金工ETF点评：跨境ETF单日净流入18.45亿元，石油石化、有色拥挤变幅较大

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 to provide investment insights[3] - Model Construction Process: The model calculates the crowding levels of various industries based on daily trading data. It identifies industries with significant changes in crowding levels, such as petroleum, petrochemicals, and non-ferrous metals, which showed notable variations in the previous trading day[3] - Model Evaluation: The model provides a useful tool for identifying industry crowding trends and potential investment opportunities[3] 2. Model Name: Premium Rate Z-Score Model - Model Construction Idea: This model is used to screen ETF products with potential arbitrage opportunities by calculating the Z-score of their premium rates over a rolling window[4] - Model Construction Process: 1. Collect historical premium rate data for ETFs 2. Calculate the Z-score of the premium rate for each ETF over a specified rolling window 3. Identify ETFs with extreme Z-scores as potential arbitrage opportunities[4] - Model Evaluation: The model effectively identifies ETFs with potential arbitrage opportunities but also highlights the need to be cautious of potential price corrections[4] --- Model Backtesting Results 1. Industry Crowding Monitoring Model - No specific numerical backtesting results were provided for this model[3] 2. Premium Rate Z-Score Model - No specific numerical backtesting results were provided for this model[4] --- Quantitative Factors and Construction Methods No specific quantitative factors were explicitly mentioned or constructed in the report --- Factor Backtesting Results No specific backtesting results for factors were provided in the report