An Experimental Evaluation of Short-Term Stock Prediction in the Nigerian Stock Market using Multilayer Perceptron Neural Network
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Date
2020-12
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African Journal Comping & ICT
Abstract
Stock prices fluctuate, are unpredictable, and this has increased interest in the stock price prediction research. This work aims at predicting stock prices in the Nigerian Stock Market using Artificial Neural Network (ANN). Seven-year data obtained from the Investing Website for ten companies listed as the top gainers in the Nigerian Stock Market were used, having attributes High, Low, Close, Open. The data set was divided into a training dataset (70%), validating dataset (15%), testing dataset (15%), a Multilayer Perceptron Neural Network (MLP) using Levenberg Marquardt algorithm to build, train and test the model. The model generated was used for a short-term prediction, predicting the next days’ opening and closing prices. The results from the training model were used for comparison with the testing data to ascertain the accuracy of the model. Results from the data analysis carried out using MATLAB revealed that Multilayer Perceptron neural network technique gives satisfactory output with best validation performance mean square value of 0.0059445 at epoch 20, with R score of 0.94654, 0.92687, 0.8584 and 0.92997 respectively for training, validation, Test and combined set. It has Mean Square Error of 5.92336e-3, 5.94448e-3 and 7.98277e-3 for training, validation and testing respectively; and regression value of 9.97966e-1, 9.97813e-1 and 9.97351e-1 respectively for training, validation and testing.
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Keywords
Stock, Neural network, MLP, Feedforward, Backpropagation