Alpha Quant Interview Guide
Alpha quant interview guide for excess return, benchmark choice, risk adjustment, factor controls, performance attribution, and caveats.
Candidates discussing performance attribution and factor controls.
Alpha depends on the benchmark
Alpha is often used for performance unexplained by a benchmark or factor model. The meaning changes with the benchmark, factor set, time period, and risk adjustment.
Excess return is not automatically alpha
A strategy can outperform because it took hidden factor exposure, leverage, illiquidity, or tail risk. Alpha claims need controls for the risks being taken.
Concrete example
If a strategy beats a broad index, ask whether it simply has small-cap, momentum, sector, or volatility exposure. Attribution changes the interpretation of performance.
Validate persistence
Alpha is noisy and can decay. Out-of-sample evidence, transaction costs, capacity, and changing market behavior all matter before calling a result robust.
Common mistakes
Candidates often call any positive backtest alpha. A stronger answer asks alpha relative to what model and after which risks, costs, and selection effects.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.