Market Overview - The A-share market experienced wide fluctuations last week, with large-cap stocks outperforming [4] - Trading sentiment turned cautious as major broad-based indices saw a decrease in volume, leading to a narrowing Alpha environment [4] - The market shifted from a high-slope upward trend to wide fluctuations, with growth sectors relatively outperforming [4] - The Shanghai Composite Index fell by 1.30%, while the ChiNext Index rose by 2.34% [4] Valuation Analysis - As of September 19, 2025, the valuation levels of major indices showed that the CSI 1000 and ChiNext Index were at "moderate" valuation levels, while the Shanghai Composite Index and others were at "danger" levels [4] - In terms of industry classification, sectors such as food and beverage, agriculture, non-bank financials, and transportation were rated as "safe" in terms of valuation [5] Volatility and Alpha Environment - Recent data indicated a decrease in cross-sectional volatility for the CSI 300, CSI 500, and CSI 1000 indices, suggesting a deterioration in the short-term Alpha environment [5] - Time series volatility also decreased for the same indices, further indicating a challenging Alpha environment [5] Fund Flow and Institutional Interest - The top five stocks attracting institutional attention last week were Huichuan Technology, Meier Technology, Xiamen Tungsten, Guanghe Technology, and Jepter [7] - Southbound capital saw a net inflow of 368.507 billion HKD, with significant contributions from both the Shanghai and Shenzhen stock connect [7] - Stock ETFs recorded a median return of 0.02% with a net inflow of 254.16 billion CNY, while commodity ETFs experienced a median return of -0.65% with a net outflow [7]

Market Overview - The A-share market experienced a volatile upward trend during the week of September 8-12, 2025, with trading volume initially suppressed but later recovering [4] - The weekly financing increase saw a significant rise compared to the previous period, while ETF funds continued to experience net outflows, indicating that leveraged funds remain in a positive buying state [4] - The market is shifting focus from broad-based indices to thematic indices, with active participation in thematic trading [4] - The Shanghai Composite Index rose by 1.52%, while the ChiNext Index increased by 2.10% during the same period [4] Valuation Analysis - As of September 12, 2025, major broad-based indices such as the Shanghai Composite, SSE 50, CSI 300, and CSI 500 are classified under the "danger" valuation category, while the CSI 1000 and ChiNext are in the "moderate" category [4] - In the CITIC primary industry classification, sectors like coal, steel, building materials, and power equipment are also in the "danger" valuation category, while food and beverage, agriculture, and transportation are in the "safe" category [5] Fund Flow and Institutional Interest - The top five stocks attracting institutional attention this week were Jing Sheng Machinery, Xiamen Tungsten, Duofu Du, Xinji Energy, and Hanzhong Precision, with 237, 186, 167, 139, and 116 institutions respectively [7] - Southbound capital saw a net inflow of 60.822 billion HKD during the week, with the Shanghai Stock Connect contributing 19.406 billion HKD and the Shenzhen Stock Connect contributing 41.416 billion HKD [8] - The median return for stock ETFs was 1.93%, with a net outflow of 4.352 billion CNY, while the median return for Hong Kong stock ETFs was 3.09% with a net inflow of 21.168 billion CNY [8] Market Sentiment - The volume timing signals for major broad-based indices indicate a cautious outlook as of September 12, 2025 [6] - The degree of separation among fund clusters has slightly increased week-on-week, with excess returns for clustered stocks showing a minor rise while excess returns for clustered funds have slightly decreased [8]

- The report discusses the "Volume Timing Signal" model, which is used to gauge market sentiment based on trading volume. The model's construction involves analyzing the volume of trades to determine whether the market sentiment is optimistic or cautious. As of August 29, 2025, the volume timing signals for the CSI 1000, ChiNext Index, and Beijing 50 Index are cautious, while other major broad-based indices show optimistic signals[26][27] - The "Number of Rising Stocks in CSI 300" sentiment indicator is another model mentioned in the report. This model calculates the proportion of stocks in the CSI 300 index that have positive returns over a given period. The formula is: $$ \text{Proportion of Rising Stocks in CSI 300 over N days} = \frac{\text{Number of CSI 300 stocks with positive returns over N days}}{\text{Total number of CSI 300 stocks}} $$ This indicator helps capture market sentiment by identifying periods when a majority of stocks are performing well, which often indicates market optimism. The recent value of this indicator is around 94%[27][28][30] - The "Moving Average Sentiment Indicator" is also discussed. This model uses the eight moving averages of the CSI 300 index to determine market trends. The moving averages used are 8, 13, 21, 34, 55, 89, 144, and 233 days. The model assigns values based on the number of moving averages that the current price exceeds. If the current price exceeds more than five moving averages, the market sentiment is considered optimistic. The formula for the moving average sentiment indicator is: $$ \text{Number of Moving Averages Exceeded by Current Price} $$ The recent analysis shows that the CSI 300 index is in an optimistic sentiment zone[35][36][37] - The "Cross-sectional Volatility" factor is used to measure the dispersion of stock returns within an index. Higher cross-sectional volatility indicates a better environment for alpha generation. The recent values for cross-sectional volatility are: - CSI 300: 1.76% - CSI 500: 1.91% - CSI 1000: 2.23% These values suggest that the short-term alpha environment is improving[41][43] - The "Time-series Volatility" factor measures the volatility of individual stock returns over time. Higher time-series volatility also indicates a better environment for alpha generation. The recent values for time-series volatility are: - CSI 300: 0.53% - CSI 500: 0.38% - CSI 1000: 0.22% These values suggest that the short-term alpha environment is improving[44][46] Model and Factor Performance Metrics - Volume Timing Signal: - CSI 1000: Cautious - ChiNext Index: Cautious - Beijing 50 Index: Cautious - Other major indices: Optimistic[26][27] - Number of Rising Stocks in CSI 300: - Recent value: 94%[27][28][30] - Moving Average Sentiment Indicator: - Recent sentiment: Optimistic[35][36][37] - Cross-sectional Volatility: - CSI 300: 1.76% - CSI 500: 1.91% - CSI 1000: 2.23%[41][43] - Time-series Volatility: - CSI 300: 0.53% - CSI 500: 0.38% - CSI 1000: 0.22%[44][46]

