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Using relative movement to support ANN-based stock forecasting in Thai stock market

Author

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  • Vatcharaporn Esichaikul
  • Pongsak Srithongnopawong

Abstract

Over the years, Artificial Neural Networks (ANNs) have become a popular and seemingly accurate model to forecast stock prices. This paper proposes data preprocessing using relative movement to improve performance of ANN-based stock forecasting. Both fundamental and technical indicators are chosen as inputs to the system. The evaluation metrics include hit ratio and total return. The k-fold cross validation is utilised on a dataset of stocks in the banking sector in the Stock Exchange of Thailand (SET). The experiments show that the proposed model outperforms a traditional model, a random walk model, and a buy & hold strategy for both hit ratio and total return.

Suggested Citation

  • Vatcharaporn Esichaikul & Pongsak Srithongnopawong, 2010. "Using relative movement to support ANN-based stock forecasting in Thai stock market," International Journal of Electronic Finance, Inderscience Enterprises Ltd, vol. 4(1), pages 84-98.
    • Handle: RePEc:ids:ijelfi:v:4:y:2010:i:1:p:84-98
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    Cited by:

    1. Ni, Yensen & Liao, Yi-Ching & Huang, Paoyu, 2015. "MA trading rules, herding behaviors, and stock market overreaction," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 253-265.

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