Quantitative Models and Construction Methods Barra Style Factors - **Model Name**: Barra Style Factors - **Construction Idea**: The model tracks the performance of common style factors such as size, value, growth, and profitability in the market - **Construction Process**: The factors are calculated based on specific financial metrics. For example: - Size factor is based on market capitalization - Book-to-market ratio is used for the value factor - Growth factor is derived from growth-related metrics - Profitability factor is based on earnings expectations[3][13] - **Evaluation**: The model provides insights into the performance of different market styles, helping investors understand factor contributions to returns[3][13] Kaiyuan Behavioral Factors - **Model Name**: Kaiyuan Behavioral Factors - **Construction Idea**: These factors are based on trading behaviors, aiming to capture alpha signals from microstructure patterns in the market - **Construction Process**: - **Ideal Reversal Factor**: Measures the reversal strength of trading days by analyzing the average transaction size. It identifies days with the strongest reversal attributes[4][13] - **Smart Money Factor**: Tracks institutional trading activity using minute-level price and volume data. It identifies "smart money" trades by sorting minute data based on a constructed indicator and calculating the volume-weighted average price (VWAP) of these trades[4][40][42] - **APM Factor**: Measures the difference in trading behavior between morning and afternoon sessions. It uses regression analysis on overnight and afternoon returns to calculate residuals, which are then used to construct the factor[4][41][43][44] - **Ideal Amplitude Factor**: Captures the structural differences in stock price amplitude under high and low price states. It calculates the difference between the average amplitude of high-price and low-price days[4][46] - **Evaluation**: These factors are recognized for their ability to capture unique trading behavior patterns and provide alpha signals[4][13] Kaiyuan Behavioral Composite Factor - **Model Name**: Kaiyuan Behavioral Composite Factor - **Construction Idea**: Combines the above behavioral factors into a single composite factor to enhance performance - **Construction Process**: - Standardizes and winsorizes individual factors within industries - Uses the past 12 periods' ICIR values as weights to calculate the composite factor - Applies industry and market capitalization neutrality adjustments[30][34] - **Evaluation**: The composite factor demonstrates superior performance in small and mid-cap stock pools compared to large-cap pools[30][35] --- Backtesting Results of Models Barra Style Factors - **Size Factor**: Return of 1.06% in December 2025[3][13] - **Book-to-Market Ratio Factor**: Return of -0.18% in December 2025[3][13] - **Growth Factor**: Return of 0.20% in December 2025[3][13] - **Profitability Factor**: Return of 0.94% in December 2025[3][13] Kaiyuan Behavioral Factors - **Ideal Reversal Factor**: - IC: -0.049 - RankIC: -0.060 - IR: 2.42 - Long-Short Monthly Win Rate: 77.8% - December 2025 Long-Short Return: 0.14% - 12-Month Long-Short Monthly Win Rate: 58.3%[5][14][17] - **Smart Money Factor**: - IC: -0.037 - RankIC: -0.061 - IR: 2.69 - Long-Short Monthly Win Rate: 80.1% - December 2025 Long-Short Return: -0.24% - 12-Month Long-Short Monthly Win Rate: 75.0%[5][19][22] - **APM Factor**: - IC: 0.028 - RankIC: 0.034 - IR: 2.25 - Long-Short Monthly Win Rate: 76.2% - December 2025 Long-Short Return: 1.08% - 12-Month Long-Short Monthly Win Rate: 41.7%[5][23][26] - **Ideal Amplitude Factor**: - IC: -0.053 - RankIC: -0.073 - IR: 2.99 - Long-Short Monthly Win Rate: 83.0% - December 2025 Long-Short Return: -0.63% - 12-Month Long-Short Monthly Win Rate: 58.3%[5][26][29] Kaiyuan Behavioral Composite Factor - **Composite Factor**: - IC: 0.066 - RankIC: 0.093 - IR: 3.25 - Long-Short Monthly Win Rate: 78.8% - December 2025 Long-Short Return: -0.04% - 12-Month Long-Short Monthly Win Rate: 58.3% - Annualized Return of Long-Short Portfolio: 8.09% - Sharpe Ratio: 2.56 - Monthly Win Rate: 77.4% - IR in Small and Mid-Cap Pools: 2.83 (China Securities 2000), 2.62 (China Securities 1000), 1.00 (China Securities 800)[5][30][34][35]

