Self-Testing Factor Coverage and Look-Ahead Bias in Stock Research

Summary

This guide presents local checks for two common failures in factor submissions: missing values near the evaluation window’s start and look-ahead bias. For time-series factors, it recommends querying enough earlier data to cover the longest lag or rolling window, calculating the factor, then trimming output back to the evaluation dates. A sample validation table lets researchers check whether early-period missingness could fail coverage requirements.

To detect future data leakage, the guide compares factor outputs from a full dataset and a version cut off at the evaluation end date. Values through that cutoff should match if the calculation uses only information available at each date. Differences can also result from nondeterministic aggregations or window functions, so the guide emphasizes explicit ordering for operations such as selecting the first or last row. It notes that tolerances can help distinguish tiny floating-point differences. These checks diagnose specific implementation risks; passing them does not establish that a factor is profitable or robust.

Key ideas

• Expand the query window backward to supply history for lags and rolling calculations.
• Trim computed factors to the requested evaluation interval before returning results.
• Compare full-data and cutoff-data outputs to identify possible future-data dependence.
• Use explicit ordering in row-selection aggregations and window functions.
• Treat small numerical discrepancies differently from missing values or material mismatches.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.