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. 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.

3. 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?

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.