Quantitative Models and Construction Methods Barra Style Factors - **Model Name**: Barra Style Factors - **Construction Idea**: The model tracks the performance of common style factors such as size, value, growth, and profitability in the market[3][13] - **Specific Construction Process**: The factors are constructed based on predefined dimensions: - Size factor: Measures the impact of market capitalization - Book-to-market ratio factor: Captures the value dimension - Growth factor: Reflects growth characteristics - Profitability factor: Tracks expected earnings performance[3][13] - **Evaluation**: The factors provide insights into the performance of different market styles, helping to understand market trends and dynamics[3][13] Open-Source Trading Behavior Factors - **Factor Name**: Ideal Reversal Factor - **Construction Idea**: Identifies the strongest reversal days by analyzing the average transaction size of large trades[4][14] - **Specific Construction Process**: 1. Retrieve the past 20 trading days' data for a stock 2. Calculate the average transaction size (transaction amount/number of transactions) for each day 3. Identify the 10 days with the highest transaction sizes and sum their returns (M_high) 4. Identify the 10 days with the lowest transaction sizes and sum their returns (M_low) 5. Compute the factor as \( M = M_{high} - M_{low} \)[43] - **Evaluation**: Captures micro-level reversal forces in the market, providing a unique perspective on trading behavior[4][14] - **Factor Name**: Smart Money Factor - **Construction Idea**: Tracks institutional trading activity by analyzing minute-level price and volume data[4][14] - **Specific Construction Process**: 1. Retrieve the past 10 days' minute-level data for a stock 2. Construct the indicator \( S_t = \frac{|R_t|}{V_t^{0.25}} \), where \( R_t \) is the return at minute \( t \) and \( V_t \) is the volume at minute \( t \) 3. Sort minute-level data by \( S_t \) in descending order and select the top 20% of minutes by cumulative volume as "smart money" trades 4. Calculate the volume-weighted average price (VWAP) for smart money trades (\( VWAP_{smart} \)) and all trades (\( VWAP_{all} \)) 5. Compute the factor as \( Q = \frac{VWAP_{smart}}{VWAP_{all}} \)[42][44] - **Evaluation**: Effectively identifies institutional trading patterns, offering a valuable alpha source[4][14] - **Factor Name**: APM Factor - **Construction Idea**: Measures the difference in stock behavior between morning (or overnight) and afternoon trading sessions[4][14] - **Specific Construction Process**: 1. Retrieve the past 20 days' data for a stock 2. Calculate daily overnight and afternoon returns for both the stock and the market index 3. Perform a regression of stock returns on market index returns to obtain residuals 4. Compute the difference between overnight and afternoon residuals for each day 5. Calculate the statistic \( \text{stat} = \frac{\mu(\delta_t)}{\sigma(\delta_t)/\sqrt{N}} \), where \( \mu \) is the mean, \( \sigma \) is the standard deviation, and \( N \) is the number of observations 6. Regress the statistic on momentum factors and use the residual as the APM factor[43][45][46] - **Evaluation**: Highlights intraday trading behavior differences, providing insights into market dynamics[4][14] - **Factor Name**: Ideal Amplitude Factor - **Construction Idea**: Measures the structural differences in amplitude information between high and low price states[4][14] - **Specific Construction Process**: 1. Retrieve the past 20 trading days' data for a stock 2. Calculate the daily amplitude as \( \text{Amplitude} = \text{(High Price/Low Price)} - 1 \) 3. Compute the average amplitude for the top 25% of days by closing price (V_high) 4. Compute the average amplitude for the bottom 25% of days by closing price (V_low) 5. Compute the factor as \( V = V_{high} - V_{low} \)[48] - **Evaluation**: Captures structural differences in price amplitude, offering a unique perspective on market behavior[4][14] - **Factor Name**: Composite Trading Behavior Factor - **Construction Idea**: Combines the above trading behavior factors using ICIR-based weights to enhance performance[32] - **Specific Construction Process**: 1. Perform industry-level outlier removal and standardization for each factor 2. Use the past 12 months' ICIR values as weights to combine the factors 3. Construct the composite factor as a weighted sum of the individual factors[32] - **Evaluation**: Demonstrates