Quantitative Models and Construction Methods - Model Name: AI Thematic Index Rotation Model Model Construction Idea: The model utilizes a full-spectrum price-volume fusion factor to score 133 thematic indices and constructs a weekly rebalancing strategy by equally allocating the top 10 thematic indices based on their scores [3][9][6] Model Construction Process: 1. Thematic Index Pool: Select thematic indices tracked by ETF funds classified by Wind, resulting in a pool of 133 thematic indices [9] 2. Factor: Full-spectrum price-volume fusion factor, which scores each thematic index based on the factor scores of its constituent stocks [9] 3. Strategy Rules: - On the last trading day of each week, select the top 10 thematic indices with the highest model scores - Allocate equally among the selected indices - Buy at the opening price of the first trading day of the following week - Weekly rebalancing with a transaction cost of 0.04% on both sides [9] Model Evaluation: The model demonstrates effective thematic index rotation and generates significant excess returns compared to the equal-weight benchmark [3][9] - Model Name: AI Concept Index Rotation Model Model Construction Idea: The model uses a full-spectrum price-volume fusion factor to score 72 concept indices and constructs a weekly rebalancing strategy by equally allocating the top 10 concept indices based on their scores [15][11][19] Model Construction Process: 1. Concept Index Pool: Select 72 popular concept indices from Wind [15] 2. Factor: Full-spectrum price-volume fusion factor, which scores each concept index based on the factor scores of its constituent stocks [15] 3. Strategy Rules: - On the last trading day of each week, select the top 10 concept indices with the highest model scores - Allocate equally among the selected indices - Buy at the opening price of the first trading day of the following week - Weekly rebalancing with a transaction cost of 0.04% on both sides [15] Model Evaluation: The model effectively identifies high-performing concept indices and generates consistent excess returns compared to the equal-weight benchmark [15][19] - Model Name: AI Industry Rotation Model Model Construction Idea: The model uses deep learning to extract information from full-spectrum price-volume data, scoring 32 primary industries and constructing a weekly rebalancing strategy by equally allocating the top 5 industries based on their scores [16][19][23] Model Construction Process: 1. Industry Pool: Includes 32 primary industries, with certain industries split into subcategories (e.g., food and beverage into food, beverages, and alcohol) [23] 2. Factor: Full-spectrum price-volume fusion factor, which scores each industry based on the factor scores of its constituent stocks [23] 3. Strategy Rules: - On the last trading day of each week, select the top 5 industries with the highest model scores - Allocate equally among the selected industries - Buy at the closing price of the first trading day of the following week - Weekly rebalancing without considering transaction costs [23] Model Evaluation: The model complements top-down strategies by leveraging AI's ability to extract patterns from multi-frequency price-volume data, achieving strong excess returns [16][23] - Model Name: AI CSI 1000 Enhanced Portfolio Model Construction Idea: The portfolio is constructed using the full-spectrum fusion factor to enhance the CSI 1000 index, aiming to achieve higher excess returns [27][29] Model Construction Process: 1. Factor: Full-spectrum fusion factor [29] 2. Portfolio Construction Rules: - Constituent stock weight must not be less than 80% - Individual stock weight deviation capped at 0.8% - Barra exposure limited to 0.3% - Weekly turnover rate controlled at 30% - Weekly rebalancing with a transaction cost of 0.4% on both sides [29] Model Evaluation: The portfolio demonstrates strong excess returns, high information ratio, and controlled tracking error [27][29] - Model Name: Text FADT_BERT Stock Selection Portfolio Model Construction Idea: The portfolio is based on the forecast_adjust_txt_bert factor, which is derived from upgraded text factors in earnings forecast adjustment scenarios, and selects the top 25 stocks for active quantitative enhancement [32] Model Construction Process: 1. Factor: Forecast_adjust_txt_bert factor, developed using text data related to earnings forecast adjustments [32] 2. Portfolio Construction Rules: - Select the top 25 stocks from the long side of the base stock pool - Active quantitative enhancement applied to the selected stocks [32] Model Evaluation: The portfolio achieves high annualized returns and excess returns relative to the CSI 500 index, with a strong Sharpe ratio [32] --- Model Backtesting Results - AI Thematic Index Rotation Model - Annualized return: 16.76% - Annualized excess return: 10.61% - Maximum drawdown of excess return: 20.79% - Excess Sharpe ratio: 0.82 - Year-to-date return: 24.22% [8] - AI Concept Index Rotation Model - Annualized return: 23.06% - Annualized excess return: 10.78% - Maximum drawdown of excess return: 19.48% - Excess Sharpe ratio: 0.91 - Year-to-date return: 25.27% - Year-to-date excess return: -0.98% [13] - AI Industry Rotation Model - Annualized return: 26.55% - Annualized excess return: 20.18% - Maximum drawdown of excess return: 12.43% - Excess Sharpe ratio: 1.96 - Year-to-date return: 23.70% - Year-to-date excess return: 1.52% [22] - AI CSI 1000 Enhanced Portfolio - Annualized return: 20.19% - Annualized excess return: 22.09% - Annualized tracking error: 6.07% - Maximum drawdown of excess return: 7.55% - Information ratio: 3.64 - Calmar ratio: 2.92 - Year-to-date excess return: 19.74% [27][30] - Text FADT_BERT Stock Selection Portfolio - Annualized return since inception: 39.96% - Annualized excess return since inception: 30.76% - Sharpe ratio: 1.39 - Year-to-date absolute return: 20.49% - Year-to-date excess return: -2.04% [32][37]
中证1000增强今年以来超额19.74%
HTSC·2025-10-19 13:38