Bankrupt Firms Research Articles

Inheriting from Bankrupt Firms? Evidence on Long-term Performance of Local Firms

Inheriting from Bankrupt Firms? Evidence on Long-term Performance of Local Firms

Regulatory Protection and Opportunistic Bankruptcy*

ABSTRACTWe document controlling shareholder (insider) opportunism in an insolvency regime that uses an accounting rule to determine bankruptcy eligibility. Our study sheds light on managerial incentives induced by weak investor protection laws. Using unique data on bankrupt firms from an emerging market, consistent with our prediction, we show insiders intentionally manage earnings downward to understate firm net worth so as to be able to file for bankruptcy. Downward pre‐bankruptcy earnings management is associated with more payments to insiders and weaker performance, post‐filing. A battery of tests suggests our results cannot be fully explained as an artifact of financial distress. Rather, they are consistent with insiders exploiting weak investor protection to extract private benefits at the expense of lenders and outside shareholders. Our study serves as a cautionary tale for all insolvency regimes that use a balance sheet test in an environment with weak creditor protection.

Vegetative cycle and bankruptcy predictors of agricultural firms

The characterisation of agricultural activity depends on what firms produce. In this article, we introduce the importance of the vegetative cycle in the prediction of bankruptcy of agricultural firms, analysing the financial ratios proposed by the classical models of Altman (1983), Ohlson (1980) and Zmijewski (1984). We consider a total of 2 228 Portuguese firms, with 83 failing between 2015 and 2019. The findings confirm that the differences between healthy and bankrupt firms depend on their vegetative cycle. Although predictors based on liquidity are helpful only in predicting the bankruptcy of non-perennial crop firms, activity predictors are better in identifying healthy perennial crop firms. In addition, we show substantial statistical differences in terms of liquidity and profitability, but only in healthy firms. The results encourage the topic of the vegetative cycle to be more present in the construction of more accurate bankruptcy prediction models.

Probability of Corporate Bankruptcy: Application to Portuguese Manufacturing Industry SMEs

This paper aims to develop a model for predicting corporate bankruptcy for SMEs in the Portuguese manufacturing industry where this question remains rather unaddressed. Using profitability, activity, liquidity, leverage, and solvency ratios, it was added the size and age variables, for a group of 208 firms, including 49 bankrupt firms and 159 active firms, during the years 2011 to 2015. The logit model allowed us to estimate a model with 82.3% of predictive capacity. The most important variables identified were profitability, solvency, and size. Estimations only with the data closest to the bankruptcy date improved predictive capacity. It is evidenced that financial and non-financial variables can predict bankruptcy probability. A possible future approach would be to analyze a larger sample. Also, a larger period could be considered, allowing to test either the effects of the 2007/8 crisis or the effects of the recent economic turmoil related to Covid-19. Important for both corporate managers and investors. Conclusions may be disclosed regarding the influence that economic turmoil certainly has on corporate defaults and bankruptcies allowing its extension to other countries. The contribution of this paper is to find the best specification for a bankruptcy prediction model applied to the Portuguese manufacturing industry SMEs. This paper also contributes to the existing literature by using non-financial variables and analyzing a sector still unexplored in Portugal, albeit its conclusions can be extended to other countries.

Do bankrupt firms recognize publicly available bad news in a timely fashion?

PurposeThe purpose of this paper is to examine whether managers of bankrupt firms are more or less conditionally conservative in their financial reporting relative to non-bankrupt firms. The study further examines the cross-sectional differences in conditional conservatism among bankrupt and non-bankrupt firms.Design/methodology/approachThe study employs a sample of US firms to investigate conditional conservatism in firms that experience financial distress and go bankrupt relative to non-stressed non-bankrupt firms. The study also uses switching regression models to identify the drivers of the cross-sectional difference in conditional conservatism among bankrupt and non-bankrupt firms.FindingsEmpirical results show that bankrupt firms are timelier in recognizing bad news than good news when compared to non-bankrupt firms. The higher level of conditional conservatism in bankrupt firms is mainly driven by their higher levels of leverage and tax-reduction incentives. The cross-sectional analyses show that these results largely hold for more leveraged firms and firms with higher tax costs. Taken together, these results suggest that the conservative tendency of managers of bankrupt firms can stem from the agency problem between lenders and managers and from tax-decreasing motivations.Originality/valueThe novelty of the authors’ research stands in studying the drivers of the cross-sectional differences in conditional conservatism between bankrupt and non-bankrupt firms and specifically, the demonstration that taxation also induces conditional conservatism in the setting of ex post bankrupt firms.

