Economic Growth Indicators - The Guosen High-Frequency Macro Diffusion Index A turned positive this week, indicating a recovery in economic growth[10] - The standardized Index B rose by 0.71, significantly above historical averages, suggesting a notable rebound in domestic economic momentum[10] - Key sectors such as consumption, investment, and real estate showed improvement this week, with all three areas performing well[10] Price Trends - Food prices decreased this week, while non-food prices remained stable; September CPI is expected to show a month-on-month increase of approximately 0.3%[12] - The forecast for September PPI indicates a month-on-month decline of about -0.1%, with a year-on-year increase expected to reach -2.4% due to a low base effect[12] Asset Price Predictions - Current domestic interest rates are low, while the Shanghai Composite Index is considered high; predictions suggest a rise in the ten-year government bond yield and a decline in the Shanghai Composite Index for the week of September 26, 2025[10] - The predicted ten-year government bond yield for the week of September 26 is 2.32%, while the Shanghai Composite Index is forecasted to be 3,193.04[19]

Investment Rating - The report maintains a "Underperform" rating for the U.S. stock market [1] Core Insights - The U.S. stock market has reached new highs as the interest rate cut cycle begins, with the S&P 500 increasing by 1.2% and the Nasdaq by 2.2% [3] - There is a significant divergence in industry performance, with 10 sectors rising and 12 falling [3] - The report highlights a steady upward revision in earnings expectations for the S&P 500 components, with a 0.3% increase in the next 12 months' EPS forecast [5] Price Trends - The S&P 500 rose by 1.2% this week, while the Nasdaq increased by 2.2% [3] - The best-performing sectors included Automotive & Components (+6.9%), Technology Hardware & Equipment (+4.6%), and Media & Entertainment (+3.9%) [3] - The sectors that saw the largest declines were Durable Goods & Apparel (-2.4%) and Healthcare Equipment & Services (-1.8%) [3] Fund Flows - The estimated fund flow for S&P 500 components was +134.6 billion USD this week, down from +215.4 billion USD the previous week [4] - Notable inflows were seen in Software & Services (+42.4 billion USD), Automotive & Components (+40.1 billion USD), and Semiconductor Products & Equipment (+26.3 billion USD) [4] - The sectors experiencing outflows included Healthcare Equipment & Services (-6.5 billion USD) and Durable Goods & Apparel (-2.1 billion USD) [4] Earnings Forecast - The report indicates a 0.3% upward adjustment in the earnings expectations for the S&P 500 components, consistent with the previous week [5] - The Semiconductor Products & Equipment sector led the upward revisions with a +0.7% increase, followed by Energy (+0.6%) and Materials (+0.5%) [5] - The Durable Goods & Apparel sector was the only one to see a downward revision, with a -0.8% adjustment [5]

Investment Rating - The report maintains an "Outperform" rating for the Hong Kong stock market [4] Core Insights - The automotive sector shows strong performance with a weekly increase of 7.1%, while the biotechnology sector has underperformed with a decrease of 2.3% [1][2] - Overall, 17 industries experienced gains, while 13 saw declines, indicating a mixed performance across sectors [1] Summary by Sections Market Performance - The Hang Seng Index rose by 0.6%, and the Hang Seng Composite Index increased by 0.4% [1] - Large-cap stocks outperformed small-cap and mid-cap stocks, with the Hang Seng Large Cap Index up by 0.7% [1] Valuation Levels - The valuation of the Hang Seng Index increased by 0.2% to 12.3x, while the Hang Seng Composite Index valuation decreased by 0.4% to 12.3x [2] - The automotive sector's valuation rose significantly by 7.3% to 15.7x, while the biotechnology sector's valuation fell by 2.3% to 30.0x [2] Earnings Expectations - The earnings per share (EPS) for the Hang Seng Index increased by 0.3%, and the Hang Seng Composite Index EPS rose by 0.7% [3] - A total of 26 industries saw upward revisions in EPS, with the coal sector experiencing the largest increase of 11.3% [3]

