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主动量化周报：8月边际谨慎：强个股，弱指数-20250810

ZHESHANG SECURITIES· 2025-08-10 11:43

Quantitative Models and Construction 1. Model Name: Fundamental Quantitative Model - **Model Construction Idea**: This model tracks the fundamental performance of industries, focusing on the transition from expectation-driven to data-driven analysis, particularly for cyclical sectors like coal and chemicals[3][13] - **Model Construction Process**: The model evaluates industry fundamentals by analyzing indicators such as industry prosperity and earnings expectations. It identifies sectors with improving fundamentals and aligns them with market sentiment shifts[3][13] - **Model Evaluation**: The model effectively captures the transition from speculative to fundamental-driven market dynamics, aligning with the observed recovery in cyclical sectors like coal and chemicals[3][13] 2. Model Name: Sentiment Quantitative Model - **Model Construction Idea**: This model measures market sentiment, particularly focusing on retail investor activity and trading dynamics in the TMT sector[3][13] - **Model Construction Process**: The model tracks metrics such as average daily turnover and retail investor participation. It identifies sectors with high trading activity and sentiment, such as TMT, which has seen sustained upward momentum since June[3][13] - **Model Evaluation**: The model successfully identifies sectors with strong trading sentiment, highlighting the TMT sector's resilience and potential for continued upward movement[3][13] 3. Model Name: Crowding Indicator Model - **Model Construction Idea**: This model assesses the crowding level in specific sectors, such as innovative drugs, to predict potential risks of pullbacks[3][13] - **Model Construction Process**: The model calculates crowding indicators based on historical data, comparing current levels to a 5-year range. For example, the crowding indicator for the innovative drug sector is at 94.93%, suggesting a high likelihood of a pullback in the next three weeks[3][13] - **Model Evaluation**: The model provides a robust framework for identifying overbought conditions, offering valuable insights for risk management in crowded sectors[3][13] --- Model Backtesting Results 1. Fundamental Quantitative Model - **Indicator: Industry Prosperity**: Coal and chemical sectors show improving fundamentals, aligning with the model's predictions for upward revisions in August[3][13] 2. Sentiment Quantitative Model - **Indicator: Average Daily Turnover**: The average daily turnover for the entire A-share market remains at approximately 1.75 trillion yuan, a historically high level, supporting the model's sentiment analysis[3][13] 3. Crowding Indicator Model - **Indicator: Crowding Level**: The crowding indicator for the innovative drug sector is at 94.93%, indicating a high risk of pullback within three weeks[3][13] --- Quantitative Factors and Construction 1. Factor Name: EP Value Factor - **Factor Construction Idea**: This factor identifies assets with high earnings-to-price ratios, which are expected to deliver superior returns[25][26] - **Factor Construction Process**: The factor is calculated as the ratio of earnings per share (EPS) to the stock price. It is used to rank assets based on their relative valuation attractiveness[25][26] - **Factor Evaluation**: The factor demonstrates strong performance, with high EP value assets delivering significant excess returns during the week[25][26] 2. Factor Name: Momentum Factor - **Factor Construction Idea**: This factor captures short-term price momentum, identifying stocks with strong recent performance[25][26] - **Factor Construction Process**: The factor is calculated based on the relative price performance of stocks over a defined short-term period. Stocks with the highest momentum scores are expected to outperform[25][26] - **Factor Evaluation**: The factor shows notable outperformance during the week, highlighting its effectiveness in capturing short-term trading opportunities[25][26] 3. Factor Name: Nonlinear Size Factor - **Factor Construction Idea**: This factor examines the nonlinear relationship between market capitalization and stock returns[25][26] - **Factor Construction Process**: The factor is derived by fitting a nonlinear regression model to the relationship between market capitalization and historical returns. It identifies deviations from the expected size-return relationship[25][26] - **Factor Evaluation**: The factor experienced a slight pullback during the week, indicating a temporary shift in market preferences away from size-based strategies[25][26] --- Factor Backtesting Results 1. EP Value Factor - **Weekly Return**: +0.2%[25][26] 2. Momentum Factor - **Weekly Return**: +0.3%[25][26] 3. Nonlinear Size Factor - **Weekly Return**: -0.3%[25][26]

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