Core Viewpoint - The article discusses a quantitative timing research framework for style indices, focusing on the identification of market bottoms and tops through valuation, liquidity, and trading congestion models, highlighting their effectiveness in generating returns since 2011 [1][2][3]. Group 1: Style Index Quantitative Timing Research Framework - The style index includes large-cap, small-cap, value, growth, and dividend indices, with a focus on their valuation and market liquidity characteristics [1]. - The average annualized return for the long positions in the style index valuation model since 2011 is 10.38%, with an average excess annualized return of 8.30% [1]. Group 2: Market Liquidity Model - The market liquidity factors include buy and sell impact costs, as well as liquidity indices for rising and falling markets, with bottom timing showing more significant accuracy compared to top timing [2]. - The average rebound return from liquidity factor bottom timing is 6.86%, and the average annualized return for the long positions in the liquidity model since 2011 is 12.38%, with an average excess annualized return of 10.30% [2]. Group 3: Trading Congestion Model - Trading congestion is identified as a top-timing risk factor, effectively complementing the valuation and liquidity models [2]. - The excess annualized return for the congestion composite model since 2011 is 4.87% [2]. Group 4: Application of Quantitative Timing Models - The combined application of valuation, liquidity, and congestion models has accurately captured style index bottoms and tops, while effectively mitigating risks from trading congestion [3]. - The average annualized return for the long positions in the timing model since 2011 is 18.54%, with an average excess annualized return of 16.46% and a SHARP ratio of 1.06, achieving an excess return win rate of 87% [3].

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]