- The report identifies the market's uptrend by analyzing the distance between the 120-day and 20-day moving averages of the WIND All A index, which has expanded from 3.04% to 4.04%, indicating a continued uptrend[2][10][17] - The core observation variable for the market's uptrend is the "profitability effect," which is currently positive at 3.76%, suggesting that incremental funds are likely to continue entering the market[2][4][11] - The industry allocation model recommends sectors such as Hong Kong innovative drugs, Hong Kong securities, and Hang Seng consumption, with additional opportunities in the photovoltaic sector due to anti-involution benefits[3][4][11] - The TWO BETA model continues to recommend the technology sector, with a focus on military and AI applications[3][4][11] - The valuation indicators show that the WIND All A index's overall PE is at the 70th percentile, indicating a moderate level, while the PB is at the 30th percentile, indicating a relatively low level[3][11] - Based on the short-term trend judgment and the position management model, the report suggests an 80% position for absolute return products with the WIND All A index as the main stock allocation[3][11] Model Backtesting Results - The distance between the 20-day and 120-day moving averages of the WIND All A index is 4.04%, indicating a continued uptrend[2][10][17] - The profitability effect value is 3.76%, which is significantly positive, suggesting that the market is likely to continue its uptrend despite short-term fluctuations[2][4][11]

Group 1 - Market sentiment indicators have improved, with the sentiment score rising from -0.9 to -0.25, indicating a shift towards a more bullish outlook [9][13][18] - The increase in industry trading congestion and the positive shift in the PCR combined with the VIX index reflect a recovery in market sentiment [13][18] - The total trading volume in the A-share market has shown a significant increase, with the highest daily trading volume reaching 1,736.61 billion RMB [18][19] Group 2 - The construction materials sector has shown a significant upward trend, with a short-term trend score increase of 21.05% [32][33] - The model indicates that small-cap growth stocks are currently favored, with a strong signal for small-cap stocks and a rapid increase in the 5-day RSI relative to the 20-day RSI [32][37] - The sectors with the strongest short-term trends include defense, media, communication, and computer industries [32][33]

Quantitative Models and Construction Methods Models Model Name: Industry Allocation Model - **Model Construction Idea**: This model aims to recommend industry sectors based on medium-term trends and specific market conditions[2][3][10] - **Model Construction Process**: - The model identifies sectors that are likely to benefit from current market trends and conditions. - It recommends sectors such as Hong Kong innovative drugs, Hong Kong securities, and photovoltaic sectors due to their potential for reversal and growth. - The model also suggests focusing on technology sectors, including military and communication, as well as A-share banks and gold stocks[2][3][10] - **Model Evaluation**: The model is effective in identifying sectors with potential growth and aligning with current market trends[2][3][10] Model Name: TWO BETA Model - **Model Construction Idea**: This model focuses on recommending technology sectors based on their beta values and market conditions[2][3][10] - **Model Construction Process**: - The model evaluates the beta values of different sectors to identify those with higher potential for growth. - It recommends technology sectors, particularly military and communication, based on their beta values and current market trends[2][3][10] - **Model Evaluation**: The model is useful for identifying high-potential technology sectors based on their beta values[2][3][10] Model Name: Position Management Model - **Model Construction Idea**: This model aims to manage stock positions based on valuation indicators and short-term trends[3][10] - **Model Construction Process**: - The model uses valuation indicators such as PE and PB ratios to determine the stock positions. - It suggests an 80% stock position for absolute return products based on the current valuation levels of the wind All A index[3][10] - **Model Evaluation**: The model provides a balanced approach to managing stock positions based on valuation and market trends[3][10] Model Backtesting Results 1. **Industry Allocation Model**: - **PE Ratio**: 70th percentile[3][10] - **PB Ratio**: 30th percentile[3][10] - **Position Suggestion**: 80%[3][10] 2. **TWO BETA Model**: - **PE Ratio**: 70th percentile[3][10] - **PB Ratio**: 30th percentile[3][10] - **Position Suggestion**: 80%[3][10] 3. **Position Management Model**: - **PE Ratio**: 70th percentile[3][10] - **PB Ratio**: 30th percentile[3][10] - **Position Suggestion**: 80%[3][10] Quantitative Factors and Construction Methods Factor Name: Moving Average Distance - **Factor Construction Idea**: This factor measures the distance between short-term and long-term moving averages to identify market trends[2][9][14] - **Factor Construction Process**: - Calculate the 20-day moving average and the 120-day moving average of the wind All A index. - Compute the distance between the two moving averages. - The formula is: $$ \text{Distance} = \frac{\text{20-day MA} - \text{120-day MA}}{\text{120-day MA}} $$ - If the distance exceeds 3%, the market is considered to be in an upward trend[2][9][14] - **Factor Evaluation**: The factor is effective in identifying market trend shifts from a volatile to an upward trend[2][9][14] Factor Name: Profitability Effect - **Factor Construction Idea**: This factor measures the market's profitability effect to predict the inflow of incremental funds[2][10][14] - **Factor Construction Process**: - Calculate the profitability effect value based on market data. - The current profitability effect value is 3.50%, indicating a positive market trend[2][10][14] - **Factor Evaluation**: The factor is useful for predicting the inflow of incremental funds based on market profitability[2][10][14] Factor Backtesting Results 1. **Moving Average Distance**: - **Distance**: 3.04%[2][9][14] - **Profitability Effect**: 3.50%[2][10][14] 2. **Profitability Effect**: - **Distance**: 3.04%[2][9][14] - **Profitability Effect**: 3.50%[2][10][14]

