Group 1 - The article discusses a macro quantitative framework that combines economic, liquidity, credit, and inflation factors for asset allocation and industry/style configuration [1][3] - The framework has been adjusted based on the changing mapping of macro variables to assets, with a focus on economic and liquidity indicators [1][5] - The performance of aggressive portfolios since 2013 shows an annualized return of approximately 8.5%, with a 0.6% excess return compared to the benchmark [3][5] Group 2 - The article highlights the impact of macroeconomic conditions on industry and style configurations, incorporating credit sensitivity into the analysis [5][7] - The macro-sensitive industry configuration has shown varying performance, with a notable decline since 2022, indicating the need for adjustments in selection criteria [7][10] - The article emphasizes the importance of market expectations in influencing macroeconomic indicators and their relationship with asset performance [13][18] Group 3 - The Factor Mimicking model is introduced to capture market expectations regarding macro variables, using a refined stock pool for better representation [19][20] - The construction of the Factor Mimicking portfolio aims to reflect the market's implicit views on economic, liquidity, inflation, and credit variables [19][23] - The article discusses the need for additional micro mappings to enhance the representation of macro variables, particularly in relation to corporate earnings and valuations [28][30] Group 4 - The article outlines the adjustments made to the macro variables based on market expectations, focusing on economic, liquidity, and credit dimensions [34][36] - The revised indicators are expected to improve asset allocation strategies, particularly in the context of equity markets [39][40] - The performance of the revised industry and style configurations indicates a positive impact from incorporating market expectations into the analysis [46][54]

金融工程 | 金工定期报告 配置方向上，我们的行业配置模型显示，中期角度行业配置继续推荐困境 反转型板块，推荐港股创新药和证券保险，上行趋势仍在延续，此外受益 政策驱动板块方面，光伏以及化工有望保持上行；TWO BETA 模型继续推 荐科技板块，继续关注军工算力以及电池。短期信号显示，黄金股有望在 调整后迎来反弹。 从估值指标来看，wind 全 A 指数整体 PE 位于 85 分位点附近，属于中等水 平，PB 位于 50 分位点附近，属于较低水平，结合短期趋势判断，根据我 们的仓位管理模型，当前以 wind 全 A 为股票配置主体的绝对收益产品建 议仓位至 80%。 金融工程 证券研究报告 量化择时周报：牛市思维，行业如何配置？ 牛市思维，行业如何配置？ 上周周报(20250817)认为：短期而言，上周市场放量加速上攻，短期日线 或有震荡，但仍建议逢低加仓；当前 WIND 全 A 趋势线位于 5625 点附近， 赚钱效应值为 3.73%，显著为正，在赚钱效应转负之前，建议耐心持有，保 持高仓位。最终 wind 全 A 继续大涨，全周上涨 3.87%。市值维度上，上周 代表小市值股票的中证 2000 上涨 3. ...

