- Model Name: Diffusion Index Model - Model Construction Idea: The model uses the diffusion index to monitor the critical point of future changes in the diffusion index[6][38] - Detailed Construction Process: The model uses the following formula to calculate the diffusion index: $$ \text{Diffusion Index} = \frac{\text{Number of Advancing Stocks}}{\text{Total Number of Stocks}} $$ The model monitors the critical point of future changes in the diffusion index by observing the values of the diffusion index at different time points[38][39] - Model Evaluation: The model is effective in predicting the high volatility of the micro-cap index in the coming week[39] - Testing Results: The current value of the diffusion index is 0.78, indicating a relatively high level[39] - Model Name: Initial Threshold Method (Left-Side Trading) - Model Construction Idea: The model triggers an opening signal when the diffusion index reaches a certain threshold[6][42] - Detailed Construction Process: The model uses the following formula to calculate the threshold: $$ \text{Threshold} = \text{Diffusion Index} \times \text{Historical Average} $$ The model triggered an opening signal on September 23, 2025, when the diffusion index reached 0.0575[42] - Model Evaluation: The model is effective in providing timely trading signals[42] - Testing Results: The model triggered an opening signal on September 23, 2025[42] - Model Name: Delayed Threshold Method (Right-Side Trading) - Model Construction Idea: The model provides an opening signal when the diffusion index reaches a delayed threshold[6][45] - Detailed Construction Process: The model uses the following formula to calculate the delayed threshold: $$ \text{Delayed Threshold} = \text{Diffusion Index} \times \text{Historical Average} + \text{Delay Factor} $$ The model provided an opening signal on September 25, 2025, when the diffusion index reached 0.1825[45] - Model Evaluation: The model is effective in providing delayed but accurate trading signals[45] - Testing Results: The model provided an opening signal on September 25, 2025[45] - Model Name: Dual Moving Average Method (Adaptive Trading) - Model Construction Idea: The model uses dual moving averages to provide trading signals[6][46] - Detailed Construction Process: The model uses the following formula to calculate the dual moving averages: $$ \text{Short-Term Moving Average} = \frac{\sum_{i=1}^{n} \text{Price}_i}{n} $$ $$ \text{Long-Term Moving Average} = \frac{\sum_{i=1}^{m} \text{Price}_i}{m} $$ The model provided a bullish signal on October 13, 2025, when the short-term moving average crossed above the long-term moving average[46] - Model Evaluation: The model is effective in providing adaptive trading signals based on market trends[46] - Testing Results: The model provided a bullish signal on October 13, 2025[46] Factor Construction and Performance - Factor Name: Dividend Yield Factor - Factor Construction Idea: The factor ranks stocks based on their dividend yield[5][16] - Detailed Construction Process: The factor uses the following formula to calculate the dividend yield: $$ \text{Dividend Yield} = \frac{\text{Annual Dividends}}{\text{Stock Price}} $$ The factor ranks stocks from highest to lowest dividend yield[16] - Factor Evaluation: The factor is effective in identifying high-yield stocks[16] - Testing Results: The factor's rank IC for the week is 0.199, with a historical average of 0.022[16] - Factor Name: PB Inverse Factor - Factor Construction Idea: The factor ranks stocks based on the inverse of their price-to-book ratio[5][16] - Detailed Construction Process: The factor uses the following formula to calculate the inverse PB ratio: $$ \text{PB Inverse} = \frac{1}{\text{Price-to-Book Ratio}} $$ The factor ranks stocks from highest to lowest PB inverse[16] - Factor Evaluation: The factor is effective in identifying undervalued stocks[16] - Testing Results: The factor's rank IC for the week is 0.112, with a historical average of 0.034[16] - Factor Name: Illiquidity Factor - Factor Construction Idea: The factor ranks stocks based on their illiquidity[5][16] - Detailed Construction Process: The factor uses the following formula to calculate illiquidity: $$ \text{Illiquidity} = \frac{\text{Absolute Return}}{\text{Trading Volume}} $$ The factor ranks stocks from highest to lowest illiquidity[16] - Factor Evaluation: The factor is effective in identifying illiquid stocks[16] - Testing Results: The factor's rank IC for the week is 0.103, with a historical average of 0.04[16] - Factor Name: Growth Factor - Factor Construction Idea: The factor ranks stocks based on their growth potential[5][16] - Detailed Construction Process: The factor uses the following formula to calculate growth: $$ \text{Growth} = \frac{\text{Current Period Earnings}}{\text{Previous Period Earnings}} - 1 $$ The factor ranks stocks from highest to lowest growth[16] - Factor Evaluation: The factor is effective in identifying high-growth stocks[16] - Testing Results: The factor's rank IC for the week is 0.019, with a historical average of -0.003[16] - Factor Name: Residual Volatility Factor - Factor Construction Idea: The factor ranks stocks based on their residual volatility[5][16] - Detailed Construction Process: The factor uses the following formula to calculate residual volatility: $$ \text{Residual Volatility} = \sqrt{\frac{\sum_{i=1}^{n} (\text{Return}_i - \text{Expected Return})^2}{n}} $$ The factor ranks stocks from highest to lowest residual volatility[16] - Factor Evaluation: The factor is effective in identifying stocks with high residual volatility[16] - Testing Results: The factor's rank IC for the week is 0.015, with a historical average of -0.039[16] Factor Backtesting Results - Dividend Yield Factor: Rank IC for the week is 0.199, historical average is 0.022[16] - PB Inverse Factor: Rank IC for the week is 0.112, historical average is 0.034[16] - Illiquidity Factor: Rank IC for the week is 0.103, historical average is 0.04[16] - Growth Factor: Rank IC for the week is 0.019, historical average is -0.003[16] - Residual Volatility Factor: Rank IC for the week is 0.015, historical average is -0.039[16]