Journal of Business Accounting and Finance Perspectives

(ISSN: 2603 7475) Open Access Journal
JBAFP 2020, 2(2), 14; doi: 10.35995/jbafp2020014

A Competitive Model to Forecast a Stock Market Index

1 PhD Candidate at Department of Economics, College of Business, Feng Chia University, Taichung City, Taiwan
2 Faculty of Business Administration, Ton Duc Thang University, No. 19 Nguyen Huu Tho Street, Tan Phong Ward, District 7, Ho Chi Minh City, Vietnam
3 Department of Product Innovation and Entrepreneurship, National Taipei University of Business, Taoyuan City 324, Taiwan;
* Corresponding author:
* Author to whom correspondence should be addressed.
Received: 27 Aug 2019 / Revised: 10 Apr 2020 / Accepted: 2020-04-12 / Published: 2020-04-14
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ABSTRACT

This study proposes a competitive model using the Box–Jenkins approach to implement a Box–Jenkins ARIMA-GARCH model in order to improve financial forecasting. Differing from previous studies, we consider optimizing the lagged terms, which assist in capturing the relationships more properly. The competitive model is then used to forecast the stock market index in Taiwan. This study conducts out-of-sample forecasting and compares the root mean square errors (RMSEs) against previous studies. The results show that the competitive model outperformed in terms of both RMSEs and consistency.
Keywords: Box–Jenkins approach; ARIMA-GARCH; fuzzy sets; stock market index
OPEN ACCESS
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
CITE
Nguyen-Thanh, N.; Huarng, K.-H. A Competitive Model to Forecast a Stock Market Index. JBAFP 2020, 2, 14.
Nguyen-Thanh N, Huarng K-H. A Competitive Model to Forecast a Stock Market Index. Journal of Business Accounting and Finance Perspectives. 2020; 2(2):14.
Nguyen-Thanh, Nhan; Huarng, Kun-Huang. 2020. "A Competitive Model to Forecast a Stock Market Index." JBAFP 2, no. 2: 14.
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