A Comparative Analysis of LSTM, ARIMA, and MCMC Models for Forecasting Financial Market

Machine learning, with its sophisticated predictive capabilities, has fundamentally transformed numerous fields, including stock trading. This research presents a comparative study of Bayesian inference using the Markov Chain Monte Carlo (MCMC) method in stock price prediction, the new paradigm of long short-term memory (LSTM) networks, and conventional auto-regressive integrated moving average (ARIMA) models. The study uses historical stock price data from a diverse array of companies to evaluate these models concerning their accuracy, robustness, and computational efficiency. The findings contribute to the ongoing discourse on effective forecasting methodologies in financial markets, offering valuable insights to stakeholders, practitioners, and researchers. These insights are particularly pertinent for navigating financial environments’ inherently unpredictable and dynamic nature.