Balancing and variable reduction of firm bankruptcy data

Financial stress experienced by supply chain elements causes stress to all members. Predictive data mining is a common tool for predicting bankruptcy. Bankruptcy often involves highly imbalanced datasets with a large number of potential variables, with bankrupt firms being by far the minority case. This study uses data from four studies of firm bankruptcy and examines the impact of data balancing and variable selection on model accuracy. The models used are random forest and gradient boosting based on decision trees, logistic regression, neural networks, and support vector machines. Two machine learning methods are used to trim the number of variables. Stepwise regression and entropy from decision trees are used to generate reduced variable sets. The complexity parameter was used to set levels on number of variables using the entropy (decision tree) option. The impact of reducing variables is examined. Error metrics used were type I and type II error (sensitivity and specificity), overall average error (accuracy), and area under the recall curve (AuC). The average error of extreme gradient boosting and random forest models was found to be better than support vector machines, which had a slight advantage over logistic regression and neural networks. Variable reduction was found to lead to mixed results with respect to relative accuracy. Overall accuracy increased with slight reduction in the number of variables (using stepwise regression), but deteriorated as the number of variables was reduced to the smaller number of variables. The experiments into balancing found that unbalanced data had high error rates, which dropped a great deal with even 10 percent balancing, but balancing beyond 10 percent was found to provide little additional accuracy.

Bankruptcy, overlapping directors, and bank loan pricing

Using a sample of loan facilities borrowed by firms that share directors with bankrupt firms, this study investigates whether the overlapping directors are a transmission channel of the bankruptcy contagion effect in the bank loan market and, if so, what the underlying mechanism is. We find that firms are charged higher loan spreads in the period following the bankruptcy filing of a firm with a common director and that overlapping directors are a relevant channel for the bankruptcy contagion effect, in addition to other channels identified in literature. We also find that the negative contagion effect on loan pricing is most likely driven by the overlapping directors' reputation loss due to their involvement in bankruptcy events, and not by competing hypotheses, such as director distraction and director career concern/experience. Further analyses reveal that the adverse contagion impact on loan spreads is more pronounced when overlapping directors have greater influence over corporate policies or when their reputation is more seriously damaged. Meanwhile, the contagion effect is mitigated when interlocked firms have a higher-quality board. These results further support our evidence of the director reputation loss hypothesis. We strengthen the identification strategy to establish causality. In sum, our study identifies common directors as a channel of bankruptcy contagion effects on loan pricing and director reputation loss as an underlying mechanism.

Structural change decomposition of productivity without share weights

Structural change is important driver of productivity. Existing decompositions of structural change rely heavily on the share weights of firms. Ideally, one would need a complete census of all firms to calculate the share weights. However, empirical data are often less than ideal. Another limitation of the structural change decompositions is that existing firms that switch from one industry to another are mixed with startups and bankrupt firms. To address both these limitations, we develop an alternative productivity decomposition of structural change that avoids the use of share weights, which can be useful whenever the share weights are problematic or unavailable. Our decomposition also takes the product switch explicitly into account and applies to both the level and percentage change of productivity. Application to Finnish agriculture illustrates the proposed approach.

Replacing key employee retention plans with incentive plans in bankruptcy

We examine executive bonus contracts in corporate bankruptcies. Introduced in 2005, Section 503(c)(1) of the United States' Chapter 11 corporate bankruptcy code regulates key employee retention plans (KERPs) but does not restrict performance incentive plans (PIPs). We find that, following the adoption of this reform, the likelihood of approval of KERPs and their coverage decrease, while those of PIPs increase. Unintended consequences of the reform include lower operating performance for PIPs and decreases in reorganization efficiency for bankrupt firms adopting KERPs or PIPs. Our results are consistent with the idea that KERPs were rent-extraction tools, which contrasts with prior evidence. PIP pay-performance link weakens after the reform, due to an increase in adoption of zero-performance thresholds and more discretion given to debtors over bonus pay. This suggests that watering down of PIPs' incentives may have re-introduced the rent extraction that the reform sought to eliminate. We conclude that the regulation of bonuses in bankruptcy should consider debtors’ reactions to such reforms.

Forecasting corporate default risk in China

Default risk prediction can not only provide forward-looking and timely risk measures for regulators and investors, but also improve the stability of the financial system. However, the determinants of corporate default risk in China have not been well-identified. An empirical analysis was conducted using a unique dataset of default events in the Chinese market to fill this gap. First, we demonstrated that the default probability estimated by a structural model, which is widely used in the literature, do not fully reveal the default risk of firms in China. Second, we classified default events into minor and major defaults for empirical analysis. We found that firms that survive minor defaults behave differently from other bankrupt firms. Our results suggest that the determinants of corporate default risk in China and the United States differ. We also found that a firm’s continued increase in cash holdings is one of the most important signs of default. Overall, our study significantly improves the accuracy of forecasting corporate default risk in China.

