Quantitative Models and Construction Methods National Enterprise Fundamental Factor Stock Selection Strategy - **Model Name**: National Enterprise Fundamental Factor Stock Selection Strategy [3] - **Model Construction Idea**: The strategy is based on fundamental factors tailored to national enterprises, considering both general and industry-specific factors [5][6] - **Model Construction Process**: - Define the sample pool using the CSI National Enterprise Index (000955.CSI) and stocks listed on the Beijing Stock Exchange for over six months with central or local state-owned enterprise attributes [3] - Classify industries into dividend-oriented and growth-oriented categories based on ZX third-level industry logic [3][4] - Select general factors such as ROE (TTM), operating cash ratio, labor productivity, asset-liability ratio, and dividend yield [5][6] - Incorporate industry-specific factors like ROIC, prepayment growth rate, inventory turnover rate, and capital expenditure/depreciation ratio for different industries [6][8] - Adjust factor weights based on industry characteristics, emphasizing dividend yield for dividend-oriented industries and reducing the weight of asset-liability ratio for growth-oriented industries [9] - Calculate scores using weighted averages of general and industry-specific factors, normalize the scores, and assign weights to stocks based on their scores [11] - Formula for stock weight: $$w_{i}={\frac{s c o r e_{i}^{3}}{\sum_{i=1}^{N}s c o r e_{i}^{3}}}$$ [11] - **Model Evaluation**: The strategy effectively captures the characteristics of national enterprises, balancing dividend stability and growth potential [5][6] Technology Theme Fundamental Factor Stock Selection Strategy - **Model Name**: Technology Theme Fundamental Factor Stock Selection Strategy [19] - **Model Construction Idea**: Focus on technology stocks with high R&D investment and strong growth potential, using fundamental factors to identify stocks in their growth and mature stages [20][23] - **Model Construction Process**: - Define the sample pool based on SW third-level industries and R&D investment criteria (R&D expenses > 5% of revenue or R&D personnel > 10% of total employees) [19][20] - Exclude stocks in the shock and decline stages based on cash flow lifecycle analysis [22][23] - Select general factors such as profitability, growth ability, technical level, supply chain concentration, and alpha factors [24][28] - Incorporate specific factors for growth and mature stages, such as management expense ratio, R&D expense ratio, accounts receivable turnover rate, and PB-ROE [24][28] - Adjust scores using R&D expense multipliers to emphasize high R&D industries [28][29] - Formula for stock weight: $$w e i g h t_{i}={\frac{s c o r e_{i}}{\sum_{i=1}^{50}s c o r e_{i}}}$$ [30] - **Model Evaluation**: The strategy highlights technology stocks with strong R&D capabilities and growth potential, effectively capturing industry-specific dynamics [24][28] Consumer Theme Fundamental Factor Stock Selection Strategy - **Model Name**: Consumer Theme Fundamental Factor Stock Selection Strategy [38] - **Model Construction Idea**: Focus on consumer stocks with direct-to-consumer business models, using fundamental factors to identify stocks with strong growth, profitability, and governance [38][39] - **Model Construction Process**: - Define the sample pool based on SW third-level industries, categorizing stocks into daily manufacturing, optional manufacturing, daily services, and optional services [38][39] - Select general factors such as growth-profitability-cash flow composite factor, operating cash flow ratio, ESG management score, and economic sensitivity [40][41] - Incorporate specific factors like market share, R&D expense ratio, accounts receivable turnover rate, and marketing expense ratio [40][41] - Adjust scores using PS (TTM) multipliers to emphasize stocks with lower price-to-sales ratios [46][47] - Formula for stock weight: $$w e l g h t_{i}={\frac{S c o r e_{i}^{a d j}}{\sum_{i=1}^{50}S c o r e_{i}^{a d j}}}$$ [48] - **Model Evaluation**: The strategy effectively identifies consumer stocks with strong fundamentals and growth potential, balancing profitability and governance [40][41] --- Model Backtesting Results National Enterprise Fundamental Factor Stock Selection Strategy - **Annualized Return**: 22.93% [12][15] - **Annualized Volatility**: 20.85% [15] - **Sharpe Ratio**: 1.0961 [15] - **Calmar Ratio**: 0.9963 [15] - **Maximum Drawdown**: -23.01% [15] Technology Theme Fundamental Factor Stock Selection Strategy - **Annualized Return**: 30.61% [31][34] - **Annualized Volatility**: 27.61% [34] - **Sharpe Ratio**: 1.1070 [34] - **Calmar Ratio**: 0.8962 [34] - **Maximum Drawdown**: -34.16% [34] Consumer Theme Fundamental Factor Stock Selection Strategy - **Annualized Return**: 24.86% [49][52] - **Annualized Volatility**: 22.99% [52] - **Sharpe Ratio**: 1.0825 [52] - **Calmar Ratio**: 1.0197 [52] - **Maximum Drawdown**: -24.38% [52]

