Review of Categorical Models for Classification Issues in Accounting and Finance

Review of Categorical Models for Classification Issues in Accounting and Finance Recent studies have extensively used the logit or probit models for classification problems in accounting and finance. More than 289 articles in prestigious journals have used these or similar methods from 1989 through 1996. This paper reviews several categorical techniques and compares the performance of logit or probit with alternative procedures. Intuitive and mathematical explanations of how the models examined differ in terms of underlying assumptions and other attributes are provided. The alternative techniques are applied to two substantive research questions: predicting bankruptcy and auditors' consistency judgements. Four empirical criteria provide some evidence that the exponential generalized beta of the second kind (EGB2), lomit, and burrit (all new to the accounting and finance literature) improve the log-likelihood functions, and the explanatory power, compared with logit and other models. EGB2, lomit and burrit also provide significantly better classifications and predictions than logit and other techniques. Review of Quantitative Finance and Accounting Springer Journals

Review of Categorical Models for Classification Issues in Accounting and Finance

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Kluwer Academic Publishers
Copyright © 1999 by Kluwer Academic Publishers
Finance; Corporate Finance; Accounting/Auditing; Econometrics; Operation Research/Decision Theory
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