Core Viewpoint - The market is expected to maintain a trend of oscillating upward in the coming week, with a notable presence of both bullish and bearish sentiments [1][2]. Market Indicators - The liquidity shock indicator for the CSI 300 index was 2.49, indicating current market liquidity is 2.49 standard deviations above the average level of the past year [2]. - The PUT-CALL ratio for the SSE 50 ETF decreased to 0.92, reflecting a reduced caution among investors regarding the short-term performance of the ETF [2]. - The five-day average turnover rates for the SSE Composite Index and Wind All A Index were 1.06% and 1.65%, respectively, indicating a decline in trading activity [2]. Macroeconomic Factors - The onshore and offshore RMB exchange rates experienced weekly increases of 0.39% and 0.05%, respectively [2]. - In July, China's CPI was reported at 0.0% year-on-year, slightly below the previous value of 0.1% but above the consensus expectation of -0.12%. The PPI remained at -3.6%, matching the previous value and below the consensus expectation of -3.44% [2]. Technical Analysis - The SAR indicator for the Wind All A Index showed an upward breakout on August 6, indicating a potential trend reversal [2]. - The market score based on the moving average strength index is currently at 246, placing it in the 91.5 percentile for 2023 [2]. - The sentiment model score is 3 out of 5, with both trend and weighted models signaling a positive outlook [2]. Market Performance - For the week of August 4-8, the SSE 50 Index rose by 1.27%, the CSI 300 Index increased by 1.23%, the CSI 500 Index grew by 1.78%, and the ChiNext Index saw a rise of 0.49% [3]. - The overall market PE (TTM) stands at 20.7 times, which is in the 67.9 percentile since 2005 [3]. Factor Crowding - The crowding degree for small-cap factors has decreased, with small-cap factor crowding at 0.79, low valuation factor crowding at 0.11, high profitability factor crowding at -0.25, and high growth factor crowding at 0.25 [3]. Industry Crowding - The industries with relatively high crowding degrees include machinery, defense and military, non-ferrous metals, comprehensive, and steel, with notable increases in crowding for defense and machinery sectors [4].

- The report defines the market environment using the distance between the long-term (120-day) and short-term (20-day) moving averages of the WIND All A index, which continues to expand, indicating an upward trend [2][9][10] - The industry allocation model recommends sectors such as innovative drugs in Hong Kong and securities for mid-term allocation, while the TWO BETA model continues to recommend the technology sector, focusing on military and computing power [2][3][10] - The current PE ratio of the WIND All A index is around the 70th percentile, indicating a moderate level, while the PB ratio is around the 30th percentile, indicating a relatively low level [3][10][15] Model and Factor Construction 1. **Model Name: Industry Allocation Model** - **Construction Idea**: Recommends sectors based on mid-term market trends - **Construction Process**: Utilizes historical data and market trends to identify sectors with potential for reversal and growth, such as innovative drugs and securities in the Hong Kong market - **Evaluation**: Effective in identifying sectors with potential for mid-term growth [2][3][10] 2. **Model Name: TWO BETA Model** - **Construction Idea**: Focuses on sectors with high beta values, indicating higher volatility and potential returns - **Construction Process**: Analyzes sectors with high beta values, recommending technology, military, and computing power sectors - **Evaluation**: Continues to recommend high-growth sectors, showing consistency in sector selection [2][3][10] Model Backtesting Results 1. **Industry Allocation Model** - **PE Ratio**: 70th percentile [3][10][15] - **PB Ratio**: 30th percentile [3][10][15] - **Moving Average Distance**: 6.92% [2][9][10] - **Profitability Effect**: 2.30% [2][9][10] 2. **TWO BETA Model** - **PE Ratio**: 70th percentile [3][10][15] - **PB Ratio**: 30th percentile [3][10][15] - **Moving Average Distance**: 6.92% [2][9][10] - **Profitability Effect**: 2.30% [2][9][10]