Quantitative Models and Construction Methods 1. Model Name: Maximized Factor Exposure Portfolio (MFE) - **Model Construction Idea**: The MFE portfolio is designed to test the effectiveness of individual factors under realistic constraints, such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach ensures that the factors deemed "effective" can genuinely contribute to the portfolio's predictive power in real-world scenarios [39][40]. - **Model Construction Process**: - The optimization model maximizes single-factor exposure while adhering to constraints such as style and industry neutrality, stock weight limits, and turnover control. - The objective function is expressed as: $ \begin{array}{ll} max & f^{T} w \\ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $ - **Explanation**: - \( f \): Factor values - \( w \): Stock weight vector - \( X \): Style factor exposure matrix - \( H \): Industry exposure matrix - \( w_b \): Benchmark stock weights - \( s_l, s_h \): Lower and upper bounds for style exposure - \( h_l, h_h \): Lower and upper bounds for industry exposure - \( w_l, w_h \): Lower and upper bounds for stock weight deviation - \( b_l, b_h \): Lower and upper bounds for benchmark stock weight proportions [39][40] - The process involves: 1. Setting constraints for style, industry, and stock weight deviations 2. Constructing the MFE portfolio at the end of each month 3. Backtesting the portfolio with historical data, accounting for transaction costs [41][43] - **Model Evaluation**: The MFE model is effective in testing factor performance under realistic constraints, ensuring that selected factors contribute to portfolio returns in practical scenarios [39][40] --- Factor Construction and Methods 1. Factor Name: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: SUE measures the deviation of actual earnings from expected earnings, standardized by the standard deviation of expected earnings. It captures the market's reaction to earnings surprises [17]. - **Factor Construction Process**: - Formula: $ SUE = \frac{(Actual\ Net\ Profit - Expected\ Net\ Profit)}{Standard\ Deviation\ of\ Expected\ Net\ Profit} $ - Parameters: - Actual Net Profit: Reported earnings for the quarter - Expected Net Profit: Consensus analyst estimates for the quarter - Standard Deviation of Expected Net Profit: Variability in analyst estimates [17] 2. Factor Name: Momentum (1-Year Momentum) - **Factor Construction Idea**: Momentum captures the tendency of stocks with strong past performance to continue outperforming in the near term [17]. - **Factor Construction Process**: - Formula: $ Momentum = \text{Cumulative Return over the Past Year (Excluding the Most Recent Month)} $ - Parameters: - Cumulative Return: Total return over the specified period, excluding the most recent month to avoid short-term reversal effects [17] 3. Factor Name: Single-Quarter Revenue Growth (YoY) - **Factor Construction Idea**: This factor measures the year-over-year growth in quarterly revenue, reflecting a company's growth potential [17]. - **Factor Construction Process**: - Formula: $ Revenue\ Growth = \frac{(Current\ Quarter\ Revenue - Revenue\ from\ Same\ Quarter\ Last\ Year)}{Revenue\ from\ Same\ Quarter\ Last\ Year} $ - Parameters: - Current Quarter Revenue: Revenue reported for the current quarter - Revenue from Same Quarter Last Year: Revenue reported for the same quarter in the previous year [17] --- Factor Backtesting Results 1. Factor: 1-Year Momentum - **Performance**: - **CSI 300 Universe**: Weekly excess return of 0.67%, monthly excess return of 3.06%, annualized historical return of 2.70% [19] - **CSI 500 Universe**: Weekly excess return of 0.92%, monthly excess return of 0.21%, annualized historical return of 3.07% [21] - **CSI 1000 Universe**: Weekly excess return of -0.27%, monthly excess return of -2.23%, annualized historical return of -0.46% [23] 2. Factor: Single-Quarter Revenue Growth (YoY) - **Performance**: - **CSI 300 Universe**: Weekly excess return of 0.66%, monthly excess return of 4.36%, annualized historical return of 4.93% [19] - **CSI 500 Universe**: Weekly excess return of 1.05%, monthly excess return of 2.95%, annualized historical return of 3.70% [21] - **CSI 1000 