Open AccessPredicting Nigerian Stock Returns using Technical Analysis and Machine Learning
Author(s) -
David Opeoluwa Oyewola,
Emmanuel Gbenga Dada,
Ezekiel Olaoluwa Omole,
K. A. Al-Mustapha
Publication year - 2019
Publication title -
european journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
ISSN - 2736-5751
DOI - 10.24018/ejece.2019.3.2.65
Subject(s) - random forest , support vector machine , technical analysis , stock (firearms) , trading strategy , computer science , artificial intelligence , machine learning , artificial neural network , econometrics , logistic regression , stock market , financial economics , economics , engineering , mechanical engineering , paleontology , horse , biology
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.