Logit Models for Bankruptcy DataImplemented in XploRe

Abstract

Numerous reasearch attempts in predicting business failures and or bankruptcy are well documented in corporate finance. Attempts to develop bankruptcy prediction continues since commercial banks, public accounting firms, bond rating agencies, for example have advocated for such information to minimize their exposure to potential client failures. The evolution of bankruptcy prediction research is geared towards the types of models that include statistical models (primarily, multiple discriminant analysis [MDA], conditional logit regression analysis, artificial neural network models and support vector machines [SVM]. Many additional bankruptcy model have been the work of Platt & Platt (1980), Gilbert, Menon, and Scwhartz (1990). Almost universally, the decision criteria to evaluate the usefulness of these models has been how well they classify a company as bankrupt or non-bankrupt compared to the company’s actual status known after the fact. In this thesis I employ logit analysis as an easily implemented analytical procedure to a bankruptcy data with the use of the XploRe software. The content is as follows: Chapter 1 and 2 introduce some models and methods used to analyse binary data and describe some stochastic properties of these models. Chapter 3 introduces data preparation for the Bankruptcy data set used in this work. In chapter 4, I examines some binary model applications in XploRe. Chapter 5 presents the logit model estimation for the Bankruptcy data. Some cases of link function and Conclusion of the analysis is in chapter 6.