Financial Factor Extraction - The generation paradigm of financial factors involves defining operational rules (operators) and calculating factors from various financial indicators, resulting in approximately 100,000 factors extracted from financial statements, forecasts, and notes [1][27][40] - A preliminary screening of factors based on RankIC mean values and annualized RankICIR led to the identification of 4,427 effective factors, indicating a significant number of factors with potential predictive power [27][35][40] Multi-Dimensional Financial Factor Expansion - The introduction of new operators and data sources enhances the performance of classic factors, with the development of a cross-sectional percentile difference operator improving the performance of classic factors significantly [1][42][45] - The addition of financial note data has provided incremental information, with the composite factor derived from notes showing a RankIC mean of 4.78% and an annualized RankICIR of 2.69, indicating strong predictive capabilities [1][62][69] Alpha Factor Library Expansion and Enhancement - Traditional factor synthesis methods face challenges such as style drift when combining numerous factors without considering their styles, leading to unstable performance [1][3] - The proposed "clustering-expansion-synthesis" method effectively groups factors based on their correlation, resulting in the creation of eight major categories of factors, which outperform direct synthesis of all factors [1][3][6] Performance of Enhanced Factors - Empirical research shows that the performance of clustered enhanced factors is superior, with a monthly RankIC mean of 12.08% and an annualized RankICIR of 5.32 since 2013, indicating strong predictive power [4][6] - The enhanced factors demonstrate significant improvements in monthly excess returns compared to traditional composite factors, particularly in value and growth categories [4][6][62]