- Short-term volume models for some broad-based indices turned bullish[1][3][11] - Feature-based institutional model turned bullish[1][3][11] - Feature-based volume model remained neutral[1][3][11] - Intelligent algorithm model for CSI 300 remained neutral, while for CSI 500 turned bullish[1][3][11] - Mid-term limit-up and limit-down model turned bullish[1][3][12] - Up-down return difference model turned bullish for all broad-based indices[1][3][12] - Calendar effect model remained neutral[1][3][12] - Long-term momentum model turned bullish for some broad-based indices[1][3][13] - Comprehensive A-share V3 model turned bullish[1][3][13] - Comprehensive A-share Guozheng 2000 model turned bullish[1][3][13] - Mid-term turnover amplitude model for Hong Kong stocks turned bullish[1][3][14] - Hang Seng Index up-down return difference model remained neutral[1][3][14]

Report Summary 1. Report Industry Investment Rating No industry investment rating is provided in the report. 2. Core Viewpoints of the Report - In the US, multiple quantitative enhancement products were issued last week, with an increasing issuance speed at the end of the year. Various asset classes in US ETFs maintained inflows, and alternative strategies such as long - short equity performed well. US domestic stock - type mutual funds still faced significant redemption pressure, while bond funds had a slight inflow [2]. 3. Summary by Directory 3.1 US ETF Innovation Products: Multiple Quantitative Enhancement Products Issued - Last week, 43 new products were issued in the US, including 6 individual stock leveraged products and 3 digital currency - related products. One product combined crude oil and Bitcoin with 2x leverage, and Simplify's US stocks + futures strategy also had a 1:1 investment ratio. Motley Fool issued 3 single - factor ETFs, each holding about 150 stocks [5][6]. - BlackRock's quantitative team issued an alternative product, and NEOS issued a long - short equity product. Hedgeye's 130/30 product also adopted a long - short strategy. Global X issued a gold miners ETF, Franklin Templeton issued a small - cap enhanced ETF, and Sterling Capital's stock option product used a quantitative stock - selection strategy [7]. - Columbia issued 6 ETFs, 3 bonds and 3 stocks. The stock products mainly used a quantitative enhancement strategy with semi - annual rebalancing [8]. 3.2 US ETF Dynamics 3.2.1 US ETF Fund Flows: All Asset Classes Maintained Inflows - In the past week, US ETF inflows remained above $40 billion, and domestic stock products had inflows of over $30 billion. There was a significant difference in fund flows between BlackRock's S&P 500 ETF (outflow) and Vanguard's products (inflow). Russell 2000 and high - yield bond ETFs had inflows, indicating a relatively high risk appetite [2][9]. - S&P 500 ETFs had significant recent fund fluctuations, Russell 2000 ETFs had continuous inflows, and gold also returned to an inflow state [13]. 3.2.2 US ETF Performance: Alternative Strategies such as Long - Short Equity Performed Well - Many long - short equity products were issued last week. In the past two years, products replicating futures and combining multiple hedge fund strategies have been increasing. Among the top ten alternative strategy products in the US, State Street's multi - strategy product and Convergence's long - short equity product performed best [14]. 3.3 Recent Fund Flows of US Ordinary Public Offering Funds - In October 2025, the total amount of non - money public offering funds in the US was $23.7 trillion, an increase of $0.22 trillion from September. The S&P 500 rose 2.27% in October, and the scale of domestic stock - type products increased by 0.9%, but the redemption pressure was still high. - From November 25th to December 3rd, domestic stock funds in the US had outflows of over $15 billion. Hybrid products had continuous outflows, while bond funds had a slight inflow [15].

