Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022)

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ETF Prediction of Leading Southeast Asian Countries Using Different Machine Learning

Authors
Weiyi Mu1, Zihan Nan2, Zhouhang Ren3, Zhixin Ye4, *
1Department of Engineering, McMaster University, Hamilton, ON, L8S 3L8, Canada
2Department of Economics and Management, Beijing Jiaotong University, Beijing, 100044, China
3Department of Applied Economics, Macau University of Science and Technology, Macau, 999078, China
4School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
*Corresponding author. Email: 2700300Y@student.gla.ac.uk
Corresponding Author
Zhixin Ye
Available Online 31 December 2022.
DOI
10.2991/978-94-6463-036-7_100How to use a DOI?
Keywords
Machine learning; Southeast Asia; stock market; LSTM
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.

Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022)
Series
Advances in Economics, Business and Management Research
Publication Date
31 December 2022
ISBN
978-94-6463-036-7
ISSN
2352-5428
DOI
10.2991/978-94-6463-036-7_100How to use a DOI?
Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Weiyi Mu
AU  - Zihan Nan
AU  - Zhouhang Ren
AU  - Zhixin Ye
PY  - 2022
DA  - 2022/12/31
TI  - ETF Prediction of Leading Southeast Asian Countries Using Different Machine Learning
BT  - Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022)
PB  - Atlantis Press
SP  - 670
EP  - 676
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-036-7_100
DO  - 10.2991/978-94-6463-036-7_100
ID  - Mu2022
ER  -