主动量化周报：标的下沉：节奏放缓，科技突围-20260118

Quantitative Models and Construction Methods 1. Model Name: ETF Fund Flow Model - Model Construction Idea: The model is designed to analyze and predict fund flows into various ETFs, identifying sectors or themes that are likely to outperform based on capital allocation trends [1][11] - Model Construction Process: The model tracks daily fund flow data for key ETFs, such as CSI 300 ETF, CSI 500 ETF, and thematic ETFs like Chip ETF, Carbon Neutral ETF, and Chip 50 ETF. It evaluates the net inflow or outflow of funds over specific time periods to determine investor preferences and market sentiment. For example, the model observed significant outflows from broad-based ETFs like CSI 300 ETF and CSI 500 ETF, while recommending thematic ETFs in technology sectors such as chips and carbon neutrality [1][11] - Model Evaluation: The model effectively identifies shifts in capital allocation, highlighting potential opportunities in technology-related sectors while cautioning against certain AI application themes [1][11] --- Model Backtesting Results 1. ETF Fund Flow Model - Key Observations: - Significant outflows from CSI 300 ETF and CSI 500 ETF, with daily net outflows reaching 114 billion, 715 billion, and 1,048 billion yuan on January 14, 15, and 16, respectively [11] - Recommendations for Chip ETF, Carbon Neutral ETF, and Chip 50 ETF, reflecting a preference for technology sectors like electronics and power equipment [11] --- Quantitative Factors and Construction Methods 1. Factor Name: Style Factors (BARRA Style Factors) - Factor Construction Idea: These factors aim to capture the performance of different market styles, such as value, growth, momentum, and size, to identify prevailing market preferences and trends [24] - Factor Construction Process: - Fundamental factors: Evaluate metrics like profitability and earnings growth to assess the performance of high-profitability assets relative to the market average - Transaction-related factors: Analyze metrics such as turnover rate, short-term momentum, and beta coefficients to identify stocks with potential for excess returns - Size factors: Examine the performance of small-cap stocks versus large-cap stocks, including non-linear size effects [24] - Factor Evaluation: The factors reveal a shift in market preferences, with high-turnover stocks reversing gains, while short-term momentum and high-beta stocks show potential for sustained excess returns. Small-cap stocks exhibit relative outperformance during the observed period [24] --- Factor Backtesting Results 1. Style Factors (BARRA Style Factors) - Key Observations: - Profitability-related factors showed recovery, with high-profitability assets outperforming the market average [24] - Transaction-related factors indicated a reversal in high-turnover stocks, while short-term momentum and high-beta stocks demonstrated potential for sustained excess returns [24] - Size factors highlighted the relative strength of small-cap stocks, with non-linear size factors experiencing larger drawdowns [24]