Group 1 - The market sentiment index has risen to 3.2, up from 1.8 the previous week, indicating a bullish outlook, but caution is advised as high sentiment levels can lead to sensitive directional changes [10][4][8] - The price-volume consistency indicator has increased, suggesting higher capital activity and reduced divergence in market sentiment, while the financing ratio continues to decline [13][4] - The total trading volume for the week remained high, with the peak on Thursday at 1961.849 billion RMB and a significant drop on Friday to 1619.884 billion RMB [17][4] Group 2 - The industry performance shows a clear upward trend in anti-involution related sectors, with basic chemicals and electronics leading the gains, while automotive, environmental, and oil sectors lag behind [26][29] - The short-term scores for most industries have generally decreased, with computer, media, communication, and food and beverage sectors showing slight increases [33][34] - The model indicates a preference for small-cap growth styles, with the RSI model also suggesting a growth style advantage, although the 5-day RSI has decreased compared to the 20-day RSI [36][37]

Quantitative Models and Construction Methods 1. Model Name: Timing System Model - **Model Construction Idea**: The model uses the distance between the short-term moving average (20-day) and the long-term moving average (120-day) of the WIND All A Index to determine the market trend[2][9] - **Model Construction Process**: - Calculate the 20-day moving average and the 120-day moving average of the WIND All A Index - Compute the percentage difference between the two moving averages: $ \text{Distance} = \frac{\text{20-day MA} - \text{120-day MA}}{\text{120-day MA}} \times 100\% $ - If the absolute value of the distance is greater than 3% and the short-term moving average is above the long-term moving average, the market is in an upward trend[2][9] - **Model Evaluation**: The model effectively identifies upward market trends and provides actionable signals for investors[2][9] 2. Model Name: Industry Allocation Model - **Model Construction Idea**: This model identifies medium-term industry allocation opportunities by focusing on sectors with potential for recovery or growth[2][9] - **Model Construction Process**: - Analyze industry-specific factors such as valuation, growth potential, and market sentiment - Recommend sectors like "distressed reversal" industries, Hong Kong innovative pharmaceuticals, Hang Seng dividend low-volatility sectors, and securities for medium-term allocation[2][9] - **Model Evaluation**: The model provides clear guidance for sector rotation and captures medium-term opportunities in specific industries[2][9] 3. Model Name: TWO BETA Model - **Model Construction Idea**: This model focuses on identifying high-growth sectors in the technology domain[2][9] - **Model Construction Process**: - Analyze beta factors related to technology sectors - Recommend sectors such as solid-state batteries, robotics, and military industries based on their growth potential and market trends[2][9] - **Model Evaluation**: The model is effective in capturing high-growth opportunities in the technology sector[2][9] --- Model Backtesting Results 1. Timing System Model - **Key Metrics**: - Moving average distance: 6.06% (absolute value > 3%, indicating an upward trend)[2][9] - WIND All A Index trendline: 5480 points[2][9] - Profitability effect: 1.45% (positive, indicating sustained market inflows)[2][9] 2. Industry Allocation Model - **Key Metrics**: - Recommended sectors: distressed reversal industries, Hong Kong innovative pharmaceuticals, Hang Seng dividend low-volatility sectors, and securities[2][9] 3. TWO BETA Model - **Key Metrics**: - Recommended sectors: solid-state batteries, robotics, and military industries[2][9] --- Quantitative Factors and Construction Methods 1. Factor Name: Profitability Effect - **Factor Construction Idea**: Measures the market's ability to generate positive returns, serving as a key indicator for market sentiment and fund inflows[2][9] - **Factor Construction Process**: - Calculate the profitability effect as a percentage value - Positive values indicate favorable market conditions for sustained fund inflows[2][9] - **Factor Evaluation**: The factor is a reliable indicator of market sentiment and a useful tool for timing investment decisions[2][9] --- Factor Backtesting Results 1. Profitability Effect - **Key Metrics**: - Profitability effect value: 1.45% (positive, indicating favorable market conditions)[2][9]

