Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industry performance[27][28] - **Model Construction Process**: The diffusion index is calculated for each industry, ranking them based on their momentum. Industries with higher diffusion index values are considered to have stronger upward trends. The model selects industries with the highest diffusion index values for allocation. - Formula: Not explicitly provided in the report - **Model Evaluation**: The model has shown mixed performance over the years. It performed well in 2021 and 2022 but faced significant drawdowns in 2023 and 2024 due to market reversals and failure to adjust to cyclical changes[27] 2. Model Name: GRU Factor Model - **Model Construction Idea**: This model leverages GRU (Gated Recurrent Unit) deep learning networks to process high-frequency price and volume data, aiming to identify industry trends and generate excess returns[34][39] - **Model Construction Process**: The GRU network is trained on historical minute-level price and volume data to predict industry rankings. The model then allocates to industries with the highest GRU factor scores. - Formula: Not explicitly provided in the report - **Model Evaluation**: The model has shown strong adaptability in short-term cycles but struggles in long-term trends and extreme market conditions. It has faced challenges in capturing excess returns in 2025 due to concentrated market themes[34][39] --- Model Backtesting Results 1. Diffusion Index Model - **2025 YTD Excess Return**: 0.37%[26][31] - **June 2025 Excess Return**: 1.99%[31] - **Weekly Average Return (June 2025)**: -0.65%[31] - **Weekly Excess Return (June 2025)**: 0.79%[31] 2. GRU Factor Model - **2025 YTD Excess Return**: -3.83%[34][37] - **June 2025 Excess Return**: 0.25%[37] - **Weekly Average Return (June 2025)**: -1.18%[37] - **Weekly Excess Return (June 2025)**: 0.25%[37] --- Quantitative Factors and Construction Methods 1. Factor Name: Diffusion Index - **Factor Construction Idea**: Measures the momentum of industries by ranking them based on their upward trends[28] - **Factor Construction Process**: The diffusion index is calculated for each industry weekly. Industries are ranked based on their index values, with higher values indicating stronger momentum. - Example Values (as of June 20, 2025): - Top Industries: Comprehensive Finance (1.0), Non-Bank Finance (0.973), Banking (0.97)[28] - Bottom Industries: Coal (0.174), Food & Beverage (0.313), Oil & Gas (0.387)[28] - **Factor Evaluation**: The factor effectively captures upward trends but may underperform during market reversals[27][28] 2. Factor Name: GRU Factor - **Factor Construction Idea**: Utilizes GRU deep learning to analyze high-frequency trading data and rank industries based on predicted performance[34][39] - **Factor Construction Process**: The GRU network processes minute-level price and volume data to generate factor scores for each industry. Industries are ranked based on these scores. - Example Values (as of June 20, 2025): - Top Industries: Coal (3.48), Non-Bank Finance (3.15), Utilities (2.65)[35] - Bottom Industries: Communication (-17.95), Media (-15.45), Defense (-11.87)[35] - **Factor Evaluation**: The factor is effective in short-term trend identification but struggles with long-term stability and extreme market conditions[34][39] --- Factor Backtesting Results 1. Diffusion Index Factor - **Top Weekly Changes (June 20, 2025)**: - Oil & Gas: +0.09 - Textiles: +0.044 - Metals: +0.036[30] - **Bottom Weekly Changes (June 20, 2025)**: - Agriculture: -0.229 - Defense: -0.086 - Building Materials: -0.078[30] 2. GRU Factor - **Top Weekly Changes (June 20, 2025)**: - Non-Bank Finance: Significant increase - Consumer Services: Significant increase - Comprehensive: Significant increase[35] - **Bottom Weekly Changes (June 20, 2025)**: - Communication: Significant decrease - Electronics: Significant decrease - New Energy Equipment: Significant decrease[35]

