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. 026What is the Kelly criterion, and why does nobody run full Kelly?Portfolio constructionIntermediatesuperdayQuantitative hedge fundsMulti-manager platforms

   Say this

   Kelly gives the bet size that maximises the long-run growth rate of capital: you bet your edge divided by the odds. For a simple even-money bet it reduces to twice your win probability minus one. Nobody runs it full because it assumes you know your edge precisely, and overestimating it is punished geometrically.

   Then walk it

   1. The formula for an even-money bet: f equals 2p minus 1. With a 55 percent chance of winning, Kelly says bet 10 percent of capital. With a 60 percent chance it says 20 percent, which already feels reckless to anyone who has run a book.
   2. It maximises the expected log of wealth, which is the geometric mean, not the arithmetic mean. That is the right objective if you are compounding one pot of capital forever, and it is why the answer is a fraction of wealth rather than a fixed amount.
   3. Full Kelly produces brutal drawdowns by design. The expected maximum drawdown on a full Kelly strategy is roughly 50 percent. No fund with outside capital survives that, because investors redeem long before the long run arrives.
   4. The estimation problem is worse than the volatility problem. Kelly is very sensitive to the edge, and betting twice the Kelly fraction takes your expected growth rate to zero. Since we estimate edge from short, noisy samples, err low.
   5. So in practice people run a quarter to a half Kelly, which gives up a modest amount of growth for a large reduction in drawdown. That trade is almost always worth it with client money.
   6. The other limitation for an equity book: Kelly is single-bet. Real portfolios have correlated positions, so the useful version is a mean-variance or risk-parity construction with a Kelly-style scaling on the whole book, not per position.

   Where candidates lose it

   Reciting the formula and stopping, or claiming you would use it. The interesting part is the estimation error and the drawdown profile. A candidate who says 'half Kelly because I cannot estimate my edge to two decimal places' has answered the question properly; one who says 'bet edge over odds' has quoted a Wikipedia line.

   Expect next

   • What happens if you bet twice Kelly?
   • How would you extend it to correlated positions?
   • How would you estimate your edge in the first place?
2. 028How do you beta-hedge a long position?Portfolio constructionIntermediatetechnicalLong-short equity

   Say this

   Short an index exposure equal to the position value times its beta. If you are long 10 million of a stock with a beta of 1.4, you short 14 million of index to be market neutral on that position. The residual is what you were trying to own in the first place.

   Then walk it

   1. The arithmetic is just that: hedge notional equals position notional times beta. Do it with index futures or an ETF, and futures are usually cheaper because there is no borrow and the financing is embedded.
   2. Beta is estimated, so it is wrong. Which lookback, which frequency, which index? A daily two-year beta and a weekly five-year beta on the same stock can differ by 0.4, and your hedge ratio inherits that error.
   3. Betas also drift with the business. A company that delevers or shifts mix becomes a different beta, and a hedge set once and forgotten stops hedging.
   4. Sector beta is usually the better hedge. If the thesis is company-specific, shorting the sector rather than the broad index strips out more of the noise you do not want, and you are left closer to pure idiosyncratic risk.
   5. The trade-off is that a sector hedge may remove exposure you wanted. If part of your edge was calling the cycle, hedging the sector hedges away your alpha.
   6. State the honest limitation: beta hedging removes the average market sensitivity, not the tails. In a sharp sell-off high-beta stocks fall much more than their historical beta implies, correlations go to one, and the hedge under-delivers exactly when you need it. That is why net exposure limits exist alongside hedges.

   Where candidates lose it

   Hedging one for one. Shorting 10 million of index against a 10 million position with beta 1.4 leaves you materially net long, and that error shows up as an unexplained market P&L in your attribution. Also, say which beta you used and over what window; interviewers probe that immediately.

   Expect next

   • Which beta would you use and over what window?
   • Index hedge or sector hedge, and why?
   • What happens to your hedge in a crash?
3. 031How do you understand portfolio risk?Portfolio constructionIntermediatetechnicalMan GroupInvestment Management · Boston · 2022

   Say this

   As three separate questions, not one number. How much do I expect to lose in a normal month, what happens in a bad one, and what am I unknowingly concentrated in? Volatility answers the first, stress tests answer the second, and factor decomposition answers the third.

   Then walk it

   1. Layer one, the normal case: volatility, VaR and contribution to risk per position. Useful for sizing and for spotting that one position is carrying a third of the risk.
   2. Layer two, the bad case: stress tests and scenarios. Rerun the book through 2008, March 2020, the 2021 momentum unwind, a 100 basis point rate shock. This is where you learn the hedges stop working.
   3. Layer three, the hidden case: factor and thematic decomposition. What is the book's net exposure to growth, to momentum, to oil, to the dollar, to one supply chain? Most surprises are a concentration nobody had named.
   4. Then liquidity risk, which sits underneath all of it. Days to exit at 20 percent of volume, and what the book looks like if you have to raise 20 percent of cash in a week. Illiquidity converts a paper loss into a realised one.
   5. And correlation instability, which is the one that actually hurts. Correlations rise in stress, so a diversified book is less diversified precisely when it matters. I would assume correlations go to one in the tail rather than trusting the historical matrix.
   6. The limitation to volunteer: every number here is backward looking and conditional on a covariance matrix estimated from a period that may not resemble the next one. That is why hard limits and drawdown stops exist alongside the models, rather than instead of them.

   Where candidates lose it

   Answering only with VaR or only with volatility. A single risk number is the wrong shape of answer to this question. Name the three layers, then add liquidity and correlation instability, and say explicitly that the models are backward looking. That last admission is what a risk-focused interviewer is listening for.

   Expect next

   • What does VaR miss?
   • How would you stress test a long-short equity book?
   • How do you think about transaction cost?

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

4. 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?

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.