Quantitative Factors and Construction Methods 1. Factor Name: Minute Capital Flow Segmentation Residual Factor - **Factor Construction Idea**: Based on the "Factor Segmentation Theory," the minute-level capital flow is segmented using specific indicators (price, return, and amplitude) to extract residual factors with differentiated information[13][14][27] - **Factor Construction Process**: - Segmentation indicators include minute price, minute return, and minute amplitude - Segmentation objects are the net inflow of large and small orders over the past 20 days - For price segmentation, the high-price (head) and low-price (tail) segments are compared, with the 50% segmentation ratio showing optimal performance - For return segmentation, the high-return segment is selected - For amplitude segmentation, the high-amplitude segment is selected - The residual factors are constructed by reprocessing the segmented variables[13][14][27] - **Factor Evaluation**: The amplitude segmentation residual factor outperforms the price and return segmentation factors, showing the best performance in stock selection[27] 2. Factor Name: Minute Capital Flow Time Period Residual Factor - **Factor Construction Idea**: Different types of investors exhibit distinct preferences for intraday trading periods, and the capital flow information in specific periods contains unique signals[30][31][36] - **Factor Construction Process**: - Intraday trading periods are divided into the first hour, second hour, third hour, fourth hour, first half-hour, and hourly intervals - The capital flow information of specific periods over the past 20 days is extracted - Residual factors are constructed for large and small orders in each period - The first half-hour period is selected as the optimal time period for constructing the residual factor due to its superior stock selection performance[30][31][36] - **Factor Evaluation**: The first half-hour residual factor demonstrates robust stock selection performance, with a stable long-short curve since 2021[34][36] 3. Factor Name: Minute Capital Flow Scenario Residual Factor - **Factor Construction Idea**: Market scenarios are divided based on specific indicators (e.g., 5-minute return), and the capital flow information under similar scenarios is used to construct residual factors[42][43][45] - **Factor Construction Process**: - Market scenarios are defined using indicators such as amplitude, 1-minute return, 5-minute return, and signed transaction volume - The top 50% of trading minutes based on the selected scenario indicator are identified - The corresponding capital flow information for individual stocks is extracted - Residual factors are constructed for large and small orders under the selected scenario - The 5-minute return indicator is chosen as the optimal scenario for constructing the residual factor due to its ability to effectively capture market trends[42][43][45] - **Factor Evaluation**: The 5-minute return scenario residual factor shows the best stock selection performance among all scenario-based factors, with stable performance since 2021[45] --- Factor Backtesting Results 1. Minute Capital Flow Segmentation Residual Factor - **Large Order (Amplitude)**: IC mean = 0.041, RankIC mean = 0.045, ICIR = 3.23, RankICIR = 3.34, Annualized Long-Short Return = 15.9%, IR = 3.39[27][30] - **Small Order (Amplitude)**: IC mean = -0.047, RankIC mean = -0.054, ICIR = -2.96, RankICIR = -3.32, Annualized Long-Short Return = 17.7%, IR = 3.02[27][30] 2. Minute Capital Flow Time Period Residual Factor - **Large Order (First Half-Hour)**: IC mean = 0.043, RankIC mean = 0.049, ICIR = 3.41, RankICIR = 3.47, Annualized Long-Short Return = 16.8%, IR = 3.64[36][42] - **Small Order (First Half-Hour)**: IC mean = -0.061, RankIC mean = -0.07, ICIR = -3.47, RankICIR = -3.82, Annualized Long-Short Return = 23.3%, IR = 3.65[36][42] 3. Minute Capital Flow Scenario Residual Factor - **Large Order (5-Minute Return)**: IC mean = 0.037, RankIC mean = 0.04, ICIR = 3.88, RankICIR = 3.86, Annualized Long-Short Return = 14.1%, IR = 4.04[45][50] - **Small Order (5-Minute Return)**: IC mean = -0.054, RankIC mean = -0.059, ICIR = -3.99, RankICIR = -4.15, Annualized Long-Short Return = 21.2%, IR = 4.22[45][50] 4. Scenario Residual Factor Performance in Different Index Samples - **Large Order (CSI 300)**: IC mean = 0.027, RankIC mean = 0.028, ICIR = 1.45, RankICIR = 1.53, Annualized Long-Short Return = 6.7%, IR = 1.14[55][60] - **Large Order (CSI 500)**: IC mean = 0.034, RankIC mean = 0.037, ICIR = 2.16, RankICIR = 2.45, Annualized Long-Short Return = 9.4%, IR = 1.83[55][60] - **Small Order (CSI 300)**: IC mean = -0.04, RankIC mean = -0.039, ICIR = -1.68, RankICIR = -1.59, Annualized Long-Short Return = 11.1%, IR = 1.63[55][60] - **Small Order (CSI 500)**: IC mean = -0.048, RankIC mean = -0.049, ICIR = -2.77, RankICIR = -2.77, Annualized Long-Short Return = 15.1%, IR = 2.48[55][60]