Core Viewpoint - The report discusses the "Leading Stock Diffusion Effect" as a mechanism driving sector trends in the A-share market, emphasizing the importance of constructing optimal investment portfolios based on improved factors like economic conditions and capital flows [1][2][3]. Research Background - The demand for industry-level beta timing has increased due to the development of flexible allocation funds and the growing industry ETF system, making sector rotation a core asset allocation need [6]. - The A-share market has seen accelerated sector rotation, which poses challenges to traditional rotation models, necessitating a reevaluation and improvement of these models [7]. Mechanism of Diffusion Effect - The diffusion effect in the A-share market typically involves capital migrating from core leading stocks to related targets, driven by policy triggers, active capital inflows, cognitive dissemination, and expectation overshoot leading to differentiation [2][16]. - The process includes vertical and horizontal expansions within the industry, market capitalization descent, and valuation arbitrage, ultimately leading to a broader sector rally [17]. Performance of Improved Factors - The report presents improved factors based on the previous discussion, showing significant performance enhancements in the revised SUE and active large order factors, with annualized excess returns of 7.9% and 10.3% respectively [21][22]. - The improved factors demonstrate better stability and lower volatility compared to traditional models, particularly in recent years [64]. Optimal Industry Portfolio - The optimal industry portfolio, constructed using a common condition screening method based on component factors, has shown superior historical performance with an annualized return of 26.0% and an annualized excess return of 19.1% since 2013 [3][64]. - The portfolio has maintained stable excess growth since 2022, with an annualized excess return of 11.7% and a maximum drawdown of 9.2% [74]. Comparison of Multi-Headed Construction Methods - The report compares two multi-headed construction methods: composite factor multi-headed and component factor common condition screening, concluding that the latter offers lower volatility and more stable excess returns [42][64]. - The composite factor multi-headed approach has shown stagnation in excess returns in recent years, while the optimal industry portfolio continues to outperform [53][64].

Group 1 - The current performance of the liquor market is influenced by external interventions, leading to a halt in the downward trend, but further declines may still occur [1] - The Hong Kong stock market has been stagnant, trading within the range of 3200 to 3400 points for eight months, causing widespread pessimism among investors [1] - The A-share market is expected to experience a rebound, particularly if capital flows from the Hong Kong market into A-shares, potentially leading to a simultaneous surge in both markets [3] Group 2 - The current market conditions may replicate the "924 market" scenario, with expectations of reaching above 3500 points, although the scale of the increase may not be as significant [5] - The concentration of trading chips is high, making a direct downward trend less likely, while a rapid upward movement could occur as chips are redistributed [5] - The complexity of individual stock performances is notable, especially in technology sectors where many investors are trapped, indicating that index growth is necessary for volume expansion [5] Group 3 - The market does not exhibit signs of a significant downturn, with expectations of a quick correction followed by a rebound, suggesting a comfortable state for investors holding positions [7] - The current market dynamics indicate that patience is required, as there has not been a substantial rally this year, and maintaining positions is advised [7]

Quantitative Models and Construction Diffusion Index Model - **Model Name**: Diffusion Index Model [6][26] - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industry performance. It selects industries with positive momentum for rotation. [26] - **Model Construction Process**: - The model calculates a diffusion index for each industry, which reflects the proportion of stocks within the industry exhibiting upward momentum. - Industries are ranked based on their diffusion index values, and the top industries are selected for portfolio allocation. [6][27] - **Model Evaluation**: The model has shown strong performance in capturing trends during momentum-driven markets but struggles during market reversals or when trends shift to mean-reversion. [26] - **Testing Results**: - 2025 YTD excess return: -0.44% [25][30] - June 2025 excess return: 1.20% [30] - Weekly average return: 0.21%, excess return over