superior performance in small and mid-cap stock pools, providing robust alpha generation[32] --- Backtesting Results of Models and Factors Barra Style Factors - **Size Factor**: Return of -0.18% in November 2025[3][13] - **Book-to-Market Ratio Factor**: Return of 0.20% in November 2025[3][13] - **Growth Factor**: Return of -0.23% in November 2025[3][13] - **Profitability Factor**: Return of -0.35% in November 2025[3][13] Open-Source Trading Behavior Factors - **Ideal Reversal Factor**: - IC: -0.049 - RankIC: -0.060 - IR: 2.44 - Monthly win rate: 77.7% - November 2025 return: -1.52% - 12-month win rate: 58.3%[5][15] - **Smart Money Factor**: - IC: -0.037 - RankIC: -0.062 - IR: 2.72 - Monthly win rate: 81.3% - November 2025 return: 0.22% - 12-month win rate: 83.3%[5][19] - **APM Factor**: - IC: 0.028 - RankIC: 0.033 - IR: 2.23 - Monthly win rate: 76.0% - November 2025 return: -0.43% - 12-month win rate: 41.7%[5][23] - **Ideal Amplitude Factor**: - IC: -0.054 - RankIC: -0.074 - IR: 3.03 - Monthly win rate: 83.4% - November 2025 return: 0.49% - 12-month win rate: 66.7%[5][27] - **Composite Trading Behavior Factor**: - IC: 0.066 - RankIC: 0.093 - IR: 3.30 - Monthly win rate: 79.4% - November 2025 return: -0.21% - 12-month win rate: 66.7%[5][32]

- The report tracks the performance of Barra style factors for October 2025, showing that the market capitalization factor recorded a return of -1.49%, the book-to-market ratio factor recorded a return of 0.39%, the growth factor recorded a return of -0.34%, and the earnings expectations factor recorded a return of 0.12%[4][14] - The report introduces a series of stock selection factors based on trading behavior, including the Ideal Reversal Factor, Smart Money Factor, APM Factor, and Ideal Amplitude Factor[5][15] - The Ideal Reversal Factor is constructed by segmenting the traditional reversal factor using W-shaped cuts, focusing on the average transaction amount per trade to identify the trading days with the strongest reversal attributes[41] - The Smart Money Factor is constructed by analyzing minute-level price and volume data to identify the involvement of institutional investors, using a specific formula to calculate the factor value[42][44] - The APM Factor measures the difference in stock price behavior between morning (or overnight) and afternoon trading sessions, using a regression model to calculate the residuals and then constructing a statistical measure to quantify the difference[43][45][46] - The Ideal Amplitude Factor measures the difference in amplitude information between high and low price states, calculating the average amplitude for the highest and lowest 25% of trading days and then taking the difference[48] - The historical performance of the Ideal Reversal Factor shows an IC mean of -0.050, rankIC mean of -0.061, IR of 2.48, and a long-short monthly win rate of 78.1%[6][16] - The historical performance of the Smart Money Factor shows an IC mean of -0.038, rankIC mean of -0.062, IR of 2.74, and a long-short monthly win rate of 81.2%[6][21] - The historical performance of the APM Factor shows an IC mean of 0.028, rankIC mean of 0.034, IR of 2.25, and a long-short monthly win rate of 76.5%[6][25] - The historical performance of the Ideal Amplitude Factor shows an IC mean of -0.054, rankIC mean of -0.074, IR of 3.03, and a long-short monthly win rate of 83.3%[6][28] - The historical performance of the composite trading behavior factor shows an IC mean of 0.067, rankIC mean of 0.093, IR of 3.33, and a long-short monthly win rate of 80.0%[6][32] - In October 2025, the Ideal Reversal Factor recorded a long-short return of 1.63% with a 12-month long-short monthly win rate of 66.7%[7][16] - In October 2025, the Smart Money Factor recorded a long-short return of 2.90% with a 12-month long-short monthly win rate of 83.3%[7][21] - In October 2025, the APM Factor recorded a long-short return of -1.13% with a 12-month long-short monthly win rate of 41.7%[7][25] - In October 2025, the Ideal Amplitude Factor recorded a long-short return of 3.33% with a 12-month long-short monthly win rate of 66.7%[7][28] - In October 2025, the composite trading behavior factor recorded a long-short return of 3.73% with a 12-month long-short monthly win rate of 75.0%[7][32]

