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]

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is used to monitor the critical points of future diffusion index changes, providing insights into potential market turning points[34][35] - **Model 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 0.33. The current diffusion index value is 0.82, indicating a relatively uniform distribution[34][35] - **Model Evaluation**: The model provides a systematic way to observe market heat and potential upward space, though it is sensitive to the dynamic updates of constituent stocks[34][35] 2. Model Name: First Threshold Method (Left-Side Trading) - **Model Construction Idea**: This method triggers a sell signal when the diffusion index reaches a predefined threshold[39] - **Model Construction Process**: The first threshold method triggered a sell signal on May 8, 2025, when the diffusion index closed at 0.9850[39] 3. Model Name: Delayed Threshold Method (Right-Side Trading) - **Model Construction Idea**: Similar to the first threshold method but with a delayed signal to confirm the trend[41][43] - **Model Construction Process**: The delayed threshold method triggered a sell signal on May 15, 2025, when the diffusion index closed at 0.8975[43] 4. Model Name: Dual Moving Average Method (Adaptive Trading) - **Model Construction Idea**: This method uses two moving averages to adaptively identify trading signals[44] - **Model Construction Process**: The dual moving average method issued a sell signal again on August 4, 2025[44] --- Model Backtesting Results 1. Diffusion Index Model - Current diffusion index value: 0.82[34][35] 2. First Threshold Method - Triggered sell signal at diffusion index value: 0.9850[39] 3. Delayed Threshold Method - Triggered sell signal at diffusion index value: 0.8975[43] 4. Dual Moving Average Method - Triggered sell signal on August 4, 2025[44] --- Quantitative Factors and Construction Methods 1. Factor Name: One-Year Volatility Factor - **Factor Construction Idea**: Measures the stock's price volatility over the past year[3][29] - **Factor Construction Process**: Rank IC for this factor this week is 0.135, with a historical average of -0.032[3][29] 2. Factor Name: Residual Volatility Factor - **Factor Construction Idea**: Captures the residual volatility of stock returns after accounting for market movements[3][29] - **Factor Construction Process**: Rank IC for this factor this week is 0.057, with a historical average of -0.039[3][29] 3. Factor Name: Growth Factor - **Factor Construction Idea**: Reflects the growth potential of stocks based on financial metrics[3][29] - **Factor Construction Process**: Rank IC for this factor this week is 0.053, with a historical average of -0.004[3][29] 4. Factor Name: Leverage Factor - **Factor Construction Idea**: Measures the financial leverage of companies[3][29] - **Factor Construction Process**: Rank IC for this factor this week is 0.042, with a historical average of -0.006[3][29] 5. Factor Name: Illiquidity Factor - **Factor Construction Idea**: Captures the illiquidity of stocks based on trading volume and price impact[3][29] - **Factor Construction Process**: Rank IC for this factor this week is 0.041, with a historical average of 0.04[3][29] 6. Factor Name: 10-Day Return Factor - **Factor Construction Idea**: Measures the stock's return over the past 10 days[3][29] - **Factor Construction Process**: Rank IC for this factor this week is -0.131, with a historical average of -0.061[3][29] 7. Factor Name: Nonlinear Market Cap Factor - **Factor Construction Idea**: Captures the nonlinear relationship between market capitalization and stock returns[3][29] - **Factor Construction Process**: Rank IC for this factor this week is -0.13, with a historical average of -0.033[3][29] 8. Factor Name: Logarithmic Market Cap Factor - **Factor Construction Idea**: Uses the logarithm of market capitalization to explain stock returns[3][29] - **Factor Construction Process**: Rank IC for this factor this week is -0.13, with a historical average of -0.033[3][29] 9. Factor Name: 10-Day Total Market Cap Turnover Factor - **Factor Construction Idea**: Measures the turnover of total market capitalization over the past 10 days[3][29] - **Factor Construction Process**: Rank IC for this factor this week is -0.13, with a historical average of -0.06[3][29] 10. Factor Name: PE_TTM Reciprocal Factor - **Factor Construction Idea**: Uses the reciprocal of the price-to-earnings ratio (trailing twelve months) as a valuation metric[3][29] - **Factor Construction Process**: Rank IC for this factor this week is -0.129, with a historical average of 0.017[3][29] --- Factor Backtesting Results Top 5 Factors by Rank IC This Week 1. One-Year Volatility Factor: 0.135[3][29] 2. Residual Volatility Factor: 0.057[3][29] 3. Growth Factor: 0.053[3][29] 4. Leverage Factor: 0.042[3][29] 5. Illiquidity Factor: 0.041[3][29] Bottom 5 Factors by Rank IC This Week 1. 