equal-weighted industry index: 0.37% [30] GRU Factor Model - **Model Name**: GRU Factor Model [7][32] - **Model Construction Idea**: This model leverages GRU (Gated Recurrent Unit) deep learning networks to process high-frequency price and volume data, aiming to identify industry rotation opportunities. [37] - **Model Construction Process**: - The model uses minute-level price and volume data as input features. - A GRU neural network is trained to predict industry factor scores, which are then used to rank industries for rotation. [37] - **Model Evaluation**: The model performs well in short-term trading environments but faces challenges in long-term trend-following scenarios, especially during extreme market conditions. [37] - **Testing Results**: - 2025 YTD excess return: -4.13% [32][35] - June 2025 excess return: 0.00% [35] - Weekly average return: 0.42%, excess return over equal-weighted industry index: 0.58% [35] --- Backtesting Results of Models Diffusion Index Model - **YTD Excess Return**: -0.44% [25][30] - **June 2025 Excess Return**: 1.20% [30] - **Weekly Average Return**: 0.21% [30] - **Weekly Excess Return**: 0.37% [30] GRU Factor Model - **YTD Excess Return**: -4.13% [32][35] - **June 2025 Excess Return**: 0.00% [35] - **Weekly Average Return**: 0.42% [35] - **Weekly Excess Return**: 0.58% [35] --- Quantitative Factors and Construction GRU Industry Factor - **Factor Name**: GRU Industry Factor [7][33] - **Factor Construction Idea**: The factor is derived from GRU neural network outputs, representing the relative attractiveness of industries based on high-frequency trading data. [37] - **Factor Construction Process**: - The GRU model processes minute-level trading data to generate factor scores for each industry. - Industries are ranked based on their factor scores, and the top industries are selected for portfolio allocation. [37] - **Factor Evaluation**: The factor effectively captures short-term trading signals but may underperform in broader market trends or during periods of concentrated market themes. [37] - **Testing Results**: - Top industries by factor score (as of June 13, 2025): Steel (2.42), Construction (1.47), Transportation (0.85), Real Estate (0.59), Utilities (-0.01), Oil & Gas (-1.52) [7][33] - Bottom industries by factor score: Food & Beverage (-49.88), Comprehensive Finance (-33.65), Consumer Services (-25.42), Media (-21.94), Automotive (-20.34), Non-Banking Finance (-18.36) [33] Diffusion Index Factor - **Factor Name**: Diffusion Index Factor [6][27] - **Factor Construction Idea**: The factor measures the proportion of stocks within an industry showing upward momentum, serving as a proxy for industry strength. [6] - **Factor Construction Process**: - Calculate the diffusion index for each industry based on the percentage of stocks with positive momentum. - Rank industries by their diffusion index values to identify the strongest performers. [6][27] - **Factor Evaluation**: The factor is effective in identifying momentum-driven industries but may lag during market reversals. [26] - **Testing Results**: - Top industries by diffusion index (as of June 13, 2025): Comprehensive Finance (1.0), Non-Banking Finance (0.997), Banking (0.97), Media (0.953), Computing (0.936), Retail (0.93) [6][27] - Bottom industries by diffusion index: Coal (0.166), Oil & Gas (0.297), Food & Beverage (0.323), Utilities (0.604), Real Estate (0.629), Building Materials (0.657) [27] --- Backtesting Results of Factors GRU Industry Factor - **Top Industries by Factor Score**: Steel (2.42), Construction (1.47), Transportation (0.85), Real Estate (0.59), Utilities (-0.01), Oil & Gas (-1.52) [7][33] - **Bottom Industries by Factor Score**: Food & Beverage (-49.88), Comprehensive Finance (-33.65), Consumer Services (-25.42), Media (-21.94), Automotive (-20.34), Non-Banking Finance (-18.36) [33] Diffusion Index Factor - **Top Industries by Diffusion Index**: Comprehensive Finance (1.0), Non-Banking Finance (0.997), Banking (0.97), Media (0.953), Computing (0.936), Retail (0.93) [6][27] - **Bottom Industries by Diffusion Index**: Coal (0.166), Oil & Gas (0.297), Food & Beverage (0.323), Utilities (0.604), Real Estate (0.629), Building Materials (0.657) [27]

