Corporate Bankruptcy Research Articles

Large Book‐Tax Differences, Bankruptcy and Firm Efficiency

ABSTRACTIn this study, I investigate the relationship between large book‐tax differences (BTDs) and the probability of firm bankruptcy and financial distress. I argue that large BTDs provide valuable information regarding corporate bankruptcy. Using the Altman Z‐score and Ohlson O‐score models, I find that firms with large negative BTDs (LNBTDs) are less likely to experience financial difficulty and bankruptcy. In contrast, firms with large positive BTDs (LPBTDs) show a higher likelihood of financial distress and bankruptcy. Additionally, the LNBTD subset, contrary to the LPBTD group, demonstrates higher firm efficiency.

A primer on Chapter 11 bankruptcy filings: why the genders of the CEO and judge (may) matter

This study investigates the impact of CEO gender on corporate bankruptcy outcomes and how this impact is influenced by the gender of the presiding judge. Using logistic regression, we find that if the bankruptcy judge is male, female-led firms emerge from bankruptcy filings less often than male-led firms, even when we control for CEO-, firm-, industry-, and bankruptcy-level explanatory factors. We argue that our outcomes mainly result from a similarity bias in which judges prefer CEOs of their own gender. Female- and male-led firms are comparable except for female-led firms showing higher pre-filing cash levels. Hence, we posit that judges do not stereotype based on systematic differences between female- and male-led firms but instead based on internalized prejudices towards female CEOs.

Regulatory Procedures for Corporate Bankruptcy: A Comparative Analysis between the Kingdom of Saudi Arabia and the United States of America

The following paper provides a qualitative comparative analysis of the regulatory processes involved in corporate bankruptcy in the Kingdom of Saudi Arabia (KSA) and the United States of America (USA). The work analyses the legislation, main subjects, procedures, and results of corporate insolvency in both countries. As such, this study seeks to analyze the differences and comparisons of these regulations to address the question of the efficiency of each regime and the gaps for changes. The features of recent changes in Saudi Arabian legislation in the sphere of bankruptcy and the influence of these changes on the business environment are discussed, as well as the obligatory American Chapter 11 procedures. The comparative approach provides policy-implementing, legal-advice, and business-useful outlooks to the policymakers, lawyers, and enterprises operating in both countries for subsequent reasons. Based on the research findings and the shift in the Bankruptcy system in Saudi Arabia, this paper also acknowledges insider influence, particularly the influence of the U.S. model as the world’s benchmark on corporate restructuring best practices.

Research on the Bankruptcy Standard of Commercial Banks in China

Crisis of commercial banks has been reported frequently, and it is normal for them to fail and quit in the market competition. In order to maintain the national financial order and prevent systemic crisis, it is of great significance to improve the legal system of commercial bank bankruptcy to deal with the problematic commercial banks in a timely manner. Commercial bank insolvency standards as the application of commercial bank insolvency legal system, scientific and reasonable commercial bank insolvency standards is the key to commercial bank insolvency legal system to play a useful role. At present, the bankruptcy of commercial banks in China applies the general corporate bankruptcy standard. Based on the special characteristics of commercial bank insolvency, the introduction of regulatory standards and the establishment of diversified commercial bank insolvency standard system can be considered.

Evolution of Financial, Non-Financial, and Macroeconomic Predictors in Corporate Bankruptcy: A Comprehensive Review

This paper analyzes the evolving interplay over the decades between financial, non-financial and macroeconomic indicators as predictors of corporate insolvency. A key goal is identifying the most frequently employed and validated models incorporating financial metrics, non-variables, and economic factors to gauge bankruptcy risk. This study conducts a rigorous review of 204 scholarly articles published from 1960 to 2024 accessed through databases, i.e. Scopus and Web of Science, using the PRISMA framework. Analysis categorizes predictors into financial, non-financial and macroeconomic variables, examining their relevance over time. Traditional financial ratios such as Debt-to-Equity and Return on Total Assets remain influential. However, management quality, governance, and economic aspects, including growth and interest rates, now complement standard finance models, enabling more nuanced predictions. The research underscores the value of internally and externally focused management systems for averting insolvencies and augmenting standard advice. Results suggest that separately weighing a firm's financial position against non-financial health, political environment, and location allows for more robust forecasting. Looking beyond solely numeric data to incorporate qualitative aspects also forms a more holistic view that benefits managers and policymakers seeking to support economic stability.