Quantitative Models and Construction Methods 1. Model Name: Volume Timing Signal - **Model Construction Idea**: The model uses volume-based signals to determine market timing, identifying bullish or cautious market views based on volume trends[23][24] - **Model Construction Process**: 1. Analyze the volume trends of major broad-based indices 2. Assign a "bullish" or "cautious" signal based on the volume dynamics 3. For example, as of April 11, 2025, the Beixin 50 index showed a "bullish" signal, while other indices like the Shanghai Composite and CSI 300 showed "cautious" signals[23][24] - **Model Evaluation**: The model provides a straightforward and intuitive approach to market timing but may lack robustness in highly volatile markets[23][24] 2. Model Name: Momentum Sentiment Indicator - **Model Construction Idea**: This model captures market sentiment by analyzing the proportion of stocks with positive returns in the CSI 300 index over a specific period[24][25] - **Model Construction Process**: 1. Calculate the proportion of CSI 300 constituent stocks with positive returns over the past N days $ \text{CSI 300 N-day Upward Proportion} = \frac{\text{Number of stocks with positive returns in N days}}{\text{Total number of stocks in CSI 300}} $ 2. Smooth the indicator using two moving averages with different windows (N1 and N2, where N1 > N2) 3. Generate signals: - If the short-term moving average (fast line) exceeds the long-term moving average (slow line), the market is considered bullish - If the fast line falls below the slow line, the market sentiment is turning cautious[27] - **Model Evaluation**: The indicator is effective in capturing upward opportunities but may fail to avoid risks in declining markets[25][27] 3. Model Name: Moving Average Sentiment Indicator - **Model Construction Idea**: This model uses an eight-moving-average system to assess the trend state of the CSI 300 index[32][33] - **Model Construction Process**: 1. Calculate the closing prices of the CSI 300 index for eight moving averages (parameters: 8, 13, 21, 34, 55, 89, 144, 233) 2. Assign values to the indicator based on the number of moving averages above or below the current price: - If the current price exceeds five or more moving averages, the market is considered bullish - Otherwise, the market is neutral or bearish[32][33] - **Model Evaluation**: The model provides a clear trend-following signal but may lag in rapidly changing markets[35] --- Model Backtesting Results 1. Volume Timing Signal - Beixin 50 Index: Bullish signal[23][24] - Other indices (e.g., Shanghai Composite, CSI 300): Cautious signal[23][24] 2. Momentum Sentiment Indicator - CSI 300 Upward Proportion: Approximately 53% in the most recent week[25] 3. Moving Average Sentiment Indicator - CSI 300 Index: Currently in a non-bullish sentiment zone[35] --- Quantitative Factors and Construction Methods 1. Factor Name: Cross-Sectional Volatility - **Factor Construction Idea**: Measures the dispersion of stock returns within an index to assess the alpha environment[37] - **Factor Construction Process**: 1. Calculate the cross-sectional volatility of constituent stocks in indices like CSI 300, CSI 500, and CSI 1000 2. Compare the recent quarter's average volatility with historical periods to determine the alpha environment[41] - **Factor Evaluation**: Higher cross-sectional volatility indicates a better alpha environment for stock selection[37][41] 2. Factor Name: Time-Series Volatility - **Factor Construction Idea**: Measures the volatility of index returns over time to assess the alpha environment[41][44] - **Factor Construction Process**: 1. Calculate the time-series volatility of indices like CSI 300, CSI 500, and CSI 1000 2. Compare the recent quarter's average volatility with historical periods to evaluate the alpha environment[44] - **Factor Evaluation**: Higher time-series volatility suggests a favorable alpha environment for active strategies[41][44] --- Factor Backtesting Results 1. Cross-Sectional Volatility - CSI 300: 1.90% (recent quarter), 77.80% of the past six months' percentile[41] - CSI 500: 2.16% (recent quarter), 42.06% of the past six months' percentile[41] - CSI 1000: 2.52% (recent quarter), 64.54% of the past six months' percentile[41] 2. Time-Series Volatility - CSI 300: 0.63% (recent quarter), 78.84% of the past six months' percentile[44] - CSI 500: 0.48% (recent quarter), 55.56% of the past six months' percentile[44] - CSI 1000: 0.29% (recent quarter), 70.12% of the past six months' percentile[44]