Core Viewpoint - The market is expected to continue its upward trend in the coming week, supported by positive signals from SAR indicators and sentiment models, as well as new upward space in the moving average strength index [1][2]. Market Indicators - The liquidity shock indicator for the CSI 300 index was 1.26, lower than the previous week's 1.73, indicating current market liquidity is 1.26 times above the average level of the past year [2]. - The PUT-CALL ratio for the SSE 50 ETF options increased to 0.66, up from 0.64 the previous week, suggesting a decrease in 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 1.67% and 2.58%, respectively, indicating trading activity is at the 83.35% and 90.49% percentile since 2005 [2]. Economic Events - The US stock market experienced fluctuations, with the Dow Jones, S&P 500, and Nasdaq indices showing weekly returns of -0.19%, -0.1%, and -0.19%, respectively [2]. - The US core PCE price index for July rose by 2.9% year-on-year, marking the highest level since February 2025, with a month-on-month increase of 0.3% [2]. Industry Insights - The State Council released an action plan for the deep implementation of "Artificial Intelligence +", aiming for significant integration of AI in six key areas by 2027 and a core industry scale of the smart economy to grow rapidly [2]. - The sentiment model score is 3 out of 5, indicating a positive trend signal, while the moving average strength index score is 244, placing it in the 89.2% percentile for 2023 [2]. Performance Overview - For the week of August 25-29, the SSE 50 Index rose by 1.63%, the CSI 300 Index by 2.71%, the CSI 500 Index by 3.24%, and the ChiNext Index by 7.74% [3]. - The current overall market PE (TTM) stands at 22.1 times, which is at the 75.5% percentile since 2005 [3]. Factor Crowding - The crowding degree for small-cap factors has significantly decreased to 0.46, while low valuation factors are at -0.65, high profitability factors at -0.18, and high profitability growth factors at 0.40 [3]. Industry Crowding - The industry crowding degree is relatively high in sectors such as non-ferrous metals, comprehensive, telecommunications, machinery equipment, and electronics, with telecommunications and non-ferrous metals showing a significant increase [4].

- The report uses the Mahalanobis distance of recent weekly price and trading volume changes to measure industry crowding[3][17] - Last week, the home appliances and non-ferrous metals industries experienced significant short-term crowding[4][17] - The first quadrant represents industries with rising prices and volumes, while the third quadrant represents industries with falling prices and volumes. Points outside the ellipse indicate industries with price and volume deviations exceeding the 99% confidence level, signifying short-term significant crowding[17]

Core Insights - Fund managers have made clear adjustments in their portfolios during Q2, indicating strong signals in their investment directions [1][2] Group 1: Sector Focus - The technology sector continues to lead, with significant investments in AI and semiconductor industries, reflecting a strong demand for AI computing power [4][9] - The defense and military industry has seen a holding increase to 4.2%, driven by geopolitical tensions, making it a preferred choice for risk-averse and aggressive investors [6] - The financial sector is experiencing a valuation recovery, with bank holdings rising to 4.9%, supported by low valuations and high dividend yields [7] Group 2: Investment Trends - Passive funds, including ETFs, have seen substantial inflows, with the CSI 300 and CSI 1000 ETFs increasing by 241 million and 115 million shares, respectively, indicating a strong market interest [8] - The electronic industry maintains a high holding of 18.8%, but the high concentration in semiconductors may limit future aggressive investments due to potential adjustment risks [9] - The wine sector has seen a significant reduction in holdings, dropping to 2% after excluding certain funds, signaling a potential exit from this market [11] Group 3: Market Dynamics - Certain sectors like automotive, food and beverage, and power equipment have experienced notable reductions in holdings, with food and beverage seeing a 2.1 percentage point decline, influenced by regulatory pressures [13] - The cyclical and defensive sectors are rising, with agriculture and livestock holdings at 1.6%, indicating a positive shift in fundamentals for these segments [6]

Group 1 - Market sentiment remains high with an index value of 3.2, showing signs of potential decline, suggesting further observation is needed [3][9] - The trading volume across the A-share market has significantly increased, with daily trading exceeding 2 trillion RMB for three consecutive days, indicating strong market activity [15][17] - The industry trend indicators show an upward breakout, reflecting a narrowing of funding viewpoint discrepancies [21][23] Group 2 - The small-cap and growth styles are currently favored, with the electronic and computer sectors showing the strongest short-term trend scores, particularly with scores reaching 100 [30][31] - The model indicates a high degree of trading concentration in sectors like machinery, electronics, and construction decoration, which may pose valuation and sentiment risks [39][41] - The report highlights that sectors with lower trading concentration, such as beauty care and public utilities, may present opportunities for gradual long-term positioning as risk appetite increases [39][41]