12月15日，外资私募腾胜投资在内地市场启动新一轮产品募资。 资事堂获悉，此次募集由中信证券、中金财富等头部券商渠道参与代销。这也是腾胜自11月初启动相关 产品发行以来，再一次面向市场的集中募资动作。 年底的私募市场，通常进入相对安静的阶段。 但就在多数机构忙着收官、复盘、放慢节奏的时候，一个意料之外的募资动作，在渠道端显露出不同寻 常的信号。 来自渠道的信息显示，一家顶级外资头部私募，借由王牌产品在内地市场的重新展开募集，一具举进入 百亿级外资私募机构的行列。 外资量化在中国，是否终于走过了试探阶段？ 01 年末募资 在外资私募的在岸展业路径中，渠道选择本身往往比募资节奏更具信息含量。与中信证券、中金财富这 类头部机构合作，几乎是多数外资资管机构进入中国市场的"标准动作"。 据悉，此次腾胜投资募集的产品以中证500指数增强策略为主体，同时配置少量商品期货CTA策略。 通俗理解，这种组合化设计的产品中，中证500指数增强可以被看作是底盘，CTA策略担当平滑收益的 角色。 可以看出，这家外资私募并不打算完全把客户资金带进高频博弈的节奏里，而是选择一种更慢一点的打 法，即指数负责收益方向，CTA负责缓冲。 限于信 ...

Core Insights - The report emphasizes the construction of an AI-based low-frequency index enhancement strategy using deep learning frameworks to build alpha and risk models [3] Market Index Performance - As of November 28, 2025, the Shanghai Composite Index rose by 1.40%, the Shenzhen Component Index increased by 3.56%, and the CSI 300 Index gained 1.64%, with most indices closing in the green [5][8] - The CSI 300 Index has increased by 15.0% year-to-date, while the CSI 300 enhanced portfolio has risen by 24.6%, resulting in an excess return of 9.5% [5][19] - The CSI 500 Index has seen a year-to-date increase of 22.8%, with its enhanced portfolio up by 28.9%, yielding an excess return of 6.1% [5][24] - The CSI A500 Index has risen by 18.1% year-to-date, with its enhanced portfolio up by 26.5%, resulting in an excess return of 8.5% [5][30] - The CSI 1000 Index has increased by 23.1% year-to-date, while its enhanced portfolio has risen by 36.7%, yielding an excess return of 13.6% [5][36] Index Enhancement Fund Performance - As of November 28, 2025, the CSI 300 index enhancement fund reported an excess return range from -1.64% to 1.93%, with a median of 0.12% [11][12] - The CSI 500 index enhancement fund showed an excess return range from -2.32% to 0.77%, with a median of -0.10% [11][12] - The CSI 1000 index enhancement fund reported an excess return range from -0.78% to 1.28%, with a median of 0.20% [11][12] Tracking Portfolio Performance - The report outlines the construction of enhanced portfolios for the CSI 300, CSI 500, and CSI 1000 indices using deep learning frameworks, with weekly rebalancing and a maximum weekly turnover rate of 10% [15] - The alpha signals are derived from a multi-source feature set and stacked multi-model strategies, while risk signals are identified using neural networks [15] CSI 300 Enhanced Portfolio - The CSI 300 enhanced portfolio has achieved a year-to-date return of 24.6%, compared to the CSI 300's 15.0%, resulting in an excess return of 9.5% [19][20] CSI 500 Enhanced Portfolio - The CSI 500 enhanced portfolio has achieved a year-to-date return of 28.9%, compared to the CSI 500's 22.8%, resulting in an excess return of 6.1% [24][25] CSI A500 Enhanced Portfolio - The CSI A500 enhanced portfolio has achieved a year-to-date return of 26.5%, compared to the CSI A500's 18.1%, resulting in an excess return of 8.5% [30][33] CSI 1000 Enhanced Portfolio - The CSI 1000 enhanced portfolio has achieved a year-to-date return of 36.7%, compared to the CSI 1000's 23.1%, resulting in an excess return of 13.6% [36][37]

Core Insights - The report emphasizes the construction of an AI-based low-frequency index enhancement strategy using deep learning frameworks to build alpha and risk models [3] Market Index Performance - As of November 21, 2025, the Shanghai Composite Index fell by 3.90%, the Shenzhen Component Index decreased by 5.13%, and the CSI 300 dropped by 3.77%, indicating a significant decline in market sentiment [5][8] - Year-to-date performance shows the CSI 300 Index has risen by 13.2%, while the CSI 300 enhanced portfolio has increased by 23.7%, resulting in an excess return of 10.5% [20] Index Enhancement Fund Performance - For the week ending November 21, 2025, the CSI 300 index enhancement fund reported an excess return ranging from -1.07% (minimum) to 3.22% (maximum), with a median of 0.29% [12][13] - Year-to-date, the CSI 500 index has increased by 19.1%, while the enhanced portfolio has risen by 26.7%, yielding an excess return of 7.7% [25][26] Tracking Portfolio Performance - The report outlines the construction of enhanced portfolios for the CSI 300, CSI 500, and CSI 1000 indices using deep learning frameworks, with weekly rebalancing and a maximum turnover rate of 10% [16] - The CSI 1000 index has shown a year-to-date increase of 18.6%, while the enhanced portfolio has risen by 32.9%, resulting in an excess return of 14.2% [37][38]