Bankruptcy spillovers and stock price crash risk of non-bankrupt firms

This paper examines whether corporate bankruptcies affect non-bankrupt firms' stock price crash risk. We observe that an upsurge in corporate bankruptcies adversely affects the crash risk of non-bankrupt firms in the industry. The increase in the crash risk following bankruptcy clustering in an industry is greater in financially constrained and financially distressed non-bankrupt firms. We also find that the creditor-in-control bankruptcy regime moderates the adverse effects of bankruptcy spillovers on the crash risk of non-bankrupt firms. Our results are robust to endogeneity tests (using GMM estimators) and tests that exclude industry downturns and macroeconomic shocks.

Predicting business bankruptcy: A comparative analysis with machine learning models

Business failure prediction has become crucially important for today's firms, enabling them to reduce financial risks and make informed decisions. This study uses a dataset of 6819 companies and 96 financial and macroeconomic variables to present a comparative analysis of machine learning (ML) models for predicting corporate bankruptcies. Behind this research is to improve the accuracy of bankruptcy prediction, which can help companies make more informed decisions and reduce financial risks. This study aims to evaluate the effectiveness of 16 ML algorithms in terms of accuracy, sensitivity, and other relevant metrics. The work uses methodologies that include data collection and cleaning, exploratory data analysis, model preprocessing and training, and model performance evaluation. Data preprocessing and hyperparameter optimization techniques were used to improve model performance. The evaluated algorithms include Classifiers such as Stacking Classifier (SCC), Randomized Search Classifier (RCV), Historical Gradient Boosting Classifier (HGBC), MLP Classifier (MLPC), K-Neighbors Classifier (KNC), Decision Tree Classifier (DTC), XGBRF Classifier (XGBRFC), Support Vector Classifier (SVC), Logistic Regression Classifier (LR), Linear SVC Classifier (LSVC). With an accuracy of 97.63 %, recall of 97.63 %, and F1-score of 97.63 %, the results show that the SCC algorithm was the best. Other models, such as RCV and DTC, also showed good results, with accuracies above 97 %. However, models such as PAC and BNB had lower performance and accuracy below 90 %. Finally, this study compares the results of ML models in predicting business failures and highlights their effectiveness. The SCC algorithm is considered the most suitable model for this task, as it suggests that it can help economic actors make more informed decisions and reduce financial risks in the context of firms.

Geopolitical conflict and firm bankruptcy risk

This study examines the impact of geopolitical risk on corporate bankruptcy. We utilize a global dataset of 36,802 firms from 42 countries covering 2004-2022. Our baseline result indicates that geopolitical risk increases firm bankruptcy risk. Our results remain robust after controlling for endogeneity using 2SLS, propensity score, and entropy balancing techniques, as well as using alternative proxies. Our analysis shows that high cash flow and strong creditor rights help mitigate bankruptcy risk, while high leverage and aggressive earnings management stimulates it. Our findings provide insights for policymakers and industry practitioners to formulate effective strategies to mitigate bankruptcy risk in globalization.

Bankruptcy as a planned business strategy? Evidence from the stock market

Purpose This paper aims to investigate how the US stock market deals with the announcement of a strategic Chapter 11, a special type of corporate bankruptcy in which companies seek the protection of the law not as a last resort but as a planned business decision. Design/methodology/approach An event study is conducted by using data concerning a group of US publicly traded companies that entered Chapter 11 for both strategic and nonstrategic reasons. Regression analysis is also used for robustness purposes. Findings This study reveals that initiating both strategic and nonstrategic Chapter 11 proceedings results in negative and statistically significant abnormal stock returns before and at the bankruptcy announcement date. However, in the period following the filing, the market gradually views strategic bankruptcy cases as positive news, whereas nonstrategic Chapter 11 filings continue to be perceived as distinctly negative. Originality/value To the best of the author’s knowledge, this is the first paper that documents an asymmetric market reaction to the announcement of Chapter 11, suggesting that, in certain circumstances, managers can add value by filing a strategic bankruptcy.