Universe**: Weekly excess return of -0.16%, monthly excess return of 4.94%, annualized historical return of 5.11% [23] 3. Factor: Standardized Unexpected Earnings (SUE) - **Performance**: - **CSI 300 Universe**: Weekly excess return of 0.02%, monthly excess return of 1.49%, annualized historical return of 3.98% [19] - **CSI 500 Universe**: Weekly excess return of 0.35%, monthly excess return of 0.22%, annualized historical return of 9.14% [21] - **CSI 1000 Universe**: Weekly excess return of -1.37%, monthly excess return of 0.77%, annualized historical return of 10.44% [23] --- Model Backtesting Results 1. CSI 300 Enhanced Portfolio - Weekly excess return: -0.65% - Year-to-date excess return: 16.53% [5][14] 2. CSI 500 Enhanced Portfolio - Weekly excess return: -0.37% - Year-to-date excess return: 8.50% [5][14] 3. CSI 1000 Enhanced Portfolio - Weekly excess return: -0.53% - Year-to-date excess return: 16.52% [5][14] 4. CSI A500 Enhanced Portfolio - Weekly excess return: 0.02% - Year-to-date excess return: 9.22% [5][14]

- The "Hong Kong Stock Selection Portfolio" strategy aims to construct a portfolio by dual-layer screening based on fundamental and technical aspects of stocks recommended by analysts. The analyst recommendation pool is built using events such as upward earnings forecast revisions, initial analyst coverage, and unexpected research report titles. Stocks with both fundamental support and technical resonance are selected to form the portfolio. The backtesting period is from January 1, 2010, to June 30, 2025, with an annualized return of 19.11% and an excess return of 18.48% relative to the Hang Seng Index[14][15][20] - The "Stable New High Stock Screening Method" identifies stocks that have reached a 250-day high in the past 20 trading days. The screening criteria include analyst attention (at least five buy or overweight ratings in the past six months), relative stock strength (top 20% in 250-day returns), and stock price stability. Stability is assessed using metrics such as price path smoothness and the average 250-day high distance over the past 120 days and the last 5 days. The formula for calculating the 250-day high distance is: $ 250\text{-day high distance} = 1 - \frac{\text{Close}_{t}}{\text{ts\_max}(\text{Close}, 250)} $ where $\text{Close}_{t}$ is the latest closing price, and $\text{ts\_max}(\text{Close}, 250)$ is the maximum closing price over the past 250 trading days[21][23][24] - The "Stable New High Stock Screening Method" evaluation highlights its effectiveness in identifying momentum stocks, aligning with research findings that stocks near their 52-week highs tend to outperform. This method also incorporates elements from established growth stock selection frameworks like CANSLIM and insights from "Stock Market Wizards"[21][23] - The backtesting results for the "Hong Kong Stock Selection Portfolio" show annualized return metrics, including excess returns relative to the Hang Seng Index, IR values, and maximum drawdown statistics across multiple years. For the full sample period, the annualized return is 19.11%, excess return is 18.48%, IR is 1.22, and maximum drawdown is 23.73%[20] - The performance of the "Stable New High Stock Screening Method" is reflected in the selection of stocks across sectors such as cyclicals, healthcare, technology, and manufacturing. For example, stocks like 中创智领 (0.0% 250-day high distance, 206.3% 250-day return) and 赤子城科技 (0.0% 250-day high distance, 433.3% 250-day return) demonstrate the method's ability to identify high-performing stocks[23][29]

Quantitative Models and Construction Methods Excellent Fund Performance Enhancement Portfolio - Model Name: Excellent Fund Performance Enhancement Portfolio - Model Construction Idea: The model aims to benchmark against active equity funds instead of broad-based indices, leveraging quantitative methods to enhance the selection of top-performing funds[4][19][50] - Model Construction Process: - Benchmark against active equity funds' median returns, using the equity hybrid fund index (885001.WI) as a proxy - Utilize quantitative methods to enhance the selection based on the holdings of top-performing funds - Consider fund performance factors