Quantitative Models and Construction Methods 1. Model Name: Industry Crowding Monitoring Model - **Model Construction Idea**: This model is designed to monitor the crowding levels of Shenwan First-Level Industry Indices on a daily basis, identifying industries with high or low crowding levels to provide actionable insights[3] - **Model Construction Process**: The model calculates the crowding levels of various industries based on specific metrics (not detailed in the report) and ranks them accordingly. For example, the report highlights that the building materials, military, and non-ferrous industries had high crowding levels, while coal, petrochemical, and transportation had low crowding levels on the previous trading day[3] - **Model Evaluation**: The model provides a useful tool for identifying industry trends and potential investment opportunities by analyzing crowding dynamics[3] 2. Model Name: Premium Rate Z-Score Model - **Model Construction Idea**: This model is used to screen ETF products by calculating their premium rate Z-scores, identifying potential arbitrage opportunities while also warning of potential pullback risks[4] - **Model Construction Process**: The model employs a rolling calculation of the Z-score of the premium rate for various ETF products. The Z-score is calculated as: $ Z = \frac{(X - \mu)}{\sigma} $ where $ X $ is the current premium rate, $ \mu $ is the mean premium rate over a rolling window, and $ \sigma $ is the standard deviation of the premium rate over the same window. This helps identify ETFs with significant deviations from their historical norms[4] - **Model Evaluation**: The model is effective in identifying ETFs with potential arbitrage opportunities and provides a risk management tool for investors[4] --- Model Backtesting Results 1. Industry Crowding Monitoring Model - **Top Crowded Industries**: Building materials, military, and non-ferrous industries had the highest crowding levels on the previous trading day[3] - **Least Crowded Industries**: Coal, petrochemical, and transportation industries had the lowest crowding levels on the previous trading day[3] 2. Premium Rate Z-Score Model - **Application Example**: The model flagged specific ETFs for potential arbitrage opportunities, such as the Battery Leaders ETF (159767.SZ), which tracks the New Energy Battery Index and has a fund size of 1.13 billion yuan[14] --- Quantitative Factors and Construction Methods No specific quantitative factors were detailed in the report beyond the models described above --- Factor Backtesting Results No specific backtesting results for individual factors were detailed in the report beyond the models described above

Group 1 - Market sentiment indicators show a slight increase to 3.25, maintaining a high level and a bullish outlook, although there is a need to monitor for potential turning points as scores show a slight decline during the week [9][12][30] - The price-volume consistency indicator remains elevated, indicating high levels of market activity, while the PCR combined with VIX has shifted from positive to negative, suggesting a change in market sentiment [12][23][24] - Total trading volume for the week showed a slight decline but remained strong, with daily trading volumes exceeding 1.6 trillion RMB on most days, indicating robust market activity [17][30] Group 2 - The report highlights that sectors with high trading congestion, such as machinery, defense, and non-ferrous metals, have seen significant price increases, but caution is advised due to potential valuation and sentiment corrections [30][34][36] - The report identifies that the small-cap growth style is currently favored, with the RSI model indicating a preference for growth stocks, although the 5-day RSI shows a rapid decline compared to the 20-day RSI, warranting further observation [30][39][41] - The report provides a detailed analysis of sector performance, with machinery, light industry, and defense showing the strongest short-term trends, particularly machinery scoring a perfect 100 [30][31][32]

Quantitative Models and Construction Methods 1. Model Name: Industry Crowding Monitoring Model - **Model Construction Idea**: This model is designed to monitor the crowding levels of Shenwan First-Level Industry Indices on a daily basis, identifying industries with high or low crowding levels and tracking changes in crowding over time[3] - **Model Construction Process**: The report does not provide specific details on the mathematical or algorithmic process used to construct the crowding monitoring model. However, it is implied that the model uses historical data and metrics to assess crowding levels across industries[3] - **Model Evaluation**: The model effectively highlights industries with significant crowding changes, such as the notable increases in crowding for the non-ferrous metals and beauty care sectors, providing actionable insights for investors[3] 2. Model Name: Premium Rate Z-Score Model - **Model Construction Idea**: This model is used to identify potential arbitrage opportunities in ETF products by calculating the Z-score of premium rates over a rolling window[4] - **Model Construction Process**: - The Z-score is calculated as: $ Z = \frac{(X - \mu)}{\sigma} $ - Where $ X $ is the current premium rate, $ \mu $ is the mean premium rate over the rolling window, and $ \sigma $ is the standard deviation of the premium rate over the same period - The model identifies ETFs with extreme Z-scores, signaling potential arbitrage opportunities or risks of price corrections[4] - **Model Evaluation**: The model provides a systematic approach to ETF selection, helping investors identify mispriced ETFs while cautioning against potential downside risks[4] --- Model Backtesting Results 1. Industry Crowding Monitoring Model - No specific numerical backtesting results or metrics are provided for this model in the report[3] 2. Premium Rate Z-Score Model - No specific numerical backtesting results or metrics are provided for this model in the report[4] --- Quantitative Factors and Construction Methods The report does not explicitly mention any standalone quantitative factors or their construction processes. It focuses on the models described above. --- Factor Backtesting Results No standalone factor backtesting results are provided in the report. The focus remains on the models and their applications.

