HTSC·2025-03-16 10:49Quantitative Models and Construction Methods AI Theme Index Rotation Model - Model Name: AI Theme Index Rotation Model - Model Construction Idea: This model uses the all-frequency volume-price fusion factor to score 133 thematic indices and constructs a weekly rebalancing strategy by selecting the top 10 indices for equal-weight allocation[3][9] - Model Construction Process: 1. Index Pool: Select thematic indices tracked by thematic ETFs based on Wind's ETF classification, resulting in 133 indices[10] 2. Factor: Use the all-frequency volume-price fusion factor to score the constituent stocks of each thematic index[10] 3. Strategy Rules: - Select the top 10 indices with the highest scores on the last trading day of each week - Allocate equally among the selected indices - Execute trades at the opening price of the first trading day of the following week - Weekly rebalancing with a transaction cost of 0.04% (two-way)[10] - Model Evaluation: The model effectively captures thematic rotation opportunities and demonstrates strong performance relative to the benchmark[3][9] AI Concept Index Rotation Model - Model Name: AI Concept Index Rotation Model - Model Construction Idea: This model uses the all-frequency volume-price fusion factor to score 72 concept indices and constructs a weekly rebalancing strategy by selecting the top 10 indices for equal-weight allocation[11][15] - Model Construction Process: 1. Index Pool: Select 72 popular concept indices from Wind[15] 2. Factor: Use the all-frequency volume-price fusion factor to score the constituent stocks of each concept index[15] 3. Strategy Rules: - Select the top 10 indices with the highest scores on the last trading day of each week - Allocate equally among the selected indices - Execute trades at the opening price of the first trading day of the following week - Weekly rebalancing with a transaction cost of 0.04% (two-way)[15] - Model Evaluation: The model leverages AI-driven factor scoring to identify high-potential concept indices, achieving notable excess returns over the benchmark[11][15] AI Industry Rotation Model - Model Name: AI Industry Rotation Model - Model Construction Idea: This model applies the all-frequency volume-price fusion factor to score 32 first-level industries and constructs a weekly rebalancing strategy by selecting the top 5 industries for equal-weight allocation[4][22] - Model Construction Process: 1. Industry Pool: Includes 32 first-level industries, with certain industries split into subcategories (e.g., food and beverage, metals)[22] 2. Factor: Use the all-frequency volume-price fusion factor to score the constituent stocks of each industry[22] 3. Strategy Rules: - Select the top 5 industries with the highest scores on the last trading day of each week - Allocate equally among the selected industries - Execute trades at the closing price of the first trading day of the following week - Weekly rebalancing without transaction cost consideration[22] - Model Evaluation: The model complements top-down strategies by leveraging AI's ability to extract patterns from multi-frequency volume-price data[16][22] --- Model Backtesting Results AI Theme Index Rotation Model - Annualized Return: 16.78%[9] - Annualized Excess Return: 12.69%[9] - Maximum Drawdown of Excess Return: 16.56%[9] - Excess Sharpe Ratio: 1.00[9] - YTD Return: 10.75%[9] - YTD Excess Return: 3.90%[9] AI Concept Index Rotation Model - Annualized Return: 21.77%[13] - Annualized Excess Return: 11.36%[13] - Maximum Drawdown of Excess Return: 18.10%[13] - Excess Sharpe Ratio: 0.96[13] - YTD Return: 3.06%[13] - YTD Excess Return: -4.45%[13] AI Industry Rotation Model - Annualized Return: 25.70%[21] - Annualized Excess Return: 21.01%[21] - Maximum Drawdown of Excess Return: 12.43%[21] - Excess Sharpe Ratio: 2.02[21] - YTD Return: 5.14%[21] - YTD Excess Return: -1.70%[21] --- Quantitative Factors and Construction Methods All-Frequency Volume-Price Fusion Factor - Factor Name: All-Frequency Volume-Price Fusion Factor - Factor Construction Idea: This factor integrates high-frequency and low-frequency volume-price data using deep learning and multi-task learning to extract comprehensive stock selection signals[26] - Factor Construction Process: 1. Train 27 high-frequency factors using deep learning to obtain high-frequency deep learning factors[26] 2. Apply multi-task learning to low-frequency volume-price data for end-to-end extraction, resulting in low-frequency multi-task factors[26] 3. Combine the high-frequency and low-frequency factors into the all-frequency fusion factor[26] - Factor Evaluation: The factor demonstrates strong stock selection capabilities, with high RankIC and significant excess returns in backtesting[26][29] Forecast_Adjust_Text_BERT Factor - Factor Name: Forecast_Adjust_Text_BERT Factor - Factor Construction Idea: This factor upgrades text-based factors in earnings forecast adjustment scenarios using BERT to enhance stock selection performance[34] - Factor Construction Process: 1. Develop the forecast_adjust_txt_bert factor based on text data related to earnings forecast adjustments[34] 2. Construct a long-only portfolio using the top 25 stocks from the factor's high-score segment[34] - Factor Evaluation: The factor effectively captures alpha signals from textual data, achieving high returns and Sharpe ratios in backtesting[34] --- Factor Backtesting Results All-Frequency Volume-Price Fusion Factor - 5-Day RankIC Mean: 0.114[29] - Annualized Excess Return (Top Layer): 30.72%[29] - YTD Excess Return (Top Layer): 3.80%[29] Forecast_Adjust_Text_BERT Factor - Annualized Return: 40.64%[41] - Annualized Excess Return: 32.25%[41] - Annualized Volatility: 28.77%[41] - Maximum Drawdown: 48.69%[41] - Sharpe Ratio: 1.41[41] - Calmar Ratio: 0.83[41]