Market Performance Review - The major stock indices showed a mixed performance in September, with growth style leading the way. As of September 26, the Shanghai Composite Index fell by 0.77%, while the Shenzhen Component Index rose by 4.04%, and the ChiNext Index increased by 9.04% [6][17] - The overall market index rose by 1.31%, with the mid-cap index up by 3.62% and the small-cap index down by 0.30%. The "茅" index increased by 3.25%, and the "宁" combination rose by 9.44% [6][17] - External disturbances were minimal, and the A-share market experienced a rebound after an initial decline following the September 3 military parade. The internal economic data remained stable, and the Federal Reserve's interest rate cut aligned with market expectations [6][17] A-Share High-Frequency Data Tracking - The dynamic HMM timing model indicated that the current market potential returns do not cover risks, leading to a recommendation for a reduced position [28] - The personal investor sentiment index showed a slight recovery, with a 7-day moving average of -4.56% as of September 27, significantly down from 15.96% on September 20 [33] - Financing sentiment has improved, maintaining a net inflow trend, with financing transactions accounting for over 20% of A-share trading volume [38] Future Outlook and Investment Views - The report suggests a "time for space" strategy, waiting for the next policy trigger. Since the market rally began on June 23, the A-share market has accumulated significant gains, and a technical stagnation is observed [7][46] - The expectation is that domestic economic policies will focus on implementing existing plans, with the "15th Five-Year Plan" policies anticipated to trigger the next market rally [7][46] - In terms of asset allocation, Hong Kong stocks are seen as having better value, and the report emphasizes the importance of identifying individual stocks with "turnaround" logic in the A-share market [8][46]

Core Insights - The report indicates that the market is in an upward trend, with the key observation variable being whether the market's profit effect can be sustained. As long as the profit effect remains positive, incremental funds are expected to continue entering the market [2][10][14] - The current WIND All A trend line is around 6184 points, with a profit effect of approximately 0.66%, still positive. It is advised to hold positions until the profit effect turns negative [2][10][14] - The industry allocation model suggests that the precious metals sector is still in an upward trend and should be monitored. Additionally, sectors benefiting from policy-driven initiatives, such as new energy and chemicals, are expected to perform well [2][10][14] Market Overview - The market is currently showing a profit effect of about 0.66%, indicating a positive environment for investment. The report suggests maintaining positions until the profit effect turns negative [2][10][14] - The valuation indicators for the WIND All A index show a PE at the 85th percentile and a PB at the 50th percentile, indicating a moderate valuation level [2][10][14] - The report recommends an 80% allocation to absolute return products based on the current market conditions and trends [2][10][14] Industry Focus - The report highlights the precious metals sector as a continuing upward trend, which should be closely monitored [2][10][14] - The technology sector, particularly chips and robotics, is recommended for continued focus based on the TWO BETA model [2][10][14] - Given the uncertainties surrounding the upcoming National Day holiday, there is a specific emphasis on focusing on dividend-paying sectors as a defensive strategy [2][10][14]

Market Overview - The market is expected to experience fluctuations next week, with liquidity shock indicators for the CSI 300 index at 1.86, indicating current market liquidity is 1.86 times higher than the average level over the past year [1] - The PUT-CALL ratio for the SSE 50 ETF options decreased to 0.91, reflecting a reduced caution among investors regarding the short-term performance of the SSE 50 ETF [1] - The average turnover rates for the SSE Composite Index and Wind All A Index were 1.27% and 1.91%, respectively, indicating a decline in trading activity [1] Macroeconomic Factors - The onshore and offshore RMB exchange rates experienced a weekly decline of -0.31% and -0.30%, respectively [1] - The US stock market showed a downward trend, with the Dow Jones, S&P 500, and Nasdaq indices recording weekly returns of -0.15%, -0.31%, and -0.65% [1] - Disagreements within the Federal Reserve regarding future monetary policy paths have increased, with some members advocating for rate cuts while others caution against it due to rising inflation [1] Industrial Performance - From January to August, China's industrial enterprises above designated size achieved a total profit of 46,929.7 billion yuan, reflecting a year-on-year growth of 0.9% [1] - In August, the profit of industrial enterprises turned from a decline of -1.5% in the previous month to a growth of 20.4% [1] Technical Analysis - The SAR indicator for the Wind All A Index showed an upward breakout on September 11 [1] - The current market score based on the moving average strength index is 150, positioned at the 53.3% percentile for 2023 [1] - The sentiment model score decreased to 1 point (out of 5), indicating a decline in market sentiment [1] Sector Analysis - The industry crowding degree is relatively high in sectors such as non-ferrous metals, communications, comprehensive, power equipment, and electronics, with notable increases in power equipment and media sectors [3]

