factor-research

To test cross-sectional factor predictive power, you analyze factor values against forward returns across multiple instruments. This process calculates IC series and quantile equity curves to validate single factors or evaluate combinations.

IC and IR analysis in factor screening measures the Information Coefficient and Information Ratio to assess factor quality. It quantifies how well a factor predicts forward returns across a cross-section of assets over time.

Quantile backtesting evaluates factor discrimination by grouping instruments into quantiles based on factor values and calculating cumulative equity curves. This reveals whether the factor effectively separates high performers from low performers.

Cross-sectional factor validity testing requires a factor-value CSV and a forward-return CSV as input data formats. These datasets are used to generate IC summaries and group equity curves for analysis.

Yes, you can analyze factor decay across different holding periods and industries. This analysis measures how the factor's predictive power diminishes over time and varies across specific industry cross-sections.

The best way to build a multi-factor strategy is through factor screening and combination. This evaluates single-factor validity and quantile backtests to construct a robust combination with genuine predictive power.