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Improved Stock Price Prediction by Integrating Data Mining Algorithms and Technical Indicators: A Case Study on Dhaka Stock Exchange

  • Syeda Shabnam Hasan
  • Rashida Rahman
  • Noel Mannan
  • Haymontee Khan
  • Jebun Nahar Moni
  • Rashedur M. Rahman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10448)

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.

Keywords

Stock prediction Machine learning Regression algorithms Time series forecast Technical indicators 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Syeda Shabnam Hasan
    • 1
  • Rashida Rahman
    • 1
  • Noel Mannan
    • 1
  • Haymontee Khan
    • 1
  • Jebun Nahar Moni
    • 1
  • Rashedur M. Rahman
    • 1
  1. 1.North South UniversityDhakaBangladesh

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