金工ETF点评：跨境ETF单日净流入20.67亿元，电子、汽车、家电拥挤低位

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 actionable insights[4] - Model Construction Process: The model calculates crowding levels for each industry index daily, using metrics such as main fund flows and single-day crowding changes. For example, the model identified that non-ferrous metals and steel had high crowding levels, while automobiles and electronics had lower levels. Additionally, significant single-day crowding changes were observed in the power equipment sector[4] - Model Evaluation: The model provides a useful tool for identifying industry crowding trends and potential investment opportunities[4] 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[5] - Model Construction Process: The Z-score is calculated for the premium rates of ETF products over a rolling window. This helps identify ETFs with significant deviations from their historical averages, signaling potential arbitrage opportunities. The model also flags ETFs with potential downside risks[5] - Model Evaluation: The model effectively identifies ETFs with potential arbitrage opportunities while also highlighting associated risks[5] --- Model Backtesting Results 1. Industry Crowding Monitoring Model - Key Observations: - Non-ferrous metals and steel had the highest crowding levels on the previous trading day[4] - Automobiles and electronics exhibited the lowest crowding levels[4] - Power equipment showed significant single-day crowding changes[4] 2. Premium Rate Z-Score Model - Key Observations: - The model identified ETFs with significant premium rate deviations, signaling potential arbitrage opportunities[5] --- Quantitative Factors and Construction Methods No specific quantitative factors were explicitly mentioned in the provided content --- Factor Backtesting Results No specific factor backtesting results were explicitly mentioned in the provided content