Quantitative Models and Construction Methods 1. Model Name: A-Share Technical Scoring Model - **Model Construction Idea**: The model aims to fully explore technical information to depict market conditions, breaking down the abstract concept of "market state" into five dimensions: price, volume, volatility, trend, and crowding. It generates a comprehensive score ranging from -1 to +1 based on equal-weighted voting of signals from 10 selected indicators across these dimensions[9][14] - **Model Construction Process**: 1. Select 10 effective market observation indicators across the five dimensions[14] 2. Generate long/short timing signals for each indicator individually 3. Aggregate the signals through equal-weighted voting to form a comprehensive score between -1 and +1[9] - **Model Evaluation**: The model provides a straightforward and timely way for investors to observe and understand the market[9] 2. Model Name: Style Timing Model (Small-Cap Crowding) - **Model Construction Idea**: The model uses a crowding-based trend approach to time large-cap and small-cap styles. Crowding is measured by the difference in momentum and trading volume ratios between small-cap and large-cap indices[3][20] - **Model Construction Process**: 1. Calculate the momentum difference between the Wind Micro-Cap Index and the CSI 300 Index across 10/20/30/40/50/60-day windows 2. Compute the trading volume ratio between the two indices over the same windows 3. Derive crowding scores for small-cap and large-cap styles by averaging the highest and lowest quantiles of the above metrics, respectively 4. Combine the momentum and volume scores to obtain the final crowding score. A score above 90% indicates high small-cap crowding, while below 10% indicates high large-cap crowding[25] - **Model Evaluation**: The model effectively captures the dynamics of style crowding and provides actionable insights for timing decisions[20][25] 3. Model Name: Industry Rotation Model (Genetic Programming) - **Model Construction Idea**: The model applies genetic programming to directly extract factors from industry indices' price, volume, and valuation data, without relying on predefined scoring rules. It uses a dual-objective approach to optimize factor monotonicity and top-group performance[28][32][33] - **Model Construction Process**: 1. Use NSGA-II algorithm to optimize two objectives: |IC| (information coefficient) and NDCG@5 (normalized discounted cumulative gain for top 5 groups) 2. Combine weakly collinear factors using a greedy strategy and variance inflation factor to form industry scores 3. Select the top 5 industries with the highest multi-factor scores for equal-weight allocation, rebalancing weekly[32][34] - **Model Evaluation**: The dual-objective genetic programming approach enhances factor diversity and reduces overfitting risks, making it a robust tool for industry rotation[32][34] 4. Model Name: China Domestic All-Weather Enhanced Portfolio - **Model Construction Idea**: The model adopts a macro-factor risk parity framework, emphasizing risk diversification across underlying macro risk sources rather than asset classes. It actively overweights favorable quadrants based on macro momentum[39][42] - **Model Construction Process**: 1. Divide macro risks into four quadrants based on growth and inflation expectations: growth above/below expectations and inflation above/below expectations 2. Construct sub-portfolios within each quadrant using equal-weighted assets, focusing on downside risk 3. Adjust quadrant risk budgets monthly based on macro momentum indicators, which combine buy-side momentum from asset prices and sell-side momentum from economic forecast surprises[42] - **Model Evaluation**: The strategy effectively integrates macroeconomic insights into portfolio construction, achieving enhanced performance through active allocation adjustments[39][42] --- Model Backtesting Results 1. A-Share Technical Scoring Model - Annualized Return: 20.78% - Annualized Volatility: 17.32% - Maximum Drawdown: -23.74% - Sharpe Ratio: 1.20 - Calmar Ratio: 0.88[15] 2. Style Timing Model (Small-Cap Crowding) - Annualized Return: 28.46% - Maximum Drawdown: -32.05% - Sharpe Ratio: 1.19 - Calmar Ratio: 0.89 - YTD Return: 11.85% - Weekly Return: 5.25%[26] 3. Industry Rotation Model (Genetic Programming) - Annualized Return: 32.92% - Annualized Volatility: 17.43% - Maximum Drawdown: -19.63% - Sharpe Ratio: 1.89 - Calmar Ratio: 1.68 - YTD Return: 6.80% - Weekly Return: 3.37%[31] 4. China Domestic All-Weather Enhanced Portfolio - Annualized Return: 11.93% - Annualized Volatility: 6.20% - Maximum Drawdown: -6.30% - Sharpe Ratio: 