Investment Rating - The report assigns an "Outperform" rating to the company, indicating an expected performance better than the market by over 10% [5][63]. Core Views - The company has shown strong revenue and profit growth, with a 13.3% year-on-year increase in operating income to HKD 40 billion and a 10.5% increase in net profit attributable to shareholders to HKD 22.2 billion in the first half of 2025 [1][3]. - Non-interest income has significantly increased, with net fee income rising by 25.8% and other non-interest income growing by 99.1%, driven by improved market conditions and increased demand for wealth management services [2][3]. - The company maintains a stable asset scale, with total assets growing by 10.0% year-on-year to HKD 4.4 trillion as of June 2025 [1][7]. Summary by Sections Financial Performance - The annualized return on equity (ROE) for the first half of 2025 is 12.9%, up by 0.5 percentage points year-on-year [1]. - The average net interest margin (NIM) decreased to 1.34%, down 12 basis points year-on-year, primarily due to the Federal Reserve's interest rate cuts [1][33]. - The company’s total deposits increased by 5.8% year-to-date to HKD 2.87 trillion, while total loans grew by 2.0% to HKD 1.71 trillion [1][7]. Asset Quality - The non-performing loan (NPL) generation rate rose to 0.40%, an increase of 0.32 percentage points year-on-year, but the overall asset quality remains strong compared to industry standards [2][33]. - The company’s NPL ratio is 1.02%, which is lower than the industry average, and the provision coverage ratio improved to 86% [2][33]. Earnings Forecast - The forecast for net profit attributable to shareholders for 2025-2027 is HKD 38.9 billion, HKD 40.2 billion, and HKD 42.7 billion, representing year-on-year growth rates of 1.8%, 3.4%, and 6.2% respectively [3][56]. - Earnings per share (EPS) for the same period is projected to be HKD 3.68, HKD 3.81, and HKD 4.04 [3][56]. Valuation - The report estimates a reasonable price range for the company's stock between HKD 43.6 and HKD 48.4, indicating a potential upside of approximately 18% to 31% from the closing price of HKD 36.86 on October 10, 2025 [3][63].

- The "Hong Kong Stock Selection Portfolio" strategy aims to construct a portfolio by dual-layer screening based on fundamental and technical aspects of analyst-recommended stocks. The analyst recommendation pool is built using events such as upward revisions of earnings forecasts, initial analyst coverage, and exceeding expectations in research report titles. The backtesting period for this portfolio spans 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 after considering transaction costs in a fully invested state [13][14][19] - 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), price stability (evaluated using metrics like price path smoothness and average 250-day high distance over the past 120 days), and trend continuation (average 250-day high distance over the past five 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 $\text{Close}_{t}$ represents the latest closing price, and $\text{ts\_max}(\text{Close}, 250)$ is the maximum closing price over the past 250 trading days. A value of 0 indicates a new high, while positive values represent the degree of fallback from the high [20][22][23] - The backtesting results for the "Hong Kong Stock Selection Portfolio" show annualized returns of 19.11%, excess returns of 18.48%, and various performance metrics such as IR of 1.22, tracking error of 14.55%, and maximum drawdown of 23.73% over the entire sample period. Specific annual performance metrics include IR values ranging from 0.00 to 2.60, tracking errors between 10.28% and 22.31%, and maximum drawdowns from 4.05% to 17.74% [19] - The "Stable New High Stock Screening Method" identified stocks across multiple sectors, with the highest number in the cyclical sector (15 stocks), followed by technology (10 stocks), healthcare (7 stocks), consumer (4 stocks), financials (3 stocks), and manufacturing (2 stocks). Examples include stocks like 中广核矿业 (China General Nuclear Mining) and 紫金矿业 (Zijin Mining) in the cyclical sector [22][23][27]

