Market anomalies analysis using Machine Learning: a case of Mexican Stock Market, MSM, 2000-2020
Keywords:
Anomalies, Mexican stock market, Mean reversion in prices, Mean reversion in prices volatility, Machine learning, Return predictionAbstract
http://doi.org/10.5281/zenodo.7415936
The purpose of this paper is to analyze the presence of anomalies in the Mexican stock market; in particular, it focuses on empirically determining the performance of a reduced set: momentum, volatility, mean reversion, and January, month-end, and weekend effects. New criteria for calculating anomalies based on price movements are proposed with the aim of reviewing their effects on returns, in contrast to the market for the period from 2000 to 2020. The study shows the results in terms of returns from the identification of an anomaly in comparison to the averages of the country’s main index: the S&P IPC (Index of Prices and Quotations), and it is the series of prices to be analyzed. Machine learning methodologies (Logistic Regression, Multilayer Perceptron (MLP), Support Vector Optimized Machines (SMO) and Logit Method) are used to analyze the models and the results are evaluated with data inside and outside the sample (cross-validation and partition). two-thirds to train and remaining to test). The results show that momentum has a greater presence, given the behavior of the index, and mean reversion and volatility are less frequent; the January effect presents percentages slightly lower than those of momentum.
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