核心观点 金融工程周报 证券研究报告 | 2025年09月27日 主动量化策略周报 科创 50 领涨，超预期精选组合年内满仓上涨 52.03% 本周，超预期精选组合绝对收益 0.70%，相对偏股混合型基金指数超额收益 0.23%。本年，超预期精选组合绝对收益 46.54%，相对偏股混合型基金指 数超额收益 14.47%。今年以来，超预期精选组合在主动股基中排名 20.61% 分位点（715/3469）。 本周，券商金股业绩增强组合绝对收益-0.54%，相对偏股混合型基金指数超 额收益-1.01%。本年，券商金股业绩增强组合绝对收益 33.26%，相对偏股 混合型基金指数超额收益 1.19%。今年以来，券商金股业绩增强组合在主动 股基中排名 43.07%分位点（1494/3469）。 本周，成长稳健组合绝对收益 0.26%，相对偏股混合型基金指数超额收益 -0.22%。本年，成长稳健组合绝对收益 51.84%，相对偏股混合型基金指数 超额收益 19.77%。今年以来，成长稳健组合在主动股基中排名 15.31%分位 点（531/3469）。 本周，股票收益中位数-1.74%，31%的股票上涨，69%的股票下跌；主 ...

Quantitative Models and Construction Methods - **Model Name**: Excellent Fund Performance Enhancement Portfolio **Model Construction Idea**: Transition from benchmarking broad-based indices to benchmarking active equity funds, leveraging quantitative methods to enhance fund selection and achieve "best of the best" [4][19][52] **Model Construction Process**: 1. Benchmark against the median return of active equity funds, represented by the biased equity hybrid fund index (885001.WI) [19][52] 2. Use a layered neutralization process for return-related factors to address style concentration issues [52] 3. Optimize the portfolio to control deviations in individual stocks, industries, and styles relative to the selected fund holdings [53] **Model Evaluation**: Demonstrates strong stability and the ability to consistently outperform the median of active equity funds [53] - **Model Name**: Outperformance Selection Portfolio **Model Construction Idea**: Focus on stocks with significant outperformance events, selecting those with both fundamental support and technical resonance [5][58] **Model Construction Process**: 1. Filter stocks based on research report titles indicating outperformance and analysts' upward revisions of net profit [5][58] 2. Conduct dual-layer screening on fundamentals and technicals to select stocks with both fundamental support and technical resonance [5][58] **Model Evaluation**: Consistently ranks in the top 30% of active equity funds annually, demonstrating strong performance [59] - **Model Name**: Securities Firms' Golden Stock Performance Enhancement Portfolio **Model Construction Idea**: Optimize the securities firms' golden stock pool to achieve stable outperformance relative to the biased equity hybrid fund index [6][63] **Model Construction Process**: 1. Use the securities firms' golden stock pool as the stock selection space and benchmark [6][33] 2. Optimize the portfolio to control deviations in individual stocks, industries, and styles relative to the golden stock pool [6][33] **Model Evaluation**: Consistently ranks in the top 30% of active equity funds annually, reflecting stable performance [64] - **Model Name**: Growth and Stability Portfolio **Model Construction Idea**: Focus on the "golden period" of excess returns for growth stocks, using a two-dimensional evaluation system based on time series and cross-sectional analysis [7][68] **Model Construction Process**: 1. Use the "excess return release map" to identify the strongest excess return periods before and after positive events [68] 2. Prioritize stocks closer to their financial report disclosure dates, and use multi-factor scoring to select high-quality stocks when the sample size is large [7][68] 3. Introduce mechanisms such as weak balancing, transition, buffering, and risk avoidance to reduce turnover and mitigate risks [68] **Model Evaluation**: Consistently ranks in the top 30% of active equity funds annually, with strong performance [69] --- Backtesting Results of Models - **Excellent Fund Performance Enhancement Portfolio**: - Annualized return: 20.31% - Excess return relative to biased equity hybrid fund index: 11.83% - Consistently ranks in the top 30% of active equity funds annually [54][57] - **Outperformance Selection Portfolio**: - Annualized return: 30.55% - Excess return relative to biased equity hybrid fund index: 24.68% - Consistently ranks in the top 30% of active equity funds annually [59][61] - **Securities Firms' Golden Stock Performance Enhancement Portfolio**: - Annualized return: 19.34% - Excess return relative to biased equity hybrid fund index: 14.38% - Consistently ranks in the top 30% of active equity funds annually [64][67] - **Growth and Stability Portfolio**: - Annualized return: 35.51% - Excess return relative to biased equity hybrid fund index: 26.88% - Consistently ranks in the top 30% of active equity funds annually [69][72]

