- The industry crowding monitoring model was constructed to monitor the crowding level of Shenwan primary industry indices daily. The model identified that the crowding levels of power equipment and environmental protection were high, while non-bank and home appliances had lower crowding levels. Additionally, significant changes in crowding levels were observed in banking and agriculture, forestry, animal husbandry, and fishery industries[3] - The Z-score model for premium rate was developed to screen ETF products with potential arbitrage opportunities. The model uses rolling calculations to identify signals and warns of potential risks of price corrections for the identified ETFs[4] - Daily net capital inflow for broad-based ETFs was 24.71 billion yuan, with top inflows observed in the following ETFs: China Securities A500ETF (+7.83 billion yuan), A500ETF (+5.14 billion yuan), and SSE 50ETF (+2.61 billion yuan). Conversely, the top outflows were seen in CSI 300ETF (-7.13 billion yuan), CSI 300ETF E Fund (-2.21 billion yuan), and ChiNext ETF (-0.43 billion yuan)[5] - Daily net capital inflow for industry-themed ETFs was 41.72 billion yuan, with top inflows observed in the following ETFs: Securities ETF (+7.78 billion yuan), Banking ETF (+6.03 billion yuan), and Power Grid Equipment ETF (+3.98 billion yuan). Conversely, the top outflows were seen in Wine ETF (-2.71 billion yuan), Robotics ETF E Fund (-2.23 billion yuan), and Battery ETF (-1.26 billion yuan)[5] - Daily net capital inflow for style-strategy ETFs was 7.92 billion yuan, with top inflows observed in the following ETFs: Dividend ETF E Fund (+3.44 billion yuan), Dividend Low Volatility ETF (+1.75 billion yuan), and Dividend Low Volatility ETF (+1.01 billion yuan). Conversely, the top outflows were seen in Dividend ETF (-0.36 billion yuan), Dividend State-Owned Enterprise ETF (-0.27 billion yuan), and Dividend Low Volatility 50ETF (-0.20 billion yuan)[5] - Daily net capital inflow for cross-border ETFs was 67.28 billion yuan, with top inflows observed in the following ETFs: Hang Seng Technology ETF (+12.00 billion yuan), Hang Seng Technology Index ETF (+9.20 billion yuan), and Hong Kong Non-Bank ETF (+6.53 billion yuan). Conversely, the top outflows were seen in Saudi ETF (-0.19 billion yuan), H-Share ETF (-0.18 billion yuan), and Hong Kong Stock Connect 100ETF (-0.08 billion yuan)[5]

- The industry crowding monitoring model was constructed to monitor the crowding level of Shenwan primary industry indices daily. The model identifies industries with high crowding levels, such as electric power equipment and environmental protection, while industries like non-bank financials exhibit lower crowding levels. The model also tracks significant changes in crowding levels for industries like environmental protection, coal, and petrochemicals[3] - The Z-score model for premium rate was developed to screen ETF products with potential arbitrage opportunities. The model uses rolling calculations to identify ETFs with significant deviations from their fair value, providing signals for potential trades while warning of possible price corrections[4] - The Z-score model for premium rate was applied to ETF products, including broad-based ETFs, industry-themed ETFs, style-strategy ETFs, and cross-border ETFs. The model identified top funds with net inflows and outflows, such as the A500ETF fund (+9.14 billion yuan) and the Shanghai 50ETF (-11.95 billion yuan), respectively[5][6] - The industry crowding monitoring model and Z-score model for premium rate provide valuable insights into market dynamics and potential trading opportunities. However, the models require continuous updates and validation to ensure accuracy and reliability in changing market conditions[3][4]