Core Viewpoint - The market uptrend is expected to continue, supported by technical indicators and optimistic market sentiment [1][2]. Market Indicators - The liquidity shock indicator for the CSI 300 index was 1.19, lower than the previous week (1.36), indicating current market liquidity is 1.19 times above the average level of the past year [2]. - The PUT-CALL ratio for the SSE 50 ETF options decreased to 0.79 from 0.95, reflecting increased investor optimism 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 0.98% and 1.62%, respectively, maintaining trading activity at 66.75% and 75.52% percentiles since 2005 [2]. Macro Factors - The onshore and offshore RMB exchange rates experienced slight fluctuations, with weekly increases of 0.05% and 0.12%, respectively [2]. - China's official manufacturing PMI for June was 49.7, up from the previous value of 49.5 and above the expected 49.3; the Caixin manufacturing PMI for June was 50.4, higher than the previous 48.3 [2]. Technical Analysis - The Wind All A index broke above the SAR indicator on June 24, signaling a buy [2]. - The current market score based on the moving average strength index is 228, placing it in the 88.8% percentile since 2021 [2]. - The sentiment model score is 3 out of 5, with both trend and weighted models indicating positive signals [2]. Market Performance - The SSE 50 index rose by 1.21%, the CSI 300 index increased by 1.54%, the CSI 500 index went up by 0.81%, and the ChiNext index gained 1.5% during the last week [3]. - The overall market PE (TTM) stands at 20.0 times, positioned at the 60.1% percentile since 2005 [3]. Factor Crowding Observations - The crowding degree for high earnings growth factors has significantly increased [3]. - The crowding degrees for small-cap factors, low valuation factors, high earnings factors, and high earnings growth factors are 0.66, -0.10, -0.21, and 0.15, respectively [3]. - The industry crowding degrees are relatively high in banking, comprehensive, non-ferrous metals, retail, and non-bank financial sectors, with construction materials and steel showing notable increases [3].

Group 1 - Market sentiment indicator decreased to -0.9, down from -0.65, indicating a bearish outlook [9][11] - The trading volatility between sectors has decreased, reflecting a lack of capital activity and increased divergence in market sentiment [11][17] - The total trading volume of the A-share market showed a gradual decline throughout the week, with the lowest daily trading volume recorded at 1.3335 trillion RMB on Thursday [15][17] Group 2 - The model indicates a preference for large-cap stocks, with the 20-day RSI close to the 60-day RSI level, suggesting potential for continued strength in large-cap stocks [29][35] - The short-term trend scores for industries such as steel, construction materials, and basic chemicals have significantly increased, with construction materials showing a rise of 90.91% [29][30] - The sectors with the strongest short-term trends include banks, communications, media, and non-ferrous metals [29][30]

