金工ETF点评：宽基ETF单日净流出109.69亿元，煤炭、石化、交运拥挤低位

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[3] - Model Construction Process: The model calculates the crowding levels of various industries based on specific metrics (not detailed in the report) and ranks them accordingly. For example, the report highlights that the building materials, military, and non-ferrous industries had high crowding levels, while coal, petrochemical, and transportation had low crowding levels on the previous trading day[3] - Model Evaluation: The model provides a useful tool for identifying industry trends and potential investment opportunities by analyzing crowding dynamics[3] 2. Model Name: Premium Rate Z-Score Model - Model Construction Idea: This model is used to screen ETF products by calculating their premium rate Z-scores, identifying potential arbitrage opportunities while also warning of potential pullback risks[4] - Model Construction Process: The model employs a rolling calculation of the Z-score of the premium rate for various ETF products. The Z-score is calculated as: $ Z = \frac{(X - \mu)}{\sigma} $ where $ X $ is the current premium rate, $ \mu $ is the mean premium rate over a rolling window, and $ \sigma $ is the standard deviation of the premium rate over the same window. This helps identify ETFs with significant deviations from their historical norms[4] - Model Evaluation: The model is effective in identifying ETFs with potential arbitrage opportunities and provides a risk management tool for investors[4] --- Model Backtesting Results 1. Industry Crowding Monitoring Model - Top Crowded Industries: Building materials, military, and non-ferrous industries had the highest crowding levels on the previous trading day[3] - Least Crowded Industries: Coal, petrochemical, and transportation industries had the lowest crowding levels on the previous trading day[3] 2. Premium Rate Z-Score Model - Application Example: The model flagged specific ETFs for potential arbitrage opportunities, such as the Battery Leaders ETF (159767.SZ), which tracks the New Energy Battery Index and has a fund size of 1.13 billion yuan[14] --- Quantitative Factors and Construction Methods No specific quantitative factors were detailed in the report beyond the models described above --- Factor Backtesting Results No specific backtesting results for individual factors were detailed in the report beyond the models described above