Quantitative Research interview questions

A first-round check that a research candidate can separate a strategy outcome from the market outcome it rode along with.

A paper portfolio returns 8% in a month while its benchmark returns 5%. What is the portfolio's active return? Is that enough to call the signal good?

Asked to see whether you can explain a counterintuitive result clearly under pressure.

Three doors: one hides a car, two hide goats. You pick door 1. The host. Who knows what's behind each door. Opens door 3 revealing a goat, then offers you the chance to switch to door 2. Should you switch? Explain why in…

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…

Researchers are expected to explain a simple factor test before writing a complex model around it.

You believe companies with the strongest earnings revisions will outperform. Explain how you would test that idea using decile portfolios. What result would make you interested, and what result would make you sceptical?

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?

A research-team scenario used to test whether a junior candidate can communicate a disciplined go or no-go decision under a deadline.

At 4pm, a senior researcher asks you to add a new vendor's "customer demand score" to tomorrow's signal run. The vendor says the history reaches 2018, but its documentation does not state when each observation became…

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 its profits came from…

Tests recursive reasoning. A staple at trading firms.

You roll a fair six-sided die. You may either take the value shown in dollars, or re-roll. You get at most two rolls total. What is the expected value of the game if you play optimally? Then: what if you had unlimited…

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?

Tests whether you can approximate rather than recall.

In a room of 23 people, what is the probability at least two share a birthday? Explain your reasoning and why the answer surprises people.

A classic Bayesian warm-up at quant trading firms.

You have two coins. One is fair; the other lands heads 75% of the time. You pick one at random and flip it 3 times, getting heads every time. What is the probability you picked the biased coin?

A research review question after a promising factor weakens in its most recent out-of-sample period.

A cross-sectional equity signal had a strong information coefficient for eight years, but its last 18 months are near zero. How would you decide whether this is noise, a regime change, or a research error?

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…

The signature exercise at proprietary trading firms.

Make me a market on the sum of the digits of a randomly chosen phone number in the room. Then I'll trade against you. Explain how you'd think through the whole exercise.

A recurring structure in quant interviews. Set up the recursion, don't simulate.

You start at position 0. Each step you move +1 with probability 0.5 and −1 with probability 0.5. What is the expected number of steps to first reach +3? Then: what changes if the walk is bounded below at −1 (you cannot…

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, a momentum variable…