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. 080What are the assumptions of linear regression?Quant and systematicIntermediatetechnicalSCSquarepoint CapitalHedge Fund · Montreal · 2024

   Say this

   Linearity in the parameters, exogenous errors with zero conditional mean, no perfect multicollinearity, homoscedastic and uncorrelated errors, and for exact small-sample inference, normally distributed errors. The first two give you unbiasedness; the rest are about whether your standard errors mean anything.

   Then walk it

   1. Separate the tiers, because that is what distinguishes someone who has used regression from someone who memorised a list. Linearity and exogeneity are needed for the coefficients to be unbiased. Homoscedasticity and no autocorrelation are needed for the usual standard errors to be correct. Normality is only needed for exact t and F inference in small samples.
   2. So a violation of homoscedasticity does not bias your beta, it biases your confidence in it. That distinction matters enormously in practice: you can still use the estimate, you just cannot trust the t-statistic.
   3. In financial time series the assumptions that actually break are autocorrelation and heteroscedasticity, because volatility clusters and returns overlap. The standard fixes are Newey-West or White standard errors, and clustered errors in panel data.
   4. Endogeneity is the serious one. If a regressor is correlated with the error, the coefficient is biased and no standard error fix helps. In finance this usually arises from omitted variables or from a feedback loop where price affects the supposed predictor.
   5. Multicollinearity does not bias anything, it just inflates variances, so coefficients become unstable and flip sign between samples. That is very common with factor exposures, and the tell is a large R-squared with no individually significant coefficient.
   6. Practical additions I would name: outliers dominate least squares because it minimises squared errors, so winsorise or use robust regression; and out-of-sample performance matters more than any in-sample diagnostic, because for a trading signal I care about prediction, not about the p-value.

   Where candidates lose it

   Reciting the list without saying what each assumption buys you. Tiering them into unbiasedness versus valid inference is the differentiator. Also, do not claim normality of the dependent variable is required; it is normality of the errors, and only for small-sample inference.

   Expect next

   • Which assumption is most often violated in financial data, and what do you do about it?
   • What is the consequence of multicollinearity?
   • How would you detect endogeneity?

   Reported by candidates at Squarepoint Capital (Hedge Fund, Montreal, 2024). Source: Wall Street Oasis.

2. 081Two series can be negatively correlated within each month but positively correlated over a full year. How?Quant and systematicHardtechnicalSCSquarepoint CapitalHedge Fund · Montreal · 2024

   Say this

   Because correlation measured within groups and correlation measured across the pooled data answer different questions. If both series share a common upward trend across months, the between-month variation is positive and can dominate the negative within-month relationship. It is Simpson's paradox in a time series.

   Then walk it

   1. Decompose the covariance into within-group and between-group parts. Total covariance equals the average within-month covariance plus the covariance of the monthly means. Those two terms can have opposite signs, and whichever has more variance wins the pooled number.
   2. Concrete picture: every month, A and B move in opposite directions day to day, so within-month correlation is negative. But each month both drift higher, so the monthly averages rise together. Pool the daily data over a year and the shared drift dominates.
   3. The generic driver is a common slow-moving factor. Both series load positively on something persistent, such as inflation, liquidity or a market trend, while their high-frequency innovations offset. Long-horizon correlation is dominated by the common factor and short-horizon correlation by the idiosyncratic part.
   4. There is also a pure measurement version of this: correlation of returns is horizon dependent when returns are autocorrelated. Compute correlation on daily returns and on annual returns for the same pair and you generally get different numbers, and neither is wrong.
   5. Why it matters practically, which is what the interviewer is really testing: hedge ratios and diversification estimated at one horizon do not hold at another. A pair that looks hedged on daily data can be a directional bet over a year, which is exactly how a relative value book acquires an unintended factor exposure.
   6. So the answer to 'which correlation is right' is neither. You choose the horizon that matches your holding period and your rebalancing frequency, and you look at both to know which part of the relationship you are actually trading.

   Where candidates lose it

   Treating it as a paradox to be resolved rather than a decomposition to be stated. Write down the within-plus-between covariance split and the answer is immediate. And do not stop at the maths: the reason they ask is the practical consequence for hedge ratios at different horizons.

   Expect next

   • Which correlation would you use to set a hedge ratio?
   • How does return autocorrelation affect measured correlation?
   • Give me another example of Simpson's paradox in markets.

   Reported by candidates at Squarepoint Capital (Hedge Fund, Montreal, 2024). 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.