- Model Name: Barra Style Factors; Model Construction Idea: The model tracks the performance of common Barra style factors; Model Construction Process: The model calculates the returns of various style factors such as market capitalization, book-to-market ratio, growth, and earnings expectations; Model Evaluation: The model provides insights into the performance of different style factors over a specific period[4][14] - Factor Name: Ideal Reversal Factor; Factor Construction Idea: The factor identifies the strongest reversal days based on the average transaction amount per trade; Factor Construction Process: 1. Retrieve the past 20 days of data for the selected stock 2. Calculate the average transaction amount per trade for each day 3. Sum the returns of the top 10 days with the highest average transaction amount (M_high) 4. Sum the returns of the bottom 10 days with the lowest average transaction amount (M_low) 5. Calculate the Ideal Reversal Factor as M = M_high - M_low 6. Repeat the above steps for all stocks to calculate their respective Ideal Reversal Factors[5][39][41] - Factor Name: Smart Money Factor; Factor Construction Idea: The factor identifies the participation of institutional investors based on minute-level price and volume data; Factor Construction Process: 1. Retrieve the past 10 days of minute-level data for the selected stock 2. Construct the indicator $St = \frac{|Rt|}{Vt^{0.25}}$, where $Rt$ is the return at minute t and $Vt$ is the volume at minute t 3. Sort the minute-level data by $St$ in descending order and select the top 20% of minutes by cumulative volume as smart money trades 4. Calculate the volume-weighted average price (VWAP) for smart money trades (VWAPsmart) 5. Calculate the VWAP for all trades (VWAPall) 6. Calculate the Smart Money Factor as $Q = \frac{VWAPsmart}{VWAPall}$[5][40][42] - Factor Name: APM Factor; Factor Construction Idea: The factor measures the difference in stock behavior between morning (or overnight) and afternoon sessions; Factor Construction Process: 1. Retrieve the past 20 days of data for the selected stock 2. Calculate the overnight and afternoon returns for the stock and the index 3. Perform a regression of the stock returns on the index returns to obtain residuals 4. Calculate the difference between overnight and afternoon residuals 5. Construct the statistic $stat = \frac{\mu(\delta_t)}{\sigma(\delta_t)/\sqrt{N}}$ 6. Perform a cross-sectional regression of the statistic on the momentum factor to obtain residuals, which are used as the APM Factor[5][41][43][44] - Factor Name: Ideal Amplitude Factor; Factor Construction Idea: The factor measures the difference in amplitude information between high and low price states; Factor Construction Process: 1. Retrieve the past 20 days of data for the selected stock 2. Calculate the daily amplitude (high price/low price - 1) 3. Calculate the average amplitude for the top 25% of days with the highest closing prices (V_high) 4. Calculate the average amplitude for the bottom 25% of days with the lowest closing prices (V_low) 5. Calculate the Ideal Amplitude Factor as V = V_high - V_low[5][46] - Composite Factor: Kaisheng Trading Behavior Composite Factor; Construction Idea: The composite factor combines multiple trading behavior factors using their ICIR values as weights; Construction Process: 1. Perform outlier removal and standardization for each trading behavior factor within the industry 2. Use the past 12 periods' ICIR values as weights to form the composite factor 3. Calculate the composite factor's returns and performance metrics[5][31] Model Backtest Results - Barra Style Factors: Market Capitalization Factor return: 1.73%, Book-to-Market Ratio Factor return: -0.31%, Growth Factor return: 0.13%, Earnings Expectations Factor return: -0.09%[4][14] Factor Backtest Results - Ideal Reversal Factor: IC: -0.050, rankIC: -0.060, IR: 2.46, Long-Short Monthly Win Rate: 77.4%, September Long-Short Return: -0.42%, Last 12 Months Long-Short Monthly Win Rate: 58.3%[6][15] - Smart Money Factor: IC: -0.037, rankIC: -0.061, IR: 2.70, Long-Short Monthly Win Rate: 81.8%, September Long-Short Return: 0.30%, Last 12 Months Long-Short Monthly Win Rate: 83.3%[6][18] - APM Factor: IC: 0.029, rankIC: 0.034, IR: 2.29, Long-Short Monthly Win Rate: 76.4%, September Long-Short Return: 1.68%, Last 12 Months Long-Short Monthly Win Rate: 50.0%[6][22] - Ideal Amplitude Factor: IC: -0.053, rankIC: -0.073, IR: 2.98, Long-Short Monthly Win Rate: 83.2%, September Long-Short Return: 0.40%, Last 12 Months Long-Short Monthly Win Rate: 66.7%[6][26] - Composite Factor: IC: 0.066, rankIC: 0.091, IR: 3.23, Long-Short Monthly Win Rate: 82.1%, September Long-Short Return: 0.57%, Last 12 Months Long-Short Monthly Win Rate: 75.0%[6][31]

