The Effect of Foreign Stock Indices on Thailand Stock Price Index Forecasts Based on ARIMAX
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Abstract
The stock price index plays an important role in the decision of an investor as a necessary profitable index. There are many factors related to stock price indexes, including foreign stock indices. The bulk of investors seek investments with low risks and high returns and analyze the fundamental factors that influence the volatility of the stock price index. This research objective was to analyze the effects of exogenous factors using foreign stock indices on the Thai stock price index using ARIMAX. The data applied in this research covered the period from 2014 to 2023 and utilized the stock price indices of global stock exchanges with significant trade values spanning the dataset, which were the S&P 500, the BOVESPA, the FTSE 100, the Hang Seng, the Nikkei 225, and the IDX Main Board. The methodology was about to apply the ARIMA model to predict the exogenous effects upon the SET price index, while the Autoregressive Integrated Moving Average with Exogenous Factors (ARIMAX) prediction model uses historical univariate time series data to analyze and predict future trends and values.
The research summarized that from a relationship point of view, there were positive relations between foreign stock indices and SET index prices listed from maximum to minimum as follows: FTSE 100 Index, IDX Main Board Index, Hang Seng Index, Bovespa Index, Nikkei 225 Index, and S&P500 Index. It can be concluded from the effects of foreign stock indices on the Thai stock price index that the Nikkei 225 Index influences SET index projections the most, followed by the FTSE 100, BOVESPA, Hang Seng, S&P500, and IDX Main Board indexes. This research suggests that investors should consider not only the foreign stock indices but also the other instruments related to investment risks in both domestic fundamentals and technical analysis.
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Articles published in the Journal of Business Administration and Innovation Rajamangala University of Technology Phra Nakhon contains information and content. The article's single author is accountable for it. In all instances, the journal's editors are not accountable for any losses incurred.
References
Omidi, A., Nourani, E., & Jalili, M. (2011). Forecasting stock prices using financial data mining and Neural Network. In 2011 3rd International Conference on Computer Research and Development, 242-246. https://doi.org/10.1109/iccrd.2011.5764290
Anggraeni, W., Andri, K. B., Sumaryanto, & Mahananto, F. (2017). The performance of ARIMAX model and Vector Autoregressive (VAR) model in forecasting strategic commodity price in Indonesia. Procedia Computer Science, 124, 189-196. https://doi.org/10.1016/j.procs.2017.12.146
Yao, T., & Zhang, Y.-J. (2017). Forecasting crude oil prices with the Google index. Energy Procedia, 105, 3772-3776. https://doi.org/10.1016/j.egypro.2017.03.880
Fitriyana, R. F., Rikumahu, B., Widiyanesti, S., & Alamsyah, A. (2020). Principal component analysis to determine main factors stock price of consumer goods industry. In 2020 International Conference on Data Science and Its Applications (ICoDSA), 1-5. https://doi.org/10.1109/icodsa50139.2020.9212845
Huy, D. T. N., Loan, B. T. T., & Anh, P. T. (2020). Impact of selected factors on stock price: a case study of Vietcombank in Vietnam. Entrepreneurship and Sustainability Issues, 7(4), 2715-2730.
Moedjahedy, J. H., Rotikan, R., Roshandi, W. F., & Mambu, J. Y. (2020). Stock price forecasting on telecommunication sector companies in Indonesia Stock Exchange using machine learning algorithms. In 2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS), 1-4. https://doi.org/10.1109/icoris50180.2020.9320758
Abd, H. T., Essa, A. K., & Jassim, F. M. (2021). Analyzing the Relationship between the Dow Jones Index and Oil Prices Using the ARIMAX Model. International Journal on Advanced Science, Engineering and Information Technology, 11(2), 465-473. https://doi.org/10.18517/ijaseit.11.2.14080
Ifeanyichukwu Ugoh, C., Alice Uzuke, C., & Obioma Ugoh, D. (2021). Application of ARIMAX Model on Forecasting Nigeria’s GDP. American Journal of Theoretical and Applied Statistics, 10(5), 216. https://doi.org/10.11648/j.ajtas.20211005.12
Chew, L. M., Yi, S. N. C., & Yeng, O. L. (2023). Gold Prices Forecasting Using Bidirectional LSTM Model Based on SPX500 Index, USD Index, Crude Oil Prices and CPI. In 2023 11th International Conference on Information and Communication Technology (ICoICT), 539-544.
Lavanya, M., & Gnanasekaran, P. (2023). Prediction of stock price using machine learning (classification) algorithms. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), 1-5.