Quantitative Models and Construction Methods 1. Model Name: Credit Impulse Timing Strategy - **Model Construction Idea**: The model uses credit impulse as a timing indicator for A-shares, where the direction of credit impulse determines the market position (full position when upward, empty position when downward) [6][13][14] - **Model Construction Process**: - Calculate the year-on-year growth rate of long-term corporate loans (TTM) as the credit impulse indicator - Use the direction of the credit impulse to determine market positions: full position when the indicator is upward, empty position when downward - Formula: $ \text{Credit Impulse} = \frac{\text{Long-term Corporate Loans (TTM)} - \text{Long-term Corporate Loans (TTM, previous year)}}{\text{Long-term Corporate Loans (TTM, previous year)}} $ - **Model Evaluation**: The model has shown high effectiveness in avoiding major downtrends in the market [6][13][14] 2. Model Name: Beta Dispersion Timing Strategy - **Model Construction Idea**: The model uses beta dispersion as an indicator to measure local market sentiment overheating, with significant monthly timing effectiveness [6][17] - **Model Construction Process**: - Calculate the monthly beta dispersion of the market - Use the beta dispersion to determine market positions: higher beta dispersion indicates higher risk - Formula: $ \text{Beta Dispersion} = \frac{\sum_{i=1}^{N} (\beta_i - \bar{\beta})^2}{N} $ where $\beta_i$ is the beta of stock i, $\bar{\beta}$ is the average beta, and N is the number of stocks - **Model Evaluation**: The model has shown significant monthly timing effectiveness since 2013 [6][17] 3. Model Name: Trading Volume Timing Strategy - **Model Construction Idea**: The model uses trading volume as an indicator for market timing, with significant daily timing effectiveness [6][17] - **Model Construction Process**: - Calculate the daily trading volume and its 60-day moving average - Use the trading volume to determine market positions: higher trading volume indicates stronger market support - Formula: $ \text{Trading Volume Indicator} = \frac{\text{Daily Trading Volume}}{\text{60-day Moving Average of Trading Volume}} $ - **Model Evaluation**: The model has shown significant daily timing effectiveness since 2013 [6][17] 4. Composite Model: Credit Impulse, Beta Dispersion, Trading Volume - **Model Construction Idea**: The composite model combines credit impulse, beta dispersion, and trading volume indicators for market timing [6][18] - **Model Construction Process**: - Use equal weighting to combine the three indicators - Adjust positions based on the combined signal: average 2-week signal change frequency - Formula: $ \text{Composite Indicator} = \frac{\text{Credit Impulse Indicator} + \text{Beta Dispersion Indicator} + \text{Trading Volume Indicator}}{3} $ - **Model Evaluation**: The composite model has shown a high annual turnover rate and significant annualized returns since 2013 [6][18] Model Backtesting Results 1. Credit Impulse Timing Strategy - **Annualized Return**: 10.83% [6][13][14] - **Avoided Major Downtrends**: 2015 H2, 2018, 2022-2024 H1 [6][13][14] 2. Beta Dispersion Timing Strategy - **Annualized Return**: 13.12% [6][17] - **Monthly Timing Effectiveness**: Significant since 2013 [6][17] 3. Trading Volume Timing Strategy - **Annualized Return**: 14.33% [6][17] - **Daily Timing Effectiveness**: Significant since 2013 [6][17] 4. Composite Model: Credit Impulse, Beta Dispersion, Trading Volume - **Annualized Return**: 19.98% [6][18] - **Annual Turnover Rate**: 24 times [6][18] Quantitative Factors and Construction Methods 1. Factor Name: Manufacturing PMI Timing Strategy - **Factor Construction Idea**: The factor uses manufacturing PMI as a timing indicator for A-shares, with positions adjusted based on PMI levels [6][13] - **Factor Construction Process**: - Calculate the rolling 5-year percentile of manufacturing PMI - Adjust positions based on PMI levels: full position when >60%, empty position when <40%, half position when between 40%-60% - Formula: $ \text{PMI Timing Indicator} = \begin{cases} \text{Full Position} & \text{if PMI Percentile} > 60\% \\ \text{Empty Position} & \text{if PMI Percentile} < 40\% \\ \text{Half Position} & \text{if 40\% \leq PMI Percentile \leq 60\%} \end{cases} $ - **Factor Evaluation**: The factor has shown poor timing performance with an annualized return of only 0.41% since 2009 [6][13] Factor Backtesting Results 1. Manufacturing PMI Timing Strategy - **Annualized Return**: 0.41% [6][13] - **Comparison with Benchmark**: Underperformed the Wind All A Index annualized return of 8.49% [6][13] Style Rotation Models and Construction Methods 1. Model Name: Growth-Value Style Rotation Model - **Model Construction Idea**: The model suggests overweighting growth based on economic cycle analysis, valuation differences, and sentiment indicators [35][36] - **Model Construction Process**: - Analyze economic cycle indicators: profitability slope, interest rate cycle, credit cycle - Calculate valuation differences: PE and PB percentiles - Assess sentiment indicators: turnover and volatility differences - Formula: $ \text{Growth-Value Rotation Indicator} = \frac{\text{Profitability Slope