微盘股突变 12月12日，市场一度向强的方向发展，但尾盘受微盘股拖累再度走弱。微盘股指数下午两点之后跳水，一度跌1.4%，本月跌幅已经超6%。 从过去几年来看，微盘股每到年底或年初都会有较大异动。去年12月，该指数杀跌近8%，去年1月更是暴跌超21%。今天跌停或跌幅达10%以上的个股一度 超过20只，ST股更是大面积跌停，其中大部分有微盘股属性。 担心可能仍未消除！ 午后，A股市场一度冲高。但微盘股在下午两点过后也开启了显著杀跌之旅。万得微盘股指数一度杀跌超1.4%。不少上午存有一定涨幅的中小盘个股午后皆 出现显著杀跌。 那么，究竟发生了什么？一方面，过去几年，每到年底或年初，微盘股都会震荡加剧，2024年1月和12月微盘股都出现了惨烈杀跌。另一方面，可能与最近 市场的传闻有关。不过，有机构人士向券商中国记者透露，截至目前，相关机制暂无变动。 那么，当下应该是切换风格，还是坚定撤退呢？分析人士认为，从目前市场的成交量来看，只要小盘股不出现大幅度回撤，主题性的中大盘还是会有一定机 会。 据方正证券，大小盘切换行情经验有三大事实： (1)A股大小盘超额收益整体呈均值回归特征，2005年以来大小盘风格变化大体可分为 ...

证券研究报告：金融工程报告 发布时间：2025-12-08 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 近期研究报告 《指数弱反弹目标补缺，融资资金净流 入 通 信 与 电 子 — — 行 业 轮 动 周 报 20251130》 - 2025.12.02 《指数回撤下融资资金净流出，ETF 资 金大幅净流入，GRU 调入传媒——行业 轮动周报 20251124》 - 2025.11.25 《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 《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 《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 《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 ...

证券研究报告：金融工程报告 发布时间：2025-12-01 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 近期研究报告 《指数回撤下融资资金净流出，ETF 资 金大幅净流入，GRU 调入传媒——行业 轮动周报 20251124》 - 2025.11.25 《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 《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 《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 《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 20 ...

