《微盘股高位盘整，增长逻辑未改变— — 微 盘 股 指 数 周 报 20251031 》 - 2025.11.03 证券研究报告：金融工程报告 近期研究报告 《微盘股继续领涨市场，扩散指数已达 较高区间 — — 微盘股指数周报 20251114》 - 2025.11.18 研究所 分析师:黄子崟 SAC 登记编号:S1340523090002 Email:huangziyin@cnpsec.com 研究助理:冯昱文 SAC 登记编号:S1340124100011 Email:fengyuwen@cnpsec.com 《连板情绪持续发酵，GRU 行业轮动调 入基础化工 — — 行业轮动周报 20251109》 - 2025.11.11 《上证周中突破 4000 点，扩散指数行业 轮动调入电力设备及新能源——行业 轮动周报 20251102》 - 2025.11.02 《微盘股触发看多信号，看好微盘 10 月 后续表现 — — 微盘股指数周报 20251017》 - 2025.10.22 《上证强于双创调整空间不大，ETF 资 金持续配置金融地产与 TMT 方向——行 业轮动周报 20251019》 - 2025 ...

Core Viewpoint - The article highlights the strong performance of micro-cap stocks, particularly in the context of the Shanghai Composite Index's fluctuations around the 4000-point mark, indicating a growing market interest in this segment [2]. Group 1: Performance Comparison - Since 2010, the micro-cap stock index has outperformed major indices like the Shanghai 50, CSI 300, CSI 500, CSI 1000, and National 2000 in most years, except for 2017 and 2020 [2]. - The absolute performance data shows that in 2015, the micro-cap index surged by 229%, while the CSI 300 only increased by 6% [3]. - In 2023, the micro-cap index recorded a 50% increase, significantly outperforming other indices [3]. Group 2: Excess Returns Analysis - The excess returns of the micro-cap index are attributed to PB (Price-to-Book) recovery and the switching between high and low valuations [8]. - The report indicates that the contribution of trading frequency to excess returns is limited, while the profitability of micro-cap stocks does not significantly influence their overall returns [8]. - The strategy behind micro-cap stocks is characterized by a "reverse selection" feature, where stocks that have risen significantly are removed from the index, allowing for a systematic "buy low, sell high" approach [6]. Group 3: Trading Strategy Insights - The micro-cap index employs a mechanism that automatically executes a rebalancing strategy, enhancing its ability to capture structural reversal opportunities during market volatility [6]. - The trading environment for micro-cap stocks is influenced by both short-term trading and momentum strategies, which can amplify volatility during periods of liquidity tightening or systemic risk [8].

Group 1 - The core viewpoint of the article highlights a significant rebound in trading sentiment, with the Wind Micro Index breaking through previous levels and achieving a one-month return of 9.31% and a year-to-date increase of 74.49% [2][3] - Small-cap stocks, represented by the CSI 2000 and CSI 1000 indices, have shown relatively strong performance in the past month, contrasting with the sluggish response of large-cap indices like the CSI 300 [2][3] Group 2 - The shift in fund preferences is driven by profit-taking in certain tech growth sectors, leading active funds to seek higher elasticity in small-cap stocks as the large-cap market lacks a clear trend [4][6] - Year-end trading characteristics are evident as some trading-oriented funds return to high-elasticity sectors, further propelling small-cap indices upward [5][6] - Policy measures aimed at expanding domestic demand and promoting innovation are more sensitive to small and medium-sized enterprises, making them more responsive to policy changes [6] Group 3 - Quantitative micro-cap strategies have shown an average return of 3.53% over the past month, significantly lagging behind the micro-cap index's over 9% increase, attributed to the different operational mechanisms of indices and quantitative strategies [7] - The recent rise in the micro-cap index is primarily driven by a few highly liquid and elastic stocks, which are difficult to weight heavily in quantitative models due to high trading costs and volatility [7][8] - Quantitative strategies focus on capturing more sustainable style premiums through a multi-factor system, which may exhibit slight delays in exposure during the initial phase of a style shift [7][8] Group 4 - The appeal of quantitative micro-cap strategies lies in their ability to provide exposure to micro-cap style returns while minimizing extreme volatility associated with indices [8][9] - These strategies have a lower correlation with other asset classes, effectively reducing portfolio volatility [9] - The quantitative framework filters out noise from extremely small stocks, focusing on fundamentals and trading quality to stabilize returns [10] - In a market environment favoring small and micro-caps, quantitative strategies offer a relatively controlled way to participate in high-elasticity stocks [11][12]