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]

Quantitative Factors and Construction Methods 1. Factor Name: Original ACT Factor - **Construction Idea**: The ACT factor measures the "active net buying intensity" by comparing the active buying amount with the active selling amount, reflecting the future price movement expectations of different types of traders [13][14] - **Construction Process**: - The ACT factor is calculated as follows: $$ \mathrm{ACT} = \frac{\text{Active Buy Amount} - \text{Active Sell Amount}}{\text{Active Buy Amount} + \text{Active Sell Amount}} $$ where "Active Buy Amount" and "Active Sell Amount" represent the total amounts of active buying and selling transactions, respectively [13] - The factor value for each stock is computed as the average ACT value over the past 20 trading days at the end of each month [14] - Stocks with insufficient trading days, suspension, or ST status are excluded from the sample [14] - **Evaluation**: The IC values of the original ACT factor are low, and its stock selection ability does not meet expectations, which has led to reduced research interest in this factor in recent years [14] 2. Factor Name: ACT Factor with Segmentation - **Construction Idea**: The segmentation method divides the ACT factor based on different market conditions (e.g., high-return and low-return days) to better capture the nuanced behavior of different trader groups [6][15] - **Construction Process**: - Calculate the daily ACT value over the past 20 trading days [20] - Identify the highest-return days as "high-return days" and the lowest-return days as "low-return days" [20] - Compute the average ACT value for high-return days (ACT_high) and low-return days (ACT_low) [20] - **Evaluation**: - Large and medium-sized orders show strong positive stock selection effects on high-return days, while small orders exhibit strong negative stock selection effects on low-return days [6][20] - The segmentation approach aligns with intuition, as institutional investors dominate large and medium orders, while retail investors dominate small orders [21] 3. Factor Name: ACT Positive and ACT Negative Factors - **Construction Idea**: Based on the segmentation analysis, ACT Positive focuses on large and medium orders with positive stock selection effects, while ACT Negative focuses on small orders with negative stock selection effects [7][24] - **Construction Process**: - ACT Positive: $$ \mathrm{ACT\ Positive} = \frac{\text{Active Buy Amount (Large + Medium Orders)} - \text{Active Sell Amount (Large + Medium Orders)}}{\text{Active Buy Amount (Large + Medium Orders)} + \text{Active Sell Amount (Large + Medium Orders)}} $$ - ACT Negative: $$ \mathrm{ACT\ Negative} = \frac{\text{Active Buy Amount (Small Orders)} - \text{Active Sell Amount (Small Orders)}}{\text{Active Buy Amount (Small Orders)} + \text{Active Sell Amount (Small Orders)}} $$ - The factor values are averaged over the past 20 trading days [26] - **Evaluation**: - ACT Positive demonstrates superior stock selection ability, particularly in smaller segmentation ratios (e.g., λ=10%), with a high return-to-volatility ratio [7][26] - ACT Negative shows stable return-to-volatility ratios but declining returns in recent years, reflecting the increasing dominance of large funds [7][31] --- Factor Backtesting Results 1. Original ACT Factor - **IC Values**: Gradually increase from negative to positive as the order size increases (small → medium → large → extra-large) [14] - **Stock Selection Ability**: Poor, as evidenced by the unsatisfactory multi-long-short net value curve [14] 2. ACT Positive Factor - **λ=10%**: - Multi-long-short return-to-volatility ratio: 3.06 - Multi-long return-to-volatility ratio: 0.87 [26][28] - **Performance in Neutralized Conditions**: - Multi-long-short return-to-volatility ratio: 2.40 - Multi-long return-to-volatility ratio: 0.66 [38] 3. ACT Negative Factor - **λ=10%**: - Multi-long-short return-to-volatility ratio: 0.82 - Multi-long return-to-volatility ratio: 0.73 [33] 4. ACT Positive Factor in Different Sample Spaces - **CSI 300**: - λ=20%: Multi-long-short return-to-volatility ratio: 1.32; Multi-long return-to-volatility ratio: 0.63 [44] - **CSI 500**: - λ=20%: Multi-long-short return-to-volatility ratio: 1.78; Multi-long return-to-volatility ratio: 0.80 [44]