Enhancing corporate bankruptcy prediction via a hybrid genetic algorithm and domain adaptation learning architecture

In the contemporary business landscape, accurately evaluating a company’s financial health is essential for stakeholders to mitigate risks and avert bankruptcy. This study presents an innovative approach to improving business bankruptcy prediction through the hybrid integration of Domain Adaptation Learning (DAL) and Genetic Algorithm (GA) techniques. The hybrid model harnesses DAL to address distributional changes in real-world scenarios and utilises GA’s proficiency in feature selection. Six machine learning models are rigorously evaluated against the proposed hybrid model: Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Gradient Boosting (GB), k-Nearest Neighbours (k-NN), and Stacking Ensemble (SE). Our hybrid model performs well on imbalanced target datasets using the Area Under the Precision–Recall Curve metric: 0.93 (RF), 0.93 (SVM), 0.89 (LR), 0.91 (GB), 0.88 (k-NN), and 0.92 (SE). These findings highlight the model’s ability to overcome the limitations of traditional approaches, offering a more reliable predictive framework for stakeholders to make informed decisions and proactively manage financial stability. Future research directions may explore the applicability of this hybrid model across different industries and the integration of additional techniques to further enhance its performance.

The Role of the Altman Z-Score Method in Predicting Corporate Bankruptcy

The Role of the Altman Z-Score Method in Predicting Corporate Bankruptcy

Corporate bankruptcy prediction based on the Adaboost algorithm for optimisation of long and short-term memory networks

In this study, a Long Short-Term Memory (LSTM) network was used to model and predict time series data, providing an innovative approach to corporate bankruptcy prediction. Subsequently, the predictions and real labels of the LSTM models were used to train the Adaboost algorithm to improve the accuracy and robustness of the models. Eventually, multiple trained LSTM models are combined into a more robust integrated model by adjusting the weights. It is worth mentioning that the integrated model achieves 95.57% prediction accuracy on the training set and 94.39% prediction accuracy on the test set, which indicates that the model has good prediction effect and generalisation ability. The method proposed in this study is of great significance, firstly, by combining LSTM and Adaboost algorithm, we not only improve the accurate prediction ability of corporate bankruptcy, but also enhance the ability of identifying anomalies. Second, by combining multiple LSTM models and adjusting the weights to form a more powerful integrated model, we effectively improve the overall prediction performance. This approach can provide financial institutions, investors, and government regulators with a more reliable and accurate tool for assessing corporate bankruptcy risk, which can help identify potential risks and take appropriate measures in a timely manner.

Probability of Corporate Bankruptcy over Two Periods of Crisis (2007-2010 vs 2019-2022): Application to Italian Manufacturing SMEs Using a Three Years Rating Model

Probability of Corporate Bankruptcy over Two Periods of Crisis (2007-2010 vs 2019-2022): Application to Italian Manufacturing SMEs Using a Three Years Rating Model

Information disclosure strategies and bank interest rates pricing decisions

A joint decision-making model for debt financing and operations is constructed in this study. The proposed model is designed so that enterprises can actively adjust the transparency of private information while considering the risks of corporate bankruptcy, bank premium behavior, and possible damage to the product market. We systematically analyzed the mechanisms of bank interest rate pricing, corporate information transparency, and corresponding stocking strategies. We examined the impact of the companies’ inventories and transparency of different types of information on bank interest rates, which revealed financing and management-related decision-making issues based on the bank’s interest rate response function. Various levels of information transparency are appropriate for companies in the context of debt financing. We discuss the impact of changes in exogenous parameters in the product and financial markets on the basis of global equilibrium, as well as corporate characteristics affecting optimal decision-making.

Recent Developments in Indian Central Banking: Flying through Turbulence but Aided by Some Tailwinds

This paper, an extension of our earlier work, presents a narrative of select broad aspects of Indian central banking during the pandemic and thereafter. As elsewhere, Indian monetary policy during the pandemic was characterized by substantial monetary expansion both in terms of rate cuts as well as asset purchase programs. The inflation targeting regime of Indian monetary policy was put to severe stress following the spurt in inflation after the pandemic. Besides, there have been two positive developments in Indian central banking. First, there has been significant improvement in the balance sheets of the Indian banking sector, following the institution of an effective corporate bankruptcy regime, write‐offs of earlier stressed assets, and recapitalization of public sector banks by the government. Second, India has made substantial progress in terms of adopting various facets of its digital financial infrastructure, where public initiatives were accompanied by private sector innovations. The digitalization of payments increased in both quantum and value and has touched the lives of the public through the ease of making daily payments, including the smallest of transactions.

