Neural network forecasting of quarterly accounting earnings

Neural network forecasting of quarterly accounting earnings

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Article ID: iaor19972359
Country: Netherlands
Volume: 12
Issue: 5
Start Page Number: 475
End Page Number: 482
Publication Date: Dec 1996
Journal: International Journal of Forecasting
Authors: , , ,
Keywords: forecasting: applications, neural networks
Abstract:

This study uses an artificial neural network model to forecast quarterly accounting earnings for a sample of 296 corporations trading on the New York stock exchange. The resulting forecast errors are shown to be significantly larger (smaller) than those generated by the parsimonious Brown-Rozeff and Griffin-Watts (Foster) linear time series models, bringing into question the potential usefulness of neural network models in forecasting quarterly accounting earnings. This study confirms the conjecture by Chatfield and Hill et al. that neural network models are context sensitive. In particular, this study shows that neural network models are not necessarily superior to linear time series models even when the data are financial, seasonal and non-linear.

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