赚钱于无形！ 在A股市场的一个普普通通的交易日里，盘面看似平静，但在系统的后台，一笔看似寻常的买单却已经 被拆分成上千条子单，几乎以毫秒为单位涌向交易所。 很少有人意识到，这些子单的流向和节奏，早已被一位"局内人"的眼睛提前锁定。 这位"局内人"正坐在杭州一座办公楼的机房角落，屏幕上显示着公司高端技术的交易终端，他的工牌上 写着"策略前端开发工程师"，听起来像是一个技术支持人员。 这位低调的"局内人"，在不到一年的时间里，不仅享受着量化大厂的丰厚薪资待遇，更在"神不知鬼不 觉"之间于一年之内赚取了8857万元。 更为关键的是，这位"局内人"并非量化投研人员。 按自然日计算，他平均每天"坐赚"25万元，远远高于他在这家知名量化巨头的日薪。 之所以能如此"暴赚"，正是因为他掌握了这家量化大厂的"核心按键"。 近日，监管部门的一纸罚单将中国内地首宗百亿量化大厂的"老鼠仓"案件公之于众。 01 "坐阵"百亿私募巨头 据浙江证监局官网，一位叫林艺平的人士牵涉了这宗老鼠仓案件。 罚单显示：2022年10月至2023年9月期间，林艺平在杭州某某科技任职，承担交易策略前端开发。 罚单还将其任职机构背景做了描写，即"浙江省内两 ...

一、本周指数增强组合表现 沪深300指数增强组合本周超额收益-0.71%，本年超额收益16.74%。 中证500指数增强组合本周超额收益0.12%，本年超额收益6.85%。 中证1000指数增强组合本周超额收益-0.94%，本年超额收益14.08%。 中证A500指数增强组合本周超额收益-1.37%，本年超额收益7.55%。 二、本周选股因子表现跟踪 沪深300成分股中一个月波动、一个月换手、三个月波动等因子表现较好。 中证500成分股中三个月机构覆盖、一个月反转、三个月反转等因子表现较 好。 中证1000成分股中一个月换手、三个月机构覆盖、单季ROA等因子表现较 好。 中证A500指数成分股中一个月换手、三个月换手、一个月波动等因子表现较 好。 公募基金重仓股中一个月波动、一个月换手、三个月换手等因子表现较好。 三、本周公募基金指数增强产品表现跟踪 沪深300指数增强产品本周超额收益最高0.70%，最低-1.26%，中位数 0.09%。 中证500指数增强产品本周超额收益最高1.17%，最低-1.13%，中位数 0.11%。 中证1000指数增强产品本周超额收益最高0.89%，最低-1.38%，中位 数-0 ...

证券研究报告 | 2025年11月22日 多因子选股周报 量价因子表现出色，沪深 300 增强组合年内超额 16.74% 核心观点 金融工程周报 国信金工指数增强组合表现跟踪 因子表现监控 以沪深 300 指数为选股空间。最近一周，一个月波动、一个月换手、三个月 波动等因子表现较好，而单季营利同比增速、三个月机构覆盖、一年动量等 因子表现较差。 以中证 500 指数为选股空间。最近一周，三个月机构覆盖、一个月反转、三 个月反转等因子表现较好，而标准化预期外盈利、DELTAROA、DELTAROE 等因子表现较差。 以中证 1000 指数为选股空间。最近一周，一个月换手、三个月机构覆盖、 单季 ROA 等因子表现较好，而单季 SP、预期 PEG、SPTTM 等因子表现 较差。 以中证 A500 指数为选股空间。最近一周，一个月换手、三个月换手、一个 月波动等因子表现较好，而预期净利润环比、单季净利同比增速、预期 PEG 等因子表现较差。 以公募重仓指数为选股空间。最近一周，一个月波动、一个月换手、三个月 换手等因子表现较好，而单季营收同比增速、单季营利同比增速、单季 ROE 等因子表现较差。 公募基金指数增强产 ...