- Liquidity shock indicator for CSI 300 index reached 1.86 on Friday, higher than the previous week's 1.33, indicating current market liquidity is 1.86 times the standard deviation above the past year's average level [7] - PUT-CALL ratio for SSE 50ETF options declined to 0.91 on Friday, lower than the previous week's 1.14, reflecting reduced investor caution regarding short-term movements of SSE 50ETF [7] - Five-day average turnover rates for SSE Composite Index and Wind All A Index were 1.27% and 1.91%, respectively, corresponding to the 75.73% and 81.47% percentiles since 2005, showing decreased trading activity [7] - SAR indicator for Wind All A Index showed a positive breakout on September 11 [10] - Moving average strength index for Wind All A Index scored 150, at the 53.3% percentile for 2023, indicating a fluctuating trend [10] - Sentiment model score was 1 out of 5, trend model signal was positive, and weighted model signal was negative [10] - Small-cap factor crowding score was 0.40, low-valuation factor crowding score was -0.67, high-profitability factor crowding score was -0.10, and high-growth factor crowding score was 0.15 [18] - Sub-scores for small-cap factor included valuation spread (1.08), pairwise correlation (0.06), market volatility (-0.42), and return reversal (0.85) [18] - Sub-scores for low-valuation factor included valuation spread (-1.25), pairwise correlation (-0.03), market volatility (-0.09), and return reversal (-1.32) [18] - Sub-scores for high-profitability factor included valuation spread (-0.17), pairwise correlation (0.14), market volatility (-0.84), and return reversal (0.48) [18] - Sub-scores for high-growth factor included valuation spread (1.91), pairwise correlation (0.46), market volatility (-0.94), and return reversal (-0.82) [18]

Core Insights - The report emphasizes the need for a multi-dimensional, AI-driven framework to effectively predict and manage risks associated with short-term market surges, particularly in the context of the A-share market [2][3] - It introduces a comprehensive multi-factor system based on four core dimensions: trend, momentum, capital flow, and valuation, which collectively enhance market state characterization [2][4] - The AI-enhanced multi-factor timing strategy is expected to provide investors with an objective risk warning tool, reducing losses from blind chasing of high prices [3][4] Trend Analysis - The report illustrates that traditional technical indicators often fail to provide timely warnings for rapid market fluctuations driven by emotions rather than fundamentals [2][6] - It highlights the "9·24" market surge as a case study, where the index rose over 21% in a short period, demonstrating the risks of emotion-driven trading [5][6] - The analysis of moving averages (MA5, MA10, MA20) indicates that a bullish trend was confirmed before the surge, while subsequent signals suggested a weakening momentum [6][8] Momentum Indicators - The report discusses the use of KDJ and RSI indicators, which reached extreme levels during the "9·24" surge, signaling potential overbought conditions [8][9] - It notes that these momentum extremes often occur at the end of price waves, serving as critical signals for potential market tops [9][10] Capital Flow Insights - The report emphasizes the correlation between trading volume and price movements, indicating that significant increases in trading volume often precede price surges [11][12] - It also points out that a decline in trading volume following a price peak can signal weakening momentum and potential market corrections [12] Valuation Metrics - The report highlights the rapid increase in the price-to-earnings (PE) ratio during the "9·24" surge, indicating a shift from undervaluation to overvaluation, which raises risk concerns [15][16] - It suggests that high PE ratios, especially when combined with momentum indicators showing overbought conditions, serve as strong signals for potential market corrections [15][16] AI-Driven Quantitative Strategy - The report outlines a comprehensive AI-driven quantitative strategy that integrates various data sources and employs machine learning algorithms to enhance decision-making [19][20] - It emphasizes the importance of feature engineering and factor processing to ensure the robustness and interpretability of the model [20][33] - The strategy's backtesting results indicate a significant annualized return of 36.41% with a Sharpe ratio of 2.30, outperforming the market benchmark [41][42] Performance Evaluation - The strategy demonstrated strong performance during market uptrends while effectively managing drawdowns during downturns, showcasing its risk management capabilities [42][45] - The report notes that the model's predictive accuracy, while modest, indicates its ability to identify market trends better than random guessing [51][56]

