A Study of Discriminant Analysis and Artificial Neural Network in Prediction of Stock Market in Nigeria

R.A. Kareem and O.A. Adeoti

Journal of Statistical and Econometric Methods, 2016, vol. 5, issue 1, 2

Abstract: This paper analyses financial and macroeconomic data responsible for predicting stock market in Nigeria using four company-specific variables and five macroeconomic variables. The variables are inflation, investment, consumer price index, unemployment, lending interest rate, net revenue, net income and net asset. Discriminant analysis and artificial neural network were employed to determine the variables responsible for good and bad investment choices. The result of study has shown that earnings per share, lending interest rate, inflation and net income are important variables that contributed towards good and poor investment choices and the ANN model trained with scaled conjugate gradient algorithm using five hidden nodes perform better in discriminating between good and poor investment choices and has higher percentages of classifying groups.

Date: 2016
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