Reorganization or Liquidation: Bankruptcy Choice and Firm Dynamics

Abstract In this article, we ask how bankruptcy law affects the financial decisions of corporations and its implications for firm dynamics. According to current U.S. law, firms have two bankruptcy options: Chapter 7 liquidation and Chapter 11 reorganization. Using Compustat data, we first document capital structure and investment decisions of non-bankrupt, Chapter 11, and Chapter 7 firms. Using those data moments, we then estimate parameters of a general equilibrium firm dynamics model with endogenous entry and exit to include both bankruptcy options. Finally, we evaluate a bankruptcy policy change similar to one recommended by the American Bankruptcy Institute that amounts to a “fresh start” for bankrupt firms. We find that changes to the law can have sizable consequences for borrowing costs and capital structure which via selection affects productivity, as well as long run welfare.

Regional Disparities and Spatial Dependence of Bankruptcy in Spain

Firm survival, bankruptcy, and turnaround are of great interest nowadays. Bankruptcy is the ultimate resource for a company to survive when it is affected by a severe decline. Thus, determinants of firm turnaround and survival in the context of bankruptcy are of interest to researchers, managers, and policy-makers. Prior turnaround literature has broadly studied firm-specific factors for turnaround success. However, location-specific factors remain relatively unstudied despite their increasing relevance. Thus, this paper aims to evaluate the existence of spatial dependence on the outcome of the bankruptcy procedure. Economic geography and business literature suggest that location matters and closer companies behave similarly to further ones. For this purpose, we designed a longitudinal analysis employing spatial correlation techniques. The analyses were conducted on a sample of 862 Spanish bankrupt firms (2004–2017) at a regional level (province). For overcoming the limitations of the broadly usually logistic model employed for the turnaround context, the Moran’s Index and the Local Association Index (LISA) were applied with gvSIG and GeoDa software. The empirical results show that the predictors GDP per capita and manufacturing specialization are related to higher bankruptcy survival rates. Both characteristics tend to be present in the identified cluster of provinces with better outcomes located in the North of Spain. We suggest that location broadly impacts the likelihood of the survival of a bankrupt firm, which can condition the strategic decision of locating in one region or another. Our findings provide policy-makers, managers, and researchers with relevant contributions and future investigation lines.

Predicting Bankruptcy with Robust Logistic Regression

Using financial ratio data from 2006 and 2007, this study uses a three-fold cross validation scheme to compare the classification and pre diction of bankrupt firms by robust logistic regression with the Bianco and Yohai (BY) estimator versus maximum likelihood (ML) logistic regression. With both the 2006 and 2007 data, BY robust logistic regression improves both the classification of bankrupt firms in the training set and the prediction of bankrupt firms in the testing set. In an out of sample test, the BY robust logistic regression correctly predicts bankruptcy for Lehman Brothers; however, the ML logistic regression never predicts bankruptcy for Lehman Brothers with either the 2006 or 2007 data. Our analysis indicates that if the BY robust logistic regression significantly changes the estimated regression coefficients from ML logistic regression, then the BY robust logistic regression method can significantly improve the classification and prediction of bankrupt firms. At worst, the BY robust logistic regression makes no changes in the estimated regression coefficients and has the same classification and prediction results as ML logistic regression. This is strong evidence that BY robust logistic regression should be used as a robustness check on ML logistic regression, and if a difference exists, then BY robust logistic regression should be used as the primary classifier.

Successful turnarounds in bankrupt firms? Assessing retrenchment in the most severe form of crisis

During economic downturns, firms file for bankruptcy in an effort to attempt a “turnaround.” The objective of this study is to assess the effectiveness of retrenchment strategies in the context of bankruptcy, as the most severe form of crisis. We conducted a longitudinal analysis of a sample of 868 bankrupt Spanish firms during the period 2004–2017. The empirical results show that stakeholder support and deep cost retrenchment increase the likelihood of survival and performance recovery, while aggressive layoffs are detrimental for turning bankrupt firms around. Surprisingly, intense asset retrenchment had no significant effects on firm survival and also pushed performance downward. The findings suggest that retrenchment should not be regarded as a general remedy for firms suffering the most severe of crisis. Bankrupt firms should focus on restoring stakeholder relationships and reducing superfluous expenses, while making employees redundant or selling assets should be evaluated carefully when attempting a turnaround. JEL CLASSIFICATION M10; G33

The CV effect: To what extent does the chance to reorganize depend on a bankruptcy judge’s profile?