Core Insights - The report emphasizes the need for a multi-dimensional, AI-driven framework to effectively predict and manage risks associated with short-term market surges, particularly in the context of the A-share market [2][3] - It introduces a comprehensive multi-factor system based on four core dimensions: trend, momentum, capital flow, and valuation, which collectively enhance market state characterization [2][4] - The AI-enhanced multi-factor timing strategy is expected to provide investors with an objective risk warning tool, reducing losses from blind chasing of high prices [3][4] Trend Analysis - The report illustrates that traditional indicators often fail to provide timely warnings for rapid market fluctuations driven by emotions rather than fundamentals [2][6] - The analysis of the "9·24" market surge shows that moving averages indicated a bullish trend before the surge, while subsequent signals indicated a weakening momentum [5][6][8] Momentum Indicators - The report highlights that extreme values in momentum indicators like KDJ and RSI often signal the end of a price surge, as seen during the "9·24" event where both indicators reached overbought levels [8][9] - The KDJ and RSI thresholds serve as critical points for identifying market cycles, aiding investors in timing their trades effectively [9] Capital Flow Insights - The report notes a strong correlation between trading volume and price movements during the "9·24" surge, indicating that volume often precedes price increases [11][12] - A decline in trading volume following price peaks serves as a warning signal for potential market corrections, as evidenced in the analysis [12] Valuation Metrics - The report discusses how valuation metrics, such as PE ratios, can indicate market risk accumulation, particularly when they exceed historical high thresholds [15][16] - The combination of high valuation levels and overbought momentum indicators has historically signaled market tops and subsequent corrections [15] AI-Driven Quantitative Strategy - The report outlines a comprehensive AI-driven quantitative strategy that automates the process of factor selection, modeling, and execution, enhancing the robustness of trading signals [19][20] - The strategy employs a closed-loop system that continuously optimizes itself based on real-time performance feedback, ensuring adaptability to changing market conditions [19][20] Factor Processing and Model Selection - The report emphasizes the importance of factor processing, including standardization and ranking, to ensure comparability and robustness of the indicators used in the model [30][33] - The HistGradientBoosting model is selected for its ability to capture non-linear relationships among factors, providing a more accurate timing signal for trades [39][40] Performance Evaluation - Backtesting results indicate that the AI-driven strategy significantly outperforms the market benchmark, achieving an annualized return of approximately 36.41% with a Sharpe ratio of 2.30 [41][42] - The strategy demonstrates strong risk management capabilities, maintaining a maximum drawdown of -19.51%, which is notably lower than the benchmark during volatile periods [45][46]

Group 1 - Market sentiment indicators showed a decline, with the sentiment index at 2, down from 2.55 the previous week, indicating a bearish outlook despite some improvement in sub-indicators [7][9][10] - The industry trend showed signs of recovery, with financing balance ratio increasing, suggesting a restoration of market leverage sentiment [9][24][39] - The total trading volume for the week increased slightly compared to the previous week, with the highest daily trading volume reaching 31,666.43 billion RMB [12][14] Group 2 - The trading volatility between industries continued to decline, indicating reduced activity in fund switching, while the RSI indicator shifted from positive to neutral, suggesting a cooling of bullish momentum [9][30] - The short-term trend scores for industries such as machinery, electric equipment, and automotive are notably strong, with machinery and electric equipment both scoring 96.61 [34][35] - The model indicates a preference for large-cap growth styles, although the strength of this signal is weakening and requires further observation [34][49] Group 3 - The report highlights high capital congestion in sectors like automotive and electric equipment, which have seen significant price increases, indicating potential volatility risks [39][44] - Conversely, sectors such as retail and transportation show high capital congestion but lower price increases, suggesting stable capital allocation [39][44] - Low congestion sectors like pharmaceuticals and beauty care, which have seen lower price increases, may present opportunities for gradual long-term investment as risk appetite improves [39][44]

