查看完整报告 特别申明： 本订阅号中所涉及的证券研究信息由光大证券研究所编写，仅面向光大证券专业投资者客 户，用作新媒体形势下研究信息和研究观点的沟通交流。非光大证券专业投资者客户，请勿 订阅、接收或使用本订阅号中的任何信息。本订阅号难以设置访问权限，若给您造成不便， 敬请谅解。光大证券研究所不会因关注、收到或阅读本订阅号推送内容而视相关人员为光大 证券的客户。 报告摘要 点击注册小程序 本周，基本面因子在多数行业表现较差，其中净利润增长率因子在煤炭行业正收益显著。估值类因子中， BP因子在综合行业正收益显著。流动性因子在交通运输、美容护理、化工、商业贸易和轻工制造行业正收 益显著。市值风格上，本周多数行业表现为小市值风格。 PB-ROE-50组合跟踪： 本周PB-ROE-50组合在中证500、中证800股票池中获取正超额收益。中证500股票池中获得超额收益 0.88%，中证800股票池中获得超额收益0.43%，全市场股票池中获得超额收益-0.02%。 量化市场跟踪 大类因子表现： 本周全市场股票池中，残差波动率因子和盈利因子分别获取正收益0.55%、0.26%；市值因子和非线性市值 因子分别获取负收益-0. ...

- The report includes a global asset allocation model based on artificial intelligence, which uses machine learning to score and rank various assets for monthly equal-weighted allocation strategy[30][31] - The global asset allocation model suggests weights for May: government bond index (66.09%), Nasdaq index (17.59%), German DAX index (13.83%), and Nikkei 225 (2.49%)[30] - Historical performance of the global asset allocation model from January 2021 to April 2025 shows an annualized return of 13.76%, Sharpe ratio of 0.75, maximum drawdown of 16.53%, and excess annualized return of 9.02%[30][36] - The dynamic macro event factor-based stock-bond rotation strategy includes three different risk preference models: conservative, balanced, and aggressive[37] - The stock-bond allocation models for April show stock weights of 45% for aggressive, 13.82% for balanced, and 0% for conservative[37][39] - Historical performance of the stock-bond allocation models from January 2005 to April 2025 shows annualized returns of 19.93% for aggressive, 11.00% for balanced, and 6.06% for conservative[37][44] - The dividend timing model uses economic growth and monetary liquidity indicators to construct a timing strategy for the dividend index, showing an annualized return of 15.84%, maximum drawdown of -21.70%, and Sharpe ratio of 0.89[45][49] - The dividend timing model's recommended position for April is 0%, with most economic growth indicators showing bearish signals and cautious monetary liquidity signals[45] Model Performance Metrics - Global asset allocation model: annualized return 13.76%, Sharpe ratio 0.75, maximum drawdown 16.53%[30][36] - Stock-bond allocation models: annualized returns 19.93% (aggressive), 11.00% (balanced), 6.06% (conservative)[37][44] - Dividend timing model: annualized return 15.84%, Sharpe ratio 0.89, maximum drawdown -21.70%[45][49]

Core Viewpoint - The article emphasizes the two fundamental logic of investment: taking on risk to earn risk premiums and capturing market mispricing, with a focus on factor investing as a primary strategy for the "Tianxingjian" fund portfolio [2]. Factor Investment Summary 1. Size Factor: The "Comeback" of Small Companies - The size factor focuses on smaller companies, which may offer excess returns due to their higher risk profile and potential undervaluation by larger institutions [4]. 2. Value Factor: The Wisdom of Buying "Cheap Goods" - The value factor targets companies with low valuations, where the risk premium arises from potential financial troubles and market overreactions to bad news, leading to mispricing [5][6]. 3. Quality Factor: The "Reward" for Good Companies - The quality factor emphasizes financially healthy companies, where excess returns may stem from investor short-sightedness and the undervaluation of stable firms [7]. 4. Dividend Factor: The "Charm" of Cash Cows - The dividend factor focuses on companies with stable and high dividend payouts, where the risk premium may relate to growth uncertainties or interest rate sensitivities, leading to systematic undervaluation [8]. 5. Low Volatility Factor: Steady Happiness - The low volatility factor targets companies with lower stock price fluctuations, where excess returns may arise from market biases favoring high-risk stocks, resulting in undervaluation of low-volatility stocks [9]. Conclusion - Each factor that consistently outperforms the market is influenced by both risk premiums and market mispricing, with understanding these dynamics aiding in the effectiveness of factor investing [10].