Indicator} + \text{Interest Rate Cycle Indicator} + \text{Credit Cycle Indicator} + \text{PE Difference Indicator} + \text{PB Difference Indicator} + \text{Turnover Difference Indicator} + \text{Volatility Difference Indicator}}{7} $ - **Model Evaluation**: The model suggests overweighting growth based on current indicators [35][36] 2. Model Name: Small-Cap Large-Cap Style Rotation Model - **Model Construction Idea**: The model suggests balanced allocation based on economic cycle analysis, valuation differences, and sentiment indicators [35][41] - **Model Construction Process**: - Analyze economic cycle indicators: profitability slope, interest rate cycle, credit cycle - Calculate valuation differences: PE and PB percentiles - Assess sentiment indicators: turnover and volatility differences - Formula: $ \text{Small-Cap Large-Cap Rotation Indicator} = \frac{\text{Profitability Slope Indicator} + \text{Interest Rate Cycle Indicator} + \text{Credit Cycle Indicator} + \text{PE Difference Indicator} + \text{PB Difference Indicator} + \text{Turnover Difference Indicator} + \text{Volatility Difference Indicator}}{7} $ - **Model Evaluation**: The model suggests balanced allocation based on current indicators [35][41] 3. Composite Model: Four-Dimensional Style Rotation Model - **Model Construction Idea**: The model combines growth-value and small-cap large-cap rotation models for allocation [35][44] - **Model Construction Process**: - Combine the signals from growth-value and small-cap large-cap rotation models - Adjust positions based on combined signals - Formula: $ \text{Four-Dimensional Rotation Indicator} = \frac{\text{Growth-Value Rotation Indicator} + \text{Small-Cap Large-Cap Rotation Indicator}}{2} $ - **Model Evaluation**: The model suggests specific allocation proportions based on current indicators [35][44] Style Rotation Model Backtesting Results 1. Growth-Value Style Rotation Model - **Annualized Return**: 11.65% [35][37] - **Comparison with Benchmark**: Outperformed the benchmark annualized return of 6.91% [35][37] 2. Small-Cap Large-Cap Style Rotation Model - **Annualized Return**: 12.32% [35][42] - **Comparison with Benchmark**: Outperformed the benchmark annualized return of 7.11% [35][42] 3. Composite Model: Four-Dimensional Style Rotation Model - **Annualized Return**: 13.22% [35][44] - **Comparison with Benchmark**: Outperformed the benchmark annualized return of 7.50% [35][44]

Market Performance - The recent five trading days saw the Sci-Tech 50 Index decline by 1.65%, the ChiNext Index by 0.74%, the large-cap value index by 1.27%, the large-cap growth index by 2.58%, the SSE 50 by 1.48%, and the CSI 2000 representing small caps by 0.19% [1] - The pharmaceutical and communication sectors performed well, while coal and non-ferrous metals lagged [1] Risk Premium Analysis - The risk premium, defined as the inverse of the static PE of the CSI All Index (EP) minus the yield of ten-year government bonds, indicates that the implied returns of equity and bond assets are at historically high levels, reaching 4.17% on April 26, 2022, and 4.08% on October 28, 2022 [1] - As of January 19, 2024, the indicator was at 4.11%, marking the fifth occurrence since 2016 of exceeding 4% [1] - The latest figure as of August 1, 2025, is 3.48%, with the two-standard-deviation boundary set at 4.76% [1] Valuation Levels - As of August 1, 2025, the CSI All Index's TTM PE is at the 64th percentile, with the SSE 50 and CSI 300 at 66% and 58% respectively, while the ChiNext Index is close to 25% [2] - The CSI 500 and CSI 1000 are at 46% and 37% respectively, indicating that the ChiNext Index's valuation is relatively low compared to historical levels [2] Long-term Market Trends - The technical analysis of the Deep 100 Index shows a pattern of bear markets every three years, followed by bull markets, with previous declines ranging from 40% to 45% [2] - The current adjustment cycle began in the first quarter of 2021, suggesting a potential for upward movement from the bottom [2] Fund Flow and Trading Activity - In the last five trading days, ETF funds experienced an outflow of 13.1 billion yuan, while margin financing increased by approximately 42.6 billion yuan [2] - The average daily trading volume across both markets was 1.7848 trillion yuan [2] AI and Machine Learning Applications - A convolutional neural network (CNN) was utilized to model price and volume data, mapping learned features to industry themes, with a focus on semiconductor materials [2][7] ETF Indexes - Various ETF indexes related to semiconductor materials and innovation were listed, including the SSE Sci-Tech Semiconductor Materials Equipment Theme Index and the CSI Semiconductor Industry Index, all dated August 1, 2025 [8]

