Quant interview preparation

Prop market making and quantitative research, weighted the way the interviews actually are: probability and expected value, statistics and machine learning, market making logic, programming and options. Every question is either traced to a named firm from a public candidate report, or tagged at desk level when we could not trace it, and every probability answer shows the reasoning path rather than just the number.

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100 questions, mapped to the firms that asked them

Questions
100
Traced to a firm
53
Firms
15
Updated
September 2026
Asked at
All firmsOld Mission Capital12Tower Research Capital10Jump Trading7Akuna Capital5Citadel4D.E. Shaw3Jane Street3AQR Capital Management2DRW2Millennium Management2Schonfeld2Squarepoint Capital2Susquehanna International Group2Belvedere Trading1Optiver1
Topic
All topicsProbability10Coins, cards and games6Expected value8Statistics11Market making15Estimation and mental maths4Stochastic processes4Regression5Machine learning6Time series6Programming10Options and derivatives8Fit and motivation7
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  1. 046What are the assumptions behind ordinary least squares, and which of them actually matter?RegressionCoretechnicalQuant researchRisk

    Say this

    Linearity in parameters, exogeneity meaning the error has zero mean conditional on the regressors, no perfect collinearity, homoskedasticity, and no autocorrelation. Only exogeneity is essential for unbiasedness. The last two affect efficiency and standard errors, not the coefficients.

    Then walk it

    1. Exogeneity, E of error given X equals zero, is the load-bearing assumption. Break it and every coefficient is biased and inconsistent, and no amount of data or robust standard errors saves you.
    2. Homoskedasticity and no autocorrelation give you Gauss-Markov efficiency and the usual standard error formula. Break them and OLS is still unbiased, just no longer the minimum-variance linear estimator, and your t-statistics are wrong. Robust or Newey-West errors fix the inference.
    3. Normality of errors is not needed for unbiasedness or consistency at all. It only buys exact small-sample t and F distributions. Asymptotically the CLT handles it.
    4. No perfect collinearity is a requirement for the estimator to exist, since X'X must be invertible. Near-collinearity is not a violation, it just inflates variances.
    5. On financial data the realistic picture is: heteroskedasticity almost always, autocorrelation often, and exogeneity frequently violated because everything is jointly determined. So I default to robust standard errors, and I spend my thinking time on whether my regressor is endogenous, because that is the one that actually changes the answer.

    Where candidates lose it

    Listing normality as a core assumption, or treating all five as equally important. Rank them. The interviewer wants to hear which violations bias the coefficients and which only bias the standard errors, because that distinction determines whether you patch the model or rebuild it.

    Expect next

    • Give me a concrete example of endogeneity in a returns regression.
    • Why is normality not needed?
    • What does Gauss-Markov actually claim?

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.