Group 1 - The report emphasizes a dynamic balance strategy that considers both win rates and odds, achieving an annualized absolute return of 18.00% and a relative return of 12.00% from January 2015 to October 2025 [2][18] - Recommended industries for November 2025 include non-ferrous metals, electric equipment and new energy, communication, computer, machinery, and electronics [2][18] - The dynamic balance strategy outperformed the benchmark in October 2025 with an absolute return of 1.66% and an excess return of 0.27% [40] Group 2 - The macro-driven strategy has achieved an excess annualized return of 4.87% and a maximum drawdown of 9.51% from January 2016 to October 2025 [3][17] - Recommended industries for November 2025 under the macro-driven strategy include food and beverage, electric equipment and new energy, automotive, basic chemicals, consumer services, and machinery [3][17] - The macro-driven strategy recorded an absolute return of 25.46% since the beginning of 2025, ranking 57.50% among active equity funds [3][17] Group 3 - The multi-strategy approach has generated an annualized relative return of 6.57% since May 2011, with a maximum drawdown of 13.03% [4][23] - Recommended industries for November 2025 under the multi-strategy approach include textiles and apparel, communication, pharmaceuticals, non-ferrous metals, electronics, non-bank financials, real estate, banking, and construction [4][23] - The multi-strategy recorded an absolute return of 16.27% since the beginning of 2025, ranking 76.50% among active equity funds [4][23] Group 4 - The report indicates that the October 2025 market saw a decline in the overall A-share market, with the CSI 300 index returning -0.001% and the ChiNext index returning -1.56% [11][12] - Among the sectors, coal, oil and petrochemicals, non-ferrous metals, and electric utilities were the top performers, while media, automotive, electronics, real estate, and defense industries lagged [12][13] Group 5 - The report highlights the importance of tracking industry crowding indicators, with multiple crowding alerts triggered in the oil and petrochemical, coal, and non-ferrous metals sectors in October [5][53] - The crowding indicators are based on four quantitative factors to assess the risk of future asset pullbacks in various industries [51][53]

- The industry congestion monitoring model is constructed to monitor the congestion levels of Shenwan first-level industry indices on a daily basis[3] - The premium rate Z-score model is used to build a related ETF product screening signal model, providing potential arbitrage opportunities and warning of potential pullback risks[4] - The industry congestion monitoring model shows that the congestion levels of the electric power equipment and non-ferrous metals industries were high on the previous trading day, while the social services and light industry had lower congestion levels[3] - The premium rate Z-score model is used to identify ETF products with potential arbitrage opportunities, but also highlights the need to be cautious of potential pullback risks[4]

- The report constructs an industry congestion monitoring model to monitor the congestion levels of Shenwan First-Level Industry Indices on a daily basis[3] - The report constructs a Z-score model based on premium rates to screen ETF products for potential arbitrage opportunities[4] - The industry congestion monitoring model indicates that the congestion levels of the power equipment and non-ferrous industries were high on the previous trading day, while the food and beverage, and social services industries had lower congestion levels[3] - The Z-score model provides signals for ETF products that may have potential arbitrage opportunities, but also warns of the risk of pullbacks[4]