Core Viewpoint - The article discusses the shift in investment strategies among private equity investors, highlighting a growing interest in dividend stocks as a safer investment option amidst market uncertainties [3][5][11]. Group 1: Market Sentiment and Investment Strategies - There is a noticeable shift from the initial enthusiasm for quantitative strategies to a more rational approach, with investors seeking more certainty in their investments [3]. - Concerns about market beta and the potential for high valuations in small-cap stocks have led to a preference for dividend-paying stocks [4][5]. - The Shanghai Composite Index faces a resistance level at 4000 points, prompting cautious behavior among investors as year-end approaches [4]. Group 2: Dividend Stocks as a Safe Haven - Dividend stocks are viewed as a natural hedge due to their higher dividend yields, providing stable cash flow and a safety net for investors [5]. - Companies that offer stable high dividends typically have lower valuations and stable cash flows, making them more resilient during market downturns [5][6]. - Historical trends show that during market volatility, funds tend to flow into dividend stocks as a defensive strategy [5][8]. Group 3: Portfolio Diversification and Risk Management - Dividend stocks can effectively hedge against aggressive investment styles, particularly those concentrated in small-cap stocks [6][11]. - The current market environment suggests an acceleration in sector rotation, which may further enhance the appeal of dividend stocks [8]. - Investors are increasingly adopting a "barbell" strategy, combining small-cap holdings with dividend strategies to balance their portfolios [8]. Group 4: Future Outlook for Dividend Stocks - The A-share premium for traditional dividend sectors is expected to rise, with the market anticipating a recovery in the AH premium index [10]. - Policies aimed at reducing competition and optimizing supply structures are likely to benefit high-dividend traditional industry leaders [10]. - Long-term confidence in A-shares is growing, with a focus on reducing volatility in investment returns [11].

Core Insights - The report suggests increasing allocation to large-cap value stocks while focusing on TMT (Technology, Media, and Telecommunications) and cyclical sectors [3][30] - The industry rotation model has been optimized to adapt to market conditions, incorporating high-frequency factors and enhancing the strategy's effectiveness [4][26] - The latest industry rotation model identifies non-bank financials, steel, media, non-ferrous metals, environmental protection, and telecommunications as preferred sectors [30][33] Market Review and Fund Flow Tracking - As of October 31, 2025, the total monthly trading volume of A-shares reached 36.78 trillion yuan, with a slight decrease in daily average trading volume by 10.49% compared to the previous month [12][18] - The average stock return dispersion for the past month was 2.41%, indicating a slight decline but remaining above the median level for the past six months [12][18] - The industry rotation speed has continued to expand, significantly exceeding the average level since 2015 [12][18] Industry Rotation Model and ETF Fund Configuration - The report emphasizes the importance of focusing on large-cap value and cyclical sectors, particularly in the context of the current unclear market leadership [3][30] - The recommended ETF portfolio includes six funds: E Fund CSI 300 Non-Bank ETF, Guotai Junan CSI Steel ETF, GF CSI Media ETF, Southern CSI Non-Ferrous Metals ETF, Southern Yangtze River Protection Theme ETF, and Guotai Junan CSI All-Share Communication Equipment ETF [3][34] - The model's historical performance has shown consistent positive excess returns, outperforming major benchmark indices [5][42] Historical Performance and Model Effectiveness - The industry rotation model has maintained a strong performance over the years, achieving excess returns compared to industry averages, with a notable performance in 2025 [5][42] - The model's win rates over the past 1, 3, and 5 years are 83.33%, 69.44%, and 71.67% respectively, indicating its robustness [43][44] - The report highlights the significance of emotional and price-volume factors in capturing market dynamics, especially in weak market conditions [42][43]

