero.
   2. Turnover is the multiplier. A signal with a two-day holding period has a fraction of the capacity of the same signal held for three months, because you pay the impact many more times.
   3. Crowding measures I would actually look at: short interest and days to cover, 13F overlap across similar funds, the share of float held by hedge funds, borrow fees trending up, and the beta of a name to a hedge fund crowding basket.
   4. The tell in the return series is a change in the character of the drawdowns. Crowded strategies lose money in sharp, correlated, liquidity-driven air pockets rather than in slow grinds, because everyone is selling the same thing on the same day.
   5. Two reference episodes make it concrete. August 2007, when quant equity books unwound together, and the early 2021 squeeze on crowded pod shorts. In both cases the fundamental signal was fine and the positioning was not.
   6. The honest limitation: crowding data is late. 13Fs are stale by 45 days, and by the time overlap is measurable the trade is already crowded. So I treat it as a sizing input and a reason to prefer less obvious expressions, not as a timing tool.

   Where candidates lose it

   Treating capacity as purely a signal decay story. The binding constraint is almost always market impact and turnover, and the crowding half of the answer needs specific observables: days to cover, 13F overlap, borrow trend. Vague talk about 'too much money chasing alpha' will not survive a follow-up.

   Expect next

   • How would you estimate capacity for a signal you just built?
   • What happened in August 2007?
   • How would you position differently if you knew a trade was crowded?
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. 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?
8. 045How do you attribute a fund's P&L?Performance and alphaIntermediatetechnicalMulti-manager platformsRisk management

   Say this

   Split it into the pieces that correspond to decisions someone made. Market, sector, style factor, then idiosyncratic stock selection, then financing and trading costs. The residual after the systematic pieces is the only part that is evidence of skill.

   Then walk it

   1. Start with market: beta-adjusted net exposure times the index return. That is the part you would have earned with no stock selection at all.
   2. Then sector or industry: the net weight in each industry times that industry's return relative to the index. This catches the PM who is really making a sector call and calling it stock picking.
   3. Then style factors from the risk model: growth, value, momentum, size, quality, volatility. Each has a net loading and a factor return, so each has a P&L line.
   4. Then the residual, which is stock selection. Split it long and short, because a book that makes all its money on the long side in a rising market has not demonstrated a short-selling capability and that matters for how much gross it should run.
   5. Then the costs that are frequently ignored: borrow fees, dividends paid on shorts, financing spread on the leverage, and realised trading cost. On a 300 percent gross book these can be well over a hundred basis points a year.
   6. The caveat to state: attribution is model dependent and the pieces do not add up cleanly. There is always an interaction and residual term, factor returns are estimated, and a PM can dispute the classification of a name. So I would use it to ask questions rather than to settle them.

   Where candidates lose it

   Attributing to positions rather than to risks. Listing the top five winners and losers is a P&L report, not an attribution. The point is to isolate whether the money came from decisions the PM is paid for. Remember to include financing and borrow costs; candidates almost always leave them out and they are large on a levered book.

   Expect next

   • What if all the alpha is on the long side?
   • How would you attribute a macro book instead?
   • What are the biggest cost lines on a levered long-short book?
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. 050A target trades at 46, the cash offer is 50, and the deal is expected to close in six months. What is your return, and what are you being paid for?Event-driven and merger arbIntermediatetechnicalMerger arbitrageEvent-driven

    Say this

    The gross spread is 4 on 46, which is about 8.7 percent over six months, or roughly 17 to 18 percent annualised before costs. You are being paid for the risk the deal breaks, and given where rates are, roughly half of that annualised number is just compensation for tying up capital.

    Then walk it

    1. Arithmetic first: 50 minus 46 is 4, over 46 is 8.7 percent. Doubling for the six-month period gives about 17.4 percent annualised, or 18.1 percent if you compound it. Say the simple number, then note the compounding refinement.
    2. Net it down. Subtract the financing cost of the long, add back any target dividend you receive, and subtract the cost of any hedge. At a 5 percent financing rate, roughly half the annualised spread disappears.
    3. Then the risk question, which is the real question. What is the undisturbed price? If the target traded at 34 before announcement, a break costs you 12 while success pays 4. Three to one against, so you need a completion probability well above 75 percent to break even.
    4. Compute the implied probability: 4 divided by 16 is 25 percent implied break risk. Then ask whether that is right. A friendly, all-cash, fully financed strategic deal with no antitrust overlap should be well below that, which would make the spread attractive.
    5. A spread this wide is a message. Eight percent over six months usually means antitrust review, a financing condition, a shareholder who has objected, or a regulatory regime with a track record of blocking. Find out which before you take the other side.
    6. And the sizing conclusion: because the payoff is three to one against you, position size and deal diversification do more for the return than spread selection. Twenty deals at 2 percent each beats four at 10 percent, and that is the whole discipline of the strategy.

    Where candidates lose it

    Quoting 8.7 percent as the return and stopping. You must annualise, and you must net the financing. Then the bigger trap: giving a return with no reference to the downside. Without the undisturbed price the spread is meaningless, and an arb interviewer will judge you almost entirely on whether you asked for it.

    Expect next

    • What was the price before the deal was announced?
    • What implied break probability does that spread give you?
    • How many deals would you hold, and why?

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.