一、国信金工主动量化策略表现跟踪 本周， 优秀基金业绩增强组合 绝对收益0.35%，相对偏股混合型基金指数超额收 益-0.12%。本年，优秀基金业绩增强组合绝对收益28.00%，相对偏股混合型基金 指数超额收益-4.08%。 今年以来，优秀基金业绩增强组合在主动股基中排名 54.37%分位点（1886/3469）。 本周， 超预期精选组合 绝对收益0.70%，相对偏股混合型基金指数超额收益 0.23%。本年，超预期精选组合绝对收益46.54%，相对偏股混合型基金指数超额 收益14.47%。 今年以来，超预期精选组合在主动股基中排名20.61%分位点 （715/3469）。 本周， 券商金股业绩增强组合 绝对收益-0.54%，相对偏股混合型基金指数超额收 益-1.01%。本年，券商金股业绩增强组合绝对收益33.26%，相对偏股混合型基金 指数超额收益1.19%。 今年以来，券商金股业绩增强组合在主动股基中排名 43.07%分位点（1494/3469）。 本 周 ， 成 长 稳 健 组 合 绝 对 收 益 0.26% ， 相 对 偏 股 混 合 型 基 金 指 数 超 额 收 益-0.22%。本年，成长稳健组合绝 ...

Quantitative Models and Construction Methods 1. Model Name: Convertible Bond Random Forest Strategy - **Model Construction Idea**: Utilizes the Random Forest machine learning method to identify convertible bonds with potential for excess returns by leveraging decision trees[16][17] - **Model Construction Process**: 1. Data preprocessing and feature engineering to prepare convertible bond datasets 2. Training a Random Forest model with historical data to identify patterns of excess return potential 3. Selecting bonds with the highest predicted scores for portfolio construction 4. Weekly rebalancing of the portfolio based on updated predictions[17] - **Model Evaluation**: Demonstrated strong performance in generating excess returns, indicating high predictive accuracy[16] 2. Model Name: Multi-Dimensional Timing Model - **Model Construction Idea**: Combines macro, meso, micro, and derivative signals to create a four-dimensional non-linear timing model for market positioning[18][19] - **Model Construction Process**: 1. Macro signals: Derived from liquidity, interest rates, credit, economic growth, and exchange rates 2. Meso signals: Based on industry-level business cycle indicators 3. Micro signals: Captures structural risks using valuation, risk premium, volatility, and liquidity factors 4. Derivative signals: Generated from the basis of stock index futures 5. Aggregation: Signals are synthesized into a composite timing signal[18][19][24] - **Model Evaluation**: Effective in identifying market trends and providing actionable signals, with the latest signal indicating a bullish stance[19][24] 3. Model Name: Industry Rotation Strategy 2.0 - **Model Construction Idea**: Constructs an industry rotation strategy based on economic quadrants and multi-dimensional industry style factors[69] - **Model Construction Process**: 1. Define economic quadrants using corporate earnings and credit conditions 2. Develop industry style factors such as expected business climate, earnings surprises, momentum, valuation bubbles, and inflation beta 3. Test factor effectiveness within each quadrant 4. Allocate to high-expected-return industries based on factor signals[69][71] - **Model Evaluation**: Demonstrates strong adaptability to the A-share market, with annualized excess returns of 9.44% (non-exclusion version) and 10.14% (double-exclusion version)[71] 4. Model Name: Genetic Programming Index Enhancement Models - **Model Construction Idea**: Uses genetic programming to discover and optimize stock selection factors for index enhancement strategies[88][93][97] - **Model Construction Process**: 1. Stock pools: Defined for CSI 300, CSI 500, CSI 1000, and CSI All Share indices 2. Training: Genetic programming generates initial factor populations and iteratively evolves them through multiple generations 3. Factor selection: Top-performing factors are combined into a composite score 4. Portfolio construction: Selects top 10% of stocks within each industry based on scores, with weekly rebalancing[88][93][97][102] - **Model Evaluation**: - CSI 300: Annualized excess return of 17.91%, Sharpe ratio of 1.05[91] - CSI 500: Annualized excess return of 11.78%, Sharpe ratio of 0.85[95] - CSI 1000: Annualized excess return of 17.97%, Sharpe ratio of 0.93[98] - CSI All Share: Annualized excess return of 24.84%, Sharpe ratio of 1.33[103] --- Model Backtest Results 1. Convertible Bond Random Forest Strategy - Weekly excess return: 0.64%[16] 2. Multi-Dimensional Timing Model - Latest composite signal: Bullish (1)[19][24] 3. Industry Rotation Strategy 2.0 - Annualized excess return (non-exclusion version): 9.44% - Annualized excess return (double-exclusion version): 10.14%[71] 4. Genetic Programming Index Enhancement Models - CSI 300: - Annualized excess return: 17.91% - Sharpe ratio: 1.05[91] - CSI 500: - Annualized excess return: 11.78% - Sharpe ratio: 0.85[95] - CSI 1000: - Annualized excess return: 17.97% - Sharpe ratio: 0.93[98] - CSI All Share: - Annualized excess return: 24.84% - Sharpe ratio: 1.33[103] --- Quantitative Factors and Construction Methods 1. Factor Name: Industry Business Climate Index 2.0 - **Factor Construction Idea**: Tracks industry fundamentals by analyzing revenue, pricing, and cost dynamics[27] - **Factor Construction Process**: 1. Analyze industry revenue and cost structures 2. Calculate daily market-cap-weighted industry indices 3. Aggregate indices into a composite business climate index[27][30] - **Factor Evaluation**: Demonstrates predictive power for A-share earnings expansion cycles[28] 2. Factor Name: Barra CNE6 Style Factors - **Factor Construction Idea**: Evaluates market performance using 9 primary and 20 secondary style factors, including size, volatility, momentum, quality, value, and growth[45] - **Factor Construction Process**: 1. Calculate factor returns for each style factor 2. Aggregate factor performance to assess market trends[45][46] - **Factor Evaluation**: Size factor performed well during the week, while volatility factor underperformed[46] 3. Factor Name: Industry Rotation Factors - **Factor Construction Idea**: Captures industry rotation dynamics using factors like expected business climate, earnings surprises, momentum, and valuation bubbles[69] - **Factor Construction Process**: 1. Define and calculate individual factors 2. Test factor effectiveness within economic quadrants 3. Combine factors for industry allocation[69] - **Factor Evaluation**: Demonstrates strong historical performance, with factors like expected business climate and momentum showing significant returns[57][59] --- Factor Backtest Results 1. Industry Business Climate Index 2.0 - Current value: 0.913 - Excluding financials: 1.288[28] 2. Barra CNE6 Style Factors - Size factor: Strong performance during the week[46] 3. Industry Rotation Factors - Historical annualized returns: - Expected business climate: 0.40% - Momentum: -0.95% - Valuation beta: 2.37%[57]

