Title: Assessing model efficacy in forecasting EPS of Chinese firms using fundamental accounting variables: a comparative study

Authors: Qing Cao, Qiwei Gan, Marc J. Schniederjans

Addresses: Rawls College of Business, Texas Tech University, Lubbock, TX 79409-2101, USA. ' Rawls College of Business, Texas Tech University, Lubbock, TX 79409-2101, USA. ' Management, CBA 272, University of Nebraska-Lincoln, P.O. Box 880491, Lincoln, NE 68588-0491, USA

Abstract: In this paper, we compare the forecasting accuracy of two neural network models in forecasting earnings per share of Chinese listed companies based upon fundamental accounting variables. In one neural network model, weights estimated by back propagation were utilised, and in the other model a genetic algorithm was utilised. Based upon a sample of 723 Chinese companies in 22 industries over a ten year period, we found that the neural network model, using a genetic algorithm in forecasting, outperforms the neural network model with back propagation. Results also showed that the addition of fundamental accounting variables used in the neural network models further improved forecasting accuracy.

Keywords: neural networks; ANNs; neural network modelling; univariate model genetic algorithms; earnings per share; EPS; forecasting accuracy; Chinese financial markets; fundamental accounting variables; back propagation; comparative analysis; hypotheses testing; China; listed companies; modelling.

DOI: 10.1504/IJSSS.2010.033491

International Journal of Society Systems Science, 2010 Vol.2 No.3, pp.207 - 225

Available online: 02 Jun 2010 *

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