 The Prediction of Earnings Using Financial Statement Information: Empirical Evidence With Logit Models and Artificial Neural NetworksAndreas Charitou and Chris Charalambous Intelligent Systems in Accounting, Finance and Management, 1996, vol. 5, issue 4, 199-215 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. Date: 1996 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wly:isacfm:v:5:y:1996:i:4:p:199-215 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Intelligent Systems in Accounting, Finance and Management from John Wiley & Sons, Ltd. |
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