- The "Hong Kong Stock Selection Portfolio" model aims to construct a portfolio by combining fundamental and technical analysis of stocks recommended by analysts. The stock pool is built based on analyst recommendation events such as upward earnings forecast revisions, initial coverage, and exceeding expectations in 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[13][14][19] - The "Stable New High Stock Screening" factor identifies stocks that have reached a 250-day high in the past 20 trading days. The screening process includes criteria such as analyst attention, relative stock strength, price path stability, and continuity of new highs. The calculation formula for the 250-day new high distance is: $ 250\text{-day new high distance} = 1 - \frac{\text{Close}_{t}}{\text{ts\_max}(\text{Close}, 250)} $ where $\text{Close}_{t}$ represents the latest closing price, and $\text{ts\_max}(\text{Close}, 250)$ is the maximum closing price over the past 250 trading days. A value of 0 indicates a new high, while positive values indicate the degree of fallback from the high[20][22][23] - The "Stable New High Stock Screening" factor evaluates stocks based on the following metrics: - Analyst attention: At least 5 buy or overweight ratings in the past 6 months - Relative stock strength: Top 20% in 250-day return within the sample pool - Price path stability: Comprehensive scoring based on price displacement ratio and average 250-day new high distance over the past 120 days - Continuity of new highs: Average 250-day new high distance over the past 5 days, selecting the top 50 ranked stocks[23] - The "Hong Kong Stock Selection Portfolio" model is evaluated positively for its ability to generate significant excess returns over the Hang Seng Index, with a robust annualized return of 19.11% during the backtesting period. The "Stable New High Stock Screening" factor is also positively assessed for its effectiveness in identifying stocks with strong momentum and stability, leveraging the proven efficacy of momentum and trend-following strategies in the Hong Kong market[13][14][20] - The backtesting results for the "Hong Kong Stock Selection Portfolio" model show annualized return of 19.11%, excess return of 18.48%, and information ratio (IR) of 1.22 over the entire sample period. The model also demonstrated a maximum relative drawdown of 23.73% and tracking error of 14.55%[19] - The "Stable New High Stock Screening" factor identified 15 stocks in the cyclical sector, 10 in technology, 7 in pharmaceuticals, 4 in consumer goods, 3 in financials, and 2 in manufacturing. Specific stocks include CGN Mining, which achieved a 250-day new high distance of 0% and a 250-day return of 129.4%[22][23][28]

Core Insights - The report highlights the performance tracking of Guosen Securities' active quantitative strategies, indicating that the "Growth and Steady" portfolio has achieved a year-to-date return of 62.19% [1][12][39]. Summary by Sections Excellent Fund Performance Enhancement Portfolio - This portfolio aims to outperform the median return of actively managed equity funds, with a year-to-date absolute return of 29.30% and a relative excess return of -4.01% against the mixed equity fund index [1][23][17]. - The portfolio's performance for the week was -0.98%, with a relative excess return of 0.54% compared to the mixed equity fund index [1][23][16]. Expected Selection Portfolio - The expected selection portfolio has achieved a year-to-date absolute return of 47.41% and a relative excess return of 14.10% against the mixed equity fund index [1][31][29]. - For the week, the portfolio's absolute return was 0.22%, with a relative excess return of 1.74% [1][31][16]. Broker's Golden Stock Performance Enhancement Portfolio - This portfolio has a year-to-date absolute return of 34.07% and a relative excess return of 0.76% against the mixed equity fund index [1][38][34]. - The weekly performance showed an absolute return of -1.51% with a relative excess return of 0.01% [1][38][16]. Growth and Steady Portfolio - The growth and steady portfolio has achieved a year-to-date absolute return of 54.84% and a relative excess return of 21.53% against the mixed equity fund index [1][42][39]. - For the week, the portfolio's absolute return was -0.08%, with a relative excess return of 1.44% [1][42][16]. Performance Monitoring of Public Funds - The report provides insights into the distribution of stock and actively managed fund returns, indicating that 55% of stocks rose while 45% fell during the week, with a median return of 0.41% for stocks and -1.63% for actively managed funds [1][46][43]. - Year-to-date, the median return for stocks was 22.40%, with 83% of stocks rising, while actively managed funds had a median return of 31.74%, with 98% rising [1][46][43].

