Authors: Vatcharaporn Esichaikul, Pongsak Srithongnopawong
Addresses: Computer Science and Information Management Program, Asian Institute of Technology, Klong Luang, Pathumthani 12120, Thailand. ' Information Technology Department, ExxonMobil Limited, Bangkok 10110, Thailand
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.
International Journal of Electronic Finance, 2010 Vol.4 No.1, pp.84 - 98
Published online: 05 Jan 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article