Predicting Nigerian Stock Returns using Technical Analysis and Machine Learning

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

doi:10.24018/ejece.2019.3.2.65

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.

Highlights

Securities exchange has pulled in much attention from academia community and business, it has the ability to procure substantial benefits if done wisely [7]
Performance evaluation We evaluate prediction performance using five measures: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Square Error (MSE) and Mean Absolute Scaled Error (MASE)
The model performance is contrasted with some other classifiers like Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM) and Neural Network model

Summary

Introduction

Securities exchange has pulled in much attention from academia community and business, it has the ability to procure substantial benefits if done wisely [7]. Stock markets are defenseless to brisk changes, causing arbitrary fluctuations in the stock price. Securities exchange arrangement are commonly unique, non-parametric, tumultuous and boisterous in nature and security exchange price movement is viewed as an irregular procedure with changes which are increasingly articulated for short time windows. Be that as it may, a few stocks generally tend to create direct patterns over long haul time windows. As to limit the hazard involved, propelled information of stock price movement in the future is required. A paper by [10] propose a novel method to limit the danger of investment in stock market by foreseeing the returns of a stock utilizing a class of groundbreaking machine learning algorithms known as ensemble

Objectives

Methods

Results

Conclusion