Assessing model efficacy in forecasting EPS of Chinese firms using fundamental accounting variables: a comparative study
نویسندگان
چکیده
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.
منابع مشابه
Forecasting EPS of Chinese Listed Companies Using Neural Network with Genetic Algorithm
In this paper we use neural network models to forecast earnings per share (EPS) of Chinese listed companies using fundamental accounting variables. The sample includes 723 Chinese companies in 22 industries over 10 years. The result shows that the neural network model with weights estimated with genetic algorithm (GA) outperforms the neural network with weights estimated with back propagation (...
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