Group 1 - The core viewpoint of the report emphasizes that the micro盘股 strategy has gained significant attention in the past two years, driven by a logic of accumulating excess returns through capital games and trading efficiency in a high-volatility environment [1][10][12] - The Wind micro盘股 index outperformed major broad-based indices twice in 2025, first from May to July with a return of +31.81% compared to +17.26% for 中证 2000 and +4.93% for 上证 50, and again in October with a +5.51% return while major indices showed minimal fluctuations [1][11][12] - The report identifies three main reasons for the leading performance of micro盘股: liquidity easing often leads micro盘股 to rebound ahead of indices, the index's "reverse selection" characteristic allows for intrinsic profit-taking and rebalancing, and the strategy focuses more on market self-repair and contrarian reactions compared to traditional cyclical strategies [2][12][13] Group 2 - A historical review shows that micro盘股 has a "double-edged sword" characteristic, providing high elasticity and excess return advantages but also amplifying volatility during liquidity tightening or systemic risk phases [3][20][22] - In bull markets dominated by public and foreign capital, micro盘股 strategies underperformed compared to cyclical investment strategies, while in bear markets, they were impacted by emotional and liquidity shocks [21][22] - The current market environment features diversified funding sources and enhanced stability, with the micro盘股 style expected to continue its upward potential, acting as a "risk appetite thermometer" and "sentiment leading indicator" for the ongoing bull market [4][26][33] Group 3 - The report suggests investment strategies focusing on the strong performance of micro盘股, particularly in the context of liquidity abundance and rising risk appetite, recommending attention to sectors like technology and cyclical rebalancing [34] - Specific sectors highlighted include photovoltaic, chemicals, steel, non-ferrous metals, and electric power, as well as technology growth areas such as AI hardware and military applications [34]

Quantitative Models and Construction Methods - **Model Name**: Diffusion Index Model **Construction Idea**: The model monitors the future critical points of the diffusion index to predict market trends[6][38][39] **Construction Process**: 1. The diffusion index is calculated based on the relative price changes of constituent stocks over a specific time window 2. Horizontal axis represents future price changes (e.g., from +10% to -10%), while vertical axis represents the length of the review window (e.g., 20 days to 10 days) 3. Example: If all constituent stocks drop by 5% after 5 days, the diffusion index value is 0.69[38] 4. The model uses methods like left-side threshold, right-side threshold, and dual moving average to generate signals - Left-side threshold method triggered an opening signal on September 23, 2025, with a value of 0.0575[43] - Right-side threshold method triggered an opening signal on September 25, 2025, with a value of 0.1825[47] - Dual moving average method provided a bullish signal on October 13, 2025[48] **Evaluation**: The model is effective in identifying high-risk zones and generating trading signals[6][39][48] - **Model Name**: Small Cap Low Volatility 50 Strategy **Construction Idea**: Select 50 stocks with small market capitalization and low volatility from micro-cap stocks[8][34] **Construction Process**: 1. Stocks are selected based on market capitalization and volatility criteria 2. Portfolio is rebalanced bi-weekly 3. Transaction cost is set at 0.3% for both sides 4. Benchmark index: Wind Micro-Cap Index (8841431.WI)[8][34] **Evaluation**: The strategy has shown strong performance in 2025, with significant YTD returns[8][34] Model Backtesting Results - **Diffusion Index Model**: - Current diffusion index value: 0.82, indicating a medium-high level[38][39] - Weekly increase from 0.78 to 0.82[39] - Future prediction: If the index rises by 2% next week, it will trigger the risk threshold[39] - **Small Cap Low Volatility 50 Strategy**: - 2024 return: 7.07%, excess return: -2.93%[8][34] - 2025 YTD return: 77.82%, weekly excess return: 1.50%[8][34] Quantitative Factors and Construction Methods - **Factor Name**: Free Float Ratio Factor **Construction Idea**: Measure the proportion of free-floating shares in total shares[5][16][32] **Construction Process**: 1. Calculate the ratio of free-floating shares to total shares 2. Rank IC value for the week: 0.108; historical average: -0.012[5][16][32] **Evaluation**: Positive weekly IC indicates strong predictive power[5][16][32] - **Factor Name**: Leverage Factor **Construction Idea**: Assess the financial leverage of companies[5][16][32] **Construction Process**: 1. Calculate