The Limitations of Bankruptcy Prediction Models: Some Cautions for the Researcher

The purpose of this study is to demonstrate potential problems associated with the use of bankruptcy prediction models in current research. The tests in this study demonstrate the problems that may arise when bankruptcy prediction models are inappropriately applied. This analysis evaluated the Zmijewski (1984) and Ohlson (1980) models using time periods, industries, and financial distress situations other than those used to originally develop the models. The findings indicated that both models were sensitive to time periods. That is, the accuracy of the models declined when applied to time periods different from those used to develop the models. The findings also suggest that the accuracy of each model continues to decline moving from the 1988–1991 to the 1992–1999 sample period. Additionally, Ohlson's (Zmijewski's) model was (was not) sensitive to industry classifications. The findings of this study also suggest that the Ohlson and Zmijewski models are not sensitive to financial distress situations other than those used to develop the models. Thus, the models appear to be more generally useful for predicting financial distress, not just bankruptcy.In sum, the results of this study suggest that researchers should use bankruptcy prediction models cautiously. Applying the models to time periods and industries other than those used to develop the models may result in a significant decline in the models' accuracies. Additionally, some bankruptcy prediction models may be more appropriate for evaluating various forms of financial distress as opposed to just bankruptcy. To avoid erroneous applications of bankruptcy prediction models in the future, it is necessary for researchers not only to understand the uses of prediction models, but also to understand the limitations of the models.     

Earnings Retention as a Specification Mechanism in Logistic Bankruptcy Models: A Test of the Free Cash Flow Theory

This paper explores the applicability of as a specification mechanism to improve forecasting methods in corporate bankruptcy. The study combines lessons from Jensen's Free Cash Flow Theory with a logisitic model of bankruptcy to improve forecasting accuracy. The model uses data from the Indian textile industry to show that data classification based on investment opportunities is yet another way of improving precision. The study also re-examines the Free Cash Flow Theory and concludes that in applying it to a bankruptcy scenario, its initial findings regarding retention policy hold true; that is, low growth firms should retain less of their earnings than their high growth counterparts.     

Dynamics in Ownership and Firm Survival: Evidence from Corporate Germany

This study investigates the determinants of changes in corporate ownership and firm failure for German firms. We find that many of the determinants of failure also affect ownership changes in this bank‐based economy. They include poor performance, weak corporate governance, high leverage, and small firm size. The ownership structure also plays a role for both events. Separate analyses of one of these events are therefore likely to miss important effects. The implications for the German corporate governance system are that the differences to countries with more market‐based systems are not as pronounced as previously speculated.     

基于非平衡大数据的公司破产评估模型研究

大数据环境下,应用机器学习数据挖掘分析技术对波兰破产及未破产公司的财务数据进行建模训练和测试验证,其中包括多层感知器中的SMOTE、SMOTE-Borderline1和BMS不平衡算法。横向对比发现SMOTE、SMOTE-Borderline1、BMS算法有效提升了F1-Score,证明了多层感知器算法在公司破产评估领域内处理非平衡类别数据手段的有效性。纵向对比表明在不同的预测时间跨度上,MLP模型和公司财务数据的分类器模型效果具有显著差异。最后,使用卡方检验筛选出公司短期负债、资金结构和经营利润等较为重要的财务指标。     

CEO turnover and bankrupt firms’ emergence

This paper examines whether CEO turnover within a bankrupt firm predicts the firm's likelihood to reemerge from bankruptcy proceedings as a reorganized entity. Using 836 bankruptcy cases filed under Chapter 11 of the United States Bankruptcy Code from 1989 through 2016, we show that firms that undergo CEO turnover are significantly more likely to emerge from Chapter 11 proceedings. We conduct further analyses to examine the potential mechanisms through which CEO turnover is linked to a firm's chance of emergence. Consistent with the perspective that CEO turnover constitutes an observable event that can signal creditor support, we find that CEO turnover in bankrupt firms is positively associated with debtor-in-possession financing. Additionally, there is a significant increase in managerial quality post-turnover. Further, we document that the predictive power of CEO turnover is stronger in bankruptcy cases with greater uncertainty, such as in free-fall bankruptcies, where there is less preexisting agreement between the firm and its creditors. Overall, our findings provide valuable insight into external investors and stakeholder groups, whose interests are significantly impacted by corporate bankruptcies.