Market Pricing Status - The overall trading heat in the market has significantly increased, with the average daily trading volume of the entire A-share market rising from 12.2 trillion to 13.8 trillion yuan, and the turnover rate of the Shanghai Composite Index increasing to 82% [1][12][11] - The number of daily limit-up stocks has decreased to 66, with the maximum consecutive limit-up stocks being 7 [1][12] A-Share Liquidity Tracking - Foreign capital has turned to inflow, with a net inflow of 0.3 million USD into the A-share market [4][47] - The net inflow of financing funds reached 125.8 billion yuan, with the total margin balance increasing to 1.8 trillion yuan [4][30] - The issuance scale of new equity funds has decreased to 12.2 billion yuan [4][30] Industry Allocation Tracking - Financing funds have shown divergence in the pharmaceutical sector, with net inflows of 22.5 billion yuan in pharmaceuticals and 17.2 billion yuan in electronics, while there were net outflows of 15.6 billion yuan in agriculture and 2.8 billion yuan in power equipment [4][30] - Foreign capital has primarily flowed into the real estate sector, while food and beverage and power equipment sectors experienced net outflows [4][30] - The top three industries on the trading leaderboard were pharmaceuticals, machinery, and environmental protection [4][30] Global Fund Flow Tracking - Southbound funds have increased, with a net inflow of 154.6 billion yuan, placing it in the 62nd percentile since 2022 [3][4] - Major global markets have shown mixed performance, with the South Korean index leading with a 2.9% increase [3][4]

Key Points - The article emphasizes a cautious approach in the short term, focusing on defensive sectors such as banks due to ongoing tariff negotiations and rising economic downward pressure [2][4] - It suggests that while small-cap growth stocks are currently favored, the overall market volatility is increasing, indicating potential risks if a turning point occurs [2][4] - The report highlights a decrease in the dispersion of excess returns among industry indices, with a slight decline in the proportion of rising constituent stocks and an increase in industry rotation speed [6][7] Market Overview - The market structure shows a stable concentration in the top 100 stocks, while the transaction share of the top five industries has slightly decreased [6][7] - Market activity has decreased, with a notable drop in the turnover rate of the Shanghai Stock Exchange 50 index, reaching its lowest level in nearly a year [6][7] Commodity Market Insights - In the commodity market, the strength of trends in precious metals and non-ferrous sectors has significantly increased, while energy and black metal sectors continue their trend [18][20] - The basis differential momentum for black and precious metals has rapidly increased, whereas it has decreased for energy and non-ferrous sectors [18][20] Options Market Analysis - The implied volatility levels for the Shanghai Stock Exchange 50 and the CSI 1000 show no significant trend, with the latter at historically low levels [23] - The skewness of the CSI 1000 put options has decreased, indicating a reduction in market concerns regarding small-cap stocks [23] Convertible Bond Market Overview - The convertible bond market remains stable in terms of valuation, with the premium rate for bonds convertible at 100 yuan and the pure debt premium rate showing steady trends [26] - The market turnover has improved, surpassing historical median levels, while credit spreads remain consistent with previous values [26]

1. Report Industry Investment Rating No information provided in the given report. 2. Core Views of the Report - The convertible bond market declined following the underlying stocks, and the valuation compressed on June 10, 2025 [1]. - The trading sentiment in the convertible bond market heated up, with the trading volume increasing [1]. - The market style favored large - cap value stocks [1]. 