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Deep learning neural network for the prediction of Asian Tiger stock markets

Author

Listed:
  • Kok-Leong Yap

    (University of Malaya, 50603 Kuala Lumpur, Malaysia2Department of Finance and Banking, Faculty of Business and Accountancy, University of Malaya, 50603 Kuala Lumpur, Malaysia)

  • Wee-Yeap Lau

    (Osaka University, Japan4Financial Engineering, National University of Singapore, Singapore5Faculty of Economics and Administration, University of Malaya, 50603 Kuala Lumpur, Malaysia)

  • Izlin Ismail

    (Department of Finance and Banking, Faculty of Business and Accountancy, University of Malaya, 50603 Kuala Lumpur, Malaysia6University of Nottingham, Nottingham, UK)

Abstract

Motivated by the recent interest of stock traders and investors towards the deep learning neural network, this study employs the deep learning neural networks, namely, multilayer perceptron, long short-term memory, and convolutional neural network, to forecast the Asian Tiger stock markets. One of the challenges to using deep learning neural networks is to select the input variable. We propose to use multiple linear regression to select the input variable that is significant to the output. Besides, we construct a regional stock market index as a significant input to forecast the Asian Tiger stock markets. A comparison study on the forecasting model shows that the deep learning model can be used as a decision-making system that assists investors to predict short-term movement and trends of stock prices.

Suggested Citation

  • Kok-Leong Yap & Wee-Yeap Lau & Izlin Ismail, 2021. "Deep learning neural network for the prediction of Asian Tiger stock markets," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 1-35, December.
    • Handle: RePEc:wsi:ijfexx:v:08:y:2021:i:04:n:s2424786321500407
    DOI: 10.1142/S2424786321500407
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