Group 1 - The report indicates a marginal increase in trading activity in the A-share market, with the average daily trading volume rising to 2.1 trillion yuan, and the turnover rate for the Shanghai Composite Index reaching 93% [2][14][20] - The report highlights a decrease in the proportion of stocks that are rising, which has dropped to 54.4%, while the median weekly return for all A-shares has decreased to 0.4% [2][15] - The report notes that the industry rotation index has shown a marginal increase, with 13 industries having turnover rates above the historical 90th percentile [2][27] Group 2 - The report tracks liquidity in the A-share market, noting an increase in ETF outflows and a shift to foreign capital inflows, with foreign capital inflowing 2.65 million USD [2][43][44] - Public funds have seen a decrease in newly established fund sizes, dropping to 5.947 billion yuan, while the overall stock positions of funds have increased [2][36] - Private equity confidence has shown a slight recovery, with the private equity fund confidence index increasing, although the overall positions have slightly decreased [2][41][42] Group 3 - The report indicates a clear divergence in capital allocation, with foreign capital flowing out of the household appliance and machinery sectors while primarily flowing into the metals sector [2][3][44] - The report highlights that the top sectors for financing inflows include electronics (+13.27 billion yuan) and machinery (+4.01 billion yuan), while coal (-0.23 billion yuan) and textiles (-0.01 billion yuan) have seen outflows [2][26] - The report also notes that the top sectors for ETF inflows include food and beverage (+0.59 billion yuan) and coal (+0.46 billion yuan), while electronics (-18.06 billion yuan) and computers (-3.90 billion yuan) have seen significant outflows [2][26] Group 4 - The report mentions that southbound capital inflows have increased, with net purchases rising to 38.12 billion yuan, marking a significant percentile since 2022 [5][4] - The report states that the Hang Seng Index rose by 1.7%, reflecting a general upward trend in global markets, with major markets showing positive performance [5][4] - The report indicates that global foreign capital has marginally flowed into developed markets, with the US and UK seeing the largest inflows, while China also experienced a net inflow of 5.6 million USD [5][4]

Core Viewpoint - The A-share market has reached a historic milestone, with the total market capitalization surpassing 100 trillion yuan for the first time, driven by significant increases in various sectors, particularly the information technology sector [1][8]. Market Performance - On August 18, the Shanghai Composite Index opened high and broke through the previous high of 3731.69 points, marking a ten-year high since August 2015 [1]. - The total market capitalization of A-shares reached 100.19 trillion yuan, an increase of 14.33 trillion yuan since the beginning of the year [1]. - The total trading volume for the year has reached 223.65 trillion yuan, with an average daily trading volume of 1.47 trillion yuan [1]. Sector Performance - The information technology sector has seen a market capitalization increase of 11.55% since August, making it the largest contributor to the overall market capitalization growth [7]. - Other sectors such as materials and industrials also experienced significant growth, with market capitalization increases of 7.10% and 6.54%, respectively [7]. - The financial sector maintained a strong position with a market capitalization of 177.02 trillion yuan, reflecting a 3.39% increase [7]. Investor Behavior - There is a notable influx of retail investors into the market, although their overall participation remains cautious due to a prevailing "fear of heights" sentiment [8][9]. - New individual investor accounts have shown marginal improvement since May, but the absolute numbers remain low, indicating a lack of significant capital inflow from retail investors [8][9]. - The trend of "capital migration" among residents is expected to continue, with a decrease in the attractiveness of low-interest savings and financial products, potentially leading to increased investment in the stock market [10]. Future Outlook - Institutional funds are anticipated to continue flowing into A-shares, with foreign capital shifting from net selling to net buying [10]. - The report suggests focusing on three investment directions: technology sectors such as consumer electronics and AI software, new consumption trends, and thematic investments like commercial aerospace and brain-computer interfaces [10].

