深度学习因子月报：Meta因子今年已实现超额收益36.8%-20250818

Quantitative Factors and Models Summary Quantitative Factors and Construction Methods 1. Factor Name: DL_EM_Dynamic - Construction Idea: Extract intrinsic stock attributes from public fund holdings using matrix decomposition, and combine these attributes with LSTM-generated factor representations to create dynamic market state factors[19][21]. - Construction Process: - Matrix decomposition is applied to fund-stock investment networks to derive intrinsic matrices for funds and stocks[19]. - Static intrinsic attributes are updated semi-annually using fund reports and transformed into dynamic attributes by calculating their similarity to current market preferences[19]. - These dynamic attributes are combined with LSTM outputs and fed into an MLP model to enhance performance[19]. - The factor is used to construct a CSI 1000 enhanced index portfolio with constraints on tracking error (5%), industry exposure (±0.02), style exposure (±0.5), and individual stock weight (3%). Weekly rebalancing is applied, and transaction costs are set at 0.2% for both sides[21]. 2. Factor Name: Meta_RiskControl - Construction Idea: Incorporate factor exposure control into deep learning models to mitigate drawdowns during rapid style factor changes[26]. - Construction Process: - Multiply model outputs by corresponding stock factor exposures and include this in the loss function[26]. - Add penalties for style deviations and style momentum to the IC-based loss function[26]. - Use an ALSTM model with style inputs as the base model and integrate it with a meta-incremental learning framework for dynamic market adaptation[26]. - Construct enhanced portfolios for CSI 300, CSI 500, and CSI 1000 indices with constraints on market cap deviation (±0.5), industry deviation (±0.02), and individual stock weight (5x benchmark weight). Weekly rebalancing and 0.2% transaction costs are applied[29]. 3. Factor Name: Meta_Master - Construction Idea: Leverage market-guided stock transformer models (MASTER) and deep risk models to capture market states and improve factor performance[36]. - Construction Process: - Incorporate market state vectors derived from recent price-volume data of CSI 300, CSI 500, and CSI 1000 indices into the MASTER model[36]. - Construct 120 new features representing market states based on the styles of recently best-performing stocks[36]. - Replace the loss function with weighted MSE to enhance long-side prediction accuracy and use online meta-incremental learning for periodic model updates[36]. - Construct enhanced portfolios for CSI 300, CSI 500, and CSI 1000 indices with constraints on market cap deviation (±0.5), industry deviation (±0.02), and individual stock weight (5x benchmark weight). Weekly rebalancing and 0.2% transaction costs are applied[38]. 4. Factor Name: Deep Learning Convertible Bond Factor - Construction Idea: Address the diminishing excess returns of traditional convertible bond strategies by using GRU deep neural networks to model the complex nonlinear pricing logic of convertible bonds[50]. - Construction Process: - Introduce convertible bond-specific time-series factors into the GRU model[50]. - Combine cross-sectional bond attributes with GRU outputs to predict future returns, significantly improving model performance[50]. --- Factor Backtesting Results 1. DL_EM_Dynamic Factor - RankIC: 11.3% (July 2025, CSI 1000)[7][10] - Excess Return: 1.3% (July 2025, CSI 1000); 11% YTD (2025)[7][10] - Annualized Return: 29.7% (since 2019)[23] - Annualized Excess Return: 23.4% (since 2019)[23] - IR: 2.03 (since 2019)[23] - Max Drawdown: -10.1% (since 2019)[23] 2. Meta_RiskControl Factor - RankIC: 15.5% (July 2025, All A-shares)[7][13] - Excess Return: - CSI 300: 1.9% (July 2025); 6.4% YTD (2025)[31] - CSI 500: 1.4% (July 2025); 4.4% YTD (2025)[33] - CSI 1000: 1.3% (July 2025); 9.3% YTD (2025)[35] - Annualized Return: - CSI 300: 20.1% (since 2019)[31] - CSI 500: 26.1% (since 2019)[33] - CSI 1000: 34.1% (since 2019)[35] - Annualized Excess Return: - CSI 300: 15.0% (since 2019)[31] - CSI 500: 19.2% (since 2019)[33] - CSI 1000: 27.0% (since 2019)[35] - IR: - CSI 300: 1.58 (since 2019)[31] - CSI 500: 1.97 (since 2019)[33] - CSI 1000: 2.36 (since 2019)[35] - Max Drawdown: - CSI 300: -5.8% (since 2019)[31] - CSI 500: -9.3% (since 2019)[33] - CSI 1000: -10.2% (since 2019)[35] 3. Meta_Master Factor - RankIC: 18.9% (July 2025, All A-shares)[7][16] - Excess Return: - CSI 300: 2.0% (July 2025); 7.9% YTD (2025)[39] - CSI 500: 1.6% (July 2025); 5.5% YTD (2025)[45] - CSI 1000: 1.4% (July 2025); 8.1% YTD (2025)[47] - Annualized Return: - CSI 300: 22.0% (since 2019)[39] - CSI 500: 23.8% (since 2019)[45] - CSI 1000: 30.7% (since 2019)[47] - Annualized Excess Return: - CSI 300: 17.5% (since 2019)[39] - CSI 500: 18.2% (since 2019)[45] - CSI 1000: 25.2% (since 2019)[47] - IR: - CSI 300: 2.09 (since 2019)[39] - CSI 500: 1.9 (since 2019)[45] - CSI 1000: 2.33 (since 2019)[47] - Max Drawdown: - CSI 300: -7.2% (since 2019)[39] - CSI 500: -5.8% (since 2019)[45] - CSI 1000: -8.8% (since 2019)[47] 4. Deep Learning Convertible Bond Factor - Absolute Return: - July 2025: 5.8% (equity-biased), 3.8% (balanced), 3.3% (debt-biased)[52] - Annualized (since 2021): 13.2% (equity-biased), 11.8% (balanced), 12.7% (debt-biased)[52] - Excess Return: - July 2025: 1.5% (equity-biased), -0.4% (balanced), -0.9% (debt-biased)[55] - Annualized (since 2021): 5.8% (equity-biased), 4.0% (balanced), 4.4% (debt-biased)[55]