and neutralize them to avoid style concentration issues - Optimize the portfolio to control deviations in individual stocks, industry, and style from the selected fund holdings[4][19][50] - Model Evaluation: The model shows good stability and can consistently outperform the median of active equity funds[50] - Model Testing Results: - Annualized return of 20.31% from 2012.1.4 to 2025.6.30, with an annualized excess return of 11.83% compared to the equity hybrid fund index[51][54] Exceeding Expectations Selection Portfolio - Model Name: Exceeding Expectations Selection Portfolio - Model Construction Idea: The model focuses on stocks with significant positive earnings surprises, selecting those with both fundamental support and technical resonance[5][24][55] - Model Construction Process: - Screen stocks based on research report titles indicating earnings surprises and analysts' upward revisions of net profit - Perform dual-layer selection on the stock pool based on fundamental and technical aspects - Construct a portfolio of stocks that meet both fundamental and technical criteria[5][24][55] - Model Evaluation: The model can consistently rank in the top 30% of active equity funds each year[55] - Model Testing Results: - Annualized return of 30.55% from 2010.1.4 to 2025.6.30, with an annualized excess return of 24.68% compared to the equity hybrid fund index[56][58] Broker Golden Stock Performance Enhancement Portfolio - Model Name: Broker Golden Stock Performance Enhancement Portfolio - Model Construction Idea: The model leverages the stock pool of broker golden stocks, optimizing the portfolio to control deviations from the stock pool in terms of individual stocks, industry, and style[6][32][60] - Model Construction Process: - Use the broker golden stock pool as the selection space and constraint benchmark - Optimize the portfolio to control deviations from the broker golden stock pool in terms of individual stocks, industry, and style[6][32][60] - Model Evaluation: The model can consistently rank in the top 30% of active equity funds each year[60] - Model Testing Results: - Annualized return of 19.34% from 2018.1.2 to 2025.6.30, with an annualized excess return of 14.38% compared to the equity hybrid fund index[61][64] Growth and Stability Portfolio - Model Name: Growth and Stability Portfolio - Model Construction Idea: The model adopts a "time-series first, cross-sectional later" approach to construct a two-dimensional evaluation system for growth stocks, focusing on the period before the official financial report release[7][38][65] - Model Construction Process: - Screen growth stocks based on research report titles indicating earnings surprises and significant earnings growth - Prioritize stocks closer to the financial report release date, and use multi-factor scoring to select high-quality stocks when the sample size is large - Introduce mechanisms to reduce portfolio turnover and avoid risks, such as weak balance, transition, buffer, and risk avoidance mechanisms[7][38][65] - Model Evaluation: The model can consistently rank in the top 30% of active equity funds each year[65] - Model Testing Results: - Annualized return of 35.51% from 2012.1.4 to 2025.6.30, with an annualized excess return of 26.88% compared to the equity hybrid fund index[66][69] Model Backtesting Results - Excellent Fund Performance Enhancement Portfolio: - Absolute return this week: -0.28%, annual absolute return: 27.54%, annual excess return: -3.91%[2][23] - Exceeding Expectations Selection Portfolio: - Absolute return this week: 1.29%, annual absolute return: 45.51%, annual excess return: 14.06%[2][31] - Broker Golden Stock Performance Enhancement Portfolio: - Absolute return this week: 0.39%, annual absolute return: 33.97%, annual excess return: 2.52%[2][37] - Growth and Stability Portfolio: - Absolute return this week: -1.23%, annual absolute return: 51.45%, annual excess return: 20.00%[3][44]