Quantitative Models and Construction Diffusion Index Model - **Model Name**: Diffusion Index Model [5][17] - **Construction Idea**: The model monitors the market's diffusion index to identify critical turning points for trading signals [5][17] - **Construction Process**: - The diffusion index is calculated based on the relative price movements of constituent stocks within the micro-cap index over a specific time window [37] - The model uses three methods: - **First Threshold Method (Left-Side Trading)**: Triggered when the diffusion index reaches a predefined risk threshold. For example, on November 14, 2025, the index value of 0.925 triggered a sell signal [41] - **Delayed Threshold Method (Right-Side Trading)**: Provides a sell signal when the index value drops below a delayed threshold, such as 0.8975 on November 17, 2025 [46] - **Dual Moving Average Method (Adaptive Trading)**: Generates buy signals based on the crossover of two moving averages, such as the buy signal on October 13, 2025 [47] - **Evaluation**: The model effectively identifies market turning points and provides actionable trading signals [5][17] Small-Cap Low-Volatility 50 Strategy - **Model Name**: Small-Cap Low-Volatility 50 Strategy [7][16][33] - **Construction Idea**: Selects 50 stocks with small market capitalization and low volatility from the micro-cap index [7][33] - **Construction Process**: - Stocks are screened based on market capitalization and volatility metrics [7][33] - Portfolio is rebalanced bi-weekly [7][33] - Transaction costs are set at 0.3% for both buying and selling [7] - **Evaluation**: The strategy demonstrates strong performance in specific market conditions but underperforms during broader market downturns [7][33] --- Model Backtesting Results Diffusion Index Model - **First Threshold Method**: Triggered sell signal at 0.925 on November 14, 2025 [41] - **Delayed Threshold Method**: Triggered sell signal at 0.8975 on November 17, 2025 [46] - **Dual Moving Average Method**: Generated buy signal on October 13, 2025 [47] Small-Cap Low-Volatility 50 Strategy - **2024 Performance**: Annual return of 7.07%, excess return of -2.93% [7][33] - **2025 YTD Performance**: Annual return of 63.78%, weekly excess return of -2.23% [7][33] --- Quantitative Factors and Construction Weekly Factor Performance - **Top 5 Factors**: - **Leverage Factor**: Weekly rank IC of 0.182, historical average of -0.005 [4] - **Free Float Ratio Factor**: Weekly rank IC of 0.138, historical average of -0.012 [4] - **Turnover Factor**: Weekly rank IC of 0.116, historical average of -0.081 [4] - **Liquidity Factor**: Weekly rank IC of 0.075, historical average of -0.041 [4] - **Dividend Yield Factor**: Weekly rank IC of 0.064, historical average of 0.022 [4] - **Bottom 5 Factors**: - **Unadjusted Stock Price Factor**: Weekly rank IC of -0.311, historical average of -0.017 [4] - **Beta Factor**: Weekly rank IC of -0.3, historical average of 0.003 [4] - **Non-Liquidity Factor**: Weekly rank IC of -0.161, historical average of 0.039 [4] - **Inverse PE_TTM Factor**: Weekly rank IC of -0.138, historical average of 0.016 [4] - **Single-Quarter ROE Factor**: Weekly rank IC of -0.089, historical average of 0.021 [4] Additional Weekly Factor Performance - **Top 5 Factors**: - **Logarithmic Market Cap Factor**: Weekly rank IC of 0.225, historical average of -0.034 [16] - **Nonlinear Market Cap Factor**: Weekly rank IC of 0.225, historical average of -0.034 [16] - **Beta Factor**: Weekly rank IC of 0.083, historical average of 0.003 [16] - **Unadjusted Stock Price Factor**: Weekly rank IC of 0.065, historical average of -0.017 [16] - **Past Year Volatility Factor**: Weekly rank IC of 0.06, historical average of -0.033 [16] - **Bottom 5 Factors**: - **Past 10-Day Return Factor**: Weekly rank IC of -0.226, historical average of -0.061 [16] - **Momentum Factor**: Weekly rank IC of -0.196, historical average of -0.006 [16] - **Leverage Factor**: Weekly rank IC of -0.114, historical average of -0.005 [16] - **Single-Quarter Net Profit Growth Factor**: Weekly rank IC of -0.11, historical average of 0.019 [16] - **Standardized Expected Earnings Factor**: Weekly rank IC of -0.104, historical average of 0.013 [16] --- Factor Backtesting Results Weekly Factor Performance - **Leverage Factor**: Weekly rank IC of 0.182 [4] - **Free Float Ratio Factor**: Weekly rank IC of 0.138 [4] - **Turnover Factor**: Weekly rank IC of 0.116 [4] - **Liquidity Factor**: Weekly rank IC of 0.075 [4] - **Dividend Yield Factor**: Weekly rank IC of 0.064 [4] - **Unadjusted Stock Price Factor**: Weekly rank IC of -0.311 [4] - **Beta Factor**: Weekly rank IC of -0.3 [4] - **Non-Liquidity Factor**: Weekly rank IC of -0.161 [4] - **Inverse PE_TTM Factor**: Weekly rank IC of -0.138 [4] - **Single-Quarter ROE Factor**: Weekly rank IC of -0.089 [4] Additional Weekly Factor Performance - **Logarithmic Market Cap Factor**: Weekly rank IC of 0.225 [16] - **Nonlinear Market Cap Factor**: Weekly rank IC of 0.225 [16] - **Beta Factor**: Weekly rank IC of 0.083 [16] - **Unadjusted Stock Price Factor**: Weekly rank IC of 0.065 [16] - **Past Year Volatility Factor**: Weekly rank IC of 0.06 [16] - **Past 10-Day Return Factor**: Weekly rank IC of -0.226 [16] - **Momentum Factor**: Weekly rank IC of -0.196 [16] - **Leverage Factor**: Weekly rank IC of -0.114 [16] - **Single-Quarter Net Profit Growth Factor**: Weekly rank IC of -0.11 [16] - **Standardized Expected Earnings Factor**: Weekly rank IC of -0.104 [16]

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]