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]

Group 1 - Main capital outflow in the last two weeks totaled 138.204 billion, with net inflows in the home appliance and coal industries, while the top three industries for net outflows were computer, basic chemicals, and national defense [2] - Current margin trading balance is 1,838.493 billion, up 0.94% from the previous period, with financing balance at 1,826.553 billion and securities lending balance at 11.940 billion [2] - In the last two weeks, the number of rising stocks exceeded the number of falling stocks, with the top three industries in terms of growth being communication, computer, and electronics, while the top three industries with declines were beauty care, oil and petrochemicals, and pharmaceuticals [2] Group 2 - The overall A-share strength analysis score is 5.97, with the CSI 300 score at 5.83, the ChiNext score at 6.28, and the STAR Market score at 6.40, indicating a neutral range [2] - The Shanghai Composite Index has broken through the March high, but there is still a gap to a bull market initiation, with short-term indices possibly reaching higher levels but unlikely to achieve significant long-term increases [3] - Future focus on opportunities in robotics and national defense industries, with a recommendation to pay attention to Hong Kong stock opportunities [3]

Group 1 - The net inflow of main funds in the Shanghai and Shenzhen stock markets today is 393 million, accounting for 0.03% [1] - The net outflow of large single funds is 5.657 billion, accounting for 0.44% [1] - The net inflow of small single funds is 7.990 billion, accounting for 0.62% [1]

Group 1 - The core point of the article indicates that the net inflow of main funds in the Shanghai and Shenzhen stock markets today is 14.02 billion, accounting for 1.18% [1] - Large single orders experienced a net outflow of 3.41 billion, representing 0.29% [1] - Small single orders saw a net inflow of 5.37 billion, which is 0.45% of the total [1]

- The liquidity factor has shown a rapid rebound, with active trading by major funds. This week's recommended sectors for the liquidity factor include electronics, electrical equipment and new energy, pharmaceuticals, machinery, non-bank finance, and non-ferrous metals[14][16] - The long-term prosperity expectation factor, which serves as a proxy for prosperity investment, has started to show a slight upward trend in effectiveness. This week's long-term prosperity expectation factor includes non-bank finance, building materials, transportation, electric power and public utilities, and non-ferrous metals[18][20] - The short-term prosperity expectation factor continues to focus on domestic demand, with significant upward movement in long-short excess returns. This week's short-term prosperity expectation factor includes agriculture, forestry, animal husbandry, and fishery, consumer services, non-bank finance, machinery, and non-ferrous metals[22][24] - The momentum reversal factor is currently unable to describe the market trend, but it is expected that sector rotation may shift to momentum in one to two weeks. This week's momentum reversal factor includes automobiles, communications, electrical equipment and new energy, machinery, and home appliances[25][26] - The composite factor for this week includes consumer services, non-bank finance, machinery, electrical equipment and new energy, electronics, and non-ferrous metals[32][33] Factor Backtesting Results - Liquidity factor, excess return of long positions: 0.7% to 2.3% over various periods[16] - Long-term prosperity expectation factor, excess return of long positions: 0.6% to 2.4% over various periods[20] - Short-term prosperity expectation factor, excess return of long positions: 0.6% to 2.0% over various periods[24] - Momentum reversal factor, excess return of long positions: 0.4% to 2.4% over various periods[26]

以下文章来源于申万宏源证券上海分公司 ，作者李金玲 申万宏源证券上海分公司 . 申万宏源证券上海分公司官微，能为您提供账户开立、软件下载、研究所及投顾资讯等综合服务，为您的财富保驾护航。 数据速看： 1.主力资金： 近两周主力资金合计净流出1079.31亿元，主力资金净流入额前三的行业为汽车、家用电器和公用事业，主力资金净流出额前三的行业为电 子、计算机和基础化工。 2.融资融券数据： 当前市场融资融券余额为18082.47亿元，较上期下降0.28%，其中融资余额17968.74亿元，融券余额113.73亿元。本期两融日均交易额为 993.88亿元，较上期下降13.93%，其中融资日均净买入988.21亿元，较两周前下降13.89%，融券日均净卖出5.67亿元，较上期下降20.41%。近两周融资净 买入前三的行业分别为汽车、电子和基础化工；融券净卖出前三的行业分别为银行、公用事业和交通运输。 3.涨跌情况： 近两周全市场上涨家数高于下跌家数，近两周涨幅前三的行业为公用事业、美容护理和汽车，跌幅前三的行业为国防军工、农林牧渔和食品 饮料。 4.强弱分析： 近两周全部A股强弱分析得分为5.90，沪深300强弱分析 ...

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股市资金流向图：今日沪深两市主力资金净流入12.95亿元，占比0.1%；大单资金净流出17.93亿元，占比0.14%；小单资金净流 入3.18亿元，占比0.02%。 ...