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. 025How do you size a position?Portfolio constructionHardsuperdayMulti-manager platformsLong-short equity

   Say this

   From the downside and the correlation, not from the upside. The question is how much the book loses if I am wrong, how likely that is, and how much of that same risk I already own elsewhere. Upside sets whether the trade is worth doing; downside sets how big it can be.

   Then walk it

   1. Start with the loss budget. If my bear case is minus 40 percent and I am unwilling to lose more than 1.5 percent of the fund on a single idea, the position caps out around 4 percent regardless of how much I like it.
   2. Then risk rather than notional. A 4 percent position in a 60 percent volatility name is a bigger risk position than 8 percent in a 20 percent volatility name. Sizing in contribution-to-risk terms is what a platform risk system will force you to do anyway.
   3. Then correlation, at the book level. Three longs expressing the same rate view are one position with three tickers. If I cannot name the common factor, I have not finished the work.
   4. Then liquidity. Days of average daily volume to exit is a hard constraint. If a position takes ten days to unwind in a normal tape, it takes thirty in a bad one, and the size should reflect the exit, not the entry.
   5. Then conviction, which is really the falsifiability of the thesis and the closeness of the test. A thesis with a print in six weeks supports more size than a five-year structural view, because I will learn sooner and can cut cheaply.
   6. Kelly is the theoretical anchor and I would say why nobody runs it full: you cannot estimate the probabilities finely enough, and the penalty for overestimating your edge is geometric. Most people run a quarter to a half of Kelly. In a pod seat much of this is imposed by the risk system, and the analyst's job is to argue for size inside those limits.

   Where candidates lose it

   Sizing by upside. Every analyst's favourite idea has the most upside, and sizing on that is precisely how books blow up. Also, giving notional percentages with no mention of volatility, correlation or liquidity. A hedge fund answer talks in risk contribution, not in weights.

   Expect next

   • What is your maximum single position, long and short?
   • How would you handle two positions with 0.8 correlation?
   • Would you add to a loser?
2. 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?
3. 027Define gross and net exposure, and tell me what each one tells you.Portfolio constructionCorephone / first roundLong-short equityMulti-manager platforms

   Say this

   Gross is longs plus shorts, net is longs minus shorts, both as a percentage of capital. Net tells you your directional market bet; gross tells you how much stock selection risk you are running. They answer different questions and a good risk conversation uses both.

   Then walk it

   1. Worked example: long 130, short 70. Gross is 200 percent, net is 60 percent. That book has moderate market exposure and a lot of single-name risk.
   2. Net is the beta-ish bet. If the market falls 10 percent and the book is 60 percent net with beta one, you lose roughly 6 percent before any stock selection.
   3. Gross is the alpha bet, and also the accident exposure. Higher gross means more spread if you are right and more pain when factors rotate violently, because both legs can move against you at once.
   4. Beta-adjusted net is the number that actually matters, and saying so is the mark of someone who has looked at a risk report. A book that is 20 percent net with high-beta longs and low-beta shorts can be 40 percent net in beta terms.
   5. Typical ranges to have in your head: fundamental long-short runs 20 to 60 percent net at 150 to 250 gross; a market-neutral pod runs around zero net at 300 to 600 gross with tight factor constraints.
   6. The limitation: neither number captures concentration or factor tilts. A 300 gross book in thirty names with no factor constraint can lose more than a 600 gross book in four hundred names that is factor neutral. Gross without a factor report is a half-measure.

   Where candidates lose it

   Getting the arithmetic right and saying nothing about beta adjustment or what the numbers are for. Every candidate can compute gross and net. The differentiator is saying that beta-adjusted net is the real directional measure and that gross means nothing without a factor decomposition alongside it.

   Expect next

   • What net exposure would you run into a Fed meeting?
   • Can a zero net book lose 5 percent in a day? How?
   • How does gross relate to leverage?
4. 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?
5. 029How would you hedge a name that does not have a similar public comp?Portfolio constructionHardtechnicalBalyasny Asset ManagementEquity Research · New York · 2026

   Say this

   Decompose the position into the risks you do not want and hedge each one with whatever trades, rather than hunting for a twin. Usually that means a basket: some index or sector for market beta, a factor or style proxy, and then something specific for the commodity, currency or customer exposure.

