Neural network earnings per share forecasting models: A comparison of backward propagation and the genetic algorithm

Abstract

Zhang, Cao, and Schniederjans [W. Zhang, Q. Cao, M. Schniederjans, Neural Network Earnings Per Share Forecasting Models: A Comparative Analysis of Alternative Methods. Decision Sciences 35(2) (2004), 205–237, hereafter ZCS] examined the relative ability of neural network models to forecast earnings per share. Their results indicate that the univariate NN model significantly outperformed four alternative univariate models examined in prior research. The authors also found that a neural network forecasting model incorporating fundamental accounting signals outperformed two variations of the multivariate forecasting model examined by Abarbanell and Bushee [J.S. Abarbanell, B.J. Bushee, Fundamental Analysis, Future EPS, and Stock Prices. Journal of Accounting Research 35(1) (1997), 1–24]. To estimate the neural network weights of their neural network models, ZCS used backward propagation (BP). In this paper we compare the forecasting accuracy of neural network weights estimated with BP to ones derived from an alternative estimation procedure, the genetic algorithm [R.S. Sexton, R.E. Dorsey, N.A. Sikander, Simultaneous Optimization of Neural Network Function and Architecture Algorithm. Decision Support Systems 36(3) (2004), 283–296]. We find that the genetic algorithm produces models that are significantly more accurate than the models examined by ZCS.