Core Viewpoint - The market is expected to experience a correction in the upcoming week due to various technical and quantitative indicators suggesting a weakening market sentiment [1][2]. Market Analysis - The liquidity shock indicator for the CSI 300 index was recorded at 1.71, indicating that current market liquidity is 1.71 times higher than the average level over the past year [2]. - The PUT-CALL ratio for the SSE 50 ETF options increased to 0.80, reflecting a growing 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 Index were 1.07% and 1.65%, respectively, indicating a decrease in trading activity [2]. Macroeconomic Factors - The onshore and offshore RMB exchange rates experienced slight declines of -0.08% and -0.1% respectively [2]. - New RMB loans in June amounted to 22,400 billion, exceeding the consensus forecast of 18,447.29 billion and the previous value of 6,200 billion [2]. - The broad money supply (M2) grew by 8.3% year-on-year, surpassing both the consensus forecast of 8.08% and the previous value of 7.9% [2]. Technical Analysis - The Wind All A Index remains above the SAR point, but the index and SAR point are now closely aligned [2]. - The moving average strength index is currently at 253, placing it in the 93.8 percentile since 2021 [2]. - The sentiment model score is 1 out of 5, indicating a decrease in market sentiment, while the trend model signal is positive and the weighted model signal is negative [2]. Performance Overview - For the week of July 14-18, the SSE 50 Index rose by 0.28%, the CSI 300 Index increased by 1.09%, the CSI 500 Index gained 1.2%, and the ChiNext Index surged by 3.17% [3]. - The overall market PE (TTM) stands at 20.4 times, which is at the 65.3 percentile since 2005 [3]. Factor Crowding Observation - The small-cap factor crowding is at a high level with a score of 1.07, while the low valuation factor crowding is at 0.36 [3]. - The industry crowding levels are relatively high in banking, comprehensive, non-ferrous metals, steel, and non-bank financial sectors, with notable increases in steel and pharmaceutical industries [3].

