Corporate Distress Research Articles

Forecasting Corporate Distress in the Asian and Pacific Region

This study analyses credit default risk for firms in the Asian and Pacific region by applying two methodologies: a Support Vector Machine (SVM) and a logistic regression (Logit). Among different financial ratios suggested as predictors of default, leverage ratios and the company size display a higher discriminating power compared to others. An analysis of the dependencies between PD and financial ratios is provided along with a comparison with Europe (Germany). With respect to forecasting accuracy the SVM has a lower model risk than the Logit on average and displays a more robust performance. This result holds true across different years.

Currency Depreciation and Emerging Market Corporate Distress

How do emerging market corporates fare during periods of currency depreciation? We find that nonfinancial firms that exploit favorable global financing conditions to issue U.S. dollar bonds and build cash balances are also those whose share price is most vulnerable to local currency depreciation. In particular, firms’ vulnerability to currency depreciation derives less from the foreign currency debt as such, and more from the cash balances that are built up by using foreign currency debt. Overall, our results point to a financial motive for dollar bond issuance by emerging market firms in carry trade–like transactions that leave them vulnerable in an environment of dollar strength.This paper was accepted by Gustavo Manso, finance.

판별분석에 의한 기업부실예측력 평가: 서울지역 특1급 호텔 사례 분석

판별분석에 의한 기업부실예측력 평가: 서울지역 특1급 호텔 사례 분석

Boarding a Sinking Ship? An Investigation of Job Applications to Distressed Firms

ABSTRACTWe use novel data from a leading online job search platform to examine the impact of corporate distress on firms’ ability to attract job applicants. Survey responses suggest that job seekers accurately perceive firms’ financial condition, as measured by companies’ credit default swap prices and accounting data. Analyzing responses to job postings by major financial firms during the Great Recession, we find that an increase in an employer's distress results in fewer and lower quality applicants. These effects are particularly evident when the social safety net provides workers with weak protection against unemployment and for positions requiring a college education.

Corporate Failure Prediction Models for Advanced Research in China: Identifying the Optimal Cut Off Point

The rapid growth of the Chinese economy has resulted in Chinese listed companies entering numerous global supply chains, and thereby contributing to the globalization of economies. Accurately predicting corporate distress is a crucial concern for enterprises, managers, investors, creditors, and supervisors. In this study, data from the 20032013 (excluding 2008) was analyzed, and a logistic model was applied to analyze critical factors. We developed Special Treatment (ST) model to measure distress of companies listed in China. The results indicate that the optimal cut-off point (one, two, three and fourth quarters before a failure), and the debt ratios (one quarter before a failure) or unadjusted economic value added (two, three and fourth quarters before a failure) is superior in predicting corporate failure in China

데이터마이닝 기법을 활용한 비외감기업의 부실화 유형 분석

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

Building a Hybrid Prediction Model to Evaluation of Financial Distress Corporate

Exploring financial distress activity within a listed target of stock markets focused on creating such prediction models can provide insight into the technological requirements of corporate and the demands placed upon a stock investor in this field. This study integrates professional knowledge to financial ratios into the emerging soft computing techniques for building up a hybrid corporate distress prediction of early warning systems in regarding application fields. Conclusively, the empirical results indicate that the proposed procedure is a great potential alternative of helpful hybrid models to demonstrate its technological merit and application value, and it has increasing the application filings. In terms of managerial implications, the analysis results may be relevant to other types of prediction models seeking to identify financial ratios for the planning processes.

Credit Default Swaps and Loss Given Default: Has the CDS Market Affected the Recovery Rates of U.S. Corporate Defaults?

We provