A Comparative Analysis of Traditional and Machine Learning Methods in Forecasting the Stock Markets of China and the US

Abstract

In the volatile and uncertain financial markets of the post-COVID-19 era, our study conducts a comparative analysis of traditional econometric models—specifically, the AutoRegressive Integrated Moving Average (ARIMA) and Holt’s Linear Exponential Smoothing (Holt’s LES)—against advanced machine learning techniques, including Support Vector Regression (SVR), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRU). Focused on the daily stock prices of the S&P 500 and SSE Index, the study utilizes a suite of metrics such as R-squared, RMSE, MAPE, and MAE to evaluate the forecasting accuracy of these methodologies. This approach allows us to explore how each model fares in capturing the complex dynamics of stock market movements in major economies like the U.S. and China amidst ongoing market fluctuations instigated by the pandemic. The findings reveal that while traditional models like ARIMA demonstrate strong predictive accuracy over short-term horizons, LSTM networks excel in capturing complex, non-linear patterns in the data, showcasing superior performance over longer forecast horizons. This nuanced comparison highlights the strengths and limitations of each model, with LSTM emerging as the most effective in navigating the unpredictable dynamics of post-pandemic financial markets. Our results offer crucial insights into optimizing forecasting methodologies for stock price predictions, aiding investors, policymakers, and scholars in making informed decisions amidst ongoing market challenges.