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 real-world constraints, such as controlling for industry exposure, style exposure, stock weight limits, and turnover rates. The goal is to maximize the exposure of a single factor while adhering to these constraints[39][40] - **Model Construction Process**: - The objective function is to maximize single-factor exposure, where $f$ represents the factor values, $f^T w$ is the weighted exposure of the portfolio to the single factor, and $w$ is the vector of stock weights - The optimization model is as follows: $$ \begin{array}{ll} max & f^{T} w \\ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $$ - The first constraint limits the portfolio's style exposure relative to the benchmark index, where $X$ is the factor exposure matrix, $w_b$ is the weight vector of the benchmark index constituents, and $s_l$ and $s_h$ are the lower and upper bounds of style factor exposure, respectively - The second constraint limits the portfolio's industry deviation, where $H$ is the industry exposure matrix, and $h_l$ and $h_h$ are the lower and upper bounds of industry deviation, respectively - The third constraint limits individual stock deviations relative to the benchmark index constituents, where $w_l$ and $w_h$ are the lower and upper bounds of individual stock deviations - The fourth constraint limits the weight proportion of the portfolio within the benchmark index constituents, where $B_b$ is a 0-1 vector indicating whether a stock belongs to the benchmark index, and $b_l$ and $b_h$ are the lower and upper bounds of the weight proportion - The fifth constraint prohibits short selling and limits the upper bound of individual stock weights - The sixth constraint ensures that the portfolio is fully invested, with the sum of weights equal to 1[39][40][41] - The MFE portfolio is constructed for a given benchmark index by applying the above optimization model. To avoid excessive concentration, the deviation of individual stock weights relative to the benchmark is typically set between 0.5% and 1%[41][43] - **Model Evaluation**: The MFE portfolio is used to evaluate the effectiveness of individual factors under realistic constraints, ensuring that the selected factors can contribute to the actual return prediction in the final portfolio[39][40] --- Model Backtesting Results 1. National Trust Quantitative Engineering Index Enhanced Portfolio - **CSI 300 Index Enhanced Portfolio**: - Weekly excess return: 0.63% - Year-to-date excess return: 17.65%[13] - **CSI 500 Index Enhanced Portfolio**: - Weekly excess return: 0.30% - Year-to-date excess return: 8.35%[13] - **CSI 1000 Index Enhanced Portfolio**: - Weekly excess return: 0.77% - Year-to-date excess return: 18.22%[13] - **CSI A500 Index Enhanced Portfolio**: - Weekly excess return: 1.57% - Year-to-date excess return: 11.17%[13] --- Quantitative Factors and Construction Methods 1. Factor Name: BP - **Factor Construction Idea**: Measures valuation by comparing book value to market value[16] - **Factor Construction Process**: - Formula: $BP = \frac{\text{Net Asset}}{\text{Total Market Value}}$[16] 2. Factor Name: Single Quarter EP - **Factor Construction Idea**: Measures profitability by comparing quarterly net profit to market value[16] - **Factor Construction Process**: - Formula: $Single\ Quarter\ EP = \frac{\text{Quarterly Net Profit}}{\text{Total Market Value}}$[16] 3. Factor Name: Single Quarter SP - **Factor Construction Idea**: Measures valuation by comparing quarterly revenue to market value[16] - **Factor Construction Process**: - Formula: $Single\ Quarter\ SP = \frac{\text{Quarterly Revenue}}{\text{Total Market Value}}$[16] 4. Factor Name: EPTTM - **Factor Construction Idea**: Measures profitability by comparing trailing twelve months (TTM) net profit to market value[16] - **Factor Construction Process**: - Formula: $EPTTM = \frac{\text{TTM