Quantitative Models and Construction Methods 1. Model Name: Industry Crowding Monitoring Model - Model Construction Idea: This model is designed to monitor the crowding levels of Shenwan First-Level Industry Indices on a daily basis, identifying industries with high or low crowding levels and significant changes in crowding dynamics[3] - Model Construction Process: The model calculates the crowding levels of various industries based on daily data. It identifies industries with the highest and lowest crowding levels and highlights industries with significant changes in crowding dynamics. Specific calculation methods or formulas are not provided in the report[3] - Model Evaluation: The model provides actionable insights into industry crowding trends, helping investors identify potential opportunities or risks in crowded or undercrowded sectors[3] 2. Model Name: Premium Rate Z-Score Model - Model Construction Idea: This model is used to screen ETF products for potential arbitrage opportunities by calculating the Z-score of premium rates on a rolling basis[4] - Model Construction Process: The Z-score of the premium rate is calculated for each ETF product over a rolling window. The Z-score helps identify ETFs with significant deviations from their historical premium rates, signaling potential arbitrage opportunities. Specific formulas or parameters are not detailed in the report[4] - Model Evaluation: The model effectively identifies ETFs with potential arbitrage opportunities, but it also warns of potential risks of price corrections in the identified ETFs[4] --- Model Backtesting Results 1. Industry Crowding Monitoring Model - No specific backtesting results or quantitative metrics are provided for this model in the report[3] 2. Premium Rate Z-Score Model - No specific backtesting results or quantitative metrics are provided for this model in the report[4] --- Quantitative Factors and Construction Methods No specific quantitative factors are mentioned or constructed in the report --- Factor Backtesting Results No specific backtesting results for factors are mentioned in the report
金工ETF点评:宽基ETF单日净流出58.37亿元,银行、地产、交运拥挤变幅较大