Predictability of Financial Markets in ASEAN Countries using Machine Learning Techniques

28 Pages Posted: 19 Jan 2019

See all articles by Dulani Jayasuriya

Dulani Jayasuriya

University of Auckland Business School

Date Written: January 18, 2019

Abstract

This paper develops several efficient machine learning models and compare their performance in forecasting the value and direction of stock prices and indices from the ASEAN countries. Although all models adequately forecast the stock indices ranging from 40% to 95% accuracy and outperform traditional regression models, ANN models outperform all other models. This study identifies several important variables as important predictors. Finally, this study concludes that the emerging economies of the ASEAN countries are indeed predictable with more than 95% accuracy.

Keywords: Machine Learning, ASEAN, Financial Markets, Support Vector Machines, Neural Networks, Random Forest, Gradient Boosting, Backpropagation, Multitask Learning

JEL Classification: C02, O33

Suggested Citation

Jayasuriya, Dulani, Predictability of Financial Markets in ASEAN Countries using Machine Learning Techniques (January 18, 2019). Available at SSRN: https://ssrn.com/abstract=3318051 or http://dx.doi.org/10.2139/ssrn.3318051

Dulani Jayasuriya (Contact Author)

University of Auckland Business School ( email )

12 Grafton Rd
Private Bag 92019
Auckland, 1010
New Zealand

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