- The report constructs an industry congestion monitoring model to monitor the congestion levels of Shenwan First-Level Industry Indexes on a daily basis[3] - The report constructs a Z-score model based on premium rates to screen ETF products for potential arbitrage opportunities[4] Quantitative Models and Construction Methods 1. **Model Name: Industry Congestion Monitoring Model** - **Model Construction Idea:** Monitor the congestion levels of Shenwan First-Level Industry Indexes daily[3] - **Model Construction Process:** The model calculates the congestion levels of various industries based on the flow of main funds. It identifies industries with high and low congestion levels and tracks the changes in congestion levels over time[3] - **Model Evaluation:** The model effectively identifies industries with significant changes in congestion levels, providing valuable insights for investment decisions[3] 2. **Model Name: Premium Rate Z-score Model** - **Model Construction Idea:** Screen ETF products for potential arbitrage opportunities based on the premium rate Z-score[4] - **Model Construction Process:** The model calculates the Z-score of the premium rates of various ETF products through rolling measurements. It identifies ETFs with potential arbitrage opportunities and warns of possible pullback risks[4] - **Model Evaluation:** The model provides a systematic approach to identify ETFs with potential arbitrage opportunities, aiding investors in making informed decisions[4] Model Backtesting Results 1. **Industry Congestion Monitoring Model** - **Congestion Levels:** Coal, Environmental Protection, and Petrochemical industries had high congestion levels, while Food & Beverage and Computer industries had low congestion levels[3] - **Main Fund Flows:** Main funds flowed into Coal and Media industries, and flowed out of Machinery and Pharmaceutical & Biological industries in the previous trading day[3] - **Three-Day Fund Allocation:** Main funds reduced allocation in Pharmaceutical, Electric Power Equipment, and increased allocation in Media over the past three days[3] 2. **Premium Rate Z-score Model** - **ETF Fund Flows:** - **Broad-based ETFs:** Net outflow of 15.91 billion yuan in a single day[5] - **Industry-themed ETFs:** Net inflow of 9.14 billion yuan in a single day[5] - **Style Strategy ETFs:** Net outflow of 2.85 billion yuan in a single day[5] - **Cross-border ETFs:** Net inflow of 24.28 billion yuan in a single day[5] Quantitative Factors and Construction Methods 1. **Factor Name: Congestion Level Factor** - **Factor Construction Idea:** Measure the congestion levels of various industries based on main fund flows[3] - **Factor Construction Process:** Calculate the congestion levels by analyzing the flow of main funds into and out of different industries. Identify industries with high and low congestion levels and track changes over time[3] - **Factor Evaluation:** The factor effectively highlights industries with significant congestion level changes, providing valuable insights for investment decisions[3] 2. **Factor Name: Premium Rate Z-score Factor** - **Factor Construction Idea:** Identify potential arbitrage opportunities in ETF products based on the premium rate Z-score[4] - **Factor Construction Process:** Calculate the Z-score of the premium rates of various ETF products through rolling measurements. Identify ETFs with potential arbitrage opportunities and warn of possible pullback risks[4] - **Factor Evaluation:** The factor provides a systematic approach to identify ETFs with potential arbitrage opportunities, aiding investors in making informed decisions[4] Factor Backtesting Results 1. **Congestion Level Factor** - **Congestion Levels:** Coal, Environmental Protection, and Petrochemical industries had high congestion levels, while Food & Beverage and Computer industries had low congestion levels[3] - **Main Fund Flows:** Main funds flowed into Coal and Media industries, and flowed out of Machinery and Pharmaceutical & Biological industries in the previous trading day[3] - **Three-Day Fund Allocation:** Main funds reduced allocation in Pharmaceutical, Electric Power Equipment, and increased allocation in Media over the past three days[3] 2. **Premium Rate Z-score Factor** - **ETF Fund Flows:** - **Broad-based ETFs:** Net outflow of 15.91 billion yuan in a single day[5] - **Industry-themed ETFs:** Net inflow of 9.14 billion yuan in a single day[5] - **Style Strategy ETFs:** Net outflow of 2.85 billion yuan in a single day[5] - **Cross-border ETFs:** Net inflow of 24.28 billion yuan in a single day[5]

Group 1: Market Sentiment Model Insights - The market sentiment score has slightly increased to 2.2 as of October 24, compared to 1.9 the previous week, indicating a partial recovery in market sentiment [6][8] - The overall market sentiment is showing increased differentiation, with a decline in price-volume consistency, suggesting reduced capital activity [8][12] - The total trading volume for the entire A-share market has significantly decreased compared to the previous week, with a peak trading volume of 1,991.617 billion RMB on October 24 [14][16] Group 2: Sector Performance Insights - As of October 24, the banking, oil and petrochemical, transportation, public utilities, and construction decoration sectors have shown an upward trend in short-term scores [33] - The coal sector currently has the highest short-term score of 93.22, indicating strong short-term performance [33][34] - The model indicates that the market is currently favoring large-cap and value styles, with strong signals for both [33][44] Group 3: Industry Crowding Insights - Recent high price increases in the electronics and power equipment sectors are accompanied by high capital crowding, suggesting potential volatility risks due to valuation and sentiment corrections [36][41] - The average crowding levels are highest in the power equipment, environmental protection, non-ferrous metals, textile and apparel, and coal sectors [37][40] - Low crowding sectors such as non-bank financials, beauty care, media, computing, and food and beverage have shown lower price increases, indicating potential for excess returns if fundamentals improve [36][40]

- The sentiment timing model indicates that the market trend has been broken, issuing a negative signal[1][2][6] - The liquidity shock indicator for the CSI 300 index was 0.84 on Friday, lower than the previous week's 1.57, indicating that current market liquidity is 0.84 standard deviations above the average level of the past year[2][7] - The PUT-CALL ratio of the SSE 50ETF options trading volume decreased to 0.72 on Friday from the previous week's 1.07, indicating an increase in short-term optimism among investors regarding the SSE 50ETF[2][7] - The five-day average turnover rates for the SSE Composite Index and Wind All A Index were 1.19% and 1.66%, respectively, indicating a decrease in trading activity compared to previous periods[2][7] - The SAR indicator shows that the Wind All A Index broke below the reversal indicator on October 17[2][11] - The moving average strength index calculated from the Wind secondary industry indices scored 197, which is at the 71.2% percentile since 2023[2][11] - The sentiment model score is 2 out of 5, the trend model signal is negative, and the weighted model signal is negative[2][11][14] - The small-cap factor congestion level increased to 0.41, the low-valuation factor congestion level was -0.26, the high-profitability factor congestion level was -0.15, and the high-growth factor congestion level was 0.35[4][15][16][18]