Quantitative Models and Construction Methods - **Model Name**: Dividend Selection Strategy **Model Construction Idea**: This strategy focuses on selecting high-quality dividend stocks by leveraging a top-down approach to identify industry and thematic core factors, aiming to refine stock-picking logic and enhance the precision of identifying potential targets within specific sectors[13][15] **Model Construction Process**: The model utilizes a fundamental factor library to screen for effective stock-picking factors. It emphasizes a combination of "stability" and "growth" styles, represented by two portfolios: the "Central SOE High Dividend 30 Portfolio" and the "Balanced Dividend 50 Portfolio"[15] **Model Evaluation**: The strategy demonstrates a significant excess return year-to-date, outperforming the benchmark by approximately 4.00%, and ranks around the 48th percentile among all dividend-related fund products[22] - **Model Name**: Industry High Success Rate Strategy **Model Construction Idea**: This strategy aims to track market hotspots and select individual stocks within high-performing industries, providing alternative perspectives for investment decisions[6][13] **Model Construction Process**: The strategy identifies industries with high success rates and selects stocks with strong thematic alignment and growth potential. It is part of the actively managed quantitative product suite launched since July 2023[6][13] Model Backtesting Results - **Dividend Selection Strategy**: - Excess return relative to the CSI Dividend Total Return Index: approximately 4.00% year-to-date[22] - Percentile ranking among dividend-related fund products: ~48%[22] - **Industry High Success Rate Strategy**: - No specific backtesting results provided in the report Quantitative Factors and Construction Methods - **Factor Name**: Dividend Quality Factor **Factor Construction Idea**: This factor emphasizes the quality of dividend-paying stocks, focusing on metrics that indicate financial stability and consistent dividend payouts[7][19] **Factor Construction Process**: The factor is derived from the CSI Dividend Quality Index, which achieved a weekly return of approximately 0.88%, outperforming pure dividend assets[7][19] **Factor Evaluation**: The factor demonstrates superior performance compared to other dividend-related indices, highlighting its effectiveness in capturing high-quality dividend stocks[7][19] Factor Backtesting Results - **Dividend Quality Factor**: - Weekly return: ~0.88%[7][19] - Outperformance relative to the CSI Dividend Index: +1.98%[19]

