ETF Prediction of Leading Southeast Asian Countries Using Different Machine Learning

Weiyi Mu, Zihan Nan, Zhouhang Ren and Zhixin Ye ()
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Weiyi Mu: McMaster University, Department of Engineering
Zihan Nan: Beijing Jiaotong University, Department of Economics and Management
Zhouhang Ren: Macau University of Science and Technology, Department of Applied Economics
Zhixin Ye: University of Glasgow, School of Mathematics and Statistics

A chapter in Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022), 2022, pp 670-676 from Springer

Abstract: Abstract The current health crisis plays a significant role in the stock market. This study aims to investigate the impact of COVID-19 on the Southeast Asia stock market, especially in Singapore, Thailand, India, Indonesia, Malaysia, and the Philippines. For this purpose, this study considered the influence on the Exchange Traded Fund (ETF) from the date the first COVID-19 case was reported in each country and the lookback period. The collected data covered the period between 3 February 2012 and 18 March 2022. Using the method of Long-Short Term Memory RNN (LTSM) to predict ETF trading with three different levels of lookback parameters of 60, 30, and 15. In terms of Singapore and India, 60 days lookback parameters had the best performance for the whole prediction. For the Philippines and Thailand, 60 days lookback parameters predicted the best before the first COVID-19 case was confirmed in each country and 15 days lookback parameters had the best prediction during the COVID-19 period. The results illustrated that most of the six countries mentioned in this study showed that with the increase of the lookback parameters, the model predicted more accurate; however, for the individual country, the lookback parameters had some differences due to the historical stock price and the COVID-19 situation in each country.

Keywords: Machine learning; Southeast Asia; stock market; LSTM (search for similar items in EconPapers)
Date: 2022
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DOI: 10.2991/978-94-6463-036-7_100

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