Net Profit}}{\text{Total Market Value}}$[16] 5. Factor Name: SPTTM - **Factor Construction Idea**: Measures valuation by comparing TTM revenue to market value[16] - **Factor Construction Process**: - Formula: $SPTTM = \frac{\text{TTM Revenue}}{\text{Total Market Value}}$[16] 6. Factor Name: One-Month Volatility - **Factor Construction Idea**: Measures risk by calculating the average intraday true range over the past 20 trading days[16] - **Factor Construction Process**: - Formula: $One\ Month\ Volatility = \text{Average of Intraday True Range over 20 trading days}$[16] 7. Factor Name: Three-Month Volatility - **Factor Construction Idea**: Measures risk by calculating the average intraday true range over the past 60 trading days[16] - **Factor Construction Process**: - Formula: $Three\ Month\ Volatility = \text{Average of Intraday True Range over 60 trading days}$[16] 8. Factor Name: One-Year Momentum - **Factor Construction Idea**: Measures momentum by calculating the return over the past year, excluding the most recent month[16] - **Factor Construction Process**: - Formula: $One\ Year\ Momentum = \text{Return over the past year excluding the most recent month}$[16] 9. Factor Name: Expected EPTTM - **Factor Construction Idea**: Measures profitability based on rolling expected earnings per share (EPS)[16] - **Factor Construction Process**: - Formula: $Expected\ EPTTM = \text{Rolling Expected EPS}$[16] 10. Factor Name: Expected BP - **Factor Construction Idea**: Measures valuation based on rolling expected book-to-price ratio[16] - **Factor Construction Process**: - Formula: $Expected\ BP = \text{Rolling Expected Book-to-Price Ratio}$[16] 11. Factor Name: Expected PEG - **Factor Construction Idea**: Measures valuation by comparing expected price-to-earnings ratio to growth rate[16] - **Factor Construction Process**: - Formula: $Expected\ PEG = \text{Expected PE Ratio / Growth Rate}$[16] 12. Factor Name: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: Measures earnings surprise by comparing actual quarterly net profit to expected net profit, normalized by the standard deviation of expected net profit[16] - **Factor Construction Process**: - Formula: $SUE = \frac{\text{Actual Quarterly Net Profit - Expected Net Profit}}{\text{Standard Deviation of Expected Net Profit}}$[16] --- Factor Backtesting Results 1. CSI 300 Index - **Best-performing factors (recent week)**: Expected EPTTM (1.19%), One-Month Volatility (1.17%), BP (1.15%)[18] - **Worst-performing factors (recent week)**: Single Quarter Revenue YoY Growth (-0.61%), Three-Month Institutional Coverage (-0.38%), Three-Month Earnings Revisions (-0.26%)[18] 2. CSI 500 Index - **Best-performing factors (recent week)**: SPTTM (1.69%), Expected BP (1.58%), Single Quarter EP (1.56%)[20] - **Worst-performing factors (recent week)**: One-Year Momentum (-1.01%), Expected PEG (-0.38%), Standardized Unexpected Revenue (-0.29%)[20] 3. CSI 1000 Index - **Best-performing factors (recent week)**: EPTTM (2.36%), SPTTM (2.14%), Expected EPTTM (2.10%)[22] - **Worst-performing factors (recent week)**: Expected Net Profit QoQ (-0.65%), One-Year Momentum (-0.48%), Single Quarter Revenue YoY Growth (-0.39%)[22] 4. CSI A500 Index - **Best-performing factors (recent week)**: Single Quarter SP (1.99%), SPTTM (1.89%), One-Month Volatility (1.69%)[24] - **Worst-performing factors (recent week)**: Single Quarter Revenue YoY Growth (-1.07%), One-Year Momentum (-0.86%), Three-Month Institutional Coverage (-0