the ratio of total debt to equity 2. Rank IC value for the week: 0.104; historical average: -0.006[5][16][32] **Evaluation**: Positive weekly IC suggests effective factor performance[5][16][32] - **Factor Name**: 10-Day Total Market Cap Turnover Rate Factor **Construction Idea**: Measure the turnover rate of total market capitalization over 10 days[5][16][32] **Construction Process**: 1. Calculate turnover rate as trading volume divided by total market capitalization over 10 days 2. Rank IC value for the week: 0.099; historical average: -0.059[5][16][32] **Evaluation**: Positive weekly IC indicates good predictive ability[5][16][32] - **Factor Name**: 10-Day Free Float Market Cap Turnover Rate Factor **Construction Idea**: Measure the turnover rate of free-floating market capitalization over 10 days[5][16][32] **Construction Process**: 1. Calculate turnover rate as trading volume divided by free-floating market capitalization over 10 days 2. Rank IC value for the week: 0.098; historical average: -0.061[5][16][32] **Evaluation**: Positive weekly IC suggests strong factor performance[5][16][32] - **Factor Name**: Dividend Yield Factor **Construction Idea**: Measure the dividend yield of stocks[5][16][32] **Construction Process**: 1. Calculate dividend yield as annual dividend divided by stock price 2. Rank IC value for the week: 0.065; historical average: 0.022[5][16][32] **Evaluation**: Positive weekly IC indicates reliable factor performance[5][16][32] Factor Backtesting Results - **Free Float Ratio Factor**: Weekly IC: 0.108; historical average: -0.012[5][16][32] - **Leverage Factor**: Weekly IC: 0.104; historical average: -0.006[5][16][32] - **10-Day Total Market Cap Turnover Rate Factor**: Weekly IC: 0.099; historical average: -0.059[5][16][32] - **10-Day Free Float Market Cap Turnover Rate Factor**: Weekly IC: 0.098; historical average: -0.061[5][16][32] - **Dividend Yield Factor**: Weekly IC: 0.065; historical average: 0.022[5][16][32]

- Model Name: Diffusion Index Model - Model Construction Idea: The model uses the diffusion index to monitor the critical point of future changes in the diffusion index[6][38] - Detailed Construction Process: The model uses the following formula to calculate the diffusion index: $$ \text{Diffusion Index} = \frac{\text{Number of Advancing Stocks}}{\text{Total Number of Stocks}} $$ The model monitors the critical point of future changes in the diffusion index by observing the values of the diffusion index at different time points[38][39] - Model Evaluation: The model is effective in predicting the high volatility of the micro-cap index in the coming week[39] - Testing Results: The current value of the diffusion index is 0.78, indicating a relatively high level[39] - Model Name: Initial Threshold Method (Left-Side Trading) - Model Construction Idea: The model triggers an opening signal when the diffusion index reaches a certain threshold[6][42] - Detailed Construction Process: The model uses the following formula to calculate the threshold: $$ \text{Threshold} = \text{Diffusion Index} \times \text{Historical Average} $$ The model triggered an opening signal on September 23, 2025, when the diffusion index reached 0.0575[42] - Model Evaluation: The model is effective in providing timely trading signals[42] - Testing Results: The model triggered an opening signal on September 23, 2025[42] - Model Name: Delayed Threshold Method (Right-Side Trading) - Model Construction Idea: The model provides an opening signal when the diffusion index reaches a delayed threshold[6][45] - Detailed Construction Process: The model uses the following formula to calculate the delayed threshold: $$ \text{Delayed Threshold} = \text{Diffusion Index} \times \text{Historical Average} + \text{Delay Factor} $$ The model provided an opening signal on September 25, 2025, when the diffusion index reached 0.1825[45] - Model Evaluation: The model is effective in providing delayed but accurate trading signals[45] - Testing Results: The model provided an opening signal on September 25, 2025[45] - Model Name: Dual Moving Average Method (Adaptive Trading) - Model Construction Idea: The model uses dual moving averages to provide trading signals[6][46] - Detailed Construction Process: The model uses the following formula to calculate the dual moving averages: $$ \text{Short-Term Moving Average} = \frac{\sum_{i=1}^{n} \text{Price}_i}{n} $$ $$ \text{Long-Term Moving Average} = \frac{\sum_{i=1}^{m} \text{Price}_i}{m} $$ The model provided a bullish signal on October 13, 2025, when the short-term moving average crossed above the long-term moving average[46] - Model Evaluation: The model is effective in providing