10-Day Return Factor: -0.131[3][29] 2. Nonlinear Market Cap Factor: -0.13[3][29] 3. Logarithmic Market Cap Factor: -0.13[3][29] 4. 10-Day Total Market Cap Turnover Factor: -0.13[3][29] 5. PE_TTM Reciprocal Factor: -0.129[3][29]

Core Viewpoint - The small-cap stocks have shown strong performance since the "9·24" market rally, leading to significant gains in related funds, with many products now entering purchase restrictions [1][4]. Group 1: Market Performance - Since the "9·24" rally, the small-cap index has surged by 120.96%, with a year-to-date increase of 55.71% despite a mid-June pullback [2]. - The average return of 39 small-cap funds reached 84.6%, with 12 funds exceeding a 100% net value increase [2]. - The ChiNext small-cap index and the Guozheng 2000 index have risen by 83% and 68%, respectively, ranking among the top two in performance among 20 Guozheng scale indices [2]. Group 2: Fund Restrictions - Currently, 21 small-cap funds are under purchase restrictions, accounting for nearly 54% of the total [4]. - The average scale of small-cap funds is below 4 billion yuan, with 32 funds having a scale under 1 billion yuan [4]. - The restrictions are attributed to the relatively weak liquidity of small-cap stocks compared to mid and large-cap stocks, which could impact trading costs if fund sizes grow too quickly [4]. Group 3: Market Drivers and Risks - The strong performance of small-cap stocks is driven by policy support, liquidity easing, valuation recovery, and capital speculation [3]. - There are concerns regarding the sustainability of small-cap stock gains, as the current market relies heavily on liquidity rather than earnings growth [5]. - The potential for increased trading costs and reduced strategy effectiveness as fund sizes expand poses risks to future performance [6].

Core Insights - Since the "9·24" market rally began, small-cap funds have averaged a return of over 84%, with more than half of these products now subject to purchase restrictions [1][4]. Performance Summary - The A-share market has seen a strong upward trend, with the Shanghai Composite Index surpassing the previous high of 3674 points set on October 8 last year, marking a nearly four-year high since December 14, 2021 [2]. - The micro-cap index has surged by 120.96% since September 24 last year, with a year-to-date increase of 55.71%. The ChiNext small-cap index and the Guozheng 2000 index have risen by 83% and 68%, respectively, ranking among the top two of 20 Guozheng scale indices [2]. - As of August 15, 39 small-cap funds have achieved an average return of 84.6%, with 12 funds seeing net value increases exceeding 100% [2]. Fund Restrictions - Currently, 21 small-cap funds are either suspended from new subscriptions or large subscriptions, accounting for nearly 54% of the total [4]. - The average fund size of small-cap funds is relatively small, with most below 4 billion yuan, and 32 funds having sizes under 1 billion yuan [4]. Market Dynamics - The strong performance of small-cap stocks is attributed to policy support, liquidity easing, valuation recovery, and capital speculation [3]. - Despite a recent pullback in June, small-cap stocks have continued to perform well due to policy dividends and liquidity support [3]. - There are differing opinions on the future performance of small-cap stocks, with some believing that the small-cap style will continue to dominate due to market sentiment and favorable liquidity conditions [4]. Valuation Concerns - Some analysts express skepticism about the sustainability of small-cap stock gains, citing high price-to-earnings ratios and a lack of earnings support for micro-cap stocks [5]. - The rise in small-cap stocks is primarily driven by liquidity rather than substantial earnings growth, raising concerns about potential valuation bubbles [6].