1.92 - Calmar Ratio: 1.89 - YTD Return: 3.59% - Weekly Return: 1.54%[43] --- Quantitative Factors and Construction Methods 1. Factor Name: Small-Cap Crowding Factor - **Factor Construction Idea**: Measures the crowding level of small-cap style based on momentum and trading volume differences between small-cap and large-cap indices[20][25] - **Factor Construction Process**: 1. Calculate momentum differences and trading volume ratios for multiple time windows 2. Derive crowding scores by averaging the highest and lowest quantiles of these metrics 3. Combine momentum and volume scores to obtain the final crowding score[25] 2. Factor Name: Industry Rotation Factor (Genetic Programming) - **Factor Construction Idea**: Extracts factors from industry indices using genetic programming, optimizing for monotonicity and top-group performance[32][34] - **Factor Construction Process**: 1. Perform cross-sectional regression of standardized daily trading volume against daily price gaps to obtain residuals (Variable A) 2. Identify the trading day with the highest standardized volume in the past 9 days (Variable B) 3. Conduct time-series regression of Variables A and B over the past 50 days to obtain intercepts (Variable C) 4. Compute the covariance of Variable C and standardized monthly opening prices over the past 45 days[38] --- Factor Backtesting Results 1. Small-Cap Crowding Factor - YTD Return: 11.85% - Weekly Return: 5.25%[26] 2. Industry Rotation Factor (Genetic Programming) - Training Set IC: 0.340 - Factor Weight: 18.7% - YTD Return: 6.80% - Weekly Return: 3.37%[31][38]

Core Viewpoint - The A-share market experienced significant volatility, particularly in micro-cap stocks, which saw a notable decline after 2 PM, with the micro-cap stock index dropping over 1.4% and a monthly decline exceeding 6% [2][4]. Group 1: Market Performance - Micro-cap stocks have historically shown large fluctuations at the end of the year or the beginning of the new year, with the index dropping nearly 8% in December last year and over 21% in January [4]. - More than 20 stocks hit the daily limit down or experienced declines of over 10%, with a significant number being ST stocks, indicating a widespread downturn among micro-cap stocks [4]. Group 2: Market Dynamics - Analysts suggest that the current market conditions may require a shift in investment style, but as long as small-cap stocks do not experience a significant pullback, there may still be opportunities in thematic mid-to-large-cap stocks [5]. - Historical data indicates that the A-share market has shown a mean-reversion characteristic in the performance of large and small-cap stocks since 2005, with small-cap stocks often leading in technology sectors during their outperformance periods [5]. Group 3: Micro-Cap Stock Characteristics - The micro-cap stock index has significantly outperformed the market in recent years, attributed to its "contrarian stock selection" characteristics, although it is currently at a historical high [6]. - The relative valuation of micro-cap stocks remains below historical extremes, suggesting potential for further growth despite existing credit and liquidity risks [6].

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The diffusion index is used to monitor the critical points of market trend changes and predict potential reversals in the micro-cap stock index[40][41] - **Model Construction Process**: The diffusion index is calculated based on the relative price changes of micro-cap stock index constituents over a specific time window. The horizontal axis represents the percentage change in stock prices (e.g., from +10% to -10%), while the vertical axis represents the length of the review or forecast window (e.g., 10 to 20 trading days). For example, a horizontal value of 0.95 and a vertical value of 15 days indicates that if all constituent stocks drop by 5% after 5 days, the diffusion index value is 0.03[40][42] - **Key Methods**: - **Left Threshold Method**: Triggered a short signal on November 14, 2025, when the index closed at 0.925[43] - **Right Threshold Method**: Triggered a short signal on November 17, 2025, when the index closed at 0.8975[46] - **Dual Moving Average Method**: Triggered a short signal on November 25, 2025[48] - **Model Evaluation**: The diffusion index is effective in identifying critical points for market reversals and provides actionable signals for trading strategies[40][41] 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 constituents, aiming to achieve stable returns with reduced risk[8][35] - **Model Construction Process**: - **Stock Selection**: Identify stocks with