Quantitative Models and Construction Methods 1. Model Name: 250-Day New High Distance - **Model Construction Idea**: This model tracks the distance of a stock's closing price from its 250-day high to identify momentum and trend-following opportunities in the market. It is inspired by studies showing that stocks near their 52-week highs tend to outperform[11][18]. - **Model Construction Process**: The formula for the 250-day new high distance is: $ 250\ Day\ New\ High\ Distance = 1 - \frac{Close_t}{ts\_max(Close, 250)} $ Where: - $Close_t$ represents the latest closing price - $ts\_max(Close, 250)$ represents the maximum closing price over the past 250 trading days If the latest closing price reaches a new high, the distance is 0. If the price has fallen from the high, the distance is a positive value indicating the percentage drop[11]. - **Model Evaluation**: This model effectively captures market momentum and highlights stocks or indices that are leading the market trends[11][18]. 2. Model Name: Stable New High Stock Screening - **Model Construction Idea**: This model identifies stocks with stable price paths and consistent momentum, as smoother price trajectories are associated with stronger momentum effects[27]. - **Model Construction Process**: The screening process involves the following steps: - **Analyst Attention**: Stocks must have at least 5 "Buy" or "Overweight" ratings in the past 3 months - **Relative Strength**: Stocks must rank in the top 20% of the market based on 250-day price performance - **Price Stability**: Stocks are scored based on two metrics: - **Price Path Smoothness**: Measured by the ratio of price displacement to the total price path length - **Momentum Consistency**: Calculated as the time-series average of the 250-day new high distance over the past 120 days - **Trend Continuation**: Stocks are ranked based on the 5-day average of the 250-day new high distance, and the top 50 stocks are selected[27][29]. - **Model Evaluation**: This model emphasizes the importance of smooth and consistent price movements, which are less likely to attract excessive attention and thus generate stronger momentum effects[27][29]. --- Model Backtesting Results 1. 250-Day New High Distance - **Indices' 250-Day New High Distance**: - Shanghai Composite: 1.63% - Shenzhen Component: 1.09% - CSI 300: 1.08% - CSI 500: 1.24% - CSI 1000: 1.54% - CSI 2000: 1.91% - ChiNext Index: 1.79% - STAR 50 Index: 1.28%[2][12][34] 2. Stable New High Stock Screening - **Selected Stocks**: 50 stocks were identified, including Industrial Fulian, Giant Network, and Shengyi Electronics. - **Sector Distribution**: - Technology: 18 stocks (e.g., Electronics) - Manufacturing: 15 stocks (e.g., Machinery)[3][30][35] --- Quantitative Factors and Construction Methods 1. Factor Name: 250-Day New High Distance - **Factor Construction Idea**: Measures the relative position of a stock's closing price to its 250-day high, capturing momentum and trend-following signals[11]. - **Factor Construction Process**: $ 250\ Day\ New\ High\ Distance = 1 - \frac{Close_t}{ts\_max(Close, 250)} $ - $Close_t$: Latest closing price - $ts\_max(Close, 250)$: Maximum closing price over the past 250 trading days[11]. 2. Factor Name: Price Path Smoothness - **Factor Construction Idea**: Quantifies the smoothness of a stock's price trajectory, as smoother paths are associated with stronger momentum effects[27]. - **Factor Construction Process**: - **Price Path Smoothness**: Ratio of price displacement to total price path length over a given period[27]. 3. Factor Name: Momentum Consistency - **Factor Construction Idea**: Measures the stability of a stock's momentum over time, emphasizing consistent performance[27]. - **Factor Construction Process**: - **Momentum Consistency**: Time-series average of the 250-day new high distance over the past 120 days[27]. 4. Factor Name: Trend Continuation - **Factor Construction Idea**: Captures short-term momentum by analyzing recent price movements[27]. - **Factor Construction Process**: - **Trend Continuation**: 5-day average of the 250-day new high distance, with stocks ranked based on this metric[27]. --- Factor Backtesting Results 1. 250-Day New High Distance - **Indices' 250-Day New High Distance**: - Shanghai Composite: 1.63% - Shenzhen Component: 1.09% - CSI 300: 1.08% - CSI 500: 1.24% - CSI 1000: 1.54% - CSI 2000: 1.91% - ChiNext Index: 1.79% - STAR 50 Index: 1.28%[2][12][34] 2. Stable New High Stock Screening Factors - **Selected Stocks**: 50 stocks were identified, including Industrial Fulian, Giant Network, and Shengyi Electronics. - **Sector Distribution**: - Technology: 18 stocks (e.g., Electronics) - Manufacturing: 15 stocks (e.g., Machinery)[3][30][35]

