中邮因子周报：小市值强势，动量风格占优-20250421

Quantitative Models and Construction 1. Model Name: GRU Model - Model Construction Idea: The GRU model is a machine learning-based quantitative model designed to capture complex patterns in stock price movements and factor relationships[3][19][23] - Model Construction Process: The GRU (Gated Recurrent Unit) model is trained on historical stock data, incorporating various financial and technical indicators as input features. The model uses a recurrent neural network structure to process sequential data, enabling it to predict stock price trends and factor performance. Specific details on the training parameters or architecture were not provided in the report[3][19][23] - Model Evaluation: The GRU model demonstrated strong performance in multi-factor strategies and outperformed other models in certain market conditions, particularly in the small-cap stock universe[7][29][33] 2. Model Name: Barra1d Model - Model Construction Idea: The Barra1d model is a factor-based quantitative model that utilizes the Barra risk model framework to analyze and predict stock returns[3][19][23] - Model Construction Process: The Barra1d model incorporates style factors such as size, value, momentum, and volatility, along with industry and country factors. It uses historical data to estimate factor exposures and risk premiums, which are then applied to construct long-short portfolios[3][19][23] - Model Evaluation: The Barra1d model experienced significant drawdowns in certain market conditions, particularly in the CSI 500 and CSI 1000 stock universes, indicating sensitivity to market volatility[5][26][29] 3. Model Name: Open1d Model - Model Construction Idea: The Open1d model focuses on short-term price movements and factor dynamics, leveraging daily open prices as a key input[3][19][29] - Model Construction Process: The Open1d model uses daily open prices and other technical indicators to construct long-short portfolios. It emphasizes short-term momentum and volatility factors to capture rapid market movements[3][19][29] - Model Evaluation: The Open1d model achieved strong performance, with its excess returns reaching new highs for the year, particularly in the CSI 1000 stock universe[6][29][33] 4. Model Name: Close1d Model - Model Construction Idea: The Close1d model is similar to the Open1d model but focuses on daily closing prices to capture end-of-day market dynamics[3][19][29] - Model Construction Process: The Close1d model uses daily closing prices and technical indicators to construct long-short portfolios. It emphasizes factors such as closing momentum and volatility to predict stock movements[3][19][29] - Model Evaluation: The Close1d model demonstrated strong performance, particularly in the CSI 1000 stock universe, with consistent positive excess returns[6][29][33] --- Model Backtesting Results 1. GRU Model - Weekly excess return: 0.46%-1.43% relative to the CSI 1000 index[7][33][34] - Year-to-date excess return: Not explicitly provided 2. Barra1d Model - Weekly excess return: 0.46%[33][34] - Year-to-date excess return: 2.10%[34] 3. Open1d Model - Weekly excess return: 1.43%[33][34] - Year-to-date excess return: 3.90%[34] 4. Close1d Model - Weekly excess return: 1.38%[33][34] - Year-to-date excess return: 1.87%[34] 5. Multi-Factor Strategy - Weekly excess return: 1.01%[33][34] - Year-to-date excess return: 2.15%[34] --- Quantitative Factors and Construction 1. Factor Name: Momentum - Factor Construction Idea: Momentum factors are designed to capture the tendency of stocks with strong past performance to continue performing well in the short term[15][16][19] - Factor Construction Process: - Historical excess return mean: $0.74 \times \text{volatility of excess returns} + 0.16 \times \text{cumulative excess return deviation} + 0.1 \times \text{residual return volatility}$[15] - Factor Evaluation: Momentum factors showed strong performance in the current market, particularly in high-volatility environments[3][19][23] 2. Factor Name: Valuation - Factor Construction Idea: Valuation factors aim to identify undervalued stocks based on financial ratios such as price-to-book (P/B) and price-to-earnings (P/E)[15][16][19] - Factor Construction Process: - Valuation factor: $1 / \text{P/B ratio}$[15] - Factor Evaluation: Valuation factors demonstrated strong performance in the small-cap stock universe, particularly in the CSI 1000 index[6][29][33] 3. Factor Name: Growth - Factor Construction Idea: Growth factors focus on identifying stocks with high earnings and revenue growth potential[15][16][19] - Factor Construction Process: - Growth factor: $0.24 \times \text{earnings growth rate} + 0.47 \times \text{revenue growth rate}$[15] - Factor Evaluation: Growth factors performed well across multiple stock universes, particularly in high-growth environments[3][19][23] 4. Factor Name: Volatility - Factor Construction Idea: Volatility factors measure the risk associated with stock price fluctuations, often used to identify low-risk investment opportunities[15][16][19] - Factor Construction Process: - Volatility factor: $0.74 \times \text{historical return volatility} + 0.16 \times \text{cumulative return deviation} + 0.1 \times \text{residual return volatility}$[15] - Factor Evaluation: Volatility factors showed mixed performance, with low-volatility stocks underperforming in certain market conditions[5][26][29] --- Factor Backtesting Results 1. Momentum Factor - Weekly excess return: 0.89%[17][19][23] - Year-to-date excess return: 42%[17][19][23] 2. Valuation Factor - Weekly excess return: 1.68%[17][19][23] - Year-to-date excess return: 1.14%[17][19][23] 3. Growth Factor - Weekly excess return: 1.20%[17][19][23] - Year-to-date excess return: 4.03%[17][19][23] 4. Volatility Factor - Weekly excess return: 0.16%[17][19][23] - Year-to-date excess return: 8.10%[17][19][23]