Quant Finance
11 statistics questions of the kind asked in quant finance interviews. Each carries a model answer, the concepts a complete response must hit, and graded feedback on your own attempt.
Quant-risk candidates are expected to translate a correlation assumption into a portfolio-risk estimate without confusing volatility with return.
A portfolio is 50% in Asset A with 20% annual volatility and 50% in Asset B with 10% annual volatility. Their correlation is 0.25. Calculate the portfolio's annual volatility and explain what drives the diversification…
An early research screen that tests statistical hygiene before a candidate proposes a signal.
You find that a stock rose on 70% of the days when a popular sports team won. Can you trade this result? What checks would you run before treating it as evidence?
A practical junior-researcher question testing whether you can calculate and interpret a simple rank-based signal diagnostic.
A signal ranks four stocks from strongest to weakest as A, B, C, D. Their next-month realised-return ranks from best to worst are A, C, B, D. Using Spearman rank correlation, calculate the information coefficient. What…
A common first-round check that you can make differently scaled signals comparable before combining them.
A stock's 12-month earnings-revision score is 18. Across the investable universe, the score has a mean of 10 and standard deviation of 4. What is its z-score, and why might a quant researcher use it rather than the raw…
Market-risk teams use this first-round question to test whether candidates understand the loss tail rather than only a headline metric.
What is expected shortfall, and why might a risk team use it alongside a 99% VaR?
Quant research interviews probe statistical judgement over formula recall.
A researcher backtests 200 signals and finds one with a t-statistic of 2.5 predicting next-day returns. Should you trade it? Explain what's wrong and what you'd require instead.
Quant research interviews hand you output and ask what it means.
You regress a stock's returns on the market and get beta 1.2 (standard error 0.15), alpha 0.3% monthly (standard error 0.4%), and R² of 0.45. What do you conclude?
A market-risk interview often tests whether you can diagnose a model exception without overreacting to one data point.
A desk's 99% one-day VaR is breached six times over 250 trading days. How do you interpret that result and investigate it?
Risk interviews use this question to distinguish a correlation calculation from an understanding of regime risk.
A portfolio manager says two positions are safe together because their trailing three-year correlation is -0.2. Why might that conclusion fail in a crisis, and how would you challenge it?
Systematic trading interviews use this to test whether you can reason about strategy economics.
A strategy makes 250 trades a year, wins 55% of the time, and wins and losses are the same size (1 unit). Estimate the annual Sharpe ratio. What does this tell you about how much edge a systematic strategy actually…
Standard for quantitative research and risk roles.
What are the assumptions behind OLS regression? Which are most frequently violated in financial data, and what do you do about it?
Write your answer, get it graded on technical accuracy, completeness and communication, and see exactly which mechanic you missed.
Firm names indicate where a question type is commonly reported in interviews. They are not sourced from, endorsed by, or affiliated with the firms named.