Research Journal of Applied Sciences
ISSN: Online 1993-6079ISSN: Print 1815-932x
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Optimization of Artificial Neural Network for Stock Market Price Prediction Using an Enhanced Firefly Algorithm
Ameen Ahmed Oloduowo, Fashoto Stephen Gbenga, Ogeh Clement, Balogun Abdullateef and Mashwama Petros
Page: 532-543 | Received 21 Sep 2022, Published online: 21 Sep 2022
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Abstract
The aim of this study is to develop a system that predicts the future closing price of daily stocks based on historic data of the stocks. The Artificial Neural Network (ANN) was employed to learn the historic data and make predictions for the next few days while the Firefly Algorithm (FA) was used to optimize the weights of the network for accurate predictions. The daily historic dataset of stock prices for five companies trading the New York Stock Exchange (NYSE) from 13-14-10-2005 was used for the experiment. This study shows that the proposed model made 6.8% better predictions with less average errors when compared with the predictions made with ANN trained with the Genetic Algorithm (GA). The introduction of the reduction scheme employed reduces the randomization parameter of the fireflies in the algorithm and this significantly improves the prediction accuracy of the proposed model.
How to cite this article:
Ameen Ahmed Oloduowo, Fashoto Stephen Gbenga, Ogeh Clement, Balogun Abdullateef and Mashwama Petros. Optimization of Artificial Neural Network for Stock Market
Price Prediction Using an Enhanced Firefly Algorithm.
DOI: https://doi.org/10.36478/rjasci.2018.532.543
URL: https://www.makhillpublications.co/view-article/1815-932x/rjasci.2018.532.543