Performance of Index Enhancement Portfolios - The CSI 300 index enhancement portfolio recorded an excess return of -0.22% for the week and 18.41% year-to-date [1][6] - The CSI 500 index enhancement portfolio had an excess return of -0.52% for the week and 7.09% year-to-date [1][6] - The CSI 1000 index enhancement portfolio showed an excess return of -0.12% for the week and 16.38% year-to-date [1][6] - The CSI A500 index enhancement portfolio achieved an excess return of 0.01% for the week and 9.75% year-to-date [1][6] Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as three-month volatility, one-month volatility, and three-month reversal performed well [1][9] - In the CSI 500 component stocks, factors like one-month turnover, BP, and illiquidity shock showed strong performance [1][9] - For the CSI 1000 component stocks, factors such as illiquidity shock, expected net profit month-on-month, and EPTTM one-year percentile performed well [1][9] - In the CSI A500 index component stocks, factors like three-month volatility, one-month volatility, and one-month turnover performed well [1][9] Public Fund Index Enhancement Products Performance Tracking - The CSI 300 index enhancement products had a maximum excess return of 1.15%, a minimum of -2.04%, and a median of 0.19% for the week [1][20] - The CSI 500 index enhancement products recorded a maximum excess return of 2.03%, a minimum of -0.65%, and a median of 0.27% for the week [1][21] - The CSI 1000 index enhancement products had a maximum excess return of 1.84%, a minimum of -0.95%, and a median of 0.00% for the week [1][23] - The CSI A500 index enhancement products achieved a maximum excess return of 0.94%, a minimum of -0.47%, and a median of 0.16% for the week [1][25] Public Fund Index Enhancement Product Quantity and Scale - There are currently 76 CSI 300 index enhancement products with a total scale of 77.9 billion [1][19] - There are 74 CSI 500 index enhancement products with a total scale of 50.5 billion [1][19] - There are 46 CSI 1000 index enhancement products with a total scale of 21.4 billion [1][19] - There are 68 CSI A500 index enhancement products with a total scale of 25.3 billion [1][19]

- The report tracks the performance of Guosen Financial Engineering's index enhancement portfolios, which are constructed based on multi-factor stock selection models targeting benchmarks such as CSI 300, CSI 500, CSI 1000, and CSI A500 indices[10][11][13] - The construction process of the index enhancement portfolios includes three main components: return prediction, risk control, and portfolio optimization[11] - The report monitors the performance of single-factor Maximized Factor Exposure (MFE) portfolios across different stock selection spaces, including CSI 300, CSI 500, CSI 1000, CSI A500 indices, and public fund heavy positions index[10][14][39] - The MFE portfolio construction process involves optimizing the portfolio to maximize single-factor exposure while controlling for constraints such as style exposure, industry exposure, individual stock weight deviation, and turnover rate[39][40][41] - The optimization model for MFE portfolios is defined as follows: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ where `f` represents factor values, `w` is the stock weight vector, and constraints include style factor deviation, industry deviation, individual stock weight deviation, and component stock weight limits[39][40] - The report highlights the weekly, monthly, and yearly performance of various factors in different stock selection spaces, such as CSI 300, CSI 500, CSI 1000, CSI A500 indices, and public fund heavy positions index[17][19][21][23][25] - Factors such as three-month volatility, one-month volatility, and three-month turnover performed well in the CSI 300 space recently, while factors like one-year momentum and single-quarter profit growth rate performed poorly[17][18] - In the CSI 500 space, factors like one-month turnover and BP showed strong performance recently, while one-year momentum and standardized unexpected earnings performed poorly[19][20] - In the CSI 1000 space, factors such as illiquidity shock and expected net profit growth performed well recently, while standardized unexpected revenue and one-year momentum showed weak performance[21][22] - In the CSI A500 space, factors like three-month volatility and one-month turnover performed well recently, while one-year momentum and standardized unexpected earnings performed poorly[23][24] - In the public fund heavy positions index space, factors such as one-month volatility and three-month turnover performed well recently, while standardized unexpected revenue and one-year momentum showed weak performance[25][26] - The report tracks the performance of public fund index enhancement products, including CSI 300, CSI 500, CSI 1000, and CSI A500 index enhancement funds, with detailed statistics on excess returns across different time periods[27][28][31][33][35][38]