Quantitative Models and Construction Methods 1. Model Name: Timing System Signal (Wind All A Moving Average Distance 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's overall trend. A positive and expanding distance indicates an upward trend[2][9]. - **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. - Latest values: 20-day MA = 5658, 120-day MA = 5241[2][9]. 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\% $ - Current distance = 7.96%[2][9]. 3. Interpret the signal: If the distance is greater than 3% and positive, the market is in an upward trend[2][9]. - **Model Evaluation**: The model effectively captures the market's upward momentum and provides a clear signal for maintaining high equity positions during positive trends[2][9]. 2. Model Name: Industry Allocation Model - **Model Construction Idea**: This model identifies industries with potential for medium-term outperformance based on factors such as policy support, valuation, and growth trends[2][10]. - **Model Construction Process**: 1. Analyze industry-specific drivers, including policy incentives and growth catalysts. 2. Identify sectors with "distressed reversal" characteristics or benefiting from policy-driven growth. 3. Recommend sectors such as innovative pharmaceuticals, securities insurance, photovoltaics, coal, and non-ferrous metals. 4. Use the TWO BETA model to emphasize technology-related sectors, including military, computing power, and batteries[2][10]. - **Model Evaluation**: The model provides actionable insights for sector rotation, aligning with macroeconomic and policy trends[2][10]. 3. Model Name: Position Management Model - **Model Construction Idea**: This model determines optimal equity allocation levels based on valuation metrics and market trends[3][10]. - **Model Construction Process**: 1. Assess valuation levels of the Wind All A Index using PE and PB ratios. - Current PE: 70th percentile (moderate level). - Current PB: 30th percentile (low level)[3][10]. 2. Combine valuation analysis with timing signals (e.g., moving average distance and profit-making effect). 3. Recommend equity allocation levels based on the above factors. - Current recommendation: 80% equity allocation[3][10]. - **Model Evaluation**: The model balances valuation and trend analysis, providing a systematic approach to equity allocation[3][10]. --- Model Backtesting Results 1. Timing System Signal - Moving average distance: 7.96% (greater than the 3% threshold, indicating an upward trend)[2][9]. 2. Industry Allocation Model - Recommended sectors: Innovative pharmaceuticals, securities insurance, photovoltaics, coal, non-ferrous metals, military, computing power, and batteries[2][10]. 3. Position Management Model - PE: 70th percentile (moderate level)[3][10]. - PB: 30th percentile (low level)[3][10]. - Recommended equity allocation: 80%[3][10]. --- Quantitative Factors and Construction Methods 1. Factor Name: Profit-Making Effect - **Factor Construction Idea**: This factor measures the market's ability to generate profits for investors, serving as a key indicator of market sentiment and potential capital inflows[2][10]. - **Factor Construction Process**: 1. Calculate the profit-making effect value based on market performance. - Current value: 3.73% (positive)[2][10]. 2. Interpret the signal: A positive value indicates sustained investor confidence and potential for further capital inflows[2][10]. - **Factor Evaluation**: The factor is a reliable indicator of market sentiment, supporting timing and allocation decisions[2][10]. --- Factor Backtesting Results 1. Profit-Making Effect - Current value: 3.73% (positive, indicating sustained market confidence)[2][10].

- 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]

Quantitative Models and Construction 1. Model Name: Odds + Win Rate Strategy - **Model Construction Idea**: This strategy combines the risk budget of the odds-based strategy and the win-rate-based strategy to create a comprehensive scoring system for asset allocation[3][48][54] - **Model Construction Process**: 1. The odds-based strategy allocates more to high-odds assets and less to low-odds assets under a target volatility constraint[48] 2. The win-rate-based strategy derives macro win-rate scores from five factors: monetary, credit, growth, inflation, and overseas, and allocates accordingly[51] 3. The combined strategy sums the risk budgets of the two strategies to form a unified allocation model[54] - **Model Evaluation**: The model demonstrates stable performance with low drawdowns and consistent returns over different time periods[54] 2. Model Name: Industry Rotation Strategy - **Model Construction Idea**: This strategy evaluates industries based