Neural network forecasting of quarterly accounting earnings |
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| Authors: | Jeffrey L Callen Clarence C.Y Kwan Patrick C.Y Yip Yufei Yuan |
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| Affiliation: | aDepartment of Accounting, Stern School of Business, New York University, 40 West 4th Street, New York, NY 10012, USA;bFinance and Business Economics Area, Michael G. DeGroote School of Business, McMaster University, Hamilton, Ont. L8S 4M4, Canada;cDepartment of Mathematics and Statistics, McMaster University, Hamilton, Ont. L8S 4M4, Canada;dInformation Systems Area, Michael G. DeGroote School of Business, McMaster University, Hamilton, Ont. L8S 4M4, Canada |
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| 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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| Keywords: | Artificial neural networks Quarterly earnings Comparative forecast performance |
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