- The report tracks the performance of Barra style factors for August 2025, showing that the market capitalization factor recorded a return of 2.54%, the book-to-market ratio factor recorded a return of -0.67%, the growth factor recorded a return of 0.42%, and the earnings expectations factor recorded a return of 0.08%[4][14] - The report introduces a series of stock selection factors based on trading behavior, including the Ideal Reversal Factor, Smart Money Factor, APM Factor, and Ideal Amplitude Factor[5][14] - The Ideal Reversal Factor is constructed by segmenting the traditional reversal factor using W-shaped cuts, focusing on the average transaction amount per day to identify the strongest reversal days[39] - The Smart Money Factor is constructed by analyzing minute-level price and volume data to identify periods with significant institutional trading activity, using the formula $St = \frac{|Rt|}{Vt^{0.25}}$, where $Rt$ is the return at minute $t$ and $Vt$ is the volume at minute $t$[40][42] - The APM Factor measures the difference in stock price behavior between morning (or overnight) and afternoon sessions, using a regression model to calculate residuals and then computing the statistic $stat = \frac{\mu(\delta_t)}{\sigma(\delta_t)/\sqrt{N}}$, where $\delta_t$ is the difference between overnight and afternoon residuals[41][43][44] - The Ideal Amplitude Factor measures the difference in amplitude information between high and low price states, calculated as $V = V_{high} - V_{low}$, where $V_{high}$ is the average amplitude on high-price days and $V_{low}$ is the average amplitude on low-price days[46] - The historical performance of the Ideal Reversal Factor shows an IC mean of -0.050, rankIC mean of -0.060, IR of 2.48, and a long-short monthly win rate of 77.8%[6][15] - The historical performance of the Smart Money Factor shows an IC mean of -0.037, rankIC mean of -0.061, IR of 2.71, and a long-short monthly win rate of 81.6%[6][18] - The historical performance of the APM Factor shows an IC mean of 0.029, rankIC mean of 0.034, IR of 2.26, and a long-short monthly win rate of 77.4%[6][22] - The historical performance of the Ideal Amplitude Factor shows an IC mean of -0.053, rankIC mean of -0.073, IR of 2.99, and a long-short monthly win rate of 83.2%[6][26] - The composite trading behavior factor, which combines the above factors, shows an IC mean of 0.066, rankIC mean of 0.092, IR of 3.25, and a long-short monthly win rate of 82.0%[6][30] - In August 2025, the Ideal Reversal Factor recorded a long-short return of -1.28%, with a 12-month long-short monthly win rate of 58.3%[7][15] - In August 2025, the Smart Money Factor recorded a long-short return of -1.17%, with a 12-month long-short monthly win rate of 83.3%[7][18] - In August 2025, the APM Factor recorded a long-short return of -0.22%, with a 12-month long-short monthly win rate of 50.0%[7][22] - In August 2025, the Ideal Amplitude Factor recorded a long-short return of -0.15%, with a 12-month long-short monthly win rate of 66.7%[7][26] - In August 2025, the composite trading behavior factor recorded a long-short return of -0.90%, with a 12-month long-short monthly win rate of 75.0%[7][30]

Quantitative Models and Construction Methods Barra Style Factors - **Model Name**: Barra Style Factors - **Construction Idea**: The Barra style factors are designed to capture the performance of different market styles, such as size, value, growth, and profitability, through specific factor definitions[4][14] - **Construction Process**: - **Size Factor**: Measures the market capitalization of stocks - **Value Factor**: Captures the book-to-market ratio of stocks - **Growth Factor**: Reflects the growth potential of stocks - **Profitability Factor**: Based on earnings expectations[4][14] - **Evaluation**: These factors are widely used in the industry to analyze market trends and style rotations[4][14] --- Open-source Trading Behavior Factors - **Factor Name**: Ideal Reversal Factor - **Construction Idea**: Identifies the strongest reversal days