Core Insights - The report emphasizes the construction of an AI-driven low-frequency index enhancement strategy using deep learning frameworks to build alpha and risk models [3] - The performance of major market indices shows positive trends, with the Shanghai Composite Index rising by 1.08% and the Shenzhen Component Index by 0.19% as of November 7, 2025 [5][8] - Year-to-date performance indicates that the CSI 300 Index has increased by 18.9%, while the CSI 300 enhanced portfolio has outperformed with a rise of 28.4%, yielding an excess return of 9.5% [19] Market Index Performance - As of November 7, 2025, the CSI 500 Index has seen a year-to-date increase of 28.0%, with its enhanced portfolio rising by 35.3%, resulting in an excess return of 7.3% [24] - The CSI 1000 Index has increased by 26.6% year-to-date, while its enhanced portfolio has risen by 40.9%, achieving an excess return of 14.4% [30] - The report highlights that sectors such as electric equipment, coal, and oil & petrochemicals performed well, with weekly returns of 4.98%, 4.52%, and 4.47% respectively [9][10] Index Enhancement Fund Performance - The CSI 300 enhanced fund reported a minimum excess return of -1.49%, a median of -0.22%, and a maximum of 0.84% for the week ending November 7, 2025 [11] - The CSI 500 enhanced fund had a minimum excess return of -1.05%, a median of 0.04%, and a maximum of 1.43% for the same period [11] - The CSI 1000 enhanced fund showed a minimum excess return of -1.69%, a median of -0.32%, and a maximum of 0.92% [11] Tracking Portfolio Performance - The report details that the AI-driven strategy involves weekly rebalancing with a maximum turnover rate of 10%, optimizing the combination of deep learning alpha signals and risk signals [15] - The CSI 300 enhanced portfolio has achieved a year-to-date return of 28.4%, significantly outperforming the CSI 300 index's 18.9% increase [19] - Historical performance data indicates that the CSI 500 enhanced portfolio has consistently outperformed its benchmark, with a year-to-date return of 35.3% compared to the CSI 500's 28.0% [25]

Core Insights - The report highlights the construction of an AI-based low-frequency index enhancement strategy using deep learning frameworks to build alpha and risk models [4][16] - The market indices showed positive performance as of October 24, 2025, with the Shanghai Composite Index rising by 2.88%, the Shenzhen Component Index by 4.73%, and the CSI 300 by 3.24%, indicating a market uptrend despite reduced trading volume [6][9][10] Market Index Performance - As of October 24, 2025, the Shanghai Composite Index reached 3950.3 points, with a weekly increase of 2.88% and a year-to-date increase of 17.86% [10] - The Shenzhen Component Index stood at 13289.2 points, increasing by 4.73% weekly and 27.60% year-to-date [10] - The CSI 300 Index was at 4660.7 points, with a weekly rise of 3.24% and a year-to-date increase of 18.44% [10] Index Enhancement Fund Performance - As of October 24, 2025, the CSI 300 index enhancement fund had a minimum excess return of -1.29%, a median of -0.08%, and a maximum of 1.86% for the week [13] - The CSI 500 index enhancement fund reported a minimum excess return of -1.78%, a median of 0.02%, and a maximum of 1.07% [13] - The CSI 1000 index enhancement fund had a minimum excess return of -1.39%, a median of 0.29%, and a maximum of 1.36% [13] Year-to-Date Performance of Index Enhancement Funds - The CSI 300 index enhancement fund has achieved a year-to-date excess return of 8.1%, with a total return of 26.5% compared to the CSI 300's 18.4% [20] - The CSI 500 index enhancement fund has recorded a year-to-date excess return of 6.4%, with a total return of 33.2% against the CSI 500's 26.8% [24] - The CSI 1000 index enhancement fund has shown a year-to-date excess return of 13.8%, with a total return of 38.3% compared to the CSI 1000's 24.5% [31] Tracking Portfolio Performance - The report emphasizes the use of deep learning frameworks to construct the CSI 300, CSI 500, and CSI 1000 index enhancement portfolios, optimizing alpha signals and risk signals through a combination of multi-source features and neural networks [16][21][25][29] - The CSI 300 index enhancement portfolio has shown a total return of 26.5% year-to-date, outperforming the CSI 300 index by 8.1% [20] - The CSI 500 index enhancement portfolio has achieved a total return of 33.2% year-to-date, with an excess return of 6.4% [24] - The CSI 1000 index enhancement portfolio has recorded a total return of 38.3% year-to-date, with an excess return of 13.8% [31]