Quantitative Models and Construction Methods 1. Model Name: 250-Day New High Distance Model - **Model Construction Idea**: This model tracks the distance of the latest closing price from the highest closing price over the past 250 trading days. It is used to identify stocks or indices that are approaching or have surpassed their historical highs, which can serve as indicators of market trends and hotspots[11][18]. - **Model Construction Process**: The formula for the 250-day new high distance is: $ 250\ Day\ New\ High\ Distance = 1 - \frac{Close_t}{ts\_max(Close, 250)} $ Where: - $ Close_t $ represents the latest closing price - $ ts\_max(Close, 250) $ represents the maximum closing price over the past 250 trading days If the latest closing price reaches a new high, the distance is 0. If the price has fallen from the high, the distance is a positive value indicating the percentage drop[11]. - **Model Evaluation**: The model is effective in identifying stocks or indices with strong momentum and can be used to monitor market trends and leading sectors[11][18]. 2. Model Name: Stable New High Stock Screening Model - **Model Construction Idea**: This model focuses on identifying stocks that not only achieve new highs but also exhibit stable price paths and consistent momentum. It incorporates factors such as analyst attention, relative strength, and price stability to refine the selection of high-momentum stocks[27][29]. - **Model Construction Process**: The screening criteria include: - **Analyst Attention**: At least 5 buy or overweight ratings in the past 3 months - **Relative Strength**: 250-day price change in the top 20% of the market - **Price Stability**: Stocks are ranked based on the following metrics: - **Price Path Smoothness**: Ratio of price displacement to the total price path - **New High Continuity**: Average 250-day new high distance over the past 120 days - **Trend Continuity**: Average 250-day new high distance over the past 5 days The top 50 stocks based on these criteria are selected as stable new high stocks[27][29]. - **Model Evaluation**: The model emphasizes the temporal characteristics of momentum and identifies stocks with smoother price paths, which are less likely to experience extreme volatility[27][29]. --- Model Backtesting Results 1. 250-Day New High Distance Model - **Indices' 250-Day New High Distance**: - Shanghai Composite: 1.63% - Shenzhen Component: 1.09% - CSI 300: 1.08% - CSI 500: 1.24% - CSI 1000: 1.54% - CSI 2000: 1.91% - ChiNext Index: 1.79% - STAR 50 Index: 1.28%[12][13][34] 2. Stable New High Stock Screening Model - **Selected Stocks**: 50 stocks were identified, including Industrial Fulian, Giant Network, and Shengyi Electronics. - **Sector Distribution**: - Technology: 18 stocks (e.g., Electronics) - Manufacturing: 15 stocks (e.g., Machinery)[30][35] --- Quantitative Factors and Construction Methods 1. Factor Name: 250-Day New High Distance - **Factor Construction Idea**: Measures the relative position of the latest closing price to the highest price in the past 250 trading days, indicating momentum strength[11]. - **Factor Construction Process**: The formula is: $ 250\ Day\ New\ High\ Distance = 1 - \frac{Close_t}{ts\_max(Close, 250)} $ Where: - $ Close_t $ is the latest closing price - $ ts\_max(Close, 250) $ is the maximum closing price over the past 250 trading days[11]. - **Factor Evaluation**: This factor effectively captures momentum and is widely used in trend-following strategies[11][18]. 2. Factor Name: Price Path Smoothness - **Factor Construction Idea**: Evaluates the stability of a stock's price movement by comparing the displacement of the price path to its total length[27]. - **Factor Construction Process**: $ Price\ Path\ Smoothness = \frac{Price\ Displacement}{Total\ Price\ Path} $ Where: - Price Displacement is the straight-line distance between the starting and ending prices - Total Price Path is the cumulative sum of absolute daily price changes over a given period[27]. - **Factor Evaluation**: Stocks with smoother price paths tend to exhibit stronger and more sustainable momentum[27]. 