Quantitative Models and Construction Methods Model Name: Wind All A Index Timing System - **Model Construction Idea**: The model aims to distinguish the overall market environment by analyzing the distance between long-term and short-term moving averages of the Wind All A Index[1][10][16] - **Model Construction Process**: - Define the long-term moving average (120-day) and short-term moving average (20-day) of the Wind All A Index[1][10] - Calculate the distance between the two moving averages: $$ \text{Distance} = \frac{\text{Short-term MA} - \text{Long-term MA}}{\text{Long-term MA}} $$ where the short-term MA is the 20-day moving average and the long-term MA is the 120-day moving average[1][10] - Monitor the distance value to determine market conditions. If the distance exceeds 3%, it signals a change from a volatile to an upward trend[1][10][16] - **Model Evaluation**: The model is effective in identifying market trends and providing signals for adjusting positions[1][10][16] Model Name: Industry Allocation Model - **Model Construction Idea**: The model recommends industry sectors based on medium-term perspectives and current market trends[2][4][11] - **Model Construction Process**: - Analyze the performance and trends of various industry sectors[2][4][11] - Identify sectors with potential for reversal or growth, such as distressed reversal sectors, innovative drugs in Hong Kong stocks, and photovoltaic sectors benefiting from anti-involution[2][4][11] - Use the TWO BETA model to recommend technology sectors, focusing on military and communication industries[2][4][11] - **Model Evaluation**: The model provides targeted industry recommendations based on current market conditions and trends[2][4][11] Model Name: Position Management Model - **Model Construction Idea**: The model manages stock positions based on valuation indicators and short-term market trends[3][12] - **Model Construction Process**: - Evaluate the overall PE and PB ratios of the Wind All A Index[3][12] - Determine the stock position based on the valuation levels and short-term market trends. For example, with the Wind All A Index at a medium PE level (70th percentile) and a low PB level (30th percentile), the recommended position is 60%[3][12] - **Model Evaluation**: The model helps in managing stock positions effectively by considering valuation levels and market trends[3][12] Model Backtest Results Wind All A Index Timing System - **Distance between Moving Averages**: 2.52%[1][10][16] Industry Allocation Model - **Recommended Sectors**: Distressed reversal sectors, innovative drugs in Hong Kong stocks, photovoltaic sectors, technology sectors (military and communication), A-share banks, and gold stocks[2][4][11] Position Management Model - **Recommended Position**: 60%[3][12]

Core Viewpoint - The market is expected to continue its upward trend in the coming week, supported by various technical and macroeconomic indicators [1][2]. Market Indicators - The liquidity shock indicator for the CSI 300 index was 1.36, indicating current market liquidity is 1.36 times higher than the average level over the past year [2]. - The PUT-CALL ratio for the SSE 50 ETF decreased to 0.95, suggesting reduced caution among investors regarding short-term movements [2]. - The five-day average turnover rates for the SSE Composite Index and Wind All A were 0.99% and 1.63%, respectively, indicating increased trading activity [2]. Macroeconomic Factors - The RMB exchange rate fluctuated, with onshore and offshore rates increasing by 0.2% and 0.09% respectively [2]. - Historical data shows that from 2005 onwards, the probability of the SSE Composite Index, CSI 300, CSI 500, and ChiNext Index rising in the first half of July is 60%, 60%, 55%, and 53%, with average gains of 0.67%, 0.93%, 1.55%, and 1.6% respectively [2]. Event-Driven Insights - The US stock market rebounded, with the Dow Jones, S&P 500, and Nasdaq indices posting weekly returns of 3.82%, 3.44%, and 4.25% respectively [2]. - Several Federal Reserve officials signaled a dovish stance, with discussions around potential interest rate cuts in July if inflation remains controlled [2]. Technical Analysis - The Wind All A index broke above the SAR point on June 24, generating a buy signal [2]. - The current market score based on the moving average strength index is 216, placing it in the 85.1% percentile since 2021 [2]. - The sentiment model score is 3 out of 5, indicating a positive trend and sentiment in the market [2]. Market Performance - For the week of June 23-27, the SSE 50 index rose by 1.27%, the CSI 300 index by 1.95%, the CSI 500 index by 3.98%, and the ChiNext index by 5.69% [3]. - The overall market PE (TTM) stands at 19.7 times, which is in the 57.5% percentile since 2005 [3]. Factor Observations - The crowding degree for small-cap factors continues to decline, with a score of 0.74 for small-cap factors, -0.48 for low valuation factors, -0.31 for high profitability factors, and -0.15 for high growth factors [3]. - The industry crowding degree is relatively high in banking, non-ferrous metals, comprehensive, non-bank financials, and retail sectors, with significant increases in non-bank financials and banking [3].