   Then walk it

   1. First, write down what you are actually exposed to. Market beta, sector, style factors like growth and momentum, one or two macro sensitivities, maybe a single large customer or an input cost. The comp problem disappears once you stop thinking in comps.
   2. Then hedge the biggest exposures with liquid instruments. Index futures for beta, a sector ETF for industry, and a factor ETF or a long-short style basket if the name is a strong growth or momentum expression.
   3. Then go up or down the value chain. If there is no comp, there is usually a supplier, a customer or an input. A specialty chemical company with no peer can often be partly hedged with the feedstock or with the auto OEMs it sells into.
   4. Then a statistical basket as the fallback. Regress the stock on a set of liquid candidates over a sensible window and build a weighted short basket from the loadings. It is crude but it is honest, and platforms do exactly this.
   5. If nothing works, the right answer is to size it smaller. An unhedgeable idiosyncratic risk is a legitimate risk to take, just not at full weight, and saying that is better than inventing a hedge.
   6. Then name the two failure modes: a regression-fitted basket can be a spurious relationship that breaks in the stress you were hedging against, and it needs rebalancing or the loadings drift. I would re-estimate monthly and cap how much of the risk I claim is hedged.

   Where candidates lose it

   Answering 'short the index' and stopping, or inventing a comp that is not really one. At a multi-manager platform this question is about whether you think in risk factors rather than in tickers. And do not miss the escape hatch: sometimes the correct answer is that the risk cannot be hedged and the position should be halved.

   Expect next

   • How would you build that regression basket and over what window?
   • What could go wrong with a statistically fitted hedge?
   • When is the right answer to just size it smaller?

   Reported by candidates at Balyasny Asset Management (Equity Research, New York, 2026). Source: Wall Street Oasis.

6. 030What does factor neutral mean, and why do platforms insist on it?Portfolio constructionHardsuperdayMulti-manager platformsQuantitative hedge funds

   Say this

   Factor neutral means the book has close to zero net exposure to the common systematic drivers of return: market, size, value, growth, momentum, quality, volatility, and the industry groups. Platforms insist on it because they are paying for idiosyncratic stock picking and can buy factor exposure themselves for a few basis points.

   Then walk it

   1. Mechanically, a risk model like Barra or Axioma decomposes every stock into factor loadings plus a residual. Neutrality means the weighted factor loadings across the book net to roughly zero, within stated limits.
   2. The business logic is straightforward: if a PM's returns come from a persistent growth tilt, the fund is paying a performance fee for something an ETF delivers. Stripping the factors leaves what the PM is actually paid for.
   3. It also makes pods additive. If every pod is factor neutral, their P&Ls are close to independent, and the centre can lever the combination. Factor tilts are the main thing that makes supposedly uncorrelated pods lose money on the same day.
   4. In practice it is enforced as limits, not perfection. Something like plus or minus 0.1 on any style factor and a cap on industry net exposure, monitored daily, with the risk team reducing you if you breach.
   5. The cost is real and worth naming. Neutralising factors removes return you might have wanted, forces trades that have nothing to do with your thesis, and creates rebalancing cost. A PM who genuinely has skill at calling the cycle is being asked to stop doing it.
   6. The deeper limitation: neutrality is only as good as the risk model. A crowding factor is not in most commercial models, so a book can be textbook factor neutral and still be one position, which is roughly what happened to quant equity in 2007 and to crowded pod longs in early 2021.

   Where candidates lose it

   Defining factor neutral and not explaining the commercial reason. The interviewer wants to hear that the fund can buy factor beta cheaply, so what they are paying you for is the residual. And if you claim factor neutrality is sufficient risk control, say the crowding caveat yourself, because that is the live criticism of the model.

   Expect next

   • Which factors would you neutralise and which would you keep?
   • How would you detect crowding if the risk model does not have it?
   • What is the cost of factor neutralisation to a PM?
7. 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.

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.