3. Factor Name: New High Continuity - **Factor Construction Idea**: Measures the consistency of a stock's ability to maintain new highs over time[29]. - **Factor Construction Process**: $ New\ High\ Continuity = Average\ (250\ Day\ New\ High\ Distance\ Over\ Past\ 120\ Days) $ This factor calculates the mean of the 250-day new high distance over a rolling 120-day window[29]. - **Factor Evaluation**: This factor highlights stocks with persistent upward trends, making them attractive for momentum-based strategies[29]. --- Factor Backtesting Results 1. 250-Day New High Distance - **Indices' 250-Day New High Distance**: - Shanghai Composite: 1.63% - Shenzhen Component: 1.09% - CSI 300: 1.08% - CSI 500: 1.24% - CSI 1000: 1.54% - CSI 2000: 1.91% - ChiNext Index: 1.79% - STAR 50 Index: 1.28%[12][13][34] 2. Price Path Smoothness and New High Continuity - **Selected Stocks**: 50 stocks were identified, including Industrial Fulian, Giant Network, and Shengyi Electronics. - **Sector Distribution**: - Technology: 18 stocks (e.g., Electronics) - Manufacturing: 15 stocks (e.g., Machinery)[30][35]

Report Industry Investment Rating No relevant content provided. Core View No specific core view was clearly presented in the given text. Summary by Related Content Government Bond Net Financing - Government bond net financing was 60.84 billion yuan in Week 37 (9/8 - 9/14) and 31.79 billion yuan in Week 38 (9/15 - 9/21). As of Week 37, the cumulative amount reached 1.11 trillion yuan, exceeding the same period last year by 490 billion yuan [1][7]. - The sum of national debt net financing and new local bond issuance was 56.22 billion yuan in Week 37 and 40.56 billion yuan in Week 38. As of Week 37, the cumulative general deficit was 870 billion yuan, with a progress of 78.5%, surpassing the same period last year [1][7]. National Debt - National debt net financing was 41.56 billion yuan in Week 37 and 28.71 billion yuan in Week 38. The total national debt net financing for the year is 666 billion yuan. As of Week 37, the cumulative amount was 530 billion yuan, with a progress of 78.9%, exceeding the average of the past five years [10]. Local Debt - Local debt net financing was 19.28 billion yuan in Week 37 and 3.09 billion yuan in Week 38. As of Week 37, the cumulative amount was 590 billion yuan, exceeding the same period last year by 280 billion yuan [12]. - New general debt issuance was 1.47 billion yuan in Week 37 and 2.07 billion yuan in Week 38. The local deficit for 2025 is 80 billion yuan. As of Week 37, the cumulative new general debt was 63.55 billion yuan, with a progress of 79.4%, exceeding the same period last year [12]. - New special - purpose debt issuance was 13.19 billion yuan in Week 37 and 9.78 billion yuan in Week 38. The planned new special - purpose debt for 2025 is 440 billion yuan. As of Week 37, the cumulative amount was 340 billion yuan, with a progress of 77.6%, exceeding the same period last year. Special new special - purpose debt of 118.19 billion yuan has been issued, including 21.4 billion yuan since September. Land reserve special - purpose debt of 33.02 billion yuan has been issued [2][15]. Special Refinancing Bonds - Special refinancing bond issuance was 2.62 billion yuan in Week 37 and 2.14 billion yuan in Week 38. As of Week 37, the cumulative amount was 196 billion yuan, with a issuance progress of 98% [2][30]. Urban Investment Bonds - Urban investment bond net financing was 1.55 billion yuan in Week 37 and is expected to be - 0.7 billion yuan in Week 38. As of this week, the balance of urban investment bonds is 1.02 trillion yuan [3][33].

The provided content does not contain any specific quantitative models or factors, nor does it include their construction processes, formulas, evaluations, or backtesting results. The documents primarily focus on market performance, sector analysis, ETF premiums/discounts, institutional activities, and other market-related data. There is no relevant information to summarize under the requested structure for quantitative models or factors.