3. Summary by Relevant Catalogs Market Overview - Index Performance: The CSI Convertible Bond Index decreased by 0.28% compared to the previous day. The Shanghai Composite Index decreased by 0.44%, the Shenzhen Component Index decreased by 0.86%, the ChiNext Index decreased by 1.17%, the SSE 50 Index decreased by 0.39%, and the CSI 1000 Index decreased by 0.92% [1]. - Market Style: Large - cap value stocks were relatively dominant. Large - cap growth decreased by 0.71%, large - cap value decreased by 0.07%, mid - cap growth decreased by 0.70%, mid - cap value decreased by 0.09%, small - cap growth decreased by 1.01%, and small - cap value decreased by 0.34% [1]. - Capital Performance: The trading volume of the convertible bond market was 74.1 billion yuan, a 6.52% increase from the previous day. The total trading volume of the Wind All - A Index was 1.451437 trillion yuan, a 10.57% increase. The net out - flow of the main funds in the Shanghai and Shenzhen stock markets was 35.972 billion yuan, and the yield of the 10 - year treasury bond increased by 0.10bp to 1.66% [1]. Convertible Bond Price - The central price of convertible bonds decreased, and the proportion of high - price bonds decreased. The weighted average closing price of convertible bonds was 119.95 yuan, a 0.31% decrease from the previous day. The closing price of equity - biased convertible bonds was 162.79 yuan, a 0.01% increase; the closing price of bond - biased convertible bonds was 111.37 yuan, a 0.20% decrease; the closing price of balanced convertible bonds was 121.15 yuan, a 0.01% decrease [2]. - The proportion of bonds with a closing price above 130 yuan was 24.42%, a 2.34 - percentage - point decrease from the previous day. The most significant change in the proportion was in the range of 110 - 120 (including 120), with a proportion of 34.82%, a 1.06 - percentage - point increase. There were 8 bonds with a closing price below 100 yuan. The median price was 121.38 yuan, a 0.67% decrease from the previous day [2]. Convertible Bond Valuation - The valuation compressed. The conversion premium rate of the 100 - yuan par - value fitted convertible bonds was 23.00%, a 0.42 - percentage - point decrease from the previous day. The overall weighted par value was 89.91 yuan, a 0.64% decrease [2]. - The premium rate of equity - biased convertible bonds was 4.93%, a 0.91 - percentage - point decrease; the premium rate of bond - biased convertible bonds was 90.04%, a 0.53 - percentage - point increase; the premium rate of balanced convertible bonds was 19.78%, a 0.08 - percentage - point increase [2]. Industry Performance - In the A - share market, the top three industries with the largest declines were national defense and military industry (-1.97%), computer (-1.87%), and electronics (-1.65%); the top three industries with the largest increases were beauty care (+1.10%), banking (+0.48%), and medicine and biology (+0.33%) [3]. - In the convertible bond market, 24 industries declined. The top three industries with the largest declines were communication (-2.17%), computer (-1.58%), and building materials (-1.49%); the top three industries with the largest increases were beauty care (+0.82%), agriculture, forestry, animal husbandry and fishery (+0.34%), and banking (+0.32%) [3]. - For different sectors: - Closing price: The large - cycle sector decreased by 0.59%, the manufacturing sector decreased by 0.65%, the technology sector decreased by 1.44%, the large - consumption sector decreased by 0.31%, and the large - finance sector increased by 0.14% [3]. - Conversion premium rate: The large - cycle sector decreased by 0.4 percentage points, the manufacturing sector increased by 0.62 percentage points, the technology sector increased by 0.86 percentage points, the large - consumption sector increased by 0.87 percentage points, and the large - finance sector increased by 1.0 percentage point [3]. - Conversion value: The large - cycle sector decreased by 0.81%, the manufacturing sector decreased by 1.51%, the technology sector decreased by 1.82%, the large - consumption sector decreased by 0.08%, and the large - finance sector decreased by 0.68% [3]. - Pure - bond premium rate: The large - cycle sector decreased by 0.7 percentage points, the manufacturing sector decreased by 0.72 percentage points, the technology sector decreased by 2.0 percentage points, the large - consumption sector decreased by 0.43 percentage points, and the large - finance sector increased by 0.13 percentage points [4]. Industry Rotation - Beauty care, banking, and medicine and biology led the rise. For beauty care, the daily increase of the underlying stocks was 1.10%, and the convertible bonds increased by 0.82%; for banking, the daily increase of the underlying stocks was 0.48%, and the convertible bonds increased by 0.32%; for medicine and biology, the daily increase of the underlying stocks was 0.33%, and the convertible bonds increased by 0.18% [53].

