factor-research

To evaluate cross-sectional factor predictive power, compute factor exposures against forward returns to obtain IC/IR metrics. This systematically tests single-factor validity and guides multi-factor stock screening across multiple instruments.

The best way to run a quantile backtest for stock-picking factors is to align cross-sectional factor CSV data with forward-return CSV data. This process outputs quantile backtest results and group equity curves to assess factor discrimination.

Yes, you can use factor analysis to test multiple factors across different markets. The cross-sectional evaluation supports cross-industry and cross-market comparison to validate factor stability and build robust multi-factor signals.

To combine multiple factors into a robust stock screening signal, evaluate individual factor predictive power using IC/IR tests and quantile backtests. This cross-sectional analysis guides factor screening and combination for robust results.

Yes, you need aligned cross-sectional factor value CSV files and corresponding forward-return CSV files. Providing these aligned inputs allows the factor analysis tool to properly compute IC/IR metrics and generate group equity curves.

Cross-sectional factor analysis requires forward returns to calculate IC because IC measures the correlation between current factor exposures and subsequent asset performance. This relationship evaluates the factor's actual predictive power.