本周估值风格显著，规模因子表现出色 ——因子周报 20250418 金融工程 1. 主要市场指数与风格表现回顾 本周主要宽基指数涨跌不一。北证 50 上涨 3.48%，上证指数上涨 1.19%，中证 2000 上涨 0.75%，沪深 300 上涨 0.59%，中证 800 上涨 0.34%；中证 500 下跌 0.37%，中证 1000 下跌 0.52%，深证成指下跌 0.54%，创业板指下跌 0.64%。 从行业来看，本周银行、房地产、煤炭、综合、石油石化等行业表现居 前；国防军工、农林牧渔、计算机、消费者服务、电子等行业表现居后。 从风格因子来看，最近一周估值因子、规模因子和非线性市值因子的表现 尤为突出，因子多空收益分别为 2.06%、-2.87%和-0.89%。 2. 选股因子表现跟踪 沪深 300 股票池中，本周 120 日成交比率、单季度 ROA 同比、BP 因子 表现较好。中证 500 股票池中，本周标准化预期外盈利、流动比率、单季度营 业收入同比增速因子表现较好。中证 800 股票池中，本周单季度 ROE 同比、 单季度净利润同比增速、单季度营业利润同比增速因子表现较好。中证 1000 股票池 ...

Quantitative Models and Construction Methods - **Model Name**: PB-ROE-50 **Model Construction Idea**: The model selects stocks based on a combination of Price-to-Book (PB) ratio and Return on Equity (ROE), aiming to capture value and profitability factors[23] **Model Construction Process**: The portfolio is constructed by ranking stocks based on PB and ROE metrics, selecting the top 50 stocks, and rebalancing periodically. Details of the construction process are referenced in earlier reports[23] **Model Evaluation**: The model underperformed this week, delivering negative excess returns across all stock pools, indicating potential challenges in the current market environment[23] - **Model Name**: Public and Private Institution Research Portfolios **Model Construction Idea**: These portfolios are based on stocks that receive significant attention from public and private institutional research, leveraging the informational advantage of institutional focus[25] **Model Construction Process**: Stocks are selected based on the frequency and intensity of institutional research coverage. The portfolios are rebalanced periodically to reflect updated research trends[25] **Model Evaluation**: The public research portfolio achieved positive excess returns this week, while the private research portfolio remained flat, suggesting varying effectiveness of institutional research strategies[25] - **Model Name**: Block Trade Portfolio **Model Construction Idea**: This model identifies stocks with high block trade activity and low volatility, hypothesizing that such stocks exhibit superior subsequent performance[29] **Model Construction Process**: Stocks are ranked based on "block trade transaction ratio" and "6-day transaction volatility." A portfolio is constructed by selecting stocks with high transaction ratios and low volatility, rebalanced monthly[29] **Model Evaluation**: The portfolio delivered strong positive excess returns this week, highlighting the effectiveness of the "high transaction, low volatility" principle[29] - **Model Name**: Private Placement Portfolio **Model Construction Idea**: This model focuses on stocks involved in private placement events, aiming to capture event-driven investment opportunities[34] **Model Construction Process**: Stocks are selected based on private placement announcements, considering factors such as market capitalization, rebalancing frequency, and position control. The portfolio is rebalanced periodically[34] **Model Evaluation**: The portfolio achieved modest positive excess returns this week, indicating the continued relevance of private placement events in generating alpha[34] Model Backtesting Results - **PB-ROE-50 Model**: - Excess return (CSI 500): -0.26% - Excess return (CSI 800): -0.83% - Excess return (All Market): -1.00%[24] - **Public Research Portfolio**: - Excess return (CSI 800): 0.81%[26] - **Private Research Portfolio**: - Excess return (CSI 800): 0.00%[26] - **Block Trade Portfolio**: - Excess return (CSI All Share): 1.55%[30] - **Private Placement Portfolio**: - Excess return (CSI All Share): 0.19%[35] Quantitative Factors and Construction Methods - **Factor Name**: Momentum Factor **Factor Construction Idea**: Captures the momentum effect by identifying stocks with strong recent performance[18] **Factor Construction Process**: Stocks are ranked based on their recent price performance, and the factor is constructed by taking long positions in high-momentum stocks and short positions in low-momentum stocks[18] **Factor Evaluation**: The factor delivered positive returns this week, indicating the presence of momentum effects in the market[18] - **Factor Name**: Nonlinear Market Cap Factor **Factor Construction Idea**: Measures the nonlinear relationship between market capitalization and stock returns[18] **Factor Construction Process**: The factor is derived by fitting a nonlinear regression model to market cap and return data, isolating the nonlinear component[18] **Factor Evaluation**: The factor underperformed this week, reflecting the dominance of small-cap stocks in the market[18] - **Factor Name**: Residual Volatility Factor **Factor Construction Idea**: Identifies stocks with low residual volatility, hypothesizing that such stocks exhibit superior risk-adjusted returns[18] **Factor Construction Process**: Residual volatility is calculated by regressing stock returns on market returns and measuring the standard deviation of residuals. Stocks with low residual volatility are favored[18] **Factor Evaluation**: The factor delivered negative returns this week, suggesting a challenging environment for low-volatility strategies[18] Factor Backtesting Results - **Momentum Factor**: Weekly return: 0.69%[18] - **Nonlinear Market Cap Factor**: Weekly return: -0.58%[18] - **Residual Volatility Factor**: Weekly return: -0.64%[18]