因子动量和反转特征下的动态调整思路

Quantitative Models and Factor Construction Quantitative Models and Construction Methods 1. Model Name: Dynamic Factor Adjustment Model Model Construction Idea: Combines factor momentum and reversal characteristics to dynamically adjust factor selection based on historical performance and failure probabilities[4][80][82] Model Construction Process: - Evaluate factor momentum using the average RankIC over the past 6 months and the average RankICIR over the past 3-12 months[4][82] - Calculate conditional failure probabilities by rolling one year of historical data to assess the likelihood of a factor transitioning from effective to ineffective[74][87] - Exclude factors with high failure probabilities and assign scores based on momentum and failure probabilities. Select the top N factors with the highest scores for equal-weighted scoring in each period[82][87][88] Model Evaluation: The model effectively balances momentum and reversal characteristics, reducing the impact of unstable factors and improving robustness in factor selection[82][87] 2. Model Name: "2+3" Dynamic Factor Model for Small-Cap Stocks Model Construction Idea: Combines two fixed factors (valuation and volatility) with three dynamically selected high-momentum factors to construct a robust small-cap stock selection model[98][99] Model Construction Process: - Fixed factors: Valuation (BTOP) and volatility (VOLATILITY) are always included due to their stable and significant performance in small-cap pools[98][99] - Dynamic factors: Exclude factors with conditional failure probabilities above 80% and select the top 3 factors based on medium- and long-term momentum scores[98][99] - Construct a portfolio of 50 equally weighted stocks based on the selected factors[98][103] Model Evaluation: The model demonstrates strong performance in small-cap pools, with high momentum and low reversal failure probabilities, making it robust against overfitting[98][103] 3. Model Name: "Exclusion + Scoring" Model for Large-Cap Stocks Model Construction Idea: Focuses on stricter exclusion of high-failure-probability factors and integrates failure information into the scoring process for large-cap stock selection[109][110] Model Construction Process: - Exclude factors with conditional failure probabilities above 70%[109][110] - Combine failure indicators into the momentum scoring model, selecting the top 5 factors with the highest comprehensive scores[109][110] - Construct a portfolio of 50 equally weighted stocks based on the selected factors[109][113] Model Evaluation: The model effectively addresses the high sensitivity and extreme reversals in large-cap pools, improving stability and performance[109][113] Model Backtesting Results 1. Dynamic Factor Adjustment Model: - Annualized return: 8.83% - Sharpe ratio: 0.42 - Excess annualized return: 11.47% - Maximum drawdown: 38.67%[103] 2. "2+3" Dynamic Factor Model for Small-Cap Stocks: - Annualized return: 8.83% - Sharpe ratio: 0.42 - Excess annualized return: 11.47% - Maximum drawdown: 38.67%[103] 3. "Exclusion + Scoring" Model for Large-Cap Stocks: - Annualized return: 8.40% - Sharpe ratio: 0.40 - Excess annualized return: 8.32% - Maximum drawdown: 36.40%[113] Quantitative Factors and Construction Methods 1. Factor Name: Valuation (BTOP) Factor Construction Idea: Measures the book-to-price ratio to capture undervalued stocks[8][39] Factor Construction Process: Calculate the ratio of book value to current market value for each stock[8][39] Factor Evaluation: Demonstrates stable and significant performance in small-cap pools, with strong selection ability in various market conditions[39][98] 2. Factor Name: Volatility (VOLATILITY) Factor Construction Idea: Measures the residual volatility of stock returns to identify low-risk stocks[8][50] Factor Construction Process: Calculate the standard deviation of residuals from a time-series regression of stock returns[8][50] Factor Evaluation: Performs well in both small-cap and large-cap pools, with low failure probabilities and consistent selection ability[50][98] 3. Factor Name: Earnings (EARNING) Factor Construction Idea: Measures earnings yield to capture profitability[8][39] Factor Construction Process: Calculate the ratio of earnings to market value for each stock[8][39] Factor Evaluation: Strong selection ability in large-cap pools, with stable performance across different market conditions[39][113] Factor Backtesting Results 1. Valuation (BTOP): - RankICIR: Consistently ranks in the top 2 across small-cap pools[39][98] 2. Volatility (VOLATILITY): - RankICIR: Demonstrates stable negative expression across all pools, with low failure probabilities[50][98] 3. Earnings (EARNING): - RankICIR: Strong performance in large-cap pools, with high selection ability and stable expression[39][113]