- The report defines a timing system signal based on the distance between the long-term moving average (120 days) and the short-term moving average (20 days) of the Wind All A Index, which is currently at 1.76%, indicating the market is still in a consolidation pattern[1][3][9] - The industry allocation model recommends mid-term allocation to sectors experiencing a turnaround, such as Hong Kong innovative drugs, new consumption, and Hong Kong finance, with the trend still intact[2][3][10] - The TWO BETA model continues to recommend the technology sector, with a focus on military and communication sectors[2][3][10] - The Wind All A Index's PE ratio is at the 65th percentile, indicating a medium level, while the PB ratio is at the 20th percentile, indicating a relatively low level[2][10] - The position management model suggests a 50% allocation to absolute return products based on the Wind All A Index[2][10] Model Backtest Results - Timing system signal: Moving average distance 1.76%[1][3][9] - Industry allocation model: Mid-term recommendation for turnaround sectors, Hong Kong innovative drugs, new consumption, and Hong Kong finance[2][3][10] - TWO BETA model: Recommendation for technology sector, focus on military and communication[2][3][10] - Wind All A Index PE ratio: 65th percentile[2][10] - Wind All A Index PB ratio: 20th percentile[2][10] - Position management model: 50% allocation to absolute return products[2][10]

Market Performance - The recent five trading days saw the Sci-Tech 50 Index increase by 3.17%, the ChiNext Index by 5.69%, the large-cap value by 1.52%, the large-cap growth by 2.61%, the SSE 50 by 1.27%, and the small-cap represented by the CSI 2000 by 4.94% [1] - The sectors showing strong performance include computers and national defense, while oil, petrochemicals, and food and beverages lagged behind [1] Risk Premium Analysis - The risk premium, calculated as the inverse of the static PE of the CSI All Index 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 stood at 4.11%, marking the fifth occurrence since 2016 to exceed 4% [1] Valuation Levels - As of June 27, 2025, the CSI All Index's PETTM percentile is at 59%, with the SSE 50 and CSI 300 at 66% and 57% respectively, while the ChiNext Index is close to 19% [2] - The ChiNext Index's valuation is relatively low compared to historical averages [2] Long-term Market Trends - The technical analysis of the Deep 100 Index suggests a cyclical pattern of bear markets every three years, followed by bull markets, with significant declines observed in previous cycles [2] - The current adjustment phase, which began in Q1 2021, appears to have sufficient time and space for a potential upward cycle [2] Fund Flow and Trading Activity - In the last five trading days, ETF funds experienced an outflow of 1.3 billion yuan, while margin trading increased by approximately 17 billion yuan, with an average daily trading volume of 1.4528 trillion yuan across the two markets [4] AI and Machine Learning Applications - The use of convolutional neural networks (CNN) for modeling price and volume data has been explored, with the latest focus on sectors such as banking and artificial intelligence [3][11]