A complex network analysis approach to bankruptcy prediction using company relational information-based drivers

Corporate bankruptcy prediction is a long-standing topic of interest for a variety of stakeholders. Various prediction methodologies have been proposed to achieve more accurate predictions. So far, most studies have focused on making predictions using artificial intelligence models fed with accounting information only or a combination of accounting information and other category or categories of information. This study proposes new corporate governance drivers that exploit company relational information using board of directors’ networks of companies and the concept of node embeddings obtained by mapping large directors’ networks to lower-dimensional spaces. The contribution of these new drivers to the overall prediction performance is assessed using data on UK companies listed on the London Stock Exchange. The proposed two-stage methodology consists of devising new drivers in the first stage followed by risk class prediction in the second stage. We examine different complex network structures that capture the diversity of connectivity patterns observed in directors’ networks. Empirical results suggest that the proposed network-based drivers that use company relational corporate governance information significantly improve the performance of bankruptcy prediction.

Societal trust and corporate bankruptcy

We find that societal trust—the extent to which residents of a country trust others—is associated with a more efficient bankruptcy process. Bankruptcy resolutions are faster, efficient outcomes are more likely, and the value lost during the bankruptcy process is lower in countries with higher societal trust. This effect of societal trust on the efficiency of the bankruptcy process is more pronounced in countries with low-income per capita, and in corrupt countries. Our results are derived from the analysis of survey data concerning the outcomes of a hypothetical firm's bankruptcy in 99 countries from 2004 to 2020, a dataset also utilized by Djankov et al. (2008).

Personal bankruptcy prediction using machine learning techniques

It has become crucial to have an early prediction model that provides accurate assurance for users about the financial situation of consumers. Recent studies focused on predicting corporate bankruptcies and credit defaults, not personal bankruptcies. Due to that, this study fills the literature gap by comparing different machine learning algorithms to predict personal bankruptcy. The main objective of the study is to examine the usefulness of machine learning models such as random forest, XGBoost, LightGBM, AdaBoost, CatBoost, and support vector machines in forecasting personal bankruptcy. The research relies on two samples of households (learning and testing) from the Survey of Consumer Finances, which was conducted in the United States. Among the estimated models, CatBoost and XGBoost showed the highest effectiveness. Among the most important variables used in the models are income, refusal to grant credit, delays in the repayment of liabilities, the revolving debt ratio, and the housing debt ratio.

Will the ‘government-court coordination’ of corporate bankruptcy disposal improve ESG performance? Evidence from China

ABSTRACT We explore the ‘government-court coordination’ (GCC) mechanism in bankruptcy disposal as a quasi-natural experiment to examine its effect on corporate Environmental, Social, and Governance (ESG) performance. Utilizing data from all Chinese A-share listed companies from 2009 to 2022, we apply the stacked difference-in-differences method. The findings indicate a significant enhancement in ESG performance among firms subjected to GCC policy, confirmed by various robustness checks. Mechanism analysis reveals that GCC’s positive impact on ESG performance is primarily driven by alleviating financial constraints for local enterprises and increasing their legal risks. The effect of the GCC on ESG performance is significant in prefectures characterized by a high presence of zombie firms and superior judicial efficiency.

Corporate bankruptcy and banking deregulation: The effect of financial leverage

We investigate the impact of deregulation-induced banking competition on corporate credit risk. Although banking competition does not, on average, affect corporate bankruptcy rates, we find that it causes corporate bankruptcies to increase significantly for high-leverage firms. We show that higher borrowing costs for high-leverage firms post-deregulation and the resulting credit rationing may be key factors behind our findings. The effect of deregulation lasts for up to seven years after the introduction of deregulation and originates mainly from firms that have high short-term debt and are financially constrained. Our results suggest that banking competition, which is expected to expand lending and reduce its cost, may, in fact, create more challenging credit conditions, particularly for firms that are more heavily dependent on external funding.