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Corresponding Author:
Roselyn Dimingo, University of Johannesburg, South Africa

Coauthors:
John Weirstrass Muteba Mwamba, University of Johannesburg, South Africa
Lumengo Bonga-Bonga, University of Johannesburg, South Africa

Prediction of Stock Market Direction: Application of Machine Learning Models

Volume 74 - Issue 4, November 2021
(pp. 499-536)
JEL classification: C55, C58, G11, G17
Keywords: Machine Learning, Cross Validation, Confusion Matrix and Performance Evaluation

Abstract

Prediction of market direction has gained more attention than the prediction of point returns over the past few years as it is essential in determining buy and sell signals. A correct forecast of the market trend leads investors and asset managers to make knowledgeable decisions about their future investments. It is in this context that this study seeks to predict the market direction of two developed markets (USA and UK) and two emerging markets (South Africa and Brazil) using five machine learning techniques, namely Support Vector Machines (SVM), Decision Trees (DTs), Random Forest (RF), K-Nearest Neighbours (K-NN) and Linear Discriminant Analysis (LDA). We use the LDA algorithm as a benchmark since it is the only algorithm that is closely related to logistic regression model. Our empirical results show that the random forest (RF) is the best model in predicting the market direction of all the markets, developed or emerging. Moreover, the study finds that stock markets in both developed and emerging markets are determined by their previous day price and the stock dividend yield.

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Institute for International Economics of the Genoa Chamber of Commerce

Institute for International Economics
of the Genoa Chamber of Commerce