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The Prediction of Earnings Using Financial Statement Information: Empirical Evidence With Logit Models and Artificial Neural Networks

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  • Andreas Charitou
  • Chris Charalambous

Abstract

In the past three decades, earnings have been one of the most researched variables in accounting. Empirical research provided substantial evidence on its usefulness in the capital markets but evidence in predicting earnings has been limited, yielding inconclusive results. The purpose of this study is to validate and extend prior research in predicting earnings by examining aggregate and industry‐specific data. A sample of 10,509 firm‐year observations included in the Compustat database for the period 1982–91 is used in the study. The stepwise logistic regression results of the present study indicated that nine earnings and non‐earnings variables can be used to predict earnings. These predictor variables are not identical to those reported in prior studies. These results are also extended to the manufacturing industry. Two new variables are identified to be significant in this industry. Moreover, an Artificial Neural Network (ANN) approach is employed to complement the logistic regression results. The ANN model's performance is at least as high as the logistic regression model's predictive ability.

Suggested Citation

  • Andreas Charitou & Chris Charalambous, 1996. "The Prediction of Earnings Using Financial Statement Information: Empirical Evidence With Logit Models and Artificial Neural Networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 5(4), pages 199-215, December.
    • Handle: RePEc:wly:isacfm:v:5:y:1996:i:4:p:199-215
    DOI: 10.1002/(SICI)1099-1174(199612)5:43.0.CO;2-C
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    References listed on IDEAS

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    2. James R. Coakley & Carol E. Brown, 2000. "Artificial neural networks in accounting and finance: modeling issues," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(2), pages 119-144, June.

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