Quantitative Models and Construction Methods 1. Model Name: Industry Crowding Monitoring Model - **Model Construction Idea**: Monitor the crowding level of industries on a daily basis[3] - **Model Construction Process**: The model is built to monitor the crowding level of Shenwan First-Level Industry Indexes daily. It tracks the main fund flows into and out of various industries, identifying those with high and low crowding levels[3] - **Model Evaluation**: The model provides valuable insights into industry crowding levels, helping investors identify potential investment opportunities and risks[3] 2. Model Name: Premium Rate Z-score Model - **Model Construction Idea**: Screen ETF products for potential arbitrage opportunities based on premium rate Z-score[4] - **Model Construction Process**: The model calculates the Z-score of the premium rate for various ETF products on a rolling basis. This helps identify ETFs with potential arbitrage opportunities while also warning of possible pullback risks[4] - **Model Evaluation**: The model is effective in identifying ETFs with potential arbitrage opportunities, but investors should be cautious of the associated risks[4] Model Backtesting Results Industry Crowding Monitoring Model - **Crowding Level**: Military, machinery equipment, coal, and finance showed significant changes in crowding levels[3] - **Main Fund Flows**: Main funds flowed into machinery, automotive, and military industries, while flowing out of pharmaceuticals and communications[3] Premium Rate Z-score Model - **ETF Products**: The model identified several ETFs with significant net inflows and outflows, indicating potential arbitrage opportunities[5][6] Quantitative Factors and Construction Methods 1. Factor Name: Main Fund Flow Factor - **Factor Construction Idea**: Track the main fund flows into and out of various industries over a period of time[3] - **Factor Construction Process**: The factor is constructed by monitoring the net inflows and outflows of main funds into Shenwan First-Level Industry Indexes daily. This helps identify industries with significant changes in fund allocation[3] - **Factor Evaluation**: The factor provides valuable insights into the allocation of main funds, helping investors make informed decisions[3] Factor Backtesting Results Main Fund Flow Factor - **Net Inflows and Outflows**: The factor showed significant net inflows into machinery, automotive, and military industries, and net outflows from pharmaceuticals and communications over the past three days[3][13] ETF Product Signals Premium Rate Z-score Model - **ETF Products to Watch**: The model identified several ETFs with potential arbitrage opportunities, including Medical Equipment ETF, China Concept Technology ETF, VR ETF, and Gold Stock ETF[14] Key Points - Industry crowding monitoring model tracks daily crowding levels of Shenwan First-Level Industry Indexes[3] - Premium rate Z-score model screens ETF products for potential arbitrage opportunities based on premium rate Z-score[4] - Main fund flow factor monitors net inflows and outflows of main funds into various industries[3] - Significant net inflows into machinery, automotive, and military industries, and net outflows from pharmaceuticals and communications[3][13] - ETF products identified for potential arbitrage opportunities include Medical Equipment ETF, China Concept Technology ETF, VR ETF, and Gold Stock ETF[14]

- The report does not contain any quantitative models or factors for analysis[1][2][3]

Group 1 - The report recommends a focus on cyclical sectors for August 2025, including oil and petrochemicals, construction, banking, agriculture, building materials, automotive, media, textiles, and pharmaceuticals [2][26][54] - The multi-strategy approach has achieved an annualized relative return of 7.08% since July 2011, with a maximum drawdown of 13.03% [2][26][62] - The dynamic balance strategy has an annualized absolute return of 16.45% from 2015 to July 2025, with a relative maximum drawdown of 10.18% [3][20][50] Group 2 - The macro-driven strategy has an annualized excess return of 4.44% since early 2016, with a maximum drawdown of 9.51% [4][18][42] - The report highlights the performance of various sectors, with the top-performing sectors in July being steel, pharmaceuticals, communications, building materials, and construction [11][12][13] - The report indicates that the current economic diffusion is the most important macro-driven factor, with an importance score of 105.52% [34][39] Group 3 - The report identifies crowded trading conditions in sectors such as coal, non-bank financials, and pharmaceuticals, indicating potential risks in these areas [5][68] - The dynamic balance strategy's absolute return in July was 4.85%, underperforming the benchmark with an excess return of -0.14% [3][50] - The multi-strategy sector allocation for August includes a high weight on oil and petrochemicals, construction, and banking, with no adjustments from the previous period [54][58][62]