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Simulation of Stock Prediction System using Artificial Neural Networks

Simulation of Stock Prediction System using Artificial Neural Networks

Omisore Olatunji Mumini, Fayemiwo Michael Adebisi, Ofoegbu Osita Edward, Adeniyi Shukurat Abidemi
Copyright: © 2020 |Pages: 20
ISBN13: 9781799804147|ISBN10: 1799804143|EISBN13: 9781799804154
DOI: 10.4018/978-1-7998-0414-7.ch029
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MLA

Mumini, Omisore Olatunji, et al. "Simulation of Stock Prediction System using Artificial Neural Networks." Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 511-530. https://doi.org/10.4018/978-1-7998-0414-7.ch029

APA

Mumini, O. O., Adebisi, F. M., Edward, O. O., & Abidemi, A. S. (2020). Simulation of Stock Prediction System using Artificial Neural Networks. In I. Management Association (Ed.), Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications (pp. 511-530). IGI Global. https://doi.org/10.4018/978-1-7998-0414-7.ch029

Chicago

Mumini, Omisore Olatunji, et al. "Simulation of Stock Prediction System using Artificial Neural Networks." In Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 511-530. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-0414-7.ch029

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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.

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