Fashoto  Stephen Gbenga, Ameen  Ahmed Oloduowo, Ogeh  Clement, Balogun  Abdullateef, Mashwama  Petros, 
    Optimization of Artificial Neural Network for Stock Market
Price Prediction Using an Enhanced Firefly Algorithm,
    Research Journal of Applied Sciences,
    Volume 13,Issue 9,
    2018,
    Pages 532-543,
    ISSN 1815-932x,
    rjasci.2018.532.543,
    (https://makhillpublications.co/view-article.php?doi=rjasci.2018.532.543)
    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.
    Keywords: Artificial neural network;firefly;optimization;price prediction;stock market;model