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.01% this week and 18.92% year-to-date [6][18] - The CSI 500 index enhanced portfolio recorded an excess return of -0.26% this week and 7.89% year-to-date [6][18] - The CSI 1000 index enhanced portfolio had an excess return of -0.63% this week and 16.63% year-to-date [6][18] - The CSI A500 index enhanced portfolio posted an excess return of 0.20% this week and 9.84% year-to-date [6][18] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as EPTTM, expected BP, and BP performed well [9][10] - In the CSI 500 component stocks, three-month volatility, expected EPTTM, and expected BP showed strong performance [9][10] - For the CSI 1000 component stocks, EPTTM, three-month volatility, and expected EPTTM were the top-performing factors [9][10] - In the CSI A500 index component stocks, expected EPTTM, EPTTM, and BP were the best-performing factors [9][10] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 0.89%, a minimum of -1.44%, and a median of -0.18% this week [22][24] - The CSI 500 index enhanced products achieved a maximum excess return of 1.65%, a minimum of -1.05%, and a median of 0.05% this week [24][28] - The CSI 1000 index enhanced products recorded a maximum excess return of 0.94%, a minimum of -1.66%, and a median of -0.30% this week [24][28] - The CSI A500 index enhanced products had a maximum excess return of 0.59%, a minimum of -1.02%, and a median of -0.16% this week [24][29]