This study explores the link between the individual profiles of French commercial judges and the bankruptcy cases they supervised between 2006 and 2012. A “Curriculum Vitae effect” prevails: the chance to reorganize after filing for bankruptcy varies with the composition of the chambers. We also confirm the existence of a limited (but not marginal) appointment bias, suggesting that bankruptcy cases are not fully randomized across judges. Several variables accounting for the judges’ profiles are found to be significant. The presence of female judges increases the chances of a successful plan (for continuation). We find a similar influence of the judges’ managerial skills and of the highest academic profiles. However, a mismatch between the judges’ profiles (mostly oriented toward big businesses) and bankrupt firms (closer to small and medium-sized enterprises) undermines the probability of reorganizing. We also focus on the two main filters for reorganization: i) the decision to open an observation period, and ii) the court supervision of such a period. Our findings provide normative recommendations to better align lay justice with litigants’ needs. The results from France can be extended to other mixed bankruptcy systems.

Real earnings management in bankrupt firms

AbstractWe investigate: (1) whether managers in bankrupt firms manipulate earnings through real earnings management (REM); (2) the incentives and tradeoff strategies to engage in REM; (3) how REM influences the subsequent firm performance and bankruptcy probability. We find that bankrupt firms are more likely to manipulate earnings via REM than continuous firms. There is an increasing trend of REM activities in the 5‐year window before bankruptcy. The major incentive for bankrupt firms to engage in REM is the issuance of new debt. Bankrupt firms treat REM and accrual‐based earnings management (AEM) as complementary tools for earnings management. We further find that REM is associated with low future firm performance. The REM score defined in this study can more accurately predict bankruptcy than the Altman Z score by 15%. Overall, the findings in this paper will help investors, regulators, and academics identify REM activities and understand the incentives and consequences of REM.

Assessment of Trajectories of Non-bankrupt and Bankrupt Enterprises

The aim of this study is to show how long-term trajectories of enterprises can be used to increase the forecasting horizon of bankruptcy prediction models. The author used seven popular forecasting models (two from Europe, two from Asia, two from North America and one from Latin America). These models (five multivariate discriminant analysis models and two logit models) were used to develop 17-year trajectories separately for non-bankrupt enterprises and those at risk of financial failure. Based on a sample of 200 enterprises, the author evaluated the differences between non-bankrupt and bankrupt firms in development during 17 years of activity. The long-term usability of the models was demonstrated. To date, these models have been used only to forecast bankruptcy risk in the short term (1–3 years’ prediction horizon). This paper demonstrates that these models can also serve to evaluate long-term growth and to identify the first symptoms of future bankruptcy risk many years before it actually occurs. It was proven and specified that long-term developmental differences exist between non-threatened and future insolvent companies. These studies proved that the process of going bankrupt is very long, perhaps even longer than the literature has previously demonstrated. This study is one of the first attempts in the literature globally to assess such long-term enterprise trajectories. Additionally by implementing a dynamic approach to the financial ratios in the risk-forecasting model let visualize the changes occurring in the company.

CEO turnover and bankrupt firms’ emergence

AbstractThis 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.

Прогнозирование банкротств предприятий с помощью экстремального градиентного бустинга

The application of models for forecasting bankruptcy of enterprises for controlling investment is the basis for monitoring activities of financial institutions. A crucial factor in allowing financial institutions to determine the amount of capital to cover credit losses is the accuracy of the forecast. Most studies use traditional statistical methods (for example, linear discriminant analysis and logistic regression) to build models of enterprise bankruptcy forecasting, but the accuracy of these models is usually quite low. The reason for that is the imbalanced nature of training data sets (the share of bankrupt firms is a small percent of the total number of firms). Nowadays, such machine learning methods as the random forest and the gradient boosting are becoming widespread. This study focuses on the use of extreme gradient boosting to predict bankruptcy. Extreme gradient boosting, using LASSO or Ridge regularization, penalizes complex models to avoid overfitting. Also, during training, extreme gradient boosting fills in the missing values of the data set, depending on the value of the loss. In this article, we proposed SMOTE technique to enhance the minority class of the training data sets, which helps to improve the performance of extreme gradient boosting. The experiment results of improved extreme gradient boosting are compared to the outcomes obtained by other methods.

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 pre-existing 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.