Quantitative Models and Construction Methods 1. Model Name: Low Volatility Uptrend Environment Model - **Model Construction Idea**: The model categorizes market environments based on rolling 60-day annualized return and volatility percentiles, defining six distinct market states: low-volatility uptrend, medium-volatility uptrend, high-volatility uptrend, low-volatility downtrend, medium-volatility downtrend, and high-volatility downtrend[5][16] - **Model Construction Process**: 1. Calculate the rolling 60-day annualized return and volatility for the CSI 300 and CSI 800 total return indices since 2010[5][16] 2. Define return > 0 as an uptrend and return ≤ 0 as a downtrend[5][16] 3. Categorize volatility percentiles: - Low volatility: below the 20th percentile - Medium volatility: between the 20th and 80th percentiles - High volatility: above the 80th percentile[5][16] 4. Combine return and volatility categories to form six market states[5][16] - **Model Evaluation**: The low-volatility uptrend environment demonstrates superior performance in terms of future returns, win rates, and payoff ratios, indicating a higher probability of sustained "slow bull" markets[5][16] 2. Model Name: Short-Term Quantitative Timing Model - **Model Construction Idea**: The model integrates macroeconomic, valuation, sentiment, and liquidity signals to generate short-term market timing recommendations[18][19][20] - **Model Construction Process**: 1. **Macroeconomic Signals**: - Manufacturing PMI percentile (44.92%): Neutral signal - Long-term loan growth percentile (0.00%): Cautious signal - M1 growth percentile (94.92%): Optimistic signal[18][22] 2. **Valuation Signals**: - PE percentile (95.70%): Neutral signal - PB percentile (79.32%): Neutral signal[19][22] 3. **Sentiment Signals**: - Beta dispersion percentile (40.68%): Neutral signal - Volume sentiment score percentile (87.76%): Optimistic signal - Volatility percentile (0.58%): Optimistic signal[19][22] 4. **Liquidity Signals**: - Money market rate percentile (33.90%): Optimistic signal - Exchange rate expectation percentile (40.68%): Neutral signal - 5-day average net financing percentile (94.04%): Neutral signal[20][22] 5. Combine signals to derive overall timing recommendations[18][19][20] - **Model Evaluation**: The model has consistently outperformed its benchmark since 2012, with an annualized return of 16.81% and a maximum drawdown of 27.70%, demonstrating robust performance[20][24] 3. Model Name: Growth-Value Style Rotation Model - **Model Construction Idea**: The model evaluates macroeconomic, valuation, and sentiment factors to recommend overweighting growth or value styles[29] - **Model Construction Process**: 1. **Macroeconomic Signals**: - Profit cycle slope (4.17): Favorable for growth - Interest rate cycle level (9.17): Favorable for value - Credit cycle change (-3.33): Favorable for value[31] 2. **Valuation Signals**: - PE spread percentile (16.36%): Favorable for growth - PB spread percentile (36.82%): Favorable for growth[31] 3. **Sentiment Signals**: - Turnover spread percentile (29.45%): Favorable for value - Volatility spread percentile (17.44%): Favorable for balance[31] 4. Combine signals to derive style rotation recommendations[29][31] - **Model Evaluation**: The strategy has delivered an annualized return of 11.71% since 2012, outperforming the benchmark by 4.80% annually[30][33] 4. Model Name: Small-Cap vs. Large-Cap Style Rotation Model - **Model Construction Idea**: The model evaluates macroeconomic, valuation, and sentiment factors to recommend overweighting small-cap or large-cap styles[34] - **Model Construction Process**: 1. **Macroeconomic Signals**: - Profit cycle slope (4.17): Favorable for small-cap - Interest rate cycle level (9.17): Favorable for large-cap - Credit cycle change (-3.33): Favorable for large-cap[36] 2. **Valuation Signals**: - PE spread percentile (78.86%): Favorable for large-cap - PB spread percentile (96.59%): Favorable for large-cap[36] 3. **Sentiment Signals**: - Turnover spread percentile (72.56%): Favorable for small-cap - Volatility spread percentile (62.60%): Favorable for large-cap[36] 4. Combine signals to derive style rotation recommendations[34][36] - **Model Evaluation**: The strategy has delivered an annualized return of 12.38% since 2012, outperforming the benchmark by 5.31% annually[35][38] 5. Model Name: Four-Dimensional Style Rotation Model - **Model Construction Idea**: Combines growth-value and small-cap-large-cap rotation models to recommend allocations across four styles: small-cap growth, small-cap value, large-cap growth, and large-cap value[39] - **Model Construction Process**: 1. Integrate signals from the growth-value and small-cap-large-cap models 2. Recommend allocations based on combined signals: - Small-cap growth: 12.5% - Small-cap value: 37.5% - Large-cap growth: 12.5% - Large-cap value: 37.5%[39][40] - **Model Evaluation**: The strategy has delivered an annualized return of 13.29% since 2012, outperforming the benchmark by 5.82% annually[39][40] --- Model Backtest Results 1. Low Volatility Uptrend Environment Model - **Annualized Return**: 18.23% (CSI 300), 10.13% (CSI 800) - **Win Rate**: 63.65% (CSI 300), 55.42% (CSI 800) - **Payoff Ratio**: 1.77 (CSI 300), 1.48 (CSI 800)[5][16][17] 2. Short-Term Quantitative Timing Model - **Annualized Return**: 16.81% - **Annualized Volatility**: 14.55% - **Maximum Drawdown**: 27.70% - **Sharpe Ratio**: 1.0033 - **Monthly Win Rate**: 69.74% - **Quarterly Win Rate**: 69.23%[20][24] 3. Growth-Value Style Rotation Model - **Annualized Return**: 11.71% - **Annualized Volatility**: 20.81% - **Maximum Drawdown**: 43.07% - **Sharpe Ratio**: 0.5409 - **Monthly Win Rate**: 58.28% - **Quarterly Win Rate**: 60.78%[30][33] 4. Small-Cap vs. Large-Cap Style Rotation Model - **Annualized Return**: 12.38% - **Annualized Volatility**: 22.69% - **Maximum Drawdown**: 50.65% - **Sharpe Ratio**: 0.5408 - **Monthly Win Rate**: 60.93% - **Quarterly Win Rate**: 58.82%[35][38] 5. Four-Dimensional Style Rotation Model - **Annualized Return**: 13.29% - **Annualized Volatility**: 21.55% - **Maximum Drawdown**: 47.91% - **Sharpe Ratio**: 0.5951 - **Monthly Win Rate**: 59.60% - **Quarterly Win Rate**: 62.75%[39][40]

- 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].

金融工程 | 金工定期报告 金融工程 证券研究报告 量化择时周报：突破震荡上轨后如何应对? 突破震荡上轨后如何应对? 上周周报(20250622)认为：短期市场宏观不确定性增加和指数在震荡格局 上沿位置的压制下，成交仍未到达低位，风险偏好较难快速提升，继续维 持中性偏低仓位，等待缩量信号。最终 wind 全 A 全周表现大超预期，上 涨 3.56%。市值维度上，上周代表小市值股票的中证 2000 上涨 5.55%，中 盘股中证 500 下跌 3.98%，沪深 300 上涨 1.95%，上证 50 上涨 1.27%;上周中 信一级行业中，表现较强行业包括综合金融、计算机，综合金融上涨 14.48%， 石油石化、食品饮料表现较弱，石油石化下跌 1.45%。上周成交活跃度上， 非银金融和国防军工资金流入明显。 从择时体系来看，我们定义的用来区别市场整体环境的 wind 全 A 长期均 线（120 日）和短期均线（20 日）的距离继续扩大，最新数据显示 20 日 线收于 5168，120 日线收于 5079 点，短期均线继续位于长线均线之上， 两线差值由上周的 1.09%扩大至 1.76%，距离绝对值继续小于 3%， ...