factor-research

Factor validity backtesting evaluates the predictive power of financial factors through cross-sectional IC/IR analysis and quantile backtesting. This skill performs statistical validation to determine if a factor has genuine stock-selection power while avoiding look-ahead bias.

Factor decay analysis measures how a financial factor's predictive validity diminishes over time. By evaluating cross-sectional IC across various forward return horizons, you can determine the optimal holding period for your quantitative investment strategies.

You combine multiple quantitative factors into a composite signal using equal-weight, IC-weighted, or orthogonalized combination methods. These techniques help build robust multi-factor strategies by isolating unique alpha sources.

Yes, you need pandas, numpy, and scipy to run cross-sectional factor backtesting. These libraries are required dependencies to process factor exposures, align forward returns, and ensure bias-free statistical computation.

To test if a momentum indicator is statistically significant, run quantile backtesting and cross-sectional IC/IR analysis on your factor and return CSV files. This validates whether the indicator has genuine predictive power before live deployment.

Factor backtesting suffers from look-ahead bias when forward returns and factor exposures are misaligned temporally. Proper cross-sectional IC analysis requires strict data alignment to ensure only past information is used for predictive validation.