Core Insights - The report indicates that the market is currently in an upward trend, with the WIND All A index showing a positive money-making effect of approximately 0.87% [2][10][15] - The report suggests maintaining a portfolio allocation of 80% in absolute return products based on the current valuation levels of the WIND All A index, which is at the 85th percentile for PE and the 50th percentile for PB, indicating a moderate valuation [11][8] Market Overview - The WIND All A index experienced a slight decline of 0.18% over the past week, with small-cap stocks represented by the CSI 2000 down by 0.02%, mid-cap stocks in the CSI 500 up by 0.32%, and large-cap indices like the CSI 300 and SSE 50 down by 0.44% and 1.98% respectively [9][10] - The report highlights strong performance in sectors such as power equipment and new energy, with new energy stocks rising by 3.61%, while the banking sector saw a decline of 4.09% [9][10] Timing System Analysis - The distance between the short-term (20-day) and long-term (120-day) moving averages continues to widen, indicating a sustained upward trend in the market, with the latest figures showing a 13.57% difference [2][10] - The report emphasizes that as long as the money-making effect remains positive, there is potential for continued inflow of incremental funds into the market [2][10][15] Sector Recommendations - The report recommends focusing on sectors that are likely to benefit from policy-driven growth, including innovative pharmaceuticals, new energy, and chemicals, while also suggesting a renewed focus on precious metals [2][10][15] - The TWO BETA model continues to recommend technology sectors, particularly in computing power and consumer electronics [2][10][15]

Quantitative Models and Construction Methods 1. Model Name: Short-term Timing Model - **Model Construction Idea**: The model evaluates the market's short-term timing signals by analyzing macro fundamentals, valuation, sentiment, and liquidity indicators. It aims to provide a comprehensive view of market conditions and guide short-term investment decisions[12][17]. - **Model Construction Process**: 1. **Macro Fundamentals**: - Manufacturing PMI: A PMI value below 50 indicates weak manufacturing activity, providing a cautious signal. - Credit Pulse: The YoY growth rate of medium- and long-term RMB loans is at the 61.02% percentile over the past five years, indicating strong credit growth and providing an optimistic signal. - M1 Growth Rate: The filtered YoY growth rate of M1 is 5.23%, at the 96.61% percentile over the past five years, indicating strong M1 growth and providing an optimistic signal[12][17]. 2. **Valuation**: - PE Median: The current PE median of the A-share market is 45.50, at the 98.84% percentile over the past five years, signaling caution. - PB Median: The current PB median is 3.02, at the 96.94% percentile over the past five years, also signaling caution[12][17]. 3. **Sentiment**: - Beta Dispersion: The current beta dispersion is 8.66%, at the 96.61% percentile over the past five years, signaling caution. - Volume Sentiment Score: The score is 0.40, at the 74.52% percentile, indicating strong volume sentiment and providing an optimistic signal. - Volatility: The annualized volatility is 20.19%, at the 77.67% percentile, providing a neutral signal[13]. 4. **Liquidity**: - Money Market Rate: The rate is 0.00, at the 38.98% percentile, indicating relatively loose liquidity and providing an optimistic signal. - Exchange Rate Expectation: The indicator is -0.42%, at the 33.90% percentile, indicating a strong RMB against the USD and providing an optimistic signal. - Average 5-day Financing Amount: The value is 126.77 billion RMB, at the 97.85% percentile, signaling caution due to high leverage[13]. - **Model Evaluation**: The model effectively integrates multiple dimensions to provide a comprehensive short-term market outlook. It has demonstrated strong performance in historical backtests, with significant excess returns and reduced drawdowns compared to benchmarks[14]. 2. Model Name: Growth-Value Style Rotation Model - **Model Construction Idea**: This model identifies optimal allocation between growth and value styles based on macroeconomic cycles, valuation spreads, and sentiment indicators. It aims to capture excess returns through style rotation[24][25]. - **Model Construction Process**: 1. **Macro Fundamentals**: - Profit Cycle Slope: A steep profit cycle slope favors growth. The current slope is high, providing a 100% growth signal. - Interest Rate Cycle: A high interest rate cycle level favors value. The current level is high, providing a 100% value signal. - Credit Cycle: A strengthening credit cycle favors growth. The current cycle is strong, providing a 100% growth signal[24][26]. 2. **Valuation**: - PE Spread: The 5-year percentile of the growth-value PE spread is 45.11%, indicating mean reversion upward, favoring growth. - PB Spread: The 5-year percentile of the growth-value PB spread is 55.48%, also indicating mean reversion upward, favoring growth[24][26]. 3. **Sentiment**: - Turnover Spread: The 5-year percentile of the turnover spread is 38.13%, favoring value. - Volatility Spread: The 5-year percentile of the volatility spread is 94.76%, favoring a balanced allocation[24][26]. - **Model Evaluation**: The model has consistently outperformed its benchmark since 2012, with significant annualized excess returns and reduced drawdowns. It effectively captures style rotation opportunities[25][27]. 3. Model Name: Small-Cap vs. Large-Cap Style Rotation Model - **Model Construction Idea**: This model evaluates 11 effective rotation indicators to determine optimal allocation between small-cap and large-cap styles. It aims to exploit market inefficiencies and generate excess returns through size-based style rotation[29][30]. - **Model Construction Process**: 1. **Indicators Favoring Small-Cap**: - Increased financing purchase amounts. - Narrowing credit spreads. - Declining implied market volatility. - Rising PB divergence. - Recovery in small-cap trading volume[29][30]. 2. **Indicators Favoring Large-Cap**: - Declining small-cap theme sentiment. - High beta dispersion. - Rising R007 rates[29][30]. - **Model Evaluation**: The model has delivered positive excess returns every year since 2014, demonstrating its robustness and effectiveness in capturing size-based rotation opportunities[30]. --- Model Backtesting Results 1. Short-term Timing Model - Annualized Return: 17.99% - Annualized Volatility: 15.87% - Maximum Drawdown: 22.44% - Sharpe Ratio: 0.9959 - Excess Return (2025 YTD): 14.15%[14][19][22] 2. Growth-Value Style Rotation Model - Annualized Return: 13.22% - Annualized Volatility: 20.80% - Maximum Drawdown: 43.07% - Sharpe Ratio: 0.6056 - Excess Return (2025 YTD): 8.50%[25][27][28] 3. Small-Cap vs. Large-Cap Style Rotation Model - Annualized Return: 19.10% - Annualized Excess Return: 11.96% - Maximum Drawdown: 39.71% - Average Small-Cap Allocation (2025 YTD): 51.41% - Excess Return (2025 YTD): 4.44%[30][32]

