Title: Predicting next trading day closing price of ASEAN+6 stock indices using artificial neural networks: evidence from Lunar New Year effect

Authors: Surachai Chancharat; Sutida Tukaew

Addresses: Faculty of Business Administration and Accountancy, Khon Kaen University, Khon Kaen, Thailand ' Faculty of Business Administration and Accountancy, Khon Kaen University, Khon Kaen, Thailand

Abstract: Many stakeholders place a premium on accurate stock market price forecasting. Artificial neural networks (ANNs) have demonstrated high accuracy in predicting stock price returns, future stock prices, and stock market direction. The main objective of this study is to predict the effect of the Lunar New Year on ASEAN+6 stock markets using historical data from September 8, 1999, to December 31, 2021. The experimental results show that ANNs are an effective modelling tool for accurately predicting the ASEAN+6 stock prices. This is the first attempt, to our knowledge, to utilise ANNs to predict the ASEAN+6 stock markets, and the results are comparable to, if not better than, many stock market predictions recorded in the literature. The ANN model also demonstrated the most important technical indicators in predicting the ASEAN+6 stock markets. The investigation results also showed that ANNs are resistant to stock market volatility.

Keywords: efficient market hypothesis; EMH; holiday effect; stock price prediction; variance-ratio test.

DOI: 10.1504/IJEPEE.2024.142493

International Journal of Economic Policy in Emerging Economies, 2024 Vol.20 No.3/4, pp.237 - 245

Received: 17 Feb 2022
Accepted: 29 May 2022
Published online: 04 Nov 2024 *

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