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Reseach Article

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
  1. Zahra Poorzamania and SeyedmohsenRahanjamb, (2014), Comparing the Capabilities of Neural Networks and Data Envelopment Analysis in Predicting Corporate Profitability, Technical Paper ISSN: 1607-7393 RRAMT 2014- Vol. 40, 1 - 111
  2. Anyaeche C. O. and Ighravwe D. E., (2013), Predicting performance measures using linear regression and neural network: A comparison, African Journal of Engineering Research Vol. 1(3), pp. 84-89,
  3. MumtazIpek, UfukBolukbas, and EminGundogar, (2011), Estimating Financial Failure of the TurkishBanks Using Artificial Neural Networks, the 2011 New Orleans International Academic Conference New Orleans, Louisiana USA 629
  4. RimvydasSimutis, Darius Dilijonas, LidijaBastina and JosifFriman, (2007), A Flexible Neural Network For Atm Cash Demand Forecasting, 6th WSEAS Int. Conference on Computational Intelligence, Man-Machine Systems and Cybernetics, Tenerife, Spain, December 14-16, 162
  5. Saad M. Darwish, (2013), A Methodology to Improve Cash Demand Forecasting for ATM Network, International Journal of Computer and Electrical Engineering, Vol. 5, No. 4,
  6. Reza Mortezapour and Mehdi Afzali, (2013), Assessment of Customer Credit through Combined Clustering of Artificial Neural Networks, Genetics Algorithm and Bayesian, Probabilities, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 11, No. 12, 2013
  7. RimvydasSimutis, Darius Dilijonas, LidijaBastina,(2008), Cash Demand Forecasting For Atm Using Neural Networks And Support Vector Regression Algorithms, 20th EURO Mini Conference “Continuous Optimization and Knowledge-Based Technologies” (EurOPT-2008), Neringa, LITHUANIA , ISBN 978-9955-28-283-9,
  8. QeetharaKadhim Al-Shayea, Ghaleb A. El-Refae, and ShurouqFathi El-Itter, (2010), Neural Networks in Bank Insolvency Prediction, IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.5, 240 – 245.
  9. Dagmar Blatná and JiříTrešl, (2011), Financial Forecasting Using Neural Networks China-USA Business Review, David Publishing ISSN 1537-1514, Vol. 10, No. 3, 169-175
Index Terms

Computer Science
Information Sciences

Keywords

Neural Network back propagation Mean Square Error Training Validation and Prediction