Core Viewpoint - The article highlights the investment philosophy and performance of DeYuan Investment, emphasizing its data-driven approach and diverse product offerings aimed at achieving long-term returns with risk-adjusted strategies [2][3]. Company Overview - DeYuan Investment, established in June 2014, is a registered private fund manager in China with a management scale of approximately 900 million [2]. - The company employs a strategy framework that integrates quantitative timing, deep value assessment, and systematic risk control to seek long-term compound growth [2]. Performance Metrics - As of June 30, 2025, DeYuan Investment's products in the 500-1,000 million scale category achieved an average return of ***%, ranking in the top 10 for semi-annual stock strategy returns [3]. - The "DeYuan Yangfan No. 1" product managed by DeYuan Investment recorded a return of ***% in the first half of 2025, placing it fourth in the semi-annual subjective long position returns [3]. Development History - DeYuan Investment was registered in Shenzhen in June 2014 with a paid-in capital of 10 million [7]. - The company received its private fund management registration certificate in July 2015 [7]. Core Team - The core investment committee consists of nine members, most with over ten years of experience, providing a stable and reliable decision-making framework [9]. Core Advantages - The company boasts a stable and professional team with no management changes in the past three years, enhancing product development and investor experience [18]. - DeYuan Investment has developed its own quantitative trading system that is fully automated and designed for low latency [18]. - Strict risk management practices are in place, focusing on preemptive risk identification and real-time monitoring [18]. - The company offers a diverse range of products, including quantitative strategies and alternative investment strategies [19]. Product Lines - The quantitative long position strategy operates fully programmatically, adjusting stock positions dynamically based on mathematical models and algorithms [20]. - The "DeYuan Haichai Quantitative No. 1" product has been established since June 23, 2022, with returns of ***% since inception [21]. - The "DeYuan Mingxuan Quantitative No. 2" product focuses on value investment principles, targeting undervalued stocks with potential for recovery [22]. Alternative Investment Strategy - DeYuan Investment identifies companies in financial distress that are undergoing bankruptcy restructuring but still possess core asset value and growth potential [27]. - The company participates in these restructurings through compliant capital increases, aiming to benefit from value recovery post-restructuring [27].

Core Viewpoint - The market sentiment score has increased, indicating a bullish outlook for the market as of July 25, with a score of 1.8, up from 0.65 the previous week [1]. Group 1: Market Sentiment Indicators - The sentiment structure indicator is calculated using a scoring method based on the direction of each sub-indicator and its position within the Bollinger Bands, resulting in a 20-day moving average score [1]. - The trading volatility between industries has shown a positive signal, suggesting increased capital activity and reduced uncertainty in short-term sentiment [4][14]. - The financing ratio has decreased, indicating a decline in the heat of margin trading, which requires further observation [4]. Group 2: Trading Activity and Volume - The overall trading volume in the A-share market has maintained an upward trend, with a peak daily trading volume of 19,286.45 billion RMB on July 25 [9]. - The consistency of price and volume remains high, indicating active participation and capital engagement in the market [6]. Group 3: Industry Performance - Industries such as basic chemicals, non-ferrous metals, and electric equipment have shown strong performance, while sectors like public utilities, media, and banking have lagged behind [16]. - The short-term trend scores for industries like coal, food and beverage, and beauty care have significantly increased, with coal showing a remarkable rise of 109.09% [19][20]. Group 4: Style and Trend Analysis - The small-cap growth style is currently favored, with the relative strength index (RSI) indicating a strong preference for growth stocks over value stocks [21][22]. - The trend scoring model shows that industries like coal, food and beverage, and construction materials have strong short-term trend scores, suggesting potential investment opportunities [19][20].

