PACIFIC SECURITIES(SH:601099) Tai Ping Yang·2025-05-12 03:35Quantitative Models and Construction 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 provide insights into market dynamics[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 major funds across industries over different time periods[4] - Model Evaluation: The model effectively highlights industries with extreme crowdedness levels and significant changes, providing actionable insights for market participants[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 over a rolling window[5] - Model Construction Process: The Z-score is calculated as follows: $ Z = \frac{(P - \mu)}{\sigma} $ - Where $P$ represents 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 period. The model identifies ETFs with extreme Z-scores as potential arbitrage opportunities[5] - Model Evaluation: The model provides a systematic approach to identify ETFs with potential mispricing, though it also highlights the need to be cautious of potential price corrections[5] --- Model Backtesting Results 1. Industry Crowdedness Monitoring Model - Top Crowded Industries (Previous Trading Day): Defense & Military, Textile & Apparel, Machinery Equipment[4] - Low Crowdedness Industry: Coal[4] - Significant Changes in Crowdedness: Communication and Non-Banking Financials experienced large single-day changes in crowdedness levels[4] - Major Fund Flows (Last 3 Days): - Inflow: Defense & Military, Communication, Electric Equipment - Outflow: Computers, Basic Chemicals, Electronics[4] 2. Premium Rate Z-Score Model - Identified ETFs with Arbitrage Signals: Specific ETFs were flagged based on their Z-scores, though detailed numerical results were not provided in the report[5] --- Quantitative Factors and Construction 1. Factor Name: Crowdedness Factor - Factor Construction Idea: Measures the level of crowdedness in industries to identify overbought or oversold conditions[4] - Factor Construction Process: The crowdedness factor is derived from daily industry-level data, incorporating metrics such as fund flows and relative changes in crowdedness levels over time[4] - Factor Evaluation: The factor is effective in identifying industries with extreme market positioning, aiding in tactical allocation decisions[4] --- Factor Backtesting Results 1. Crowdedness Factor - Top Industries by Crowdedness (Previous Trading Day): Defense & Military, Textile & Apparel, Machinery Equipment[4] - Industries with Low Crowdedness: Coal[4] - Industries with Significant Crowdedness Changes: Communication, Non-Banking Financials[4]