Quantitative Models and Construction Methods 1. Model Name: Annualized Discount Model for CSI 500 Futures - **Model Construction Idea**: The model identifies optimal entry points for building positions based on historical performance when the annualized discount of CSI 500 futures exceeds a certain threshold, indicating market pessimism. [1][11] - **Model Construction Process**: - The model uses the annualized discount rate of the next-month contract of CSI 500 index futures as the key metric. - Historical data from 2017 onwards is analyzed to determine the relationship between the discount rate and subsequent returns. - Key findings: - When the annualized discount exceeds 15%, holding the index for more than 12 trading days results in average cumulative returns trending upward. - Holding for over 33 trading days yields a probability of positive cumulative returns exceeding 50%. - Holding for over 50 trading days increases the probability of positive returns to approximately 60%. - Formula: $ \text{Annualized Discount} = \frac{\text{Spot Price} - \text{Futures Price}}{\text{Futures Price}} \times \frac{365}{\text{Days to Maturity}} $ - Spot Price: Current index level - Futures Price: Price of the futures contract - Days to Maturity: Remaining days until the futures contract expires [11] - **Model Evaluation**: The model effectively captures market pessimism and identifies potential rebound opportunities, making it a useful tool for timing market entry. [11] --- Model Backtesting Results 1. Annualized Discount Model for CSI 500 Futures - **Key Metrics**: - Holding for 12 trading days: Average cumulative returns trend upward. - Holding for 33 trading days: Positive return probability > 50%. - Holding for 50 trading days: Positive return probability ~60%. [1][11] --- Quantitative Factors and Construction Methods 1. Factor Name: Proprietary Active Trader Activity Indicator - **Factor Construction Idea**: This factor measures the activity level of speculative funds (e.g., proprietary traders) to gauge market sentiment and risk appetite. [3][13] - **Factor Construction Process**: - Data Source: Derived from "Dragon and Tiger List" (龙虎榜) data. - The indicator tracks the marginal changes in active trader participation over time. - Observations: - From late April, the indicator showed a consistent decline, reflecting reduced risk appetite and cautious market sentiment. - Recently, the indicator has shown marginal improvement, suggesting a potential rebound in risk appetite. [3][13] - **Factor Evaluation**: The factor provides timely insights into the behavior of speculative funds, which can serve as a leading indicator for shifts in market sentiment. [3][13] 2. Factor Name: BARRA Style Factors - **Factor Construction Idea**: These factors assess the performance of various style attributes (e.g., momentum, volatility, size) to understand market preferences. [23][24] - **Factor Construction Process**: - Data Source: BARRA factor model. - Key Observations for the Week: - Fundamental factors (e.g., profitability) showed significant positive excess returns. - Stocks with high short-term momentum and high volatility outperformed. - Size-related factors (e.g., market capitalization) continued to underperform, indicating a preference for mid- to small-cap stocks. - Formula: Factor returns are calculated as the weighted average of stock returns within each style category. [23][24] - **Factor Evaluation**: The factors effectively capture shifts in market preferences, providing actionable insights for portfolio adjustments. [23][24] --- Factor Backtesting Results 1. Proprietary Active Trader Activity Indicator - **Key Metrics**: - Indicator showed consistent decline from late April, reflecting reduced risk appetite. - Recent marginal improvement suggests a potential rebound in speculative activity. [3][13] 2. BARRA Style Factors - **Key Metrics**: - Momentum: +0.2% weekly return. - Volatility: +0.2% weekly return. - Profitability: +0.3% weekly return. - Size: -0.5% weekly return. - Nonlinear Size: -0.3% weekly return. [23][24]

- The report primarily focuses on the weekly performance of the equity market, highlighting the resilience of the A-share market amidst global trade policy fluctuations and the rising prominence of technology and new consumption sectors [13][14][18] - It mentions the issuance of multiple quantitative products, including A500 Index Enhanced and Sci-Tech Composite Index Enhanced funds, which aim to enrich the market's product offerings [13][72][73] - The report tracks the weekly net inflow and outflow of funds across various ETF categories, showing significant movements in wide-base indices, industry-specific ETFs, and thematic ETFs [42][43][46] - Quantitative models such as the "Cinda Financial Engineering Industry Rotation Strategy" are referenced, which monitor marginal changes in holdings by high-performing funds to identify over-allocated and under-allocated sectors [37][38][41] - The report provides detailed fund flow data, including top-performing sectors like electronics, communication, and non-bank finance, as well as sectors with significant outflows such as automobiles, machinery, and pharmaceuticals [60][65][67]