Group 1 - Market sentiment indicators have risen to 1.8, up from 0.65 last week, indicating a bullish outlook [10][18] - Inter-industry trading volatility has increased, signaling a recovery in capital activity and reduced uncertainty in short-term sentiment [14][23] - The total trading volume of the A-share market has continued to rise, with a peak daily trading volume of 1,928.645 billion RMB on Wednesday [18][27] Group 2 - The coal industry shows a significant upward trend, with a short-term trend score increase of 109.09% [32][34] - The model indicates a preference for small-cap growth styles, with strong signals for growth styles as evidenced by the RSI metrics [36][37] - The top five industries with the strongest short-term trends include environmental protection, basic chemicals, social services, non-ferrous metals, and comprehensive sectors [32][34]

Quantitative Models and Construction 1. Model Name: Timing System Model - **Model Construction Idea**: This model uses the distance between the short-term moving average (20-day) and the long-term moving average (120-day) of the WIND All A Index to determine the market trend. If the short-term moving average is above the long-term moving average and the absolute distance exceeds 3%, the market is considered to be in an upward trend[2][10][16] - **Model Construction Process**: 1. Calculate the 20-day moving average (short-term) and the 120-day moving average (long-term) of the WIND All A Index 2. Compute the percentage difference between the two moving averages: $ \text{Distance} = \frac{\text{20-day MA} - \text{120-day MA}}{\text{120-day MA}} \times 100\% $ - 20-day MA: Short-term moving average - 120-day MA: Long-term moving average 3. If the distance is greater than 3% and the short-term moving average is above the long-term moving average, the market is in an upward trend[2][10][16] - **Model Evaluation**: The model effectively identifies upward market trends and provides a clear signal for timing decisions[2][10][16] 2. Model Name: Industry Allocation Model - **Model Construction Idea**: This model identifies sectors with potential for outperformance based on medium-term trends and specific themes, such as "distressed reversal" and "high elasticity" sectors[3][11][16] - **Model Construction Process**: 1. Analyze sector performance and valuation metrics 2. Identify sectors with medium-term growth potential, such as distressed reversal sectors (e.g., Hong Kong innovative drugs, Hong Kong securities, and Hang Seng consumption) 3. Highlight high-elasticity sectors like technology, military, AI applications, and solid-state batteries based on the TWO BETA model[3][11][16] - **Model Evaluation**: The model provides actionable insights for sector allocation during upward market trends, focusing on high-growth and high-elasticity sectors[3][11][16] 3. Model Name: Position Management Model - **Model Construction Idea**: This model determines the optimal stock allocation ratio based on valuation levels and short-term market trends[3][11] - **Model Construction Process**: 1. Assess the valuation levels of the WIND All A Index using PE and PB metrics 2. Combine valuation levels with short-term market trends to recommend stock allocation ratios 3. Current recommendation: Allocate 80% of absolute return products to stocks based on the WIND All A Index[3][11] - **Model Evaluation**: The model provides a systematic approach to managing stock positions, balancing valuation levels and market trends[3][11] --- Model Backtesting Results 1. Timing System Model - **Distance between Moving Averages**: 5.21% (greater than the 3% threshold, indicating an upward trend)[2][10][16] 2. Industry Allocation Model - **Recommended Sectors**: - Distressed reversal sectors: Hong Kong innovative drugs, Hong Kong securities, Hang Seng consumption - High-elasticity sectors: Technology, military, AI applications, solid-state batteries[3][11][16] 3. Position Management Model - **Stock Allocation Recommendation**: 80% allocation to stocks based on the WIND All A Index[3][11]