by analyzing the average transaction size of large trades[5][15] - **Construction Process**: 1. Retrieve the past 20 trading days' data for a stock 2. Calculate the average transaction size per day (transaction amount/number of transactions) 3. Identify the 10 days with the highest transaction sizes and sum their returns (M_high) 4. Identify the 10 days with the lowest transaction sizes and sum their returns (M_low) 5. Compute the factor as $M = M_{high} - M_{low}$[43] - **Evaluation**: Captures the microstructure of reversal forces in the A-share market[5][15] - **Factor Name**: Smart Money Factor - **Construction Idea**: Tracks institutional trading activity by analyzing minute-level price and volume data[5][15] - **Construction Process**: 1. Retrieve the past 10 days' minute-level data for a stock 2. Construct the indicator $S_t = |R_t| / V_t^{0.25}$, where $R_t$ is the return at minute $t$, and $V_t$ is the trading volume at minute $t$ 3. Sort minute-level data by $S_t$ in descending order and select the top 20% of minutes by cumulative trading volume 4. Calculate the volume-weighted average price (VWAP) for smart money trades ($VWAP_{smart}$) and all trades ($VWAP_{all}$) 5. Compute the factor as $Q = VWAP_{smart} / VWAP_{all}$[42][44] - **Evaluation**: Effectively identifies institutional trading patterns[5][15] - **Factor Name**: APM Factor - **Construction Idea**: Measures the difference in trading behavior between morning (or overnight) and afternoon sessions[5][15] - **Construction Process**: 1. Retrieve the past 20 days' data for a stock 2. Calculate daily overnight and afternoon returns for both the stock and the index 3. Perform a regression of stock returns on index returns to obtain residuals 4. Compute the difference between overnight and afternoon residuals for each day 5. Calculate the statistic $\mathrm{stat} = \frac{\mu(\delta_t)}{\sigma(\delta_t) / \sqrt{N}}$, where $\mu$ is the mean, $\sigma$ is the standard deviation, and $N$ is the sample size 6. Regress the statistic on momentum factors and use the residual as the APM factor[43][45][46] - **Evaluation**: Captures intraday trading behavior differences[5][15] - **Factor Name**: Ideal Amplitude Factor - **Construction Idea**: Measures the structural differences in amplitude information between high and low price states[5][15] - **Construction Process**: 1. Retrieve the past 20 trading days' data for a stock 2. Calculate the daily amplitude as $(\text{High Price}/\text{Low Price}) - 1$ 3. Compute the average amplitude for the top 25% of days with the highest closing prices ($V_{high}$) 4. Compute the average amplitude for the bottom 25% of days with the lowest closing prices ($V_{low}$) 5. Compute the factor as $V = V_{high} - V_{low}$[48] - **Evaluation**: Highlights amplitude differences across price states[5][15] - **Factor Name**: Composite Trading Behavior Factor - **Construction Idea**: Combines the above trading behavior factors using ICIR-based weights to enhance predictive power[31] - **Construction Process**: 1. Standardize and winsorize the individual factors within industries 2. Use the past 12 periods' ICIR values as weights to compute the composite factor[31] - **Evaluation**: Demonstrates superior performance in small-cap stock pools[32] --- Backtesting Results of Models and Factors Barra Style Factors - **Size Factor**: Return of 0.64% in July 2025[4][14] - **Value Factor**: Return of 0.59% in July 2025[4][14] - **Growth Factor**: Return of 0.16% in July 2025[4][14] - **Profitability Factor**: Return of -0.32% in July 2025[4][14] Open-source Trading Behavior Factors - **Ideal Reversal Factor**: - IC: -0.050 - RankIC: -0.061 - IR: 2.52 - Long-short monthly win rate: 78.3% (historical), 66.7% (last 12 months) - July 2025 long-short return: 0.47%[6][16] - **Smart Money Factor**: - IC: -0.037 - RankIC: -0.061 - IR: 2.76 - Long-short monthly win rate: 82.2% (historical), 91.7% (last 12 months) - July 2025 long-short return: 1.78%[6][19] - **APM Factor**: - IC: 0.029 - RankIC: 0.034 - IR: 2.30 - Long-short monthly win rate: 77.4% (historical), 58.3% (last 12 months) - July 2025 long-short return: 1.42%[6][23] - **Ideal Amplitude Factor**: - IC: -0.054 - RankIC: -0.073 - IR: 3.03 - Long-short monthly win rate: 83.6% (historical), 75.0% (last 12 months) - July 2025 long-short return: 3.86%[6][28] - **Composite Trading Behavior Factor**: - IC: 0.067 - RankIC: 0.092 - IR: 3.30 - Long-short monthly win rate: 82.6% (historical), 83.3% (last 12 months) - July 2025 long-short return: 2.13%[6][31]

