Quantitative Models and Factor Construction Quantitative Models and Construction Process - **Model Name**: Standardized Average Transaction Amount Factor (SATD) **Construction Idea**: This factor captures the trading behavior of major funds by normalizing the average transaction amount during specific time periods against the daily average transaction amount[1][25][26] **Construction Process**: 1. Calculate the average transaction amount for specific time periods: $ ATD_{P} = \frac{\sum_{t \in P} Amt_{t}}{\sum_{t \in P} DealNum_{t}} $ Here, $ ATD_{P} $ represents the average transaction amount for the specific time period $ P $, $ Amt_{t} $ is the transaction amount at time $ t $, and $ DealNum_{t} $ is the number of transactions at time $ t $[26][27] 2. Calculate the daily average transaction amount: $ ATD_{T} = \frac{\sum_{t \in T} Amt_{t}}{\sum_{t \in T} DealNum_{t}} $ Here, $ ATD_{T} $ represents the daily average transaction amount[27] 3. Normalize the specific time period's average transaction amount: $ SATD_{P} = \frac{ATD_{P}}{ATD_{T}} $ Here, $ SATD_{P} $ is the standardized average transaction amount factor for the specific time period $ P $[28][29] Quantitative Factors and Construction Process - **Factor Name**: Downward Price Movement Factor **Construction Idea**: This factor identifies the predictive power of major fund activity during periods of price decline[39][40] **Construction Process**: 1. Classify minute-level price movements into upward, downward, and flat periods using the following formulas: $ UpFlag_{t} = \begin{cases} 1, & if\ ret_{i} > 0 \\ 0, & if\ ret_{i} \leq 0 \end{cases} $ $ DownFlag_{t} = \begin{cases} 0, & if\ ret_{i} \geq 0 \\ 1, & if\ ret_{i} < 0 \end{cases} $ $ ZeroFlag_{t} = \begin{cases} 0, & if\ ret_{i} \neq 0 \\ 1, & if\ ret_{i} = 0 \end{cases} $ Here, $ ret_{i} $ represents the minute-level return[39][40] 2. Calculate the average transaction amount for downward periods and normalize it against the daily average transaction amount: $ SATDDown = \frac{ATD_{DownFlag}}{ATD_{T}} $[43][44] - **Factor Name**: Maximum Downward Price Movement Factor **Construction Idea**: This factor focuses on the periods with the largest price declines, hypothesizing that major funds are more active during these times[59][60] **Construction Process**: 1. Rank minute-level price movements by their magnitude of decline 2. Select the top N% of minutes with the largest price declines 3. Calculate the average transaction amount for these periods and normalize it against the daily average transaction amount[59][60] - **Factor Name**: Lowest Price Factor **Construction Idea**: This factor identifies periods when the stock price is at its lowest, hypothesizing that major funds are more active during these times[87][89] **Construction Process**: 1. Rank minute-level prices from lowest to highest 2. Select the bottom N% of minutes with the lowest prices 3. Calculate the average transaction amount for these periods and normalize it against the daily average transaction amount[89][91] - **Factor Name**: Highest Volume Factor **Construction Idea**: This factor identifies periods with the highest trading volume, hypothesizing that these periods contain more significant information[109][110] **Construction Process**: 1. Rank minute-level trading volumes from highest to lowest 2. Select the top N% of minutes with the highest volumes 3. Calculate the average transaction amount for these periods and normalize it against the daily average transaction amount[109][110] - **Factor Name**: Volume-Price Divergence Factor **Construction Idea**: This factor identifies periods where trading volume and price movements are negatively correlated, hypothesizing that these periods contain more significant information[128][129] **Construction Process**: 1. Calculate the correlation coefficient between transaction prices and volumes for each minute 2. Rank minutes by their correlation coefficients 3. Select the bottom N% of minutes with the lowest correlation coefficients 4. Calculate the average transaction amount for these periods and normalize it against the daily average transaction amount[129][134] - **Factor Name**: Composite Factor **Construction Idea**: This factor combines the most effective factors (e.g., maximum downward price movement, lowest price, and highest volume factors) to enhance predictive power[160][161] **Construction Process**: 1. Combine the selected factors using equal weighting: $ CompositeFactor = DownwardFactor + LowestPriceFactor + HighestVolumeFactor $[160][161] Backtesting Results for Factors - **Downward Price Movement Factor**: RankIC Mean = 6.84%, Annualized RankICIR = 3.23, Monthly Win Rate = 83.93%[46][48] - **Maximum Downward Price Movement Factor**: RankIC Mean = 7.31%, Annualized RankICIR = 4.04, Monthly Win Rate = 86.49%[60][61] - **Lowest Price Factor**: RankIC Mean = 7.21%, Annualized RankICIR = 4.52, Monthly Win Rate = 91.96%[91][92] - **Highest Volume Factor**: RankIC Mean = 9.70%, Annualized RankICIR = 3.67, Monthly Win Rate = 83.04%[110][113] - **Volume-Price Divergence Factor**: RankIC Mean = 5.41%, Annualized RankICIR = 3.20, Monthly Win Rate = 81.25%[134][135] - **Composite Factor**: RankIC Mean = 10.33%, Annualized RankICIR = 4.32, Monthly Win Rate = 90.18%[160][161]