adaptive trading signals based on market trends[46] - Testing Results: The model provided a bullish signal on October 13, 2025[46] Factor Construction and Performance - Factor Name: Dividend Yield Factor - Factor Construction Idea: The factor ranks stocks based on their dividend yield[5][16] - Detailed Construction Process: The factor uses the following formula to calculate the dividend yield: $$ \text{Dividend Yield} = \frac{\text{Annual Dividends}}{\text{Stock Price}} $$ The factor ranks stocks from highest to lowest dividend yield[16] - Factor Evaluation: The factor is effective in identifying high-yield stocks[16] - Testing Results: The factor's rank IC for the week is 0.199, with a historical average of 0.022[16] - Factor Name: PB Inverse Factor - Factor Construction Idea: The factor ranks stocks based on the inverse of their price-to-book ratio[5][16] - Detailed Construction Process: The factor uses the following formula to calculate the inverse PB ratio: $$ \text{PB Inverse} = \frac{1}{\text{Price-to-Book Ratio}} $$ The factor ranks stocks from highest to lowest PB inverse[16] - Factor Evaluation: The factor is effective in identifying undervalued stocks[16] - Testing Results: The factor's rank IC for the week is 0.112, with a historical average of 0.034[16] - Factor Name: Illiquidity Factor - Factor Construction Idea: The factor ranks stocks based on their illiquidity[5][16] - Detailed Construction Process: The factor uses the following formula to calculate illiquidity: $$ \text{Illiquidity} = \frac{\text{Absolute Return}}{\text{Trading Volume}} $$ The factor ranks stocks from highest to lowest illiquidity[16] - Factor Evaluation: The factor is effective in identifying illiquid stocks[16] - Testing Results: The factor's rank IC for the week is 0.103, with a historical average of 0.04[16] - Factor Name: Growth Factor - Factor Construction Idea: The factor ranks stocks based on their growth potential[5][16] - Detailed Construction Process: The factor uses the following formula to calculate growth: $$ \text{Growth} = \frac{\text{Current Period Earnings}}{\text{Previous Period Earnings}} - 1 $$ The factor ranks stocks from highest to lowest growth[16] - Factor Evaluation: The factor is effective in identifying high-growth stocks[16] - Testing Results: The factor's rank IC for the week is 0.019, with a historical average of -0.003[16] - Factor Name: Residual Volatility Factor - Factor Construction Idea: The factor ranks stocks based on their residual volatility[5][16] - Detailed Construction Process: The factor uses the following formula to calculate residual volatility: $$ \text{Residual Volatility} = \sqrt{\frac{\sum_{i=1}^{n} (\text{Return}_i - \text{Expected Return})^2}{n}} $$ The factor ranks stocks from highest to lowest residual volatility[16] - Factor Evaluation: The factor is effective in identifying stocks with high residual volatility[16] - Testing Results: The factor's rank IC for the week is 0.015, with a historical average of -0.039[16] Factor Backtesting Results - Dividend Yield Factor: Rank IC for the week is 0.199, historical average is 0.022[16] - PB Inverse Factor: Rank IC for the week is 0.112, historical average is 0.034[16] - Illiquidity Factor: Rank IC for the week is 0.103, historical average is 0.04[16] - Growth Factor: Rank IC for the week is 0.019, historical average is -0.003[16] - Residual Volatility Factor: Rank IC for the week is 0.015, historical average is -0.039[16]

Quantitative Models and Construction - **Model Name**: Diffusion Index Model **Construction Idea**: The model monitors the future diffusion index's critical points to predict market changes[39][40] **Construction Process**: The diffusion index is calculated based on the relative price changes of constituent stocks over a specific time window. The horizontal axis represents the future price change percentage (from +10% to -10%), while the vertical axis represents the review period length (from 20 days to 10 days). For example, at a horizontal axis value of 0.95 and a vertical axis value of 15 days, the diffusion index is 0.42, indicating that if all constituent stocks drop by 5% after 5 days, the diffusion index value will be 0.42[39][41] **Evaluation**: The model is effective in identifying potential turning points in the market[40] - **Model Name**: First Threshold Method (Left-Side Trading) **Construction Idea**: This method triggers a buy signal when the diffusion index reaches a predefined threshold[43] **Construction Process**: The first threshold method triggered a buy signal on September 23, 2025, when the diffusion index reached 0.0575[43] **Evaluation**: The method is suitable for early-stage market