on three dimensions: momentum/trend, turnover/volatility/beta (crowding), and IR (information ratio) over the past 12 months[43] - **Model Construction Process**: 1. Momentum and trend are measured using the IR of industries over the past 12 months[43] 2. Crowding is assessed using turnover ratio, volatility ratio, and beta ratio[43] 3. The strategy ranks industries based on these metrics and allocates to those with strong trends, low crowding, and high IR[43] - **Model Evaluation**: The strategy has shown strong excess returns and low tracking errors, making it a robust framework for industry allocation[43] --- Model Backtesting Results 1. Odds + Win Rate Strategy - **Annualized Return**: - 2011 onwards: 7.0% - 2014 onwards: 7.6% - 2019 onwards: 7.2%[54] - **Maximum Drawdown**: - 2011 onwards: 2.8% - 2014 onwards: 2.7% - 2019 onwards: 2.8%[54] - **Sharpe Ratio**: - 2011 onwards: 2.86 - 2014 onwards: 3.26 - 2019 onwards: 2.85[56] 2. Industry Rotation Strategy - **Excess Return**: - 2011 onwards: 13.1% - 2014 onwards: 13.0% - 2019 onwards: 10.8%[44] - **Tracking Error**: - 2011 onwards: 11.0% - 2014 onwards: 12.0% - 2019 onwards: 10.7%[44] - **IR**: - 2011 onwards: 1.18 - 2014 onwards: 1.08 - 2019 onwards: 1.02[44] --- Quantitative Factors and Construction 1. Factor Name: Value Factor - **Factor Construction Idea**: Measures stocks with strong trends, low crowding, and moderate odds[27] - **Factor Construction Process**: 1. Trend is measured at zero standard deviation[27] 2. Odds are at 0.3 standard deviation[27] 3. Crowding is at -1.3 standard deviation[27] - **Factor Evaluation**: The factor ranks highest among all style factors, making it a key focus for allocation[27] 2. Factor Name: Quality Factor - **Factor Construction Idea**: Focuses on high odds, weak trends, and low crowding, with potential for future trend confirmation[29] - **Factor Construction Process**: 1. Odds are at 1.7 standard deviation[29] 2. Trend is at -1.4 standard deviation[29] 3. Crowding is at -0.8 standard deviation[29] - **Factor Evaluation**: The factor shows left-side buy signals but requires trend confirmation for stronger allocation[29] 3. Factor Name: Growth Factor - **Factor Construction Idea**: Represents high odds, moderate trends, and moderate crowding, suitable for standard allocation[32] - **Factor Construction Process**: 1. Odds are at 0.9 standard deviation[32] 2. Trend is at -0.2 standard deviation[32] 3. Crowding is at 0.1 standard deviation[32] - **Factor Evaluation**: The factor is recommended for standard allocation due to its balanced characteristics[32] 4. Factor Name: Small-Cap Factor - **Factor Construction Idea**: Characterized by low odds, strong trends, and high crowding, with high uncertainty[35] - **Factor Construction Process**: 1. Odds are at -0.7 standard deviation[35] 2. Trend is at 1.6 standard deviation[35] 3. Crowding is at 0.6 standard deviation[35] - **Factor Evaluation**: The factor is not recommended due to its high uncertainty and crowding[35] --- Factor Backtesting Results 1. Value Factor - **Odds**: 0.3 standard deviation - **Trend**: 0 standard deviation - **Crowding**: -1.3 standard deviation[27] 2. Quality Factor - **Odds**: 1.7 standard deviation - **Trend**: -1.4 standard deviation - **Crowding**: -0.8 standard deviation[29] 3. Growth Factor - **Odds**: 0.9 standard deviation - **Trend**: -0.2 standard deviation - **Crowding**: 0.1 standard deviation[32] 4. Small-Cap Factor - **Odds**: -0.7 standard deviation - **Trend**: 1.6 standard deviation - **Crowding**: 0.6 standard deviation[35]

Market Pricing Status - The trading heat in the market has slightly declined, with turnover rates decreasing and net capital inflows reducing [8][24][28] - The average daily trading volume for the entire A-share market decreased to 18.1 billion, down from 18.5 billion the previous week [8] - The proportion of stocks rising in the A-share market dropped to 31.9%, with a median weekly return of -1.48% [8][9] A-Share Liquidity Tracking - ETF outflows have accelerated, with overall capital inflows slowing down [24][28] - The new issuance scale of equity funds decreased to 8.87 billion, down from 19.41 billion [35] - Foreign capital inflow into the A-share market was 25.9 million USD, with the northbound capital transaction proportion dropping to 11.6% [46][48] A-Share Industry Allocation - Financing capital is flowing into the pharmaceutical and electronics sectors, while foreign capital is entering the banking sector [3][46] - The net inflow for the pharmaceutical sector was 6.7 billion, and for electronics, it was 6.06 billion [3] - The ETF capital flow showed net inflows in food and beverage (+0.95 billion) and coal (+0.22 billion), while electronics (-11.09 billion) and pharmaceuticals (-6.46 billion) experienced net outflows [3][46] Hong Kong and Global Capital Flow - Southbound capital inflows increased, with net purchases rising to 59.02 billion, the highest since 2022 [4][48] - The Hang Seng Index fell by 3.5%, with major global markets also experiencing declines, particularly the French CAC40 index, which dropped by 3.7% [4][48] - Foreign capital primarily flowed into developed markets, with the US receiving 4.06 billion and the UK 0.98 billion [4][48]

