A Heterogeneous Ensemble Model for Forecasting Stock Market Monthly Direction

Main Article Content

Linus Lavi Raymond
Etemi Joshua Garba
Asabe Sandra Ahmadu

Abstract

It has never been easy to invest in a set of assets, the abnormally of financial market does not allow simple models to forecast future asset values with higher accuracy. Machine learning, which consists of making computers perform tasks that normally requiring human intelligence is currently the dominant trend in scientific research. This article aims to ensemble a model using Support Vector Machine (SVM) and Long-Short Term Memory model (LSTM) to predict the Nigerian Stock Exchange values. The main objective of this paper is to see in which precision a Machine learning algorithm can predict and how much the epochs can improve our model.

Downloads

Download data is not yet available.

Article Details

Section
Articles
Author Biographies

Linus Lavi Raymond, Computer Science Department. Modibbo Adama University Yola, Nigeria

Computer Science Department. Modibbo Adama University

 Yola, Nigeria

Etemi Joshua Garba, Computer Science Department. Modibbo Adama University Yola, Nigeria

Computer Science Department. Modibbo Adama University

Yola, Nigeria

 

 

Asabe Sandra Ahmadu, Computer Science Department. Modibbo Adama University Yola, Nigeria

Computer Science Department. Modibbo Adama University

Yola, Nigeria