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

Vatcharaporn Esichaikul and Pongsak Srithongnopawong

International Journal of Electronic Finance, 2010, vol. 4, issue 1, 84-98

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.

Keywords: e-finance; stock forecasting; ANNs; artificial neural networks; relative movement; performance; fundamental indicators; technical indicators; Thai stock market; Thailand; electronic finance; stock market returns; banking industry. (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijelfi:v:4:y:2010:i:1:p:84-98

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