Core Viewpoint - Since the "9·24" market rally, small-cap funds have seen an average return of over 84%, with more than half of the products now subject to purchase restrictions [2][6]. Performance Summary - The A-share market has shown strong upward movement, with the Shanghai Composite Index surpassing the previous high of 3674 points set on October 8 last year, reaching a nearly four-year high since December 14, 2021 [4]. - The Wind data indicates that since September 24 last year, the Wind Micro-Cap Index has surged by 120.96%, with a year-to-date increase of 55.71%. The ChiNext Small Cap Index and the CSI 2000 Index have risen by 83% and 68%, respectively, ranking among the top two in performance among 20 national indices [4]. - As of August 15, 39 small-cap funds have achieved an average return of 84.6%, with 12 funds exceeding a 100% increase in net value [4]. Fund Purchase Restrictions - With rising net values, the number of small-cap funds imposing purchase restrictions has increased. Currently, 21 small-cap funds are either suspended from new subscriptions or large subscriptions, accounting for nearly 54% [7]. - The average fund size of small-cap funds is relatively small, with most below 4 billion yuan, and 32 funds having sizes under 1 billion yuan [8]. Market Dynamics and Future Outlook - The underlying logic for the excess returns of small-cap stocks is attributed to policy catalysts, liquidity easing, valuation recovery, and capital speculation. In a weak economic recovery environment, small and medium-sized enterprises are seen as innovation carriers [5]. - There are differing opinions on the future performance of small-cap stocks. Some believe that small-cap styles will continue to outperform due to market sentiment, liquidity environment, industry trends, and policy benefits [8]. - However, skepticism exists regarding the sustainability of small-cap stock gains, with concerns about high price-to-earnings ratios and the reliance on liquidity rather than earnings growth [9].

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is used to monitor the critical points of trend changes in the micro-cap stock index by analyzing the distribution of stock price movements over a specific time window [5][39] - **Model Construction Process**: The diffusion index is calculated based on the relative price changes of constituent stocks over a retrospective or forward-looking window. For example, if the horizontal axis is 0.95 and the vertical axis is 15 days, the value of 0.31 indicates that if all constituent stocks drop by 5% after 5 days, the diffusion index value is 0.31. The model uses thresholds to signal trading actions: - **First Threshold Method (Left-Side Trading)**: Triggered a sell signal on May 8, 2025, when the index reached 0.9850 [43] - **Delayed Threshold Method (Right-Side Trading)**: Triggered a sell signal on May 15, 2025, at 0.8975 [47] - **Double Moving Average Method (Adaptive Trading)**: Triggered a buy signal on July 3, 2025 [48] - **Model Evaluation**: The model effectively identifies trend changes but may be influenced by the distribution of constituent stocks and their updates [39][40] 2. Model Name: Small-Cap Low-Volatility 50 Strategy - **Model Construction Idea**: This strategy selects 50 stocks with small market capitalization and low volatility from the micro-cap stock index, rebalancing every two weeks [7][36] - **Model Construction Process**: - Select stocks with the smallest market capitalization and lowest volatility from the micro-cap index - Rebalance the portfolio bi-weekly - Benchmark: Wind Micro-Cap Stock Index (8841431.WI) - Transaction cost: 0.3% on both sides [7][36] - **Model Evaluation**: The strategy demonstrates strong performance in 2025 but underperformed in 2024, indicating sensitivity