Abstract
This paper employs a number of machine learning algorithms to predict the future stock price of Dhaka Stock Exchange. The outcomes of the different machine learning algorithms are combined to form an ensemble to improve the prediction accuracy. In addition, two popular and widely used technical indicators are combined with the machine learning algorithms to further improve the prediction performance. To evaluate the proposed techniques, historical price and volume data over the past 15 months of three prominent stocks enlisted in Dhaka Stock Exchange are collected, which are used as training and test data for the algorithms to predict the 1-day, 1-week and 1-month-ahead prices of these stocks. The predictions are made both on training and test data sets and results are compared with other existing machine learning algorithms. The results indicate that the proposed ensemble approach as well as the combination of technical indicators with the machine learning algorithms can often provide better results, with reduced overall prediction error compared to many other existing prediction algorithms.
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Hasan, S.S., Rahman, R., Mannan, N., Khan, H., Moni, J.N., Rahman, R.M. (2017). Improved Stock Price Prediction by Integrating Data Mining Algorithms and Technical Indicators: A Case Study on Dhaka Stock Exchange. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10448. Springer, Cham. https://doi.org/10.1007/978-3-319-67074-4_28
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DOI: https://doi.org/10.1007/978-3-319-67074-4_28
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