金工ETF点评：宽基ETF单日净流入68.60亿元，恒生创新药、创新药ETF可关注

Quantitative Models and Construction Methods 1. Model Name: Industry Crowdedness Monitoring Model - Model Construction Idea: This model is designed to monitor the crowdedness levels of Shenwan First-Level Industry Indices on a daily basis, identifying industries with high or low crowdedness levels to guide investment focus[4] - Model Construction Process: The model calculates the crowdedness levels of various industries based on daily data. It identifies industries with significant changes in crowdedness levels and tracks the inflow and outflow of main funds over different time windows (e.g., one day, three days)[4] - Model Evaluation: The model provides a useful tool for identifying industries with significant fund movements, which can help in making tactical allocation decisions[4] 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[5] - Model Construction Process: The Z-score is calculated as follows: $ Z = \frac{(P - \mu)}{\sigma} $ - Where $ P $ is the premium rate of the ETF, $ \mu $ is the mean premium rate over the rolling window, and $ \sigma $ is the standard deviation of the premium rate over the same window[5] - Model Evaluation: The model is effective in identifying ETFs with significant deviations from their historical premium rates, which may indicate arbitrage opportunities or potential risks[5] --- Backtesting Results of Models 1. Industry Crowdedness Monitoring Model - Top crowded industries (previous trading day): Textile & Apparel, Light Manufacturing, Environmental Protection[4] - Least crowded industries (previous trading day): Real Estate, Electronics, Coal, Social Services, Steel[4] - Industries with significant changes in crowdedness (previous trading day): Agriculture, Forestry, Animal Husbandry & Fishery; Petroleum & Petrochemicals; Computers[4] - Main fund flows (previous trading day): - Inflows: Computers, Electronics, Non-Banking Financials - Outflows: Machinery Equipment, Electrical Equipment, Retail[4] - Main fund flows (last 3 trading days): - Increased allocation: Computers, Beauty & Personal Care, Environmental Protection - Decreased allocation: Machinery Equipment, Electrical Equipment, Electronics[4] 2. Premium Rate Z-Score Model - Application: The model identifies ETFs with potential arbitrage opportunities and highlights the need to be cautious of potential pullback risks[5] --- Quantitative Factors and Construction Methods No specific quantitative factors were explicitly mentioned in the report content provided --- Backtesting Results of Factors No specific backtesting results for quantitative factors were explicitly mentioned in the report content provided