Front Office Investing interview prep.

Behavioural questions to expect

1. Walk me through your CV.
2. Tell me about your most rigorous piece of research.
3. Tell me about a weakness, a failure, or feedback you've received and worked on.
4. Why systematic / quant investing? Why not discretionary?
5. Why the firm?
6. Why a {multi-manager pod / single-manager} platform — or why {mid-frequency / high-frequency} — over the alternative?
7. How would you describe the firm's research process and edge in your own words?
8. How do you think the firm manages risk on its signals and at the book level?

Technical concepts to master

• Designing a signal — the workflow

  Hypothesis first · Data + point-in-time · Construction · Evaluation · Economics

• Backtesting hygiene

  In-sample vs out-of-sample · Walk-forward + cross-validation · Multiple testing + deflated Sharpe · Survivorship + look-ahead · Parsimony

• The economics of a signal

  Turnover · Transaction costs + market impact · Capacity · Alpha decay / half-life · Crowding

• The fundamental law + combining signals

  Grinold's fundamental law · Breadth + independence · Signal combination · Risk model + neutralization · Optimization

• Why signals fail live

  Overfitting (the big one) · Costs + capacity reality · Regime change · Crowding + decay

Practical drills

• Design an alpha signal for the firm's asset class and horizon. 5 min prep, 5-7 min delivery. Be ready to be probed for 10-15 min on bias controls, net-of-cost economics, and what would kill it.
• Your signal has an information coefficient of 0.05 and you make ~200 independent bets per year. (a) What's the implied information ratio? (b) What if you raise breadth to 800 independent bets? (c) You add a second, uncorrelated signal with the same IC — roughly what happens to the combined IR?
• A signal shows 8% gross annual alpha. It turns the book over 20x per year (round-trip), and round-trip transaction cost is 20bp. (a) What's the net alpha? (b) The firm wants to 5x the capital and market impact doubles the cost to 40bp — now what? (c) What does this tell you about capacity?

Smart-question anchors

• Research-to-production pipeline — how a signal goes from idea to live capital, and who owns data, execution, and risk
• Data + tooling edge — proprietary / alternative data, the backtesting framework, and how research is validated before deployment
• Signal ownership + collaboration — whether researchers own signals end-to-end or feed a central book; how credit and P&L are attributed
• Capacity + cost discipline — how the firm models transaction costs / market impact and thinks about capacity vs assets
• Risk + monitoring — factor neutralization, crowding monitoring, live-IC-vs-backtest tracking, and kill criteria for decaying signals

Sourced from

Deutsche Bank Markets Research — Seven Sins of Quantitative Investing · Bailey & Lopez de Prado — Deflated Sharpe Ratio / backtest overfitting · Grinold & Kahn — Fundamental Law of Active Management · Wall Street Oasis (WSO) · Stefan Jansen — Machine Learning for Trading (alpha factor research, Alphalens) · Lopez de Prado — Advances in Financial Machine Learning