factor-research

You can evaluate stock factor predictive power by calculating Information Coefficient and Information Ratio statistics alongside running quantile backtests. This framework assesses single factors like momentum or value to determine their effectiveness in investment strategies.

The best way to test a multi-factor model is by combining multiple single factors to analyze historical returns and factor exposure across different instruments. This comprehensive view guides portfolio construction and validates the combined investment strategy.

Yes, you need pandas, numpy, and scipy installed in your Python environment to conduct quantitative factor analysis. These libraries provide the necessary data manipulation and statistical functions required for IC/IR calculations and backtesting.

Yes, you can backtest momentum, value, and quality factors across multiple instruments. The framework supports single-factor analysis to test the predictive power of individual factors before combining them into a multi-factor model for broader portfolio evaluation.

To perform quantile backtesting for stock factor screening, apply the factor research framework to divide instruments into quantiles based on factor values, then analyze the historical returns of each quantile to identify factors with consistent predictive power.