Leveraging Artificial Intelligence for Cross-border Investment Behavior Analysis: A Data Mining and Pattern Recognition Approach to Predict Market Liquidity and Price Discovery Efficiency

The hybrid algorithm model developed in this study successfully broke the "black box effect" of cross-border capital flows by integrating the three engines of random forest, support vector machine, and LSTM neural network. Based on full-cycle data of the S&P 500, FTSE 100, and Shanghai Composite indices from 2019 to 2024, the model achieved an 85% response rate in warning of volatility in Southeast Asian emerging markets, 37 percentage points lower than the forecast error of the traditional ARCH-GARCH model. The specially designed timing analysis module successfully captured the abnormal signal of the daily withdrawal of $12.7 billion of northbound funds during the global market meltdown in March 2020, and issued a liquidity depletion warning 18 hours in advance by analyzing the dynamic correlation between the VIX fear index and the offshore RMB exchange rate.