Simulation of Stock Prediction System using Artificial Neural Networks

Omisore Olatunji Mumini, Fayemiwo Michael Adebisi, Ofoegbu Osita Edward and Adeniyi Shukurat Abidemi
Additional contact information
Omisore Olatunji Mumini: Centre for Information Technology and Systems, University of Lagos, Lagos, Nigeria
Fayemiwo Michael Adebisi: Department of Computer Science, Oduduwa University Ipetumodu, Ile Ife, Nigeria
Ofoegbu Osita Edward: Department of Computer Science, Oduduwa University Ipetumodu, Ile Ife, Nigeria
Adeniyi Shukurat Abidemi: Department of Computer Science, Oduduwa University Ipetumodu, Ile Ife, Nigeria

International Journal of Business Analytics (IJBAN), 2016, vol. 3, issue 3, 25-44

Abstract: Stock trading, used to predict the direction of future stock prices, is a dynamic business primarily based on human intuition. This involves analyzing some non-linear fundamental and technical stock variables which are recorded periodically. This study presents the development of an ANN-based prediction model for forecasting closing price in the stock markets. The major steps taken are identification of technical variables used for prediction of stock prices, collection and pre-processing of stock data, and formulation of the ANN-based predictive model. Stock data of periods between 2010 and 2014 were collected from the Nigerian Stock Exchange (NSE) and stored in a database. The data collected were classified into training and test data, where the training data was used to learn non-linear patterns that exist in the dataset; and test data was used to validate the prediction accuracy of the model. Evaluation results obtained from WEKA shows that discrepancies between actual and predicted values are insignificant.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJBAN.2016070102 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jban00:v:3:y:2016:i:3:p:25-44

Access Statistics for this article

International Journal of Business Analytics (IJBAN) is currently edited by John Wang

More articles in International Journal of Business Analytics (IJBAN) from IGI Global
Bibliographic data for series maintained by Journal Editor ().