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 single factors under real-world constraints, such as industry exposure, style exposure, stock weight limits, and turnover rate. This approach ensures that the factors deemed "effective" can genuinely contribute to return prediction in the final portfolio[38][39]. - **Model Construction Process**: - The objective function is to maximize single-factor exposure, represented as $f^{T}w$, where $f$ is the factor value, and $w$ is the stock weight vector. - The optimization model includes the following constraints: 1. **Style Exposure Constraint**: Limits the portfolio's deviation from the benchmark in terms of style factors. $X$ is the factor exposure matrix, $w_b$ is the benchmark weight vector, and $s_l, s_h$ are the lower and upper bounds for style factor exposure[39]. 2. **Industry Exposure Constraint**: Limits the portfolio's deviation from the benchmark in terms of industry exposure. $H$ is the industry exposure matrix, and $h_l, h_h$ are the lower and upper bounds for industry exposure[39]. 3. **Stock Weight Deviation Constraint**: Limits individual stock weight deviations from the benchmark. $w_l, w_h$ are the lower and upper bounds for stock weight deviations[39]. 4. **Constituent Stock Weight Constraint**: Limits the weight of constituent stocks within the portfolio. $B_b$ is a binary vector indicating whether a stock is a benchmark constituent, and $b_l, b_h$ are the lower and upper bounds for constituent stock weights[39]. 5. **No Short Selling Constraint**: Ensures no short positions and limits individual stock weights to a maximum value $l$[39]. 6. **Full Investment Constraint**: Ensures the portfolio is fully invested, with the sum of weights equal to 1[40]. - The optimization model is expressed as: $$ \begin{array}{ll} max & f^{T}w \\ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T}w = 1 \end{array} $$ - The MFE portfolio is constructed monthly, and historical returns are backtested with a 0.3% transaction cost applied on both sides[42]. - **Model Evaluation**: The MFE portfolio effectively tests factor performance under realistic constraints, making it a robust tool for evaluating factor predictability in practical scenarios[38][39]. --- Factor Construction and Methods 1. Factor Name: EPTTM (Earnings to Price Trailing Twelve Months) - **Factor Construction Idea**: Measures the profitability of a company relative to its market value, using trailing twelve months' earnings[15]. - **Factor Construction Process**: - Formula: $EPTTM = \frac{\text{Net Income (TTM)}}{\text{Market Value}}$ - The numerator represents the trailing twelve months' net income, while the denominator is the company's total market value[15]. 2. Factor Name: BP (Book-to-Price Ratio) - **Factor Construction Idea**: Evaluates the valuation of a company by comparing its book value to its market value[15]. - **Factor Construction Process**: - Formula: $BP = \frac{\text{Book Value}}{\text{Market Value}}$ - The numerator is the company's book value, and the denominator is its total market value[15]. 3. Factor Name: Three-Month Volatility - **Factor Construction Idea**: Captures the stock's price fluctuation over the past three months, reflecting its risk level[15]. - **Factor Construction Process**: - Formula: $Volatility = \text{Average True Range (ATR)}$ over the past 60 trading days. - The ATR is calculated as the average of the daily high-low range over the specified period[15]. 4. Factor Name: One-Month Reversal - **Factor Construction Idea**: Measures the short-term reversal effect by analyzing the stock's return over the past month[15]. - **Factor Construction Process**: - Formula: $Reversal = \text{Return over the past 20 trading days}$ - Positive values indicate a reversal effect, while negative values suggest momentum continuation[15]. --- Factor Backtesting Results 1. EPTTM - **HS300**: Weekly return 1.35%, monthly return 4.28%, YTD return 5.95%, historical annualized return 4.60%[18]. - **CSI500**: Weekly return 1.54%, monthly return 3.55%, YTD return -3.61%, historical annualized return 4.78%[20]. - **CSI1000**: Weekly return 1.44%, monthly return 2.78%, YTD return 0.15%, historical annualized return 6.84%[22]. - **CSIA500**: Weekly return 1.72%, monthly return 3.92%, YTD return 2.62%, historical annualized return 3.71%[24]. - **Public Fund Index**: Weekly return 1.82%, monthly return 5.32%, YTD return 4.75%, historical annualized return 1.42%[26]. 2. BP - **HS300**: Weekly return 1.25%, monthly return 2.83%, YTD return -1.86%, historical annualized return 2.72%[18]. - **CSI500**: Weekly return 1.36%, monthly return 2.23%, YTD return 3.09%, historical annualized return 3.47%[20]. - **CSI1000**: Weekly return 0.99%, monthly return 1.56%, YTD return -0.45%, historical annualized return 3.07%[22]. - **CSIA500**: Weekly return 1.50%, monthly return 3.44%, YTD return -4.52%, historical annualized return 2.89%[24]. - **Public Fund Index**: Weekly return 1.45%, monthly return 3.20%, YTD return -8.75%, historical annualized return 0.74%[26]. 3. Three-Month Volatility - **HS300**: Weekly return 0.52%, monthly return 1.75%, YTD return -3.56%, historical annualized return 1.84%[18]. - **CSI500**: Weekly return 1.76%, monthly return 3.07%, YTD return -7.17%, historical annualized return 3.50%[20]. - **CSI1000**: Weekly return 1.40%, monthly return 2.54%, YTD return -8.22%, historical annualized return 4.33%[22]. - **CSIA500**: Weekly return 0.79%, monthly return 2.15%, YTD return -9.34%, historical annualized return 2.77%[24]. - **Public Fund Index**: Weekly return 0.97%, monthly return 2.04%, YTD return -15.34%, historical annualized return 1.54%[26]. 4. One-Month Reversal - **HS300**: Weekly return -0.93%, monthly return 0.98%, YTD return -0.57%, historical annualized return -0.33%[18]. - **CSI500**: Weekly return -1.83%, monthly return -0.84%, YTD return 2.56%, historical annualized return -0.84%[20]. - **CSI1000**: Weekly return -1.49%, monthly return -0.55%, YTD return -4.63%, historical annualized return -3.84%[22]. - **CSIA500**: Weekly return -1.28%, monthly return 0.51%, YTD return -1.07%, historical annualized return -2.34%[24]. - **Public Fund Index**: Weekly return -1.11%, monthly return 0.95%, YTD return 4.67%, historical annualized return -1.80%[26].

