中邮因子周报：beta风格显著，高波占优-20250630

Quantitative Models and Construction - Model Name: barra1d Model Construction Idea: Focuses on short-term factor performance using daily data Model Construction Process: Utilizes historical data to calculate factor exposures and applies industry-neutral adjustments. Stocks are ranked based on factor scores, with the top 10% selected for long positions and the bottom 10% for short positions. Adjustments include equal weighting and monthly rebalancing[19][21][30] Model Evaluation: Demonstrates strong performance in short-term factor analysis[19][21][30] - Model Name: barra5d Model Construction Idea: Focuses on medium-term factor performance using five-day data Model Construction Process: Similar to barra1d, but uses a five-day rolling window for factor calculations. Stocks are ranked and selected based on factor scores, with monthly rebalancing and equal weighting applied[19][21][30] Model Evaluation: Exhibits robust medium-term factor performance, outperforming other models in cumulative returns[19][21][30] - Model Name: open1d Model Construction Idea: Focuses on factor performance using daily open prices Model Construction Process: Factors are calculated using daily open price data, with industry-neutral adjustments applied. Stocks are ranked based on factor scores, and the top 10% are selected for long positions, while the bottom 10% are shorted. Monthly rebalancing is implemented[19][21][30] Model Evaluation: Performs well in certain market conditions but shows higher volatility compared to other models[19][21][30] - Model Name: close1d Model Construction Idea: Focuses on factor performance using daily close prices Model Construction Process: Factors are calculated using daily close price data, with industry-neutral adjustments applied. Stocks are ranked based on factor scores, and the top 10% are selected for long positions, while the bottom 10% are shorted. Monthly rebalancing is implemented[19][21][30] Model Evaluation: Demonstrates weaker performance compared to other models, with significant drawdowns observed[19][21][30] Model Backtesting Results - barra1d: Weekly excess return 0.17%, monthly excess return 0.32%, six-month excess return 4.09%, year-to-date excess return 3.93%[32] - barra5d: Weekly excess return 0.13%, monthly excess return 0.39%, six-month excess return 7.59%, year-to-date excess return 7.56%[32] - open1d: Weekly excess return -0.35%, monthly excess return -0.71%, six-month excess return 5.85%, year-to-date excess return 6.30%[32] - close1d: Weekly excess return 0.55%, monthly excess return 0.40%, six-month excess return 6.40%, year-to-date excess return 6.31%[32] - Multi-factor model: Weekly excess return -0.38%, monthly excess return -0.04%, six-month excess return 3.56%, year-to-date excess return 2.82%[32] Quantitative Factors and Construction - Factor Name: Beta Factor Construction Idea: Measures historical beta to assess market sensitivity Factor Construction Process: Calculated using historical beta values derived from regression analysis of stock returns against market returns[15][16] Factor Evaluation: Demonstrates strong performance in high-volatility environments[15][16] - Factor Name: Momentum Factor Construction Idea: Captures historical excess return trends Factor Construction Process: Combines weighted averages of historical excess return volatility, cumulative excess return deviation, and residual return volatility using the formula: $ Momentum = 0.74 * Historical Excess Return Volatility + 0.16 * Cumulative Excess Return Deviation + 0.1 * Residual Return Volatility $[15][16] Factor Evaluation: Performs well in trending markets but struggles in reversal scenarios[15][16] - Factor Name: Volatility Factor Construction Idea: Measures stock price fluctuation intensity Factor Construction Process: Combines weighted averages of monthly, quarterly, and annual turnover rates using the formula: $ Volatility = 0.35 * Monthly Turnover Rate + 0.35 * Quarterly Turnover Rate + 0.3 * Annual Turnover Rate $[15][16] Factor Evaluation: Strong performance in high-volatility stocks[15][16] - Factor Name: Valuation Factor Construction Idea: Assesses stock valuation using price-to-book ratio Factor Construction Process: Calculated as the inverse of the price-to-book ratio[15][16] Factor Evaluation: Performs well in identifying undervalued stocks[15][16] Factor Backtesting Results - Beta: Weekly excess return 0.17%, monthly excess return 0.32%, six-month excess return 4.09%, year-to-date excess return 3.93%[32] - Momentum: Weekly excess return -0.38%, monthly excess return -0.04%, six-month excess return 3.56%, year-to-date excess return 2.82%[32] - Volatility: Weekly excess return 0.55%, monthly excess return 0.40%, six-month excess return 6.40%, year-to-date excess return 6.31%[32] - Valuation: Weekly excess return 0.13%, monthly excess return 0.39%, six-month excess return 7.59%, year-to-date excess return 7.56%[32]