量化择时研究系列03：风格指数如何择时：通过估值、流动性和拥挤度构建量化择时策略

Group 1 - The report introduces a quantitative timing strategy for style indices based on valuation, liquidity, and crowding models, emphasizing that "efficient markets" are dynamic processes rather than static states [1][6] - The quantitative timing model effectively captures the characteristics of style index bottoms and tops while mitigating risks associated with crowded trades, achieving an average annual return of 18.54% and an average excess annual return of 16.46% since 2011 [1][6] - The report highlights the performance of the mixed style index model, which has achieved an annual return of 20.10% and an excess annual return of 16.24% since December 2013 [1][6] Group 2 - The style index valuation model includes factors such as PB, PE, PBPE, and equity risk premium, with an average annual return of 10.38% and an average excess annual return of 8.30% since 2011 [1][6][17] - The market liquidity model incorporates factors like buy and sell impact costs and liquidity indices, showing a significant accuracy in bottom timing with an average rebound return of 6.86% [1][6][19] - The trading crowding model serves as a top-timing hedge factor, effectively complementing the valuation and liquidity models, achieving an excess annual return of 4.87% since 2011 [1][6][19] Group 3 - The report outlines a quantitative timing research framework that includes data processing, model factor calculation, model testing, and composite model synthesis [1][6][19] - The valuation factors are constructed by calculating the historical percentile levels of the style index valuation factors, which are then compared against set thresholds to trigger buy or sell signals [1][6][21] - The report emphasizes the need for timing factors to be logical and mean-reverting, with specific thresholds established for different style indices to determine market conditions [1][6][20]