金融工程定期：开源交易行为因子绩效月报（2025年11月）-20251128

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]