BMV stocks return prediction using macro economics variables, technical analysis, and machine learning

Abstract

Historical data, macroeconomic variables, technical analysis, and machine learning are some of the tools used to predict the price of shares of companies listed on the Mexican stock ex-change.The present thesis’s purpose is to reach a robust investment strategy, capable of coping with unforeseen events, and maximizing returns by selecting stocks quoted in the Mexican Stock Market. Our strategy predicts stock returns considering the influence of macroeconomic variables filtered by a causal analysis to determine the most significant ones, and a layered architecture, where machine learning methodologies are endowed with technical analysis applied to the stock historical data.The results from this thesis work show profitable strategies that outperform the free-risk rate of return and the Mexican Index performance. Results demonstrate even good performances when unforeseen events are present as the Covid-19 pandemic in 2020-2021.