Investment Rating - The report maintains an "Outperform" rating for the agricultural products sector [4]. Core Views - The report is optimistic about the reversal of the livestock cycle in 2025, with domestic and international beef and raw milk markets expected to resonate positively [3]. - The report highlights the support for long-term pig prices due to industry restructuring, and it sees potential recovery for undervalued leading companies in the pig farming sector [3]. - The pet consumption sector is identified as a growing industry benefiting from demographic changes [3]. - The report anticipates a recovery in aquaculture, benefiting companies like Haida Group [3]. - The poultry sector is expected to see a long-term increase in consumption, with yellow chicken likely to benefit first from improved domestic demand [3]. Summary by Sections Livestock - Beef prices have started to rise, with the average market price for beef at 61.13 yuan/kg as of October 10, 2025, showing a year-on-year increase of 21.05% [2]. - The report expects a significant acceleration in raw milk prices by the end of the year, with the average price at 3.04 yuan/kg [2]. Swine - The report notes a decrease in pig prices to 11.14 yuan/kg, down 7.48% week-on-week and 38.45% year-on-year, but anticipates a long-term price stabilization due to industry restructuring [1][3]. Poultry - The report indicates a slight increase in supply for white chickens, with prices at 6.68 yuan/kg, down 0.30% week-on-week [1]. - Yellow chicken prices are expected to benefit from improved domestic demand, with prices for various types of yellow chicken ranging from 5.00 to 8.70 yuan/kg [1]. Feed and Ingredients - Soybean meal prices are supported by supply-demand dynamics, with current prices at 3006 yuan/ton, up 0.6% week-on-week [2]. - Corn prices are expected to maintain a moderate upward trend, with current prices at 2233 yuan/ton, down 5.06% week-on-week but up 3.24% year-on-year [2]. Investment Recommendations - The report recommends investing in leading companies in the livestock, pig, poultry, pet, and feed sectors, including YouRan Agriculture, MuYuan Co., and HaiDa Group [3][4].

Investment Rating - The report maintains an "Outperform" rating for the fluorochemical industry [5][9]. Core Views - The fluorochemical industry is experiencing a significant price increase in refrigerants, driven by supply constraints and rising demand from both domestic and international markets [2][5][8]. - The transition to liquid cooling technologies in data centers is expected to boost the demand for fluorinated liquids and refrigerants, indicating a positive outlook for companies involved in this sector [3][6][8]. Summary by Sections 1. Industry Performance - As of September 30, the fluorochemical index rose by 7.61% compared to the end of August, outperforming major indices such as the Shanghai Composite and the CSI 300 [1][16]. 2. Refrigerant Market Review - The long-term contract prices for R32 and R410A have increased by 18.97% and 7.26% respectively in Q4, reflecting a strong market sentiment [1][23]. - R32's external trade demand is growing due to environmental regulations, with prices reaching 62,000 CNY/ton for exports and 61,000-63,000 CNY/ton for domestic sales [2][25]. 3. Production and Export Data - Domestic air conditioning production is expected to adjust upwards in Q4 2025, despite a decline in September-October due to high inventory levels from the previous year [3][4]. - The export of refrigerants like R32 has shown a 19% increase year-on-year, while R22 exports have decreased by 33% due to quota reductions [33][4]. 4. Liquid Cooling Demand - The shift towards liquid cooling in data centers is anticipated to significantly increase the demand for fluorinated liquids, with the market expected to exceed 100 billion CNY by 2027 [6][67]. - Companies such as Juhua Co., Dongyue Group, and Sanmei Co. are highlighted as key players benefiting from this trend [3][69]. 5. Regulatory Environment - China's commitment to the Montreal Protocol includes significant reductions in HCFCs and HFCs, which will impact the production quotas for refrigerants like R22 and R32 [70][73]. - The report emphasizes that the tightening of refrigerant quotas will support long-term price increases and profitability for leading companies in the fluorochemical sector [8][73].

证券研究报告 | 2025年10月11日 AI 服务器散热技术领先，将深度受益 AI 服务器液冷需求爆发增长。液冷散 热应用是 AI 算力需求下产业趋势，正在迎来黄金发展期。根据 Markets and Markets 数据，全球数据中心液冷市场规模将从 2025 年的 28.4 亿美元增长 至 2032 年的 211.4 亿美元，2025-2032 年复合增速达 33.21%。公司掌握单 相/两相液冷模组、3D-VC 散热模组等核心技术，AI 服务器散热产品已获得 批量订单并实现量产交付，深度受益数据中心液冷量价齐升的产业趋势。 人形机器人：领先布局散热解决方案，控股收购灵巧手领先企业。2025 年上 半年，公司战略投资控股果力智能，果力智能是业内领先的灵巧手及具身智 能机器人专精特新企业，研发的灵巧手拥有业内独创柔软智能单元，具备触 觉、本体与柔软智能多模态感知能力，技术领先。公司基于散热、电磁屏蔽 领域的核心能力，已开发出应用于机器人灵巧手、关节模组、控制板等部位 的散热与电磁屏蔽解决方案，有望深度受益人形机器人产业发展机遇。 投资建议：公司是领先的热管理平台型公司，受益下游消费电子需求回暖和 AI 服 ...

Investment Rating - The report maintains an "Outperform" rating for the industry [5] Core Insights - The North Exchange's stock trading activity remains active, with a total of 277 listed companies and a total market capitalization of 868.79 billion yuan as of September 30, 2025 [11][10] - The North Exchange 50 index has a PE-TTM of 50.90 times, ranking in the 88.25th percentile over the past two years, while the PB-MRQ is 13.18 times, in the 95.46th percentile [24][25] - The report highlights a mixed performance across industries, with the only rising sector being construction materials, while defense, agriculture, social services, food and beverage, and telecommunications sectors saw declines [32][29] Summary by Sections Market Overview - The North Exchange's trading volume for September was 26.028 billion shares, with a transaction value of 615.638 billion yuan, showing a 0.2% increase in volume but a 4.1% decrease in value compared to the previous month [10][15] - The average daily margin balance reached 7.673 billion yuan, an increase of 11.28% month-on-month, with peak margin balances nearing 8 billion yuan [21][21] Valuation - As of September 30, 2025, the North Exchange 50 index's PE-TTM is 50.90 times, and the PB-MRQ is 13.18 times, indicating high valuation levels compared to historical data [24][25] - The highest median PE among industries is in light manufacturing at 104 times, followed by computer and construction materials at 98 and 88 times respectively [25][28] Industry Performance - The North Exchange's dual indices showed a downward trend in September, with the specialized index down 4.44% and the North Exchange 50 down 2.90% [29][30] - The only sector that saw an increase was construction materials, while significant declines were noted in defense, agriculture, social services, food and beverage, and telecommunications [32][29] New Listings - Three new companies were listed this month: Sanxie Electric (920100.BJ), Jinhua New Materials (920015.BJ), and Shichang Co., Ltd. (920022.BJ) [11][3] - The total number of listed companies on the North Exchange is now 277, with a total market capitalization of 868.79 billion yuan [11][10]

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]