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio recorded a weekly excess return of -0.02% and a year-to-date excess return of 18.75% [6][18] - The CSI 500 index enhanced portfolio had a weekly excess return of -0.64% and a year-to-date excess return of 8.25% [6][18] - The CSI 1000 index enhanced portfolio experienced a weekly excess return of -1.24% and a year-to-date excess return of 17.45% [6][18] - The CSI A500 index enhanced portfolio achieved a weekly excess return of 1.03% and a year-to-date excess return of 9.51% [6][18] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as EPTTM, expected BP, and expected EPTTM performed well [9][10] - In the CSI 500 component stocks, factors like single-season SP, SPTTM, and expected PEG showed strong performance [10][11] - In the CSI 1000 component stocks, factors such as specificity, three-month turnover, and expected net profit month-on-month performed well [10][12] - In the CSI A500 index component stocks, single-season SP, SPTTM, and BP factors performed well [10][14] - Among publicly offered fund heavy stocks, factors like SPTTM, one-month reversal, and single-season SP performed well [10][16] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 1.06%, a minimum of -0.51%, and a median of 0.13% for the week [20][22] - The CSI 500 index enhanced products recorded a maximum excess return of 0.79%, a minimum of -1.26%, and a median of -0.13% for the week [23][25] - The CSI 1000 index enhanced products had a maximum excess return of 0.75%, a minimum of -1.44%, and a median of -0.34% for the week [24][25] - The CSI A500 index enhanced products achieved a maximum excess return of 0.91%, a minimum of -0.59%, and a median of 0.07% for the week [24][25]

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.53% this week and 18.86% year-to-date [1][7] - The CSI 500 index enhanced portfolio recorded an excess return of 0.45% this week and 9.03% year-to-date [1][7] - The CSI 1000 index enhanced portfolio had an excess return of 0.34% this week and 19.00% year-to-date [1][7] - The CSI A500 index enhanced portfolio experienced an excess return of -0.46% this week and 8.18% year-to-date [1][7] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as quarterly ROA, quarterly ROE, and one-year momentum performed well [1][10] - In the CSI 500 component stocks, factors like SPTTM, executive compensation, and three-month institutional coverage showed strong performance [1][10] - For the CSI 1000 component stocks, factors such as three-month earnings revisions, standardized unexpected revenue, and standardized unexpected earnings performed well [1][10] - In the CSI A500 index component stocks, factors like one-year momentum, quarterly revenue year-on-year growth, and DELTAROA showed good performance [1][10] - Among publicly offered fund heavy stocks, factors like one-year momentum, standardized unexpected revenue, and three-month earnings revisions performed well [1][10] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 2.02%, a minimum of -1.13%, and a median of 0.06% this week [1][23] - The CSI 500 index enhanced products recorded a maximum excess return of 1.24%, a minimum of -1.61%, and a median of 0.19% this week [1][25] - The CSI 1000 index enhanced products achieved a maximum excess return of 1.52%, a minimum of -1.23%, and a median of 0.45% this week [1][29] - The CSI A500 index enhanced products had a maximum excess return of 0.84%, a minimum of -0.53%, and a median of 0.03% this week [1][30]

Core Insights - The domestic index investment has seen significant growth, with investors increasingly seeking clear risk-return characteristics [1] - The company, Xingzheng Global Fund, has rapidly developed a diverse range of index-enhanced products, leveraging its expertise in quantitative investment [1] Group 1: Quantitative Investment Team Development - The quantitative research team has been established over the past two years, developing over 2,000 alpha factors and a modular quantitative management system [2] - The team operates in a collaborative environment that encourages sharing of results and strategies, enhancing overall productivity [2] - The focus is on achieving full automation in the quantitative system, ensuring stable operations and enhancing modularity and fault tolerance [2][3] Group 2: Alpha Factor Exploration - The core focus of the quantitative strategy is on the exploration of alpha factors, which are crucial for generating excess returns while closely tracking index characteristics [4] - The team employs a systematic approach to develop and optimize alpha factors, ensuring their effectiveness is tested over longer periods [4][5] - Continuous iteration and optimization of alpha factors are conducted to adapt to market changes and incorporate the latest machine learning models [4] Group 3: Product Line Expansion - The company has recognized the growth potential in index-enhanced funds, which currently represent only a fraction of the scale of equity ETFs [6] - Recent product launches include various index-enhanced funds, particularly in the Hong Kong market, where the company has developed proprietary risk models and factor libraries [7] - The company aims to build a comprehensive product line that includes various styles such as quality, value, and growth to meet diverse investor needs [8]