PACIFIC SECURITIES(SH:601099) Tai Ping Yang Zheng Quan·2025-08-12 14:44Quantitative 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 insights for potential investment opportunities[3] - Model Construction Process: The model calculates the crowding levels of various industries based on daily data. It identifies industries with significant changes in crowding levels and tracks the inflow and outflow of main funds across industries. For example, the model highlighted that the crowding levels of military, non-ferrous metals, building materials, and electrical equipment were high on the previous trading day, while retail, coal, and transportation had lower crowding levels[3] - Model Evaluation: The model provides a systematic approach to identifying industry crowding trends, which can help investors focus on industries with significant changes in crowding levels[3] 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 over a rolling window[4] - Model Construction Process: The model calculates the Z-score of the premium rate for each ETF product over a specified rolling window. A high Z-score indicates a potential overvaluation, while a low Z-score suggests undervaluation. The model also flags ETFs with potential risks of price corrections[4] - Model Evaluation: The model is effective in identifying ETFs with significant deviations from their fair value, providing actionable signals for arbitrage strategies[4] --- Backtesting Results of Models 1. Industry Crowding Monitoring Model - Key Observations: On the previous trading day, the model identified high crowding levels in industries such as military, non-ferrous metals, building materials, and electrical equipment. Conversely, retail, coal, and transportation exhibited low crowding levels. Additionally, the model noted significant changes in crowding levels for media and electrical equipment industries[3] 2. Premium Rate Z-Score Model - Key Observations: The model flagged ETF products with potential arbitrage opportunities based on their premium rate Z-scores. Specific ETFs were highlighted for further attention, though detailed numerical results were not provided in the report[4] --- Quantitative Factors and Construction Methods 1. Factor Name: Main Fund Flow Factor - Factor Construction Idea: This factor tracks the inflow and outflow of main funds across industries to identify trends in capital allocation[3][10] - Factor Construction Process: The factor aggregates main fund flow data over different time horizons (e.g., daily, three-day) for Shenwan First-Level Industry Indices. For instance, the report highlighted that main funds flowed into industries like non-ferrous metals and banks while flowing out of industries like machinery and media over the past three trading days[3][10] - Factor Evaluation: The factor provides valuable insights into capital allocation trends, which can guide investment decisions[3][10] --- Backtesting Results of Factors 1. Main Fund Flow Factor - Key Observations: Over the past three trading days: - Inflow: Non-ferrous metals (+15.61 billion), banks (+7.68 billion) - Outflow: Machinery (-97.50 billion), media (-57.39 billion), and computers (-142.99 billion)[10]