Group 1: Market Overview - The SAR indicator has shown a bullish breakout, indicating strong market volatility and a potential upward trend in the coming week [1][3] - The liquidity shock index for the CSI 300 was 0.78, indicating higher liquidity compared to the past year's average [1] - The PUT-CALL ratio for the SSE 50 ETF decreased to 0.67, reflecting increased investor optimism about short-term market movements [1] Group 2: Macroeconomic Factors - The onshore and offshore RMB exchange rates experienced weekly increases of 0.22% and 0.04%, respectively [2] - August CPI in China was -0.4%, lower than the previous value of 0% and below the consensus expectation of -0.2% [2] - New RMB loans in August amounted to 590 billion, exceeding both the consensus expectation and the previous value [2] Group 3: Technical Analysis - The Wind All A Index broke above the SAR indicator on September 11, signaling a potential upward trend [3] - The market score based on moving averages is 246, placing it in the 89.6 percentile for 2023 [3] - The sentiment model score is 2 out of 5, indicating mixed market emotions with a positive trend signal and a negative weighted model signal [3] Group 4: Market Performance - The SSE 50 Index rose by 0.89%, the CSI 300 Index increased by 1.38%, and the ChiNext Index grew by 2.1% during the last week [4] - The current market PE (TTM) is 22.2 times, which is in the 76.4 percentile since 2005 [4] Group 5: Factor and Industry Observations - Small-cap factor crowding remains stable at 0.57, while low valuation and high profitability factors show negative crowding [5] - The industry crowding is relatively high in sectors such as non-ferrous metals, communications, and power equipment, with notable increases in power equipment and media [6]