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]

Report Industry Investment Rating No relevant content provided. Core Viewpoints The report analyzes the Q2 2025 positions of public funds, covering aspects such as scale changes, stock positions, A-share sector and style allocation, industry and individual stock positions, and Hong Kong stock market allocation changes [2]. Summary by Directory 1. Q2 Public Fund Scale Changes - By the end of Q2 2025, there were 12,907 public funds in China, an increase of 307 from Q1 2025. Among them, there were 3,015 stock funds, 4,702 hybrid funds, and 3,862 bond funds, increasing by 209, 31, and 54 respectively compared to Q1 2025 [4]. - The total net asset value of all public funds at the end of Q2 2025 was 33.72 trillion yuan, a growth of 2.1112 trillion yuan from Q1 2025. Stock funds reached 4.27 trillion yuan, hybrid funds 3.21 trillion yuan, bond funds 10.91 trillion yuan, and money market funds 14.23 trillion yuan [5]. - In terms of equity fund sub - types, passive index funds and enhanced index funds both saw increases in quantity and net asset value [11]. - By the end of Q2 2025, the total number of actively managed equity - oriented funds was 4,582, an increase of 45 from Q1 2025, but the total net asset value decreased by 21.18 billion yuan [13]. 2. Actively Managed Equity - Oriented Funds: Stock Positions Continue to Rise - In Q2 2025, actively managed equity - oriented funds held stocks worth 2.94 trillion yuan, a decrease of 0.02 trillion yuan from the end of Q1. However, the stock position in asset allocation continued to rise, from 84.01% at the end of Q1 to 84.24%, a historical high since 2005. The proportion of A - shares in the fund's asset allocation continued to decline [2]. - Most of the stock positions of the four types of actively managed equity - oriented funds increased. The positions of common stock, balanced hybrid, and flexible allocation funds rose by 0.57, 2.05, and 0.49 percentage points respectively, while the position of partial - stock hybrid funds remained basically unchanged [24]. 3. A - Share Sector Distribution and Style Allocation (1) Increased Allocation in the GEM - In Q2 2025, the allocation ratio of the GEM reversed the previous two - quarter decline, rising from 16.58% at the end of Q1 to 18.93%. The allocation ratio of the Sci - Tech Innovation Board increased by 0.18 percentage points, and the allocation ratio of the Beijing Stock Exchange rose from 0.23% at the end of Q1 to 0.41%. The market value of main - board holdings decreased by 2.71 percentage points [25]. (2) Positioning Style Tends towards Growth and Finance - In the A - share market, the market value ratio of large - cap stocks represented by the CSI 300 decreased by 2.55 percentage points in Q2, and the investment enthusiasm for large - cap stocks continued to decline. The allocation ratio of small - cap stocks also decreased by 0.93 percentage points. In terms of growth and value styles, the growth style increased by 0.92 percentage points, and the value style increased by 0.42 percentage points [26]. - From the perspective of the five - style index classification, the growth style increased by 3.98 percentage points, the financial style by 1.72 percentage points, and the stable style by 0.02 percentage points. The consumption and cyclical styles decreased [27]. 4. A - Share Industry Allocation: Increased Allocation in the Communication Industry and Rising Finance Popularity (1) First - Tier Industry Allocation - In Q2 2025, the industries with high market value ratios were electronics (18.67%), pharmaceutical biology (10.91%), power equipment (9.89%), food and beverage (6.73%), and automobiles (6.32%). Industries with relatively low ratios included comprehensive (0.11%), steel (0.34%), coal (0.37%), petroleum and petrochemicals (0.38%), and textile and apparel (0.41%) [30]. - In Q2 2025, industries such as electronics, pharmaceutical biology, power equipment, communication, and household appliances were significantly over - allocated, while non - bank finance, computer, bank, public