entry[43] - **Model Name**: Delayed Threshold Method (Right-Side Trading) **Construction Idea**: This method provides a buy signal after the diffusion index stabilizes above the threshold[46] **Construction Process**: The delayed threshold method triggered a buy signal on September 25, 2025, when the diffusion index reached 0.1825[46] **Evaluation**: The method is more conservative and reduces false signals compared to the first threshold method[46] - **Model Name**: Dual Moving Average Method (Adaptive Trading) **Construction Idea**: This method uses two moving averages to generate trading signals based on their crossover[47] **Construction Process**: The dual moving average method provided a buy signal on October 13, 2025, at the market close[47] **Evaluation**: The method adapts to market trends and is effective in capturing medium-term signals[47] Model Backtesting Results - **Diffusion Index Model**: Current diffusion index value is 0.67, indicating a medium-high level. If the market rises by 4% next week, it will trigger the risk threshold[39][40] - **First Threshold Method**: Triggered buy signal at diffusion index value of 0.0575[43] - **Delayed Threshold Method**: Triggered buy signal at diffusion index value of 0.1825[46] - **Dual Moving Average Method**: Triggered buy signal on October 13, 2025[47] Quantitative Factors and Construction - **Factor Name**: Non-Liquidity Factor **Construction Idea**: Measures the illiquidity of stocks to predict their future performance[5][33] **Construction Process**: The rank IC for this factor is calculated weekly. This week, the rank IC is 0.147, compared to the historical average of 0.04[5][33] **Evaluation**: The factor shows strong predictive power this week[5][33] - **Factor Name**: Beta Factor **Construction Idea**: Captures the sensitivity of stock returns to market movements[5][33] **Construction Process**: The rank IC for this factor is 0.134 this week, compared to the historical average of 0.004[5][33] **Evaluation**: The factor demonstrates significant improvement in predictive ability this week[5][33] - **Factor Name**: Standardized Expected Earnings Factor **Construction Idea**: Reflects the expected profitability of stocks[5][33] **Construction Process**: The rank IC for this factor is 0.132 this week, compared to the historical average of 0.014[5][33] **Evaluation**: The factor is effective in identifying profitable stocks[5][33] - **Factor Name**: Single-Quarter Net Profit Growth Factor **Construction Idea**: Measures the growth rate of net profit in a single quarter[5][33] **Construction Process**: The rank IC for this factor is 0.088 this week, compared to the historical average of 0.02[5][33] **Evaluation**: The factor is moderately effective in predicting stock performance[5][33] - **Factor Name**: Single-Quarter ROE Factor **Construction Idea**: Reflects the return on equity for a single quarter[5][33] **Construction Process**: The rank IC for this factor is 0.045 this week, compared to the historical average of 0.022[5][33] **Evaluation**: The factor shows limited predictive power this week[5][33] Factor Backtesting Results - **Non-Liquidity Factor**: Rank IC 0.147, historical average 0.04[5][33] - **Beta Factor**: Rank IC 0.134, historical average 0.004[5][33] - **Standardized Expected Earnings Factor**: Rank IC 0.132, historical average 0.014[5][33] - **Single-Quarter Net Profit Growth Factor**: Rank IC 0.088, historical average 0.02[5][33] - **Single-Quarter ROE Factor**: Rank IC 0.045, historical average 0.022[5][33] Composite Strategy and Construction - **Strategy Name**: Small-Cap Low-Volatility 50 Strategy **Construction Idea**: Selects 50 stocks with small market capitalization and low volatility from micro-cap stocks[8][17][35] **Construction Process**: The strategy rebalances every two weeks. In 2024, the strategy achieved a return of 7.07% with an excess return of -2.93%. In 2025, the YTD return is 71.25%, with a weekly excess return of 2.65%. The benchmark is the Wind Micro-Cap Index (8841431.WI), and transaction costs are 0.3% on both sides[8][17][35] **Evaluation**: The strategy is highly effective in capturing the performance of small-cap stocks in 2025[8][17][35] Strategy Backtesting Results - **Small-Cap Low-Volatility 50 Strategy**: - 2024 Return: 7.07%, Excess Return: -2.93%[8][17][35] - 2025 YTD Return: 71.25%, Weekly Excess Return: 2.65%[8][17][35]

