A Bankruptcy Prediction Model With Nontraditional Measures
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Type of WorkText
DepartmentBusiness and Management
ProgramDoctor of Philosophy
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The purpose of this study is to examine whether a more relevant bankruptcy model is derived by combining unconventional measures with traditional financial statement ratios. A growing literature suggests that instead of only financial accounting ratios, long-term firm performance models are enhanced, balanced and more relevant by adding such alternative measures. Qualitative factors are included in the model to capture perceivable threats to operations, to measure management's reaction to those threats and the association of those factors with filing bankruptcy. The relevance of traditional bankruptcy models has conceivably declined with the changing economic and technological environment. Hence, this study was developed in response to the gap in the literature surrounding the relevance of existing bankruptcy models: limited theory related to qualitative factors, thus, the inclusion of only financial statement ratios, inclusion of only specific industries, changes in the legal, technological, and economic environment. Each sample and model is comprehensively tested and analyzed utilizing both logistic regressions and discriminant analyses. First, I examine the traditional Altman model's performance using four separate samples created from the data. The cross-industry samples consist of three pre-bankruptcy observation subsamples and a pooled sample. Then, I examine a composite bankruptcy model consisting of the Altman model and the measures of operational effects. Finally, I investigate the performance of only the operational effects section of the model. The proxies for operational effects analyzed in this study are market share change, customer satisfaction, employee change, litigation effects, restructuring effects and auditing effects. The model developed in this study is a useful screening and signaling device for practitioners, capital providers, regulators and other stakeholders. Moreover, the model is straightforward, timely, and can be implemented from frequently-reported, easily-accessible public information. Sensitivity tests of the model's performance utilizing a holdout sample show that the enhanced model identifies nearly all bankruptcy firms prior to filing. My findings show that by adding nontraditional variables to a financial bankruptcy model, a relevant and timely model with significant explanatory power is developed.