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]

Core Viewpoint - The report tracks the performance of various active quantitative strategies by GuoXin JinGong, focusing on their absolute and relative returns against benchmarks, specifically the active equity mixed fund index and the performance of individual stock pools [2][3][6]. Group 1: Performance Overview - The "Excellent Fund Performance Enhancement Portfolio" achieved an absolute return of 2.52% this week and a year-to-date return of 27.90%, underperforming the mixed fund index by 0.12% and 2.68% respectively [1][10]. - The "Super Expected Selection Portfolio" recorded an absolute return of 1.55% this week and 43.65% year-to-date, outperforming the mixed fund index by 13.07% [1][18]. - The "Brokerage Golden Stock Performance Enhancement Portfolio" had an absolute return of 2.22% this week and 33.45% year-to-date, with a relative outperformance of 2.87% against the mixed fund index [1][19]. - The "Growth and Stability Portfolio" achieved an absolute return of 2.83% this week and 53.34% year-to-date, outperforming the mixed fund index by 22.76% [1][28]. Group 2: Strategy Summaries - The "Excellent Fund Performance Enhancement Portfolio" aims to outperform the median return of active equity funds by utilizing a quantitative approach based on the holdings of top-performing funds [7][33]. - The "Super Expected Selection Portfolio" selects stocks based on positive earnings surprises and analyst upgrades, focusing on both fundamental and technical criteria to build a robust stock selection [12][39]. - The "Brokerage Golden Stock Performance Enhancement Portfolio" leverages the stock pool identified by brokerage analysts, optimizing the selection to maintain alignment with the broader market while aiming for superior returns [16][41]. - The "Growth and Stability Portfolio" employs a time-series evaluation method to identify growth stocks that are likely to outperform around earnings announcements, incorporating various risk management mechanisms [24][45].

- The report discusses the performance of various quantitative strategies, including the "Dividend Selection Strategy" and the "Industry High Success Rate Strategy," which were launched by the Changjiang Financial Engineering team since July 2023[5][13][15] - The "Dividend Selection Strategy" includes two products: the "Central State-Owned Enterprises High Dividend 30 Portfolio" and the "Balanced Dividend 50 Portfolio"[15] - The "Industry Enhancement Series" focuses on the electronics sector and includes two products: the "Electronics Balanced Allocation Enhancement Portfolio" and the "Electronics Sector Preferred Enhancement Portfolio"[15] Quantitative Models and Construction Methods 1. **Model Name: Central State-Owned Enterprises High Dividend 30 Portfolio** - **Model Construction Idea**: Focuses on high dividend stocks within central state-owned enterprises[15] - **Model Construction Process**: The portfolio is constructed by selecting 30 high dividend stocks from central state-owned enterprises, aiming to outperform the CSI Dividend Total Return Index[15] - **Model Evaluation**: The model has shown to outperform the CSI Dividend Total Return Index by approximately 0.01% in the reported period[16][25] 2. **Model Name: Electronics Balanced Allocation Enhancement Portfolio** - **Model Construction Idea**: Aims to balance allocation within the electronics sector to achieve enhanced returns[15] - **Model Construction Process**: The portfolio is constructed by selecting stocks within the electronics sector and balancing the allocation to achieve a defensive attribute and enhanced returns[15] - **Model Evaluation**: The model outperformed the electronics index by approximately 0.81% in the reported period[6][35] 3. **Model Name: Electronics Sector Preferred Enhancement Portfolio** - **Model Construction Idea**: Focuses on leading companies in mature sub-sectors within the electronics industry[15] - **Model Construction Process**: The portfolio is constructed by selecting leading companies in mature sub-sectors within the electronics industry, aiming to achieve enhanced returns[15] - **Model Evaluation**: The model outperformed the electronics index by approximately 1.48% in the reported period[6][35] Model Backtest Results 1. **Central State-Owned Enterprises High Dividend 30 Portfolio** - **Outperformance**: Approximately 0.01% over the CSI Dividend Total Return Index[16][25] 2. **Electronics Balanced Allocation Enhancement Portfolio** - **Outperformance**: Approximately 0.81% over the electronics index[6][35] 3. **Electronics Sector Preferred Enhancement Portfolio** - **Outperformance**: Approximately 1.48% over the electronics index[6][35]