- Model Name: Barra Style Factors; Model Construction Idea: Measure the performance of common Barra style factors in April 2025; Model Construction Process: Calculate the returns of various factors such as market capitalization, book-to-market ratio, growth, and earnings expectations; Model Evaluation: Provides insights into the performance of different style factors in the market[4][14] - Factor Name: Ideal Reversal Factor; Factor Construction Idea: Identify the strongest reversal days based on the average transaction amount per trade; Factor Construction Process: 1. Retrieve the past 20 days of data for the selected stock 2. Calculate the average transaction amount per trade for each day 3. Sum the returns of the top 10 days with the highest average transaction amount, denoted as M_high 4. Sum the returns of the bottom 10 days with the lowest average transaction amount, denoted as M_low 5. Calculate the Ideal Reversal Factor as M = M_high - M_low[5][46][49] - Factor Name: Smart Money Factor; Factor Construction Idea: Identify the participation of smart money in trading based on minute-level price and volume data; Factor Construction Process: 1. Retrieve the past 10 days of minute-level data for the selected stock 2. Construct the indicator $ S_t = \frac{|R_t|}{V_t^{0.25}} $, where $ R_t $ is the return at minute t and $ V_t $ is the volume at minute t 3. Sort the minute data by $ S_t $ in descending order and select the top 20% of minutes by cumulative volume as smart money trades 4. Calculate the volume-weighted average price (VWAP) of smart money trades, denoted as VWAP_smart 5. Calculate the VWAP of all trades, denoted as VWAP_all 6. Calculate the Smart Money Factor as $ Q = \frac{VWAP_{smart}}{VWAP_{all}} $[5][47] - Factor Name: APM Factor; Factor Construction Idea: Measure the difference in stock price behavior between morning (or overnight) and afternoon sessions; Factor Construction Process: 1. Retrieve the past 20 days of data for the selected stock 2. Record the overnight and afternoon returns for both the stock and the index 3. Perform a regression of the form $ r_t = \alpha + \beta R_t + \epsilon_t $ to obtain residuals 4. Calculate the difference between overnight and afternoon residuals 5. Construct the statistic $ \text{stat} = \frac{\mu(\delta_t)}{\sigma(\delta_t)/\sqrt{N}} $ 6. Regress the statistic against the momentum factor to obtain the APM Factor[5][48][50] - Factor Name: Ideal Amplitude Factor; Factor Construction Idea: Measure the difference in amplitude information between high and low price states; Factor Construction Process: 1. Retrieve the past 20 days of data for the selected stock 2. Calculate the daily amplitude as (highest price/lowest price - 1) 3. Calculate the average amplitude for the top 25% of days with the highest closing prices, denoted as V_high 4. Calculate the average amplitude for the bottom 25% of days with the lowest closing prices, denoted as V_low 5. Calculate the Ideal Amplitude Factor as V = V_high - V_low[5][51] Model and Factor Performance - Barra Style Factors: Market Capitalization Factor return: 0.09%, Book-to-Market Ratio Factor return: 0.11%, Growth Factor return: -0.19%, Earnings Expectations Factor return: -0.02%[4][14] - Ideal Reversal Factor: IC: -0.051, rankIC: -0.061, IR: 2.55, Long-Short Monthly Win Rate: 78.5%, April 2025 Long-Short Return: 0.89%, Last 12 Months Long-Short Monthly Win Rate: 66.7%[6][16] - Smart Money Factor: IC: -0.038, rankIC: -0.061, IR: 2.78, Long-Short Monthly Win Rate: 82.5%, April 2025 Long-Short Return: 0.89%, Last 12 Months Long-Short Monthly Win Rate: 100.0%[6][21] - APM Factor: IC: 0.030, rankIC: 0.034, IR: 2.32, Long-Short Monthly Win Rate: 77.6%, April 2025 Long-Short Return: -0.27%, Last 12 Months Long-Short Monthly Win Rate: 75.0%[6][25] - Ideal Amplitude Factor: IC: -0.054, rankIC: -0.073, IR: 3.04, Long-Short Monthly Win Rate: 83.9%, April 2025 Long-Short Return: 2.52%, Last 12 Months Long-Short Monthly Win Rate: 83.3%[6][30] - Composite Trading Behavior Factor: IC: 0.068, rankIC: 0.092, IR: 3.36, Long-Short Monthly Win Rate: 82.2%, April 2025 Long-Short Return: 0.99%, Last 12 Months Long-Short Monthly Win Rate: 83.3%[6][35]