Quantitative Models and Construction Methods Model Name: DBD-GRU Model - **Model Construction Idea**: Combining factor slicing theory with deep learning to enhance information extraction and prediction capabilities[4][25] - **Model Construction Process**: - Assume input data x contains features A and B, where feature A is the slicing indicator[4] - Use the median of feature A in the time series as the threshold to construct two masks: mask_Ahigh and mask_Alow[4] - Input the masked data into two branch networks: GRU_high and GRU_low[4] - Take the difference between the outputs of the last time step of the two networks as the input to the output layer[4] - Formula: $ \text{DBD-GRU} = \text{GRU}_\text{high} - \text{GRU}_\text{low} $[4] - **Model Evaluation**: The DBD-GRU model provides significant information increment compared to the original slicing theory factors and baseline GRU model factors[5][55] - **Model Testing Results**: - Ideal Amplitude-DBD RankIC: -10.33%[5] - Ideal Reversal-DBD RankIC: -10.31%[5] - Active Buy-Sell-DBD RankIC: -9.81%[5] Quantitative Factors and Construction Methods Factor Name: Ideal Reversal Factor - **Factor Construction Idea**: Improve traditional reversal factors by slicing the time series data based on transaction amount[14][15] - **Factor Construction Process**: - Retrieve past 20 days of data for selected stocks[17] - Calculate the average transaction amount per trade for each day[17] - Sum the price changes for the 10 days with the highest transaction amounts, denoted as M_high[18] - Sum the price changes for the 10 days with the lowest transaction amounts, denoted as M_low[18] - Ideal Reversal Factor M = M_high - M_low[18] - **Factor Evaluation**: The slicing process effectively distinguishes between strong and weak reversal periods, enhancing the factor's stability and predictive power[24] - **Factor Testing Results**: - RankIC: -6.06%[40] - RankICIR: -2.98[40] - Annualized long-short return: 24.26%[40] - Annualized long-short volatility: 9.38%[40] - Long-short return volatility ratio: 2.59[40] - Maximum long-short drawdown: 7.80%[40] - Monthly win rate: 78.57%[40] Factor Name: Ideal Amplitude Factor - **Factor Construction Idea**: Measure the difference in amplitude information between high and low price states of stocks[78] - **Factor Construction Process**: - Retrieve past 20 days of data for selected stocks[80] - Calculate the daily amplitude (highest price/lowest price - 1)[80] - Calculate the average amplitude for the 25% of days with the highest closing prices, denoted as Amplitude_high[80] - Calculate the average amplitude for the 25% of days with the lowest closing prices, denoted as Amplitude_low[80] - Ideal Amplitude Factor = Amplitude_high - Amplitude_low[80] - **Factor Evaluation**: The factor effectively captures the difference in amplitude information between high and low price states, providing a stable and predictive measure[24] - **Factor Testing Results**: - RankIC: -7.00%[40] - RankICIR: -3.47[40] - Annualized long-short return: 21.02%[40] - Annualized long-short volatility: 10.53%[40] - Long-short return volatility ratio: 2.00[40] - Maximum long-short drawdown: 17.67%[40] - Monthly win rate: 76.98%[40] Factor Name: Active Buy-Sell Factor - **Factor Construction Idea**: Measure retail investors' trading behavior in a declining market environment[79] - **Factor Construction Process**: - Retrieve past 20 days of data for selected stocks[79] - Calculate the daily stock price change and small order inflow intensity[79] - Formula for small order inflow intensity: $ \frac{\text{Active Buy Amount (small orders)} - \text{Active Sell Amount (small orders)}}{\text{Active Buy Amount (small orders)} + \text{Active Sell Amount (small orders)}} $[79] - Calculate the average small order inflow intensity for the 25% of days with the lowest closing prices to obtain the Active Buy-Sell Factor[79] - **Factor Evaluation**: The factor effectively captures retail investors' trading behavior in a declining market, providing a stable and predictive measure[24] - **Factor Testing Results**: - RankIC: -3.39%[40] - RankICIR: -1.27[40] - Annualized long-short return: 10.20%[40] - Annualized long-short volatility: 12.57%[40] - Long-short return volatility ratio: 0.81[40] - Maximum long-short drawdown: 25.26%[40] - Monthly win rate: 70.63%[40] Factor Backtesting Results DBD-GRU Model Factors - **Ideal Amplitude-DBD**: - RankIC: -10.33%[47] - RankICIR: -3.68[47] - Annualized long-short return: 34.31%[52] - Annualized long-short volatility: 15.17%[52] - Long-short return volatility ratio: 2.26[52] - Maximum long-short drawdown: 17.98%[52] - Monthly win rate: 76.98%[52] - **Ideal Reversal-DBD**: - RankIC: -10.31%[47] - RankICIR: -3.57[47] - Annualized long-short return: 37.62%[52] - Annualized long-short volatility: 12.55%[52] - Long-short return volatility ratio: 3.00[52] - Maximum long-short drawdown: 8.96%[52] - Monthly win rate: 80.95%[52] - **Active Buy-Sell-DBD**: - RankIC: -9.81%[47] - RankICIR: -3.63[47] - Annualized long-short return: 33.33%[52] - Annualized long-short volatility: 13.32%[52] - Long-short return volatility ratio: 2.50[52] - Maximum long-short drawdown: 13.82%[52] - Monthly win rate: 75.40%[52] DBD-Combine Factor - **Performance in Major Broad-Based Indices**: - **CSI 300**: - RankIC: -5.76%[63] - RankICIR: -1.87[61] - Annualized long-short return: 14.9%[63] - Annualized excess return: 7.64%[67] - Excess IR: 1.84[61] - Maximum excess drawdown: 3.37%[61] - **CSI 500**: - RankIC: -7.40%[68] - RankICIR: -2.58[65] - Annualized long-short return: 17.5%[69] - Annualized excess return: 7.23%[70] - Excess IR: 1.37[65] - Maximum excess drawdown: 6.43%[65] - **CSI 1000**: - RankIC: -9.84%[75] - RankICIR: -3.48[71] - Annualized long-short return: 30.8%[72] - Annualized excess return: 11.8%[76] - Excess IR: 2.21[71] - Maximum excess drawdown: 3.94%[71]