Inderscience Publishers

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

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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

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