Group 1 - The core viewpoint of the article is to track the performance of various active quantitative strategies developed by GuoXin Securities, focusing on their relative performance against benchmarks such as the active equity fund median and the mixed equity fund index [2][3][6]. Group 2 Active Quantitative Strategy Performance Tracking - The "Excellent Fund Performance Enhancement Portfolio" had an absolute return of -1.31% this week and a year-to-date return of 24.74%, ranking in the 48.80 percentile among active equity funds [1][14]. - The "Super Expected Selection Portfolio" reported an absolute return of -1.63% this week and a year-to-date return of 41.45%, ranking in the 17.96 percentile among active equity funds [1][25]. - The "Brokerage Golden Stock Performance Enhancement Portfolio" had an absolute return of -2.58% this week and a year-to-date return of 30.55%, ranking in the 36.12 percentile among active equity funds [1][36]. - The "Growth and Stability Portfolio" showed an absolute return of -0.07% this week and a year-to-date return of 49.12%, ranking in the 10.90 percentile among active equity funds [1][44]. Group 3 Excellent Fund Performance Enhancement Portfolio - This portfolio aims to outperform the median return of public active equity funds by utilizing a quantitative approach based on the holdings of top-performing funds [7][49]. - The portfolio's construction considers the median position of active equity funds, with a current position of 90% [7][49]. Group 4 Super Expected Selection Portfolio - This portfolio selects stocks based on the occurrence of super expected events and analyst profit upgrades, focusing on both fundamental and technical criteria [19][56]. - The portfolio's construction also considers a 90% position based on the mixed equity fund index [19][56]. Group 5 Brokerage Golden Stock Performance Enhancement Portfolio - This portfolio is constructed using a selection of stocks from the brokerage golden stock pool, aiming to optimize performance while controlling deviations from the pool's characteristics [28][60]. - The portfolio also maintains a 90% position based on the ordinary equity fund index [28][60]. Group 6 Growth and Stability Portfolio - This portfolio employs a two-dimensional evaluation system for growth stocks, prioritizing stocks closer to their earnings report dates to capture potential excess returns [37][64]. - The portfolio also maintains a 90% position based on the mixed equity fund index [37][64].

Group 1 - The core viewpoint of the article emphasizes the performance tracking of GuoXin's active quantitative strategies, which aim to outperform the median returns of actively managed equity funds [2][3][5] - The report includes four main strategies: Excellent Fund Performance Enhancement Portfolio, Super Expected Selection Portfolio, Broker Golden Stock Performance Enhancement Portfolio, and Growth Stability Portfolio [2][3][5] Group 2 Excellent Fund Performance Enhancement Portfolio - This portfolio aims to benchmark against the median returns of actively managed equity funds, utilizing quantitative methods to enhance performance based on the holdings of top-performing funds [6][50] - As of this week, the portfolio achieved an absolute return of 3.51% and a relative excess return of 0.65% against the mixed equity fund index [11][12] Super Expected Selection Portfolio - This strategy selects stocks based on super expected events and analyst profit upgrades, focusing on both fundamental and technical criteria to build a portfolio [16][57] - The portfolio reported an absolute return of 2.90% this week and a year-to-date return of 43.79%, outperforming the mixed equity fund index by 15.51% [22][21] Broker Golden Stock Performance Enhancement Portfolio - This portfolio is constructed using a selection of stocks recommended by brokers, aiming to optimize performance while controlling deviations from the broker stock pool [25][61] - The portfolio achieved an absolute return of 4.77% this week and a year-to-date return of 34.01%, with a relative excess return of 5.72% against the mixed equity fund index [34][27] Growth Stability Portfolio - This strategy focuses on growth stocks, utilizing a two-dimensional evaluation system to prioritize stocks close to their earnings announcement dates, aiming to capture excess returns during favorable events [35][65] - The portfolio reported an absolute return of 2.47% this week and a year-to-date return of 49.23%, outperforming the mixed equity fund index by 20.94% [44][39]