Quantitative Models and Construction Methods - **Model Name**: Diffusion Index Model **Construction Idea**: The model monitors the critical point of future diffusion index changes to identify potential turning points in the market[37][38] **Construction Process**: The diffusion index is calculated based on the relative price changes of constituent stocks over a specific time window. For example, if all constituent stocks drop by 5% after 5 days, the diffusion index value is 0.05. The formula is: $ Diffusion Index = \frac{\text{Number of stocks with positive changes}}{\text{Total number of stocks}} $ The model uses thresholds (e.g., 0.1) to trigger buy signals. Observations show that the current diffusion index is at 0.1, indicating sufficient adjustment and a potential rise in the next 5 days[37][38][39] **Evaluation**: The model effectively identifies market turning points and provides actionable signals for trading[38] - **Model Name**: First Threshold Method (Left-side Trading) **Construction Idea**: This method triggers buy signals when the diffusion index reaches a predefined threshold[41] **Construction Process**: The first threshold method triggered a buy signal on September 23, 2025, when the diffusion index reached 0.0575[41] **Evaluation**: The method is straightforward and provides early signals for market entry[41] - **Model Name**: Delayed Threshold Method (Right-side Trading) **Construction Idea**: This method provides buy signals after the diffusion index stabilizes above a certain threshold[44] **Construction Process**: The delayed threshold method triggered a buy signal on September 25, 2025, when the diffusion index reached 0.1825[44] **Evaluation**: The method reduces false signals by waiting for confirmation of market trends[44] - **Model Name**: Dual Moving Average Method (Adaptive Trading) **Construction Idea**: This method uses two moving averages to identify buy signals based on trend changes[45] **Construction Process**: The dual moving average method triggered a buy signal on October 13, 2025, based on the crossover of short-term and long-term moving averages[45] **Evaluation**: The method adapts to market trends and provides reliable signals for trading[45] Model Backtesting Results - **Diffusion Index Model**: Current value is 0.10, indicating a potential rise in the next 5 days[37][38][39] - **First Threshold Method**: Triggered buy signal at diffusion index value of 0.0575 on September 23, 2025[41] - **Delayed Threshold Method**: Triggered buy signal at diffusion index value of 0.1825 on September 25, 2025[44] - **Dual Moving Average Method**: Triggered buy signal on October 13, 2025[45] Quantitative Factors and Construction Methods - **Factor Name**: PB Inverse Factor **Construction Idea**: This factor ranks stocks based on the inverse of their price-to-book ratio[31] **Construction Process**: $ PB\_Inverse = \frac{1}{PB} $ The factor is calculated weekly and ranks stocks based on their PB inverse values[31] **Evaluation**: The factor shows strong predictive power for stock performance[31] - **Factor Name**: Dividend Yield Factor **Construction Idea**: This factor ranks stocks based on their dividend yield[31] **Construction Process**: $ Dividend\_Yield = \frac{\text{Annual Dividend}}{\text{Stock Price}} $ The factor is calculated weekly and ranks stocks based on their dividend yield values[31] **Evaluation**: The factor is effective in identifying high-yield stocks with stable returns[31] - **Factor Name**: Non-liquidity Factor **Construction Idea**: This factor ranks stocks based on their non-liquidity characteristics[31] **Construction Process**: $ Non\_Liquidity = \frac{\text{Average Daily Volume}}{\text{Total Shares Outstanding}} $ The factor is calculated weekly and ranks stocks based on their non-liquidity values[31] **Evaluation**: The factor captures stocks with low trading activity, which may have higher potential for price appreciation[31] - **Factor Name**: Free Float Ratio Factor **Construction Idea**: This factor ranks stocks based on the proportion of free-floating shares[31] **Construction Process**: $ Free\_Float\_Ratio = \frac{\text{Free-floating Shares}}{\text{Total Shares Outstanding}} $ The factor is calculated weekly and ranks stocks based on their free float ratio values[31] **Evaluation**: The factor identifies stocks with higher market accessibility and liquidity[31] - **Factor Name**: Profitability Factor **Construction Idea**: This factor ranks stocks based on their profitability metrics[31] **Construction Process**: $ Profitability = \frac{\text{Net Income}}{\text{Total Revenue}} $ The factor is calculated weekly and ranks stocks based on their profitability values[31] **Evaluation**: The factor effectively identifies companies with strong financial performance[31] Factor Backtesting Results - **PB Inverse Factor**: Weekly rank IC value is 0.214, historical average is 0.034[31] - **Dividend Yield Factor**: Weekly rank IC value