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Predicting Nigerian Stock Returns using Technical Analysis and Machine Learning

Author

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  • D. O. Oyewola
  • Emmanuel Gbenga Gbenga Dada
  • Omole Ezekiel Olaoluwa
  • K.A. Al-Mustapha

Abstract

Models of stock price prediction have customarily utilized technical indicators alone to produce trading signals. In this paper, we construct trading techniques by applying machine-learning methods to technical analysis indicators and stock market returns data. The resulting prediction models can be utilized as an artificial trader used to trade on any given stock trade. Here the issue of stock trading decision prediction is enunciated as a classification problem with two class values representing the buy and sell signals. The stacking technique utilized in this paper is to assist trader with applying the proposed algorithms in their trading using random forest which was staked with different algorithms which incorporates Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN). The experimental results indicated that Top Layer of Random Forest (TRF) produced the best performance among all the algorithms compared. This is an indication that it is a promising strategy for forecasting Nigerian stock returns.

Suggested Citation

  • D. O. Oyewola & Emmanuel Gbenga Gbenga Dada & Omole Ezekiel Olaoluwa & K.A. Al-Mustapha, 2019. "Predicting Nigerian Stock Returns using Technical Analysis and Machine Learning," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 3(2), March.
    • Handle: RePEc:epw:ejece0:v:3:y:2019:i:2:id:19065
    DOI: 10.24018/ejece.2019.3.2.65
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    References listed on IDEAS

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    1. Amin Hedayati & Moein Hedayati & Morteza Esfandyari, 2016. "Stock market index prediction using artificial neural network," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 21(41), pages 89-93.
    2. Johnson, Neil F. & Jefferies, Paul & Hui, Pak Ming, 2003. "Financial Market Complexity," OUP Catalogue, Oxford University Press, number 9780198526650.
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