to market conditions [7][36] --- Model Backtesting Results 1. Diffusion Index Model - **First Threshold Method**: Triggered sell signal at 0.9850 on May 8, 2025 [43] - **Delayed Threshold Method**: Triggered sell signal at 0.8975 on May 15, 2025 [47] - **Double Moving Average Method**: Triggered buy signal on July 3, 2025 [48] 2. Small-Cap Low-Volatility 50 Strategy - **2024 Return**: 7.07%, underperformed by -2.93% relative to the benchmark [7][36] - **2025 YTD Return**: 69.79%, underperformed by -1.88% relative to the benchmark [7][36] --- Quantitative Factors and Construction Methods 1. Factor Name: Unadjusted Stock Price Factor - **Factor Construction Idea**: Measures the rank IC of unadjusted stock prices within the micro-cap stock index [4][17] - **Factor Construction Process**: - Calculate the rank IC of unadjusted stock prices weekly - Compare with historical averages for evaluation [4][17] - **Factor Evaluation**: Demonstrated strong performance this week with a rank IC of 0.177, significantly above the historical average of -0.015 [4][17] 2. Factor Name: Beta Factor - **Factor Construction Idea**: Measures the systematic risk of stocks within the micro-cap stock index [4][17] - **Factor Construction Process**: - Calculate the beta of each stock relative to the market - Evaluate weekly rank IC and compare with historical averages [4][17] - **Factor Evaluation**: Performed well this week with a rank IC of 0.15, above the historical average of 0.006 [4][17] 3. Factor Name: Illiquidity Factor - **Factor Construction Idea**: Captures the illiquidity of stocks within the micro-cap stock index [4][17] - **Factor Construction Process**: - Measure illiquidity based on trading volume and price impact - Evaluate weekly rank IC and compare with historical averages [4][17] - **Factor Evaluation**: Strong performance this week with a rank IC of 0.143, above the historical average of 0.04 [4][17] 4. Factor Name: 10-Day Return Factor - **Factor Construction Idea**: Tracks the short-term momentum of stocks within the micro-cap stock index [4][17] - **Factor Construction Process**: - Calculate the 10-day return for each stock - Evaluate weekly rank IC and compare with historical averages [4][17] - **Factor Evaluation**: Positive performance this week with a rank IC of 0.105, above the historical average of -0.061 [4][17] 5. Factor Name: PE_TTM Reciprocal Factor - **Factor Construction Idea**: Measures valuation based on the reciprocal of the trailing twelve-month price-to-earnings ratio [4][17] - **Factor Construction Process**: - Calculate the reciprocal of PE_TTM for each stock - Evaluate weekly rank IC and compare with historical averages [4][17] - **Factor Evaluation**: Moderate performance this week with a rank IC of 0.041, above the historical average of 0.017 [4][17] --- Factor Backtesting Results Top 5 Factors by Weekly Rank IC 1. **Unadjusted Stock Price Factor**: Weekly rank IC = 0.177, Historical Average = -0.015 [4][17] 2. **Beta Factor**: Weekly rank IC = 0.15, Historical Average = 0.006 [4][17] 3. **Illiquidity Factor**: Weekly rank IC = 0.143, Historical Average = 0.04 [4][17] 4. **10-Day Return Factor**: Weekly rank IC = 0.105, Historical Average = -0.061 [4][17] 5. **PE_TTM Reciprocal Factor**: Weekly rank IC = 0.041, Historical Average = 0.017 [4][17] Bottom 5 Factors by Weekly Rank IC 1. **Turnover Factor**: Weekly rank IC = -0.189, Historical Average = -0.082 [4][17] 2. **Momentum Factor**: Weekly rank IC = -0.132, Historical Average = -0.005 [4][17] 3. **Residual Volatility Factor**: Weekly rank IC = -0.13, Historical Average = -0.04 [4][17] 4. **10-Day Free Float Turnover Factor**: Weekly rank IC = -0.12, Historical Average = -0.062 [4][17] 5. **Liquidity Factor**: Weekly rank IC = -0.118, Historical Average = -0.041 [4][17]