is 0.103, historical average is 0.022[31] - **Non-liquidity Factor**: Weekly rank IC value is 0.07, historical average is 0.04[31] - **Free Float Ratio Factor**: Weekly rank IC value is 0.054, historical average is -0.012[31] - **Profitability Factor**: Weekly rank IC value is 0.049, historical average is 0.022[31] Composite Strategy and Construction Methods - **Strategy Name**: Small Cap Low Volatility 50 Strategy **Construction Idea**: Select 50 stocks with small market capitalization and low volatility from the micro-cap stock universe[8][17] **Construction Process**: - Filter stocks based on market capitalization and volatility criteria - Select the top 50 stocks meeting the criteria - Rebalance the portfolio bi-weekly[8][17] **Evaluation**: The strategy effectively captures the performance of small-cap stocks with stable returns[8][17] Strategy Backtesting Results - **Small Cap Low Volatility 50 Strategy**: - 2024 annual return: 7.07%, excess return: -2.93%[8][17] - 2025 YTD return: 56.99%, weekly excess return: 0.62%[8][17]

Quantitative Models and Construction Methods - **Model Name**: Diffusion Index Model **Model Construction Idea**: The model is designed to monitor the future critical points of diffusion index changes and provide trading signals based on different methods such as threshold methods and moving average methods [6][37][38] **Model Construction Process**: - The diffusion index is calculated based on the relative price changes of micro-cap stock index constituent stocks over a specific time window. - The horizontal axis represents the percentage change in stock prices from +10% to -10% over the next N days, while the vertical axis represents the length of the retrospective window T days or the future N days. - For example, at a horizontal axis value of 0.95 and a vertical axis value of 15 days, the diffusion index value is 0.04, indicating that if all micro-cap stock index constituent stocks drop by 5% after N=5 days, the diffusion index value will be 0.04. - The model uses three methods to generate trading signals: - **First Threshold Method (Left-side Trading)**: Triggered a buy signal on September 23, 2025, with a closing value of 0.0575 [40] - **Delayed Threshold Method (Right-side Trading)**: Gave a buy signal on September 25, 2025, with a closing value of 0.1825 [44] - **Double Moving Average Method (Adaptive Trading)**: Provided a sell signal on August 4, 2025 [45] **Model Evaluation**: The diffusion index is currently at a medium level, indicating a short-term downward trend but not expected to trigger the 0.1 threshold in the next 10 trading days [37][38] Model Backtesting Results - **Diffusion Index Model**: - Current diffusion index value: 0.50 [37] - First Threshold Method: Buy signal triggered at 0.0575 on September 23, 2025 [40] - Delayed Threshold Method: Buy signal triggered at 0.1825 on September 25, 2025 [44] - Double Moving Average Method: Sell signal triggered on August 4, 2025 [45] --- Quantitative Factors and Construction Methods - **Factor Name**: Leverage Factor **Factor Construction Idea**: Measures the financial leverage of companies to assess their risk and return potential [5][31] **Factor Construction Process**: - The leverage factor is calculated as the ratio of total debt to total equity. - The rank IC for this factor is calculated weekly to evaluate its predictive power for stock returns [31] **Factor Evaluation**: This factor showed a positive rank IC this week, indicating its effectiveness in predicting stock returns [5][31] - **Factor Name**: Free Float Ratio Factor **Factor Construction Idea**: Evaluates the proportion of shares available for trading in the market [5][31] **Factor Construction Process**: - The free float ratio factor is calculated as the ratio of free float shares to total shares outstanding. - Weekly rank IC is used to measure its predictive ability [31] **Factor Evaluation**: This factor demonstrated a positive rank IC this week, suggesting its utility in forecasting stock performance [5][31] - **Factor Name**: Dividend Yield Factor **Factor Construction Idea**: Measures the dividend yield of stocks to identify value opportunities [5][31] **Factor Construction Process**: - The dividend yield factor is calculated as the ratio of annual dividend per share to the current stock price. - Weekly rank IC is computed to assess its predictive power [31] **Factor Evaluation**: This factor showed a positive rank IC this week, indicating its effectiveness in predicting stock returns [5][31] - **Factor Name**: Single-quarter ROE Factor **Factor Construction Idea**: Measures the return on equity for a single quarter to evaluate profitability [5][31] **Factor Construction Process**: - The single-quarter ROE factor is calculated as the ratio of net income to shareholders' equity for a single quarter. - Weekly rank IC is used to measure its predictive ability [31] **Factor Evaluation**: This factor demonstrated a positive rank IC this week, suggesting its utility in forecasting stock performance [5][31] - **Factor Name**: Growth Factor **Factor Construction Idea**: Measures the growth potential of companies based on financial metrics [5][31] **Factor Construction Process**: - The growth factor is calculated using metrics such as revenue growth, earnings growth, and other growth indicators. - Weekly rank IC is computed to assess its predictive power [31] **Factor Evaluation**: This factor showed a positive rank IC this week, indicating its effectiveness in predicting stock returns [5][31] --- Factor Backtesting Results - **Leverage Factor**: Rank IC this week: 0.176, historical average: -0.006 [5][31] - **Free Float Ratio Factor**: Rank IC this week: 0.156, historical average: -0.013 [5][31] - **Dividend Yield Factor**: Rank IC this week: 0.109, historical average: 0.021 [5][31] - **Single-quarter ROE Factor**: Rank IC this week: 0.091, historical average: 0.022 [5][31] - **Growth Factor**: Rank IC this week: 0.091, historical average: -0.003 [5][31]

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Timing - Initial Threshold Method (Left-Side Trading) - **Model Construction Idea**: The model uses the proportion of stocks in the "Wind Micro-Cap Index" that have been in an upward trend over the past 20 trading days as a diffusion index. It applies a threshold-based strategy to determine market timing[65][66] - **Model Construction Process**: - When the diffusion index exceeds 0.9, the strategy signals to exit positions (empty portfolio) - When the diffusion index falls below 0.1, the strategy signals to fully enter positions (full portfolio) - In other periods, the signal from the previous period is maintained - The model requires a full position before signaling an empty position, and vice versa[66] - **Model Evaluation**: - The model failed to outperform the "Wind Micro-Cap Index" in-sample - It struggled to identify major bear markets in 2024, leading to a "buy-the-dip" behavior - However, it captured the bottom well in 2025 and aligned with the upward trend[66] - **Key Signal**: Triggered an empty portfolio signal on May 8, 2025[66] 2. Model Name: Diffusion Index Timing - Delayed Threshold Method (Right-Side Trading) - **Model Construction Idea**: This model modifies the left-side trading approach by introducing momentum-based logic, aiming to avoid premature reversals[69] - **Model Construction Process**: - If the diffusion index was above 0.9 the previous day and falls below 0.9 today, the strategy signals to exit positions - If the diffusion index was below 0.1 the previous day and rises above 0.1 today, the strategy signals to fully enter positions - In other periods, the signal from the previous period is maintained[69] - **Model Evaluation**: - The model failed to outperform the "Wind Micro-Cap Index" in-sample - It avoided premature exits during the 2024 bull market, preserving gains - The strategy's net value reached a new high recently, but overall performance in 2025 was mediocre[69] - **Key Signal**: Triggered an empty portfolio signal on May 15, 2025[69] 3. Model Name: Diffusion Index Timing - Dual Moving Average Method (Adaptive Trading) - **Model Construction Idea**: This model introduces a dual moving average approach to adapt to market trends, reducing reliance on fixed thresholds[72] - **Model Construction Process**: - The short-term moving average is the 10-day moving average (MA) of the diffusion index - The long-term moving average is the 20-day MA of the short-term moving average - When the short-term MA crosses above the long-term MA, the strategy signals to fully enter positions - When the short-term MA crosses below the long-term MA, the strategy signals to exit positions[72] - **Model Evaluation**: - The model controlled drawdowns effectively in-sample - It avoided significant losses during the 2024 bear market and preserved gains during the bull market - However, the model showed some instability in trend recognition due to parameter smoothing issues[72] - **Key Signal**: Triggered an empty portfolio signal on August 4, 2025[72] --- Model Backtesting Results 1. Initial Threshold Method - **Key Signal**: Empty portfolio signal on May 8, 2025[66] 2. Delayed Threshold Method - **Key Signal**: Empty portfolio signal on May 15, 2025[69] 3. Dual Moving Average Method - **Key Signal**: Empty portfolio signal on August 4, 2025[72]