Quantitative Models and Construction 国证 2000 Index Enhancement Strategy - **Model Name**: 国证 2000 Index Enhancement Strategy - **Model Construction Idea**: Focused on the small-cap stock rotation phenomenon in A-shares, aiming to select stocks effectively within 国证 2000 index components to enhance returns [11] - **Model Construction Process**: - Selected factors such as technical, reversal, and idiosyncratic volatility, which showed strong performance on 国证 2000 index components [12] - Addressed high correlation among factors by regressing volatility factors on technical and reversal factors to obtain residual volatility factors [12] - Combined all major factors equally and performed industry and market capitalization neutralization to construct the 国证 2000 enhancement factor [12] - Formula: Residual volatility factor = Volatility factor - Regression(Technical factor, Reversal factor) [12] - **Model Evaluation**: Demonstrated strong predictive performance with an IC mean of 12.63% and T-statistic of 12.70 [12] - **Strategy Construction**: - Monthly rebalancing at the end of each month, buying the top 10% ranked stocks based on factor values, constructing an equal-weighted long portfolio [15] - Backtesting period: April 2014 to present, benchmarked against 国证 2000 index, with a transaction fee rate of 0.2% per side [15] Machine Learning Index Enhancement Strategy - **Model Name**: TSGRU+LGBM Machine Learning Index Enhancement Strategy - **Model Construction Idea**: Improved machine learning stock selection model by integrating TimeMixer framework with GRU and LightGBM, leveraging multi-scale mixing and seasonal/trend decomposition mechanisms [21] - **Model Construction Process**: - Original strategy used GBDT and NN models trained on different feature datasets and prediction labels, but showed signs of failure due to market style adjustments [21] - Enhanced model incorporated TimeMixer framework into GRU, combined LightGBM with TSGRU latent vectors and traditional quantitative factors [21] - Optimized portfolio construction by controlling tracking error and individual stock weight deviation to maximize factor exposure [25] - **Model Evaluation**: Improved ability to capture recent market information, showing strong performance [21] Dividend Style Timing + Dividend Stock Selection Strategy - **Model Name**: Dividend Style Timing + Dividend Stock Selection Strategy - **Model Construction Idea**: Leveraged the long-term stability and high dividend characteristics of dividend stocks to reduce risk during weak market conditions [36] - **Model Construction Process**: - Used 10 indicators related to economic growth and monetary liquidity to construct a dynamic event factor system for dividend index timing [36] - Applied AI models to test stock selection within 中证红利 index components, achieving stable excess returns [36] - **Model Evaluation**: Demonstrated significant stability improvement compared to 中证红利 index total return [36] --- Model Backtesting Results 国证 2000 Index Enhancement Strategy - **IC Mean**: 12.63% [12] - **Latest Month IC**: 25.34% [12] - **Annualized Excess Return**: 13.30% [16] - **Information Ratio (IR)**: 1.73 [16] - **Tracking Error**: 7.68% [19] - **October Excess Return**: 2.92% [16] TSGRU+LGBM Machine Learning Index Enhancement Strategy - **沪深 300 Index**: - **Annualized Excess Return**: 6.96% [26] - **Information Ratio (IR)**: 1.40 [26] - **Tracking Error**: 4.97% [26] - **October Excess Return**: 2.25% [26] - **中证 500 Index**: - **Annualized Excess Return**: 10.11% [30] - **Information Ratio (IR)**: 1.96 [30] - **Tracking Error**: 5.16% [30] - **October Excess Return**: -0.59% [30] - **中证 1000 Index**: - **Annualized Excess Return**: 13.52% [35] - **Information Ratio (IR)**: 2.37 [35] - **Tracking Error**: 5.70% [35] - **October Excess Return**: 2.63% [35] Dividend Style Timing + Dividend Stock Selection Strategy - **Stock Selection Strategy**: - **Annualized Return**: 18.98% [38] - **Sharpe Ratio**: 0.90 [38] - **October Return**: 2.52% [38] - **Timing Strategy**: - **Annualized Return**: 13.83% [38] - **Sharpe Ratio**: 0.90 [38] - **October Return**: 3.28% [38] - **固收+ Strategy**: - **Annualized Return**: 7.39% [38] - **Sharpe Ratio**: 2.19 [38] - **October Return**: 0.92% [38]

- The recent phenomenon of "strong index, weak quantitative Alpha" is attributed to style mismatches, with cumulative excess returns driven by small-cap and short-term momentum factors initially, and later by analyst consensus expectations and growth styles[2][3] - The Guozheng 2000 Index enhancement strategy involves factor testing and selection, including technical, reversal, and idiosyncratic volatility factors, which have shown excellent performance in the Guozheng 2000 Index constituents[4] - The machine learning index enhancement strategy based on multiple objectives and models uses GBDT and NN models, trained on different feature datasets and combined to construct a GBDT+NN stock selection factor, which has performed well across various broad-based indices in the A-share market[5] - The dividend style timing + dividend stock selection fixed income+ strategy uses 10 indicators related to economic growth and monetary liquidity to construct a dynamic event factor system for dividend index timing, showing significant stability improvement compared to the CSI Dividend Index total return[6] - The Guozheng 2000 Index enhancement factor's IC mean is 12.54%, with a T-statistic of 12.56, indicating good predictive performance[4] - The GBDT+NN machine learning stock selection factor in the CSI 300 constituents has an IC mean of 11.43% and an annualized excess return of 15.39%[43] - The GBDT+NN machine learning stock selection factor in the CSI 500 constituents has an IC mean of 9.77% and an annualized excess return of 29.48%[48] - The GBDT+NN machine learning stock selection factor in the CSI 1000 constituents has an IC mean of 13.49% and an annualized excess return of 16.10%[53] - The Guozheng 2000 Index enhancement strategy has an annualized excess return of 13.18% and an IR of 1.73[38] - The GBDT+NN CSI 300 Index enhancement strategy has an annualized excess return of 10.86% and an IR of 1.81[47] - The GBDT+NN CSI 500 Index enhancement strategy has an annualized excess return of 10.27% and an IR of 1.71[52] - The GBDT+NN CSI 1000 Index enhancement strategy has an annualized excess return of 15.83% and an IR of 2.34[57] - The dividend stock selection strategy has an annualized return of 18.83% and a Sharpe ratio of 0.89[58] - The dividend timing strategy has an annualized return of 13.58% and a Sharpe ratio of 0.88[58] - The fixed income+ strategy has an annualized return of 7.34% and a Sharpe ratio of 2.17[58]