Survivorship Bias Quant Interview Guide
Survivorship bias quant interview guide for surviving samples, delisted examples, performance inflation, and backtest mitigation.
Candidates preparing for data-quality and research process questions.
Survivorship bias excludes failures
Survivorship bias occurs when analysis includes only entities that survived to be observed, excluding failures or exits.
Why it matters in backtests
If failed or delisted names are missing, historical performance can look cleaner and stronger than a real-time strategy would have seen.
Concrete example
Testing a stock rule only on companies that exist today ignores firms that disappeared, merged, or went bankrupt during the period.
Mitigation
Use point-in-time universes and inclusion rules that match what would have been known at the time, when the prompt allows that discussion.
Common mistakes
Candidates often describe survivorship bias vaguely. Tie it to what was excluded and how that exclusion changes the estimate or backtest.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.