Quantitative Models and Construction Methods - **Model Name**: Excellent Fund Performance Enhancement Portfolio **Construction Idea**: Shift from benchmarking broad-based indices to benchmarking active equity funds, leveraging quantitative methods to enhance fund holdings for optimal selection [4][48][49] **Construction Process**: 1. Benchmark against active equity fund median returns, represented by the biased equity hybrid fund index (885001.WI) [18][48] 2. Select funds based on performance layering, neutralizing return-related factors to mitigate style concentration risks [48] 3. Optimize the portfolio to control deviations in individual stocks, industries, and styles compared to selected fund holdings [49] **Evaluation**: Demonstrates stability and ability to outperform active equity fund medians [49] - **Model Name**: Outperformance Selection Portfolio **Construction Idea**: Focus on stocks with significant earnings surprises, combining fundamental and technical analysis for selection [5][54] **Construction Process**: 1. Identify stocks with earnings surprises based on research titles and analysts' profit revisions [5][54] 2. Conduct dual-layer screening on fundamental and technical dimensions to select stocks with both fundamental support and technical resonance [5][54] **Evaluation**: Consistently ranks in the top 30% of active equity funds annually [55] - **Model Name**: Brokerage Golden Stock Performance Enhancement Portfolio **Construction Idea**: Use brokerage golden stock pools as the stock selection space and constraint benchmark, optimizing the portfolio to control deviations [6][59] **Construction Process**: 1. Benchmark against active equity fund medians, represented by the biased equity hybrid fund index [33][59] 2. Optimize the portfolio to control deviations in individual stocks, industries, and styles compared to the brokerage golden stock pool [6][59] **Evaluation**: Stable performance, consistently ranking in the top 30% of active equity funds annually [60] - **Model Name**: Growth and Stability Portfolio **Construction Idea**: Focus on the time-series release intensity of excess returns for growth stocks, constructing a two-dimensional evaluation system [7][64] **Construction Process**: 1. Use "excess return release maps" to identify the strongest release periods of excess returns around positive events [64] 2. Prioritize stocks closer to financial report disclosure dates, and apply multi-factor scoring to select high-quality stocks when sample size is large [7][64] 3. Introduce mechanisms like weak balance, transition, buffer, and risk avoidance to reduce turnover and mitigate risks [64] **Evaluation**: High efficiency in capturing excess returns during optimal periods, consistently ranking in the top 30% of active equity funds annually [64][65] --- Model Backtesting Results - **Excellent Fund Performance Enhancement Portfolio**: - Annualized return (2012.1.4-2025.6.30): 20.31% - Annualized excess return vs. biased equity hybrid fund index: 11.83% - Most years ranked in the top 30% of active equity funds [50][53] - **Outperformance Selection Portfolio**: - Annualized return (2010.1.4-2025.6.30): 30.55% - Annualized excess return vs. biased equity hybrid fund index: 24.68% - Most years ranked in the top 30% of active equity funds [55][57] - **Brokerage Golden Stock Performance Enhancement Portfolio**: - Annualized return (2018.1.2-2025.6.30): 19.34% - Annualized excess return vs. biased equity hybrid fund index: 14.38% - Most years ranked in the top 30% of active equity funds [60][63] - **Growth and Stability Portfolio**: - Annualized return (2012.1.4-2025.6.30): 35.51% - Annualized excess return vs. biased equity hybrid fund index: 26.88% - Most years ranked in the top 30% of active equity funds [65][68] --- Portfolio Weekly and Yearly Performance - **Excellent Fund Performance Enhancement Portfolio**: - Weekly absolute return: -0.98% - Weekly excess return vs. biased equity hybrid fund index: 0.54% - Yearly absolute return: 29.30% - Yearly excess return vs. biased equity hybrid fund index: -4.01% - Yearly ranking: 54.63% percentile (1895/3469) [2][24][17] - **Outperformance Selection Portfolio**: - Weekly absolute return: 0.22% - Weekly excess return vs. biased equity hybrid fund index: 1.74% - Yearly