- Model Name: Short-term Quantitative Timing Model; Model Construction Idea: The model is based on market sentiment indicators, valuation, macro liquidity, and macro fundamentals to generate timing signals; Model Construction Process: The model uses various indicators such as manufacturing PMI, long-term loan balance growth rate, M1 growth rate, PE and PB valuation percentiles, Beta dispersion, volume sentiment score, volatility, monetary rate, exchange rate expectation, and net financing amount to generate signals. For example, the formula for the volume sentiment score is: $$ \text{Volume Sentiment Score} = \frac{\text{Current Volume} - \text{Mean Volume}}{\text{Standard Deviation of Volume}} $$ where the current volume is the trading volume of the current period, the mean volume is the average trading volume over a specified period, and the standard deviation of volume is the standard deviation of trading volumes over the same period. The model evaluates these indicators to determine the overall market sentiment and generates a timing signal accordingly[9][14][15]; Model Evaluation: The model is highly sensitive to market sentiment indicators, which can lead to frequent signal changes[9] - Model Name: Growth-Value Style Rotation Model; Model Construction Idea: The model uses economic cycle analysis to determine the allocation between growth and value styles; Model Construction Process: The model evaluates the slope of the profit cycle, the level of the interest rate cycle, and the changes in the credit cycle. For example, the formula for the profit cycle slope is: $$ \text{Profit Cycle Slope} = \frac{\text{Current Profit} - \text{Previous Profit}}{\text{Previous Profit}} $$ where the current profit is the profit of the current period, and the previous profit is the profit of the previous period. The model also considers PE and PB valuation differences and turnover and volatility differences between growth and value styles to generate allocation signals[25][26]; Model Evaluation: The model provides significant improvement over the benchmark in terms of annualized returns and Sharpe ratio[25][26] - Model Name: Small-Cap vs. Large-Cap Style Rotation Model; Model Construction Idea: The model uses economic cycle analysis to determine the allocation between small-cap and large-cap styles; Model Construction Process: The model evaluates the slope of the profit cycle, the level of the interest rate cycle, and the changes in the credit cycle. For example, the formula for the interest rate cycle level is: $$ \text{Interest Rate Cycle Level} = \frac{\text{Current Interest Rate} - \text{Mean Interest Rate}}{\text{Standard Deviation of Interest Rate}} $$ where the current interest rate is the interest rate of the current period, the mean interest rate is the average interest rate over a specified period, and the standard deviation of interest rate is the standard deviation of interest rates over the same period. The model also considers PE and PB valuation differences and turnover and volatility differences between small-cap and large-cap styles to generate allocation signals[30][31][32]; Model Evaluation: The model provides significant improvement over the benchmark in terms of annualized returns and Sharpe ratio[30][31][32] - Model Name: Four-Style Rotation Model; Model Construction Idea: The model combines the conclusions of the growth-value and small-cap vs. large-cap rotation models to determine the allocation among four styles: small-cap growth, small-cap value, large-cap growth, and large-cap value; Model Construction Process: The model uses the signals generated by the growth-value and small-cap vs. large-cap rotation models to allocate the portfolio among the four styles. For example, if the growth-value model suggests overweighting value and the small-cap vs. large-cap model suggests overweighting large-cap, the allocation would be adjusted accordingly[33][34]; Model Evaluation: The model provides significant improvement over the benchmark in terms of annualized returns and Sharpe ratio[33][34] Model Backtest Results - Short-term Quantitative Timing Model: Annualized Return 16.24%, Annualized Volatility 14.70%, Maximum Drawdown 27.70%, Sharpe Ratio 0.9613, IR 0.5862, Monthly Win Rate 68.21%, Quarterly Win Rate 68.63%, Annual Win Rate 85.71%[16][19][22] - Growth-Value Style Rotation Model: Annualized Return 11.51%, Annualized Volatility 20.85%, Maximum Drawdown 43.07%, Sharpe Ratio 0.5316, IR 0.2672, Monthly Win Rate 58.00%, Quarterly Win Rate 60.00%, Annual Win Rate 85.71%[27][29] - Small-Cap vs. Large-Cap Style Rotation Model: Annualized Return 11.92%, Annualized Volatility 22.75%, Maximum Drawdown 50.65%, Sharpe Ratio 0.5283, IR 0.2386, Monthly Win Rate 60.67%, Quarterly Win Rate 56.00%, Annual Win Rate 85.71%[32] - Four-Style Rotation Model: Annualized Return 13.03%, Annualized Volatility 21.60%, Maximum Drawdown 47.91%, Sharpe Ratio 0.5834, IR 0.2719, Monthly Win Rate 59.33%, Quarterly Win Rate 62.00%, Annual Win Rate 85.71%[34][35]