the smallest market capitalization and lowest volatility within the micro-cap stock index[35] - **Rebalancing Frequency**: Rebalance the portfolio bi-weekly[35] - **Transaction Costs**: Assume a bidirectional transaction cost of 0.3%[35] - **Benchmark**: Use the Wind Micro-Cap Stock Index (8841431.WI) as the benchmark[35] - **Model Evaluation**: The strategy demonstrates strong performance in capturing excess returns, particularly in 2025, with a significant year-to-date (YTD) return[35] --- Model Backtesting Results 1. Diffusion Index Model - **Left Threshold Method**: Short signal triggered at 0.925 on November 14, 2025[43] - **Right Threshold Method**: Short signal triggered at 0.8975 on November 17, 2025[46] - **Dual Moving Average Method**: Short signal triggered on November 25, 2025[48] 2. Small-Cap Low-Volatility 50 Strategy - **2024 Return**: 7.07%, with an excess return of -2.93%[35] - **2025 YTD Return**: 72.07%, with a weekly excess return of 0.001%[35] --- Quantitative Factors and Construction Methods 1. Factor Name: PE_TTM Inverse Factor - **Factor Construction Idea**: This factor uses the inverse of the price-to-earnings ratio (trailing twelve months) to identify undervalued stocks[5][33] - **Factor Construction Process**: - Calculate the trailing twelve months (TTM) PE ratio for each stock - Take the reciprocal of the PE ratio to construct the factor[33] - **Factor Evaluation**: Demonstrates strong predictive power with a weekly rank IC of 0.237, significantly above its historical average of 0.016[5][33] 2. Factor Name: PB Inverse Factor - **Factor Construction Idea**: This factor uses the inverse of the price-to-book ratio to identify undervalued stocks[5][33] - **Factor Construction Process**: - Calculate the price-to-book (PB) ratio for each stock - Take the reciprocal of the PB ratio to construct the factor[33] - **Factor Evaluation**: Shows strong performance with a weekly rank IC of 0.213, well above its historical average of 0.034[5][33] 3. Factor Name: Profitability Factor - **Factor Construction Idea**: Measures the profitability of companies to identify fundamentally strong stocks[5][33] - **Factor Construction Process**: - Use metrics such as return on equity (ROE) and net profit margin to construct the factor[33] - **Factor Evaluation**: Exhibits robust predictive power with a weekly rank IC of 0.185, outperforming its historical average of 0.022[5][33] 4. Factor Name: Dividend Yield Factor - **Factor Construction Idea**: Focuses on stocks with high dividend yields to capture stable income[5][33] - **Factor Construction Process**: - Calculate the dividend yield for each stock - Rank stocks based on their dividend yield to construct the factor[33] - **Factor Evaluation**: Performs well with a weekly rank IC of 0.179, exceeding its historical average of 0.022[5][33] 5. Factor Name: Single-Quarter ROE Factor - **Factor Construction Idea**: Evaluates the return on equity for a single quarter to identify high-performing stocks[5][33] - **Factor Construction Process**: - Calculate the ROE for the most recent quarter - Rank stocks based on their single-quarter ROE to construct the factor[33] - **Factor Evaluation**: Delivers strong results with a weekly rank IC of 0.163, above its historical average of 0.021[5][33] --- Factor Backtesting Results 1. PE_TTM Inverse Factor - Weekly Rank IC: 0.237 - Historical Average Rank IC: 0.016[5][33] 2. PB Inverse Factor - Weekly Rank IC: 0.213 - Historical Average Rank IC: 0.034[5][33] 3. Profitability Factor - Weekly Rank IC: 0.185 - Historical Average Rank IC: 0.022[5][33] 4. Dividend Yield Factor - Weekly Rank IC: 0.179 - Historical Average Rank IC: 0.022[5][33] 5. Single-Quarter ROE Factor - Weekly Rank IC: 0.163 - Historical Average Rank IC: 0.021[5][33]

Quantitative Models and Construction Methods - **Model Name**: Diffusion Index Model **Construction Idea**: The model monitors the critical points of future diffusion index changes to predict market trends[38][39] **Construction Process**: 1. The horizontal axis represents the percentage change in stock prices from the current level, ranging from +10% to -10% 2. The vertical axis represents the length of the retrospective or future window period, ranging from 20 days to 10 days 3. For example, at a horizontal axis value of 0.95 and a vertical axis value of 15 days, the diffusion index value is 0.07, indicating that if all stocks in the micro-cap index drop by 5% after 5 days, the diffusion index value will be 0.07[38] **Evaluation**: The model is effective in identifying potential turning points in the market[39] - **Model Name**: First Threshold Method (Left-Side Trading) **Construction Idea**: This method triggers signals based on predefined threshold values[42] **Construction Process**: 1. On November 14, 2025, the closing value of the diffusion index reached 0.925, triggering a sell signal[42] **Evaluation**: Useful for early signal generation but may require further validation[42] - **Model Name**: Delayed Threshold Method (Right-Side Trading) **Construction Idea**: Signals are generated after a delay to confirm trends[44][46] **Construction Process**: 1. On November 17, 2025, the closing value of the diffusion index reached 0.8975, triggering a sell signal[46] **Evaluation**: Provides more reliable signals by avoiding premature actions[46] - **Model Name**: Dual Moving Average Method (Adaptive Trading) **Construction Idea**: Uses moving averages to adapt to market changes[47] **Construction Process**: 1. On November 25, 2025, the model issued a sell signal based on the dual moving average strategy[47] **Evaluation**: Effective in capturing adaptive trends but may lag in volatile markets[47] Model Backtesting Results - **Diffusion Index Model**: Current value is 0.49, indicating a medium level with potential high volatility in the coming week[38][39] - **First Threshold Method**: Triggered sell signal at 0.925 on November 14, 2025[42] - **Delayed Threshold Method**: Triggered sell signal at 0.8975 on November 17, 2025[46] - **Dual Moving Average Method**: Triggered sell signal on November 25, 2025[47] Quantitative Factors and Construction Methods - **Factor Name**: 10-Day Total Market Cap Turnover Rate **Construction Idea**: Measures liquidity by turnover rate over a 10-day period[5][16][32] **Construction Process**: 1. Calculate the turnover rate using the formula: $ TurnoverRate = \frac{Volume}{MarketCap} $ 2. Aggregate data over a 10-day window[5][16][32] **Evaluation**: High rank IC indicates strong predictive power[5][16][32] - **Factor Name**: 10-Day Free Float Market Cap Turnover Rate **Construction Idea**: Similar to the above but focuses on free float market cap[5][16][32] **Construction Process**: 1. Use the same formula as above but replace market cap with free float market cap[5][16][32] **Evaluation**: High rank IC suggests good performance in liquidity prediction[5][16][32] - **Factor Name**: Beta Factor **Construction Idea**: Measures stock sensitivity to market movements[5][16][32] **Construction Process**: 1. Calculate beta using regression analysis: $ Beta = \frac{Cov(R_i, R_m)}{Var(R_m)} $ where $ R_i $ is the return of the stock and $ R_m $ is the return of the market[5][16][32] **Evaluation**: Positive rank IC indicates effective market sensitivity measurement[5][16][32] - **Factor Name**: Liquidity Factor **Construction Idea**: Assesses stock liquidity based on trading volume and price impact[5][16][32] **Construction Process**: 1. Calculate liquidity using the formula: $ Liquidity = \frac{Volume}{PriceImpact} $ 2. Normalize the data for comparison[5][16][32] **Evaluation**: High rank IC reflects strong predictive ability for liquidity[5][16][32] - **Factor Name**: Standardized Expected Earnings Factor **Construction Idea**: Evaluates expected earnings adjusted for market conditions[5][16][32] **Construction Process**: 1. Standardize expected earnings using z-scores: $ Z = \frac{Earnings - Mean(Earnings)}{StdDev(Earnings)} $ 2. Use standardized values for ranking[5][16][32] **Evaluation**: Positive rank IC indicates reliable earnings prediction[5][16][32] Factor Backtesting Results - **10-Day Total Market Cap Turnover Rate**: Rank IC 0.17, historical average -0.058[5][16][32] - **10-Day Free Float Market Cap Turnover Rate**: Rank IC 0.159, historical average -0.06[5][16][32] - **Beta Factor**: Rank IC 0.152, historical average 0.003[5][16][32] - **Liquidity Factor**: Rank IC 0.151, historical average -0.04[5][16][32] - **Standardized Expected Earnings Factor**: Rank IC 0.133, historical average 0.013[5][16][32] Composite Strategy Performance - **Strategy Name**: Small Cap Low Volatility 50 Strategy **Construction Idea**: Selects 50 stocks with small market cap and low volatility from micro-cap index components[8][16][34] **Construction Process**: 1. Filter stocks based on market cap and volatility criteria 2. Rebalance portfolio bi-weekly[8][16][34] **Evaluation**: Demonstrates strong performance in 2025 but underperformed in 2024[8][16][34] Strategy Backtesting Results - **Small Cap Low Volatility 50 Strategy**: - 2024 Return: 7.07%, Excess Return: -2.93%[8][16][34] - 2025 YTD Return: 74.15%, Weekly Excess Return: 0.22%[8][16][34]

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]