金工ETF点评：宽基ETF单日净流出21.69亿元，国防、恒生医疗指数ETF可关注

• The report constructs an industry crowding monitoring model to monitor the crowding degree of Shenwan First-Level Industry Indexes on a daily basis[4] - The Z-score model is used to build a related ETF product screening signal model, providing potential arbitrage opportunities through rolling calculations[5] Model Construction and Process Industry Crowding Monitoring Model 1. Model Name: Industry Crowding Monitoring Model 2. Model Construction Idea: Monitor the crowding degree of various industries to identify potential investment opportunities and risks[4] 3. Model Construction Process: - Daily monitoring of the crowding degree of Shenwan First-Level Industry Indexes - Identify industries with high and low crowding levels - Track the main fund flows in and out of these industries over recent trading days[4] 4. Model Evaluation: Provides insights into industry crowding levels, helping investors to make informed decisions[4] Z-score Model for ETF Product Screening 1. Model Name: Z-score Model 2. Model Construction Idea: Identify potential arbitrage opportunities in ETF products by calculating the Z-score of their premium rates[5] 3. Model Construction Process: - Calculate the Z-score of the premium rates of various ETF products - Identify ETFs with significant deviations from their historical average premium rates - Provide signals for potential arbitrage opportunities and caution for potential pullback risks[5] 4. Model Evaluation: Helps in identifying ETFs with potential arbitrage opportunities, but also warns of possible pullback risks[5] Model Backtest Results 1. Industry Crowding Monitoring Model: - Daily monitoring results show that industries like basic chemicals, textiles and apparel, and light manufacturing have high crowding levels, while home appliances, real estate, and electronics have low crowding levels[4] - Significant changes in crowding levels were observed in industries like construction and decoration, and non-ferrous metals[4] 2. Z-score Model for ETF Product Screening: - Identified ETFs with potential arbitrage opportunities, such as the National Defense ETF and the Hang Seng Medical Index ETF[14] Factor Construction and Process Industry Crowding Factor 1. Factor Name: Industry Crowding Factor 2. Factor Construction Idea: Measure the crowding degree of industries to identify potential investment opportunities and risks[4] 3. Factor Construction Process: - Calculate the crowding degree of each industry based on fund flows and other relevant metrics - Identify industries with high and low crowding levels[4] 4. Factor Evaluation: Provides valuable insights into industry crowding levels, aiding in investment decision-making[4] Factor Backtest Results 1. Industry Crowding Factor: - High crowding levels were observed in industries like basic chemicals, textiles and apparel, and light manufacturing[4] - Low crowding levels were observed in industries like home appliances, real estate, and electronics[4] - Significant changes in crowding levels were noted in industries like construction and decoration, and non-ferrous metals[4]