- The report suggests that the market is currently at a good point for building positions due to sufficient pessimistic expectations indicated by factors such as futures discount and reduced trading volume[1][10][11] - The report highlights that if the annualized discount of the CSI 500 index futures exceeds 15%, buying the CSI 500 index and holding it for more than 12 trading days can lead to average cumulative returns increasing, with a win rate exceeding 50% after 33 trading days and reaching about 60% after 50 trading days[1][11] - The report mentions that the public funds are inclined to adjust their benchmark to the CSI 800, which has higher allocations in pharmaceuticals, chemicals, and computers compared to the previous mainstream benchmark CSI 300, potentially bringing event-driven returns[2][12] - The report constructs a "active capital activity indicator" based on the Dragon and Tiger list data, which has been declining since the end of April, indicating a decrease in risk appetite among active funds, but has recently shown signs of marginal recovery[3][13] - The report monitors the activity of informed traders, noting that the informed trader activity indicator has been hovering near the zero line, suggesting a cautious attitude towards the market[15] - The report calculates the rolling 12-month ROE and net profit growth rate changes for Shenwan first-level industries, with industries like light manufacturing, building materials, and real estate showing significant growth in net profit expectations[18][19] - The report observes that the net inflow of margin trading funds this week is highest in the pharmaceutical and biological industry, with a net inflow of 2.69 billion yuan[20][21] - The report analyzes the performance of BARRA style factors, noting that fundamental-related factors have shown significant positive excess returns, and funds prefer growth over value in the short term[23][24] Quantitative Models and Construction Methods 1. **Model Name: Active Capital Activity Indicator** - **Construction Idea**: Based on Dragon and Tiger list data to measure the activity level of active funds - **Construction Process**: The indicator is constructed by tracking the trading activity of active funds listed on the Dragon and Tiger list, with a focus on their buying and selling behavior over time[3][13] - **Evaluation**: Indicates the risk appetite of active funds, useful for understanding market sentiment[3][13] 2. **Model Name: Informed Trader Activity Indicator** - **Construction Idea**: Measures the activity level of informed traders to gauge market sentiment - **Construction Process**: The indicator is constructed by analyzing the trading activity of informed traders, focusing on their buying and selling patterns and their impact on market movements[15] - **Evaluation**: Provides insights into the cautious or optimistic attitudes of informed traders towards the market[15] Quantitative Factors and Construction Methods 1. **Factor Name: Futures Discount Factor** - **Construction Idea**: Based on the annualized discount of the CSI 500 index futures - **Construction Process**: The factor is constructed by calculating the annualized discount of the CSI 500 index futures and analyzing its impact on the index's performance over different holding periods[1][11] - **Evaluation**: Historical data shows that a significant discount can indicate a good entry point for building positions[1][11] 2. **Factor Name: Industry ROE and Net Profit Growth Factor** - **Construction Idea**: Based on the rolling 12-month ROE and net profit growth rate changes for Shenwan first-level industries - **Construction Process**: The factor is constructed by calculating the rolling 12-month ROE and net profit growth rate changes for each industry, and identifying industries with significant growth[18][19] - **Evaluation**: Helps identify industries with strong growth potential and positive market expectations[18][19] Factor Backtesting Results 1. **Futures Discount Factor** - **Win Rate after 33 Trading Days**: >50%[1][11] - **Win Rate after 50 Trading Days**: ~60%[1][11] 2. **Industry ROE and Net Profit Growth Factor** - **Light Manufacturing Net Profit Growth**: 0.63%[18][19] - **Building Materials Net Profit Growth**: 0.56%[18][19] - **Real Estate Net Profit Growth**: 0.49%[18][19] Style Factor Performance 1. **Turnover**: -0.3%[24] 2. **Financial Leverage**: 0.0%[24] 3. **Earnings Volatility**: 0.3%[24] 4. **Earnings Quality**: 0.2%[24] 5. **Profitability**: 0.3%[24] 6. **Investment Quality**: 0.1%[24] 7. **Long-term Reversal**: 0.1%[24] 8. **EP Value**: -0.1%[24] 9. **BP Value**: 0.0%[24] 10. **Growth**: 0.1%[24] 11. **Momentum**: 0.2%[24] 12. **Non-linear Size**: -0.3%[24] 13. **Size**: -0.5%[24] 14. **Volatility**: 0.2%[24] 15. **Near-term Reversal**: 0.4%[24] 16. **Dividend Yield**: -0.1%[24]