MACHINE LEARNING MODELS FOR FORECASTING STOCK TRENDS

CAMBA, GIACOMO

First

Methodology

;Conversano, Claudio

Last

Conceptualization

2019-01-01

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Abstract

This research addresses the problem of predicting the trends of two stocks and two stock indexes for the American stock market. In this study, the predictive performance of four machine learning models, are compared. The models investigated include Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest and Naive-Bayes. Supervised models training is performed through a 10-fold CV approach repeated 3 times, using 10 of the main indicators and oscillators of technical analysis as input. The experiments conducted show that among the 4, the Naive-Bayes model gives the worst predictive performance, the Random Forest obtains discrete results, while the SVM and the ANN are the best performing models.