A Comparative Analysis of the Classical and Machine learning Forecasting Methods for the Mexican Stock Exchange

Abstract

There is no recent comparison in the literature on the application of classical and machine learning methods for forecasting financial assets on the Mexican Stock Exchange (BMV). These methods divide the time series into three sections: training, validation, and testing. They predict future values using the training data and are evaluated in the validation phase using error metrics; once the lowest error is obtained, the best parameters and algorithms are used to predict values using the test section. This paper aims to find the most accurate regression algorithm to make predictions in the financial time series of the BMV. The regression methods compared include linear regression, neural networks, decision trees, and support vector regression. The study uses historical BMV asset price data to compare the accuracy of each of these algorithms.