absolute return: 47.41% - Yearly excess return vs. biased equity hybrid fund index: 14.10% - Yearly ranking: 21.71% percentile (753/3469) [2][32][17] - **Brokerage Golden Stock Performance Enhancement Portfolio**: - Weekly absolute return: -1.51% - Weekly excess return vs. biased equity hybrid fund index: 0.01% - Yearly absolute return: 34.07% - Yearly excess return vs. biased equity hybrid fund index: 0.76% - Yearly ranking: 44.42% percentile (1541/3469) [2][39][17] - **Growth and Stability Portfolio**: - Weekly absolute return: -0.08% - Weekly excess return vs. biased equity hybrid fund index: 1.44% - Yearly absolute return: 54.84% - Yearly excess return vs. biased equity hybrid fund index: 21.53% - Yearly ranking: 13.00% percentile (451/3469) [3][43][17]

Investment Rating - The report maintains an "Outperform the Market" rating for the company [5][18]. Core Views - The company is expected to achieve significant year-on-year growth in net profit for the first three quarters of 2025, with estimates ranging from 384 million to 394 million yuan, representing a growth of 649.71% to 669.25% [1][8]. - The increase in profit is attributed to rising sales and prices of key products, improved gross margins, and increased investment income from affiliated companies [1][8]. - The company has signed a registration agreement for the sale of its product in Brazil, which is the largest market for the product globally, indicating strong future sales potential [2][13]. Summary by Sections Financial Performance - The company forecasts a net profit of 529 million yuan for 2025, with a projected earnings per share (EPS) of 1.26 yuan, corresponding to a price-to-earnings (PE) ratio of 15.4 [4][18]. - Revenue is expected to grow from 4.24 billion yuan in 2023 to 4.96 billion yuan in 2025, reflecting a growth rate of 17% [4][22]. Product Pricing and Market Trends - Key products such as甲维盐 and 阿维菌素 have seen price increases, with甲维盐 rising from 500,000 yuan/ton to 650,000 yuan/ton and 阿维菌素 from 350,000 yuan/ton to 455,000 yuan/ton since March 2024 [2][14]. - The price of代森锰锌 has increased from 23,500 yuan/ton to 27,500 yuan/ton since March 2025, contributing to improved profitability [2][13]. New Business Developments - The company has accelerated its new business layout by acquiring a 51% stake in 德彦智创, which focuses on global pesticide creation using AI technology [3][17]. - Strategic partnerships with various technology companies aim to develop innovative agricultural products, potentially leading to high-barrier new products and growth opportunities [3][17].

Investment Rating - The oil and gas industry is rated as "Outperform" [6] Core Views - Oil prices experienced wide fluctuations in September due to geopolitical tensions and OPEC+ production policies, with Brent crude averaging $67.6 per barrel and WTI averaging $63.6 per barrel [2][14] - OPEC+ announced an extension of production increases for October and November, aiming to gradually lift voluntary production cuts established earlier [3][18] - Major energy agencies project an increase in global oil demand, with expected growth of 740,000 to 1.3 million barrels per day in 2025 and 700,000 to 1.38 million barrels per day in 2026 [4][19] Summary by Sections Oil Price Review - In September, Brent crude futures averaged $67.6 per barrel, up $0.3 from the previous month, while WTI averaged $63.6 per barrel, down $0.4 [2][14] - Geopolitical events, including U.S. actions against Venezuela and conflicts in the Middle East, contributed to price volatility [2][14] Supply Side Analysis - OPEC+ plans to continue increasing production, with a collective reduction target extended to 2026 and voluntary cuts to be gradually lifted [3][18] - The group has increased production by 41,100 barrels per day in May, June, and July, and by 54,800 barrels per day in August and September [3][18] Demand Side Analysis - Forecasts indicate that oil demand will rise in 2025, with OPEC, IEA, and EIA projecting demand increases of 130,000 to 1.05 million barrels per day [4][19] - The demand for oil is expected to continue growing into 2026, with similar projections for increased consumption [4][19] Industry Policy and Outlook - China's petrochemical industry is facing overcapacity, leading to stricter controls on new refining projects and a focus on optimizing supply [5][20] - The expected price range for Brent crude in 2025 is projected to be between $65 and $75 per barrel, while WTI is expected to range from $60 to $70 per barrel [5][20] Company Performance and Recommendations - Key companies such as CNOOC, PetroChina, Satellite Chemical, and CNOOC Development are recommended for investment, all rated as "Outperform" [6][5]

- The report introduces a quantitative model named "250-day new high distance" to track market trends and identify hot spots. The model is based on momentum and trend-following strategies, emphasizing stocks that consistently hit new highs. The formula for calculating the 250-day new high distance is: $ 250\text{-day new high distance} = 1 - \frac{Close_t}{ts\_max(Close, 250)} $ where $ Close_t $ represents the latest closing price, and $ ts\_max(Close, 250) $ is the maximum closing price over the past 250 trading days. If the latest closing price hits a new high, the distance equals 0; otherwise, it reflects the percentage drop from the peak [11][19][27] - The report evaluates the model positively, citing its ability to capture market leaders and trends effectively. It references studies by George (2004), William O'Neil, and Mark Minervini, which highlight the importance of tracking stocks near their 52-week highs for superior returns [11][18][21] - The model's backtesting results show that as of October 10, 2025, major indices such as the Shanghai Composite Index, Shenzhen Component Index, and others have respective 250-day new high distances of 0.94%, 2.70%, 1.97%, 2.00%, 1.49%, 2.61%, 4.55%, and 5.61%. Industry indices like power utilities, steel, and basic chemicals are closer to their 250-day highs, while sectors like food and beverage, banking, and transportation are farther away [12][13][31] - A factor named "Stable New High Stocks" is constructed to identify stocks with smooth price paths and consistent momentum. The factor considers analyst attention (minimum 5 buy/hold ratings in the past 3 months), relative price strength (top 20% in 250-day returns), price path smoothness (measured by price displacement ratio), and trend continuation (average 250-day new high distance over the past 120 days and past 5 days). The top 50 stocks meeting these criteria are selected [25][27][28] - The factor is positively evaluated for its focus on smooth momentum and its ability to identify stocks with strong and consistent performance. It references studies by Turan G Bali (2011) and Da Gurun (2012), which highlight the advantages of smooth price paths in momentum strategies [25][27][28] - Backtesting results for the "Stable New High Stocks" factor show that 50 stocks were selected, with the highest representation in cyclical and technology sectors. Examples include Industrial Internet, Xiangnong Chip, and Xingye Yinxi. The cyclical sector is dominated by basic chemicals, while the technology sector is led by electronics [28][30][32]

证券研究报告 | 2025年10月10日 **Makel'sid.** **Hil's.Makel's.** **Hil's.Makel's. **Hil's.Makel's. 乘势而起：市场新高趋势追踪：截至 2025 年 10 月 10 日，上证指数、深 证成指、沪深 300、中证 500、中证 1000、中证 2000、创业板指、科创 50 指数 250 日新高距离分别为 0.94%、2.70%、1.97%、2.00%、1.49%、 2.61%、4.55%、5.61%。中信一级行业指数中电力及公用事业、钢铁、 建材、有色金属、基础化工行业指数距离 250 日新高较近，食品饮料、 银行、消费者服务、综合金融、交通运输行业指数距离 250 日新高较远。 概念指数中，林木、充电桩、风力发电、钢铁、钢铁Ⅳ、发电设备、可 转债正股等概念指数距离 250 日新高较近。 见微知著：利用创新高个股进行市场监测：截至 2025 年 10 月 10 日，共 1235 只股票在过去 20 个交易日间创出 250 日新高。其中创新高个股数量最多的 是电子、机械、基础化工行业，创新高个股数量占比最高的是有色金属、电 子、电力设备 ...