Big 5 ASEAN capital markets forecasting using WEMA method

Hansun, Seng and Kristanda, Marcel Bonar and Winarno, P.M (2020) Big 5 ASEAN capital markets forecasting using WEMA method. TELKOMNIKA Telecommunication, Computing, Electronics and Control, 17 (1). ISSN 1693-6930

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ASEAN through ASEAN Economics Community (AEC) 2020 treaty has proposed financial integration via capital markets integration in order to aim comprehensive ASEAN economic integration. Therefore, the need to have a proper prediction of ASEAN capital market becomes a major issue. In this study, we took big 5 ASEAN capital markets, i.e. Straits Times Index (STI), Kuala Lumpur Stock Exchange (KLSE), Stock Exchange of Thailand (SET), Jakarta Stock Exchange (JKSE), and Philippine Stock Exchange (PSE) to be forecasted using WEMA method. Weighted Exponential Moving Average (WEMA) is a new hybrid moving average method which combines the weighting factor calculation in Weighted Moving Average (WMA) with the procedure of Exponential Moving Average (EMA). WEMA has successfully been implemented and used to forecaste discrete time series data, but never being used to forecast ASEAN capital markets. In this study, we took further action by implementing the WEMA method with brute force approach for scaling factor tuning on big 5 ASEAN capital markets. From the experimental results, we found that WEMA has successfully forecasted all those exchanges. By looking at the forecast error measurement, it gives the best performance on PSE and worst performance on SET dataset among all datasets being considered in this study.

Item Type: Article
Subjects: 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 006 Special Computer Methods
300 Social Sciences > 330 Economics > 337 International Economics
500 Science and Mathematic > 510 Mathematics > 519 Probabilities and Applied Mathematics
Divisions: Faculty of Engineering & Informatics > Informatics
Depositing User: Administrator UMN Library
Date Deposited: 19 Oct 2021 08:25
Last Modified: 19 Oct 2021 08:25

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