Group 1: Market Sentiment Model Insights - The market sentiment score has slightly increased to 2.2 as of October 24, compared to 1.9 the previous week, indicating a partial recovery in market sentiment [9][12]. - The overall market sentiment is showing increased differentiation, with a decline in price-volume consistency, suggesting reduced capital activity and a cautious risk appetite among investors [12][19]. - The total trading volume for the entire A-share market has significantly decreased compared to the previous week, with a peak trading volume of 1,991.617 billion RMB on October 24 [19][22]. Group 2: Industry Trends and Insights - As of October 24, 2025, industries such as banking, oil and petrochemicals, transportation, public utilities, and construction decoration have shown an upward trend in short-term scores, with coal being the strongest at a score of 93.22 [40][41]. - The model indicates that the banking sector's short-term score has rapidly increased, maintaining a favorable signal for both value and large-cap styles [40][41]. - The analysis of industry crowding shows that sectors like electronics and power equipment have high returns but also high capital crowding, which may pose volatility risks [43][44]. Group 3: Technical Indicators and Market Dynamics - The Relative Strength Index (RSI) has shown a decline, indicating weak upward momentum and reduced buying interest in the market [32][35]. - The main capital inflow has improved, suggesting an increase in institutional buying power and a gradual warming of market sentiment [35][37]. - The model maintains a signal indicating that large-cap and value styles are currently dominant, although the strength of this signal may weaken in the future [52][53].

Core Viewpoint - The recent instability in the Sino-US trade environment has led to a valuation correction in certain stocks, resulting in a rise in market risk aversion. The market is expected to maintain a volatile trend in the short term [1]. Market Overview - The market is anticipated to remain volatile in the short term. The liquidity shock indicator for the CSI 300 index was 1.57, higher than the previous week's 1.36, indicating current market liquidity is 1.57 times the average level over the past year [2]. - The put-call ratio for the SSE 50 ETF options increased to 1.07 from 0.85, reflecting heightened caution among investors regarding the short-term performance of the SSE 50 ETF [2]. - The five-day average turnover rates for the SSE Composite Index and Wind All A were 1.42% and 1.93%, respectively, consistent with the trading activity levels since 2005 [2]. - The RMB exchange rate fluctuated last week, with onshore and offshore rates showing weekly changes of -0.05% and 0.29%, respectively [2]. - In September, China's CPI decreased by 0.3% year-on-year, slightly better than the previous -0.4%, but worse than the consensus expectation of -0.15%. The PPI was -2.3%, also better than the previous -2.9% but below the expected -2.4% [2]. - New RMB loans in September amounted to 1.29 trillion yuan, lower than the expected 1.39 trillion yuan but higher than the previous 590 billion yuan. M2 growth was 8.4%, below both the expected 8.51% and the previous 8.8% [2]. Technical Analysis - The SAR indicator for the Wind All A index broke downwards on October 17, indicating a bearish trend [2]. - The market score based on the moving average strength index is currently at 141, which is at the 49.9% percentile for 2023 [2]. - The sentiment model score is 2 out of 5, indicating moderate market sentiment, while the trend model signal is positive and the weighted model signal is negative [2]. Performance Summary - For the week of October 13-17, the SSE 50 index fell by 0.24%, the CSI 300 index dropped by 2.22%, the CSI 500 index decreased by 5.17%, and the ChiNext index declined by 5.71% [3]. - The overall market PE (TTM) stands at 22.0 times, which is at the 74.0% percentile since 2005 [3]. Industry Insights - The industry crowding levels are relatively high in sectors such as non-ferrous metals, comprehensive, power equipment, telecommunications, and electronics. The crowding levels in the steel and public utilities sectors have increased significantly [4].