15 questions reported in D. E. Shaw interviews, organised by the group that asks them. Every question carries a model answer and graded feedback on your own attempt.
Questions
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Model builds
Built in the spreadsheet grid
Multiple testing, out-of-sample discipline, capacity and decay. 9 questions
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…
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…
A foundational research-process question for candidates who may use machine learning on financial data.
What are training, validation and test sets in quantitative research? Why should a time-series financial dataset be split chronologically rather than randomly shuffled?
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.
An offer-level research case: interviewers want a decision and a validation plan, not another feature idea.
You inherit a monthly equity signal with a 1.1 gross Sharpe ratio from 2005–2024. It rebalances the full universe, has 180% annual turnover, loses half its Sharpe after estimated costs, and most of…
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 Bayesian question testing whether you anchor on the prior or the evidence.
A test for a condition affecting 1 in 1,000 people is 99% accurate. 99% true positive rate and 99% true negative rate. Someone tests positive. What is the probability they have the condition?
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?
A judgement-heavy research review testing whether you can reject a persuasive model output for the right technical reason.
A machine-learning equity model has a strong backtest and a positive out-of-sample result. But when you rerun it across adjacent training windows, its top feature alternates between a valuation ratio,…
Expected value under pressure, adverse selection and inventory risk. 3 questions
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 research discussion at a systematic or market-making firm tests whether your backtest resembles tradeable reality.
You propose a pairs trade that buys the underperformer and shorts the outperformer when two historically correlated stocks diverge. How would you test whether the strategy is real before trading…
A systematic-trading interview tests whether a candidate follows a risk process when a live position conflicts with a backtest.
Your mean-reversion strategy is long a stock after a three-standard-deviation selloff. The position is down another 4% intraday, while the model still says buy. What do you check before deciding…
Policy reaction functions, positioning, carry and expressing a view cleanly. 1 question
A foundational EM and FX macro question.
What is a current account deficit and why can it matter for a currency?
Variant perception, catalyst mapping, short construction and sizing. 1 question
An accessible calculation that tests whether a candidate can connect earnings to an equity valuation.
A company earns $120m of net income and has 60m diluted shares. Its share price is $30. Calculate EPS and P/E. If earnings rise 10% next year and the P/E stays unchanged, what share price does that…
Stochastic calculus, VaR and expected shortfall, and model limitations. 1 question
Quant research and derivatives interviews test conceptual understanding over derivation.
Why do we model stock prices as geometric Brownian motion rather than arithmetic Brownian motion? What does Itô's lemma tell us, and why is the drift of log returns lower than the drift of prices?
Write your answer, get it graded on technical accuracy, completeness and communication, and see exactly which mechanic you missed.
Company tags reflect where a question type is commonly reported in interviews. They are not sourced from, endorsed by, or affiliated with D. E. Shaw.