utilities, and machinery were under - allocated [30]. - In Q2 2025, the market value ratios of 15 first - tier industries increased. Industries with an increase of over 0.5 percentage points included communication, bank, national defense and military industry, non - bank finance, and media. Industries with a decline included food and beverage, automobiles, power equipment, household appliances, and machinery [32]. - In terms of the change in the over - allocation ratio, communication, national defense and military industry, non - bank finance, bank, and media increased significantly, while food and beverage, automobiles, power equipment, machinery, and household appliances decreased [35]. (2) Second - Tier Industry Allocation - In Q2 2025, semiconductor, chemical pharmaceutical, battery, Baijiu II, communication equipment, components, automobile parts, white goods, consumer electronics, and industrial metals ranked high in terms of market value ratio. Chemical pharmaceutical rose to the second place, and Baijiu II dropped to the fourth place [41]. - The top ten industries with increased holdings were communication equipment, components, chemical pharmaceutical, city commercial banks II, insurance II, aviation equipment II, logistics, games II, joint - stock commercial banks II, and feed industry. Industries with significant reductions included Baijiu II, passenger cars, consumer electronics, white goods, and construction machinery [43]. 5. Heavy - Positioned Individual Stocks: Decreased Concentration - Among the top 20 individual stocks by total market value held by actively managed equity - oriented funds, there were 14 A - shares and 6 Hong Kong stocks. Compared with Q1, Zijin Mining and Xiaomi Group - W rose to the 5th and 6th places respectively, and Wuliangye and Shanxi Fenjiu dropped significantly. Newly included stocks were 3 A - shares and 2 Hong Kong stocks [51]. - The top ten stocks with increased holdings were Zhongji Innolight, New Fiber Optic, Hudian Co., Ltd., Cinda Bio (HK), Pop Mart (HK), Shenghong Technology, 3SBio (HK), SF Holding, Haid Group, and AVIC Shenfei. The top ten stocks with reduced holdings were BYD, Alibaba Group Holding Limited - W (HK), Luxshare Precision Industry Co., Ltd., Tencent Holdings Limited (HK), Kweichow Moutai Co., Ltd., Wuliangye, Luzhou Laojiao Co., Ltd., Midea Group Co., Ltd., Shanxi Fenjiu, and Semiconductor Manufacturing International Corporation (HK) [52]. - In Q2 2025, the concentration of heavy - positioned individual stocks in actively managed equity - oriented funds decreased overall. The proportions of the top 10, 20, 30, 40, and 50 stocks in the total market value of heavy - positioned stocks decreased by 3.16, 3.31, 2.90, 2.60, and 2.19 percentage points respectively compared with the end of Q1 [59]. 6. Hong Kong Stock Market Allocation Changes - The allocation ratio of the A - share market in the heavy - positioned stocks of actively managed equity - oriented funds has declined for six consecutive quarters, from 91.34% at the end of 2023 to 80.09% at the end of Q2 2025. The allocation ratio of the Hong Kong stock market has increased from 8.66% at the end of 2023 to 19.91% at the end of Q2 2025, rising by 0.81 percentage points compared with Q1 2025 [62]. - By the end of Q2 2025, there were 360 Hong Kong stocks in the heavy - positioned stocks of actively managed equity - oriented funds, an increase of 33 from Q1. The market value of Hong Kong stock holdings was 326.5 billion yuan, an increase of 8.2 billion yuan from Q1 [63]. - In terms of the Hang Seng primary industries, the market value of information technology, non - essential consumer goods, healthcare, and finance accounted for 32.41%, 26.87%, 14.32%, and 6.33% respectively. The market value and proportion of healthcare and finance increased, while information technology and non - essential consumer goods decreased [63]. - In terms of the Hang Seng secondary industries, the top five industries were software services, pharmaceuticals and biotechnology, professional retail, information technology equipment, and household appliances and products. The market value of eight industries such as pharmaceuticals and biotechnology increased by over 1 billion yuan, while the professional retail industry had the largest decline [67][70]. - In Q2 2025, actively managed equity - oriented funds significantly increased their holdings of Cinda Bio, Pop Mart, 3SBio, JD Health, and Xiaomi Group - W, and significantly reduced their holdings of Alibaba Group Holding Limited - W, Tencent Holdings Limited, Semiconductor Manufacturing International Corporation, XPeng Inc. - W, and Geely Automobile [73].