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A Neural Network Approach to Financial Forecasting

by P. Enyindah, Onwuachu Uzochukwu C.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 135 - Number 8
Year of Publication: 2016
Authors: P. Enyindah, Onwuachu Uzochukwu C.
10.5120/ijca2016908463

P. Enyindah, Onwuachu Uzochukwu C. . A Neural Network Approach to Financial Forecasting. International Journal of Computer Applications. 135, 8 ( February 2016), 28-32. DOI=10.5120/ijca2016908463

@article{ 10.5120/ijca2016908463,
author = { P. Enyindah, Onwuachu Uzochukwu C. },
title = { A Neural Network Approach to Financial Forecasting },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 135 },
number = { 8 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 28-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume135/number8/24072-2016908463/ },
doi = { 10.5120/ijca2016908463 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:35:15.696060+05:30
%A P. Enyindah
%A Onwuachu Uzochukwu C.
%T A Neural Network Approach to Financial Forecasting
%J International Journal of Computer Applications
%@ 0975-8887
%V 135
%N 8
%P 28-32
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

As the world economy keeps on changing, financial institutions and investors always look forward to a system by which they can monitor the dynamic financial state of the world. This calls for a system that could simulate and predict financial positions based on financial market trends in order to manage and identify the best package to invest in. The paper is aimed at developing a neural network application to predict interest rate on loan investment in Nigerian bank using the back propagation neural network.It forecastinterest rate on loan investment in three areas which include commerce, education, and rent/housing. The simulation was done using Matlab 2008. From the results obtained the Mean Squared Error values 3.99104e-6 in the Training, 3.597228e-5 in the validation and 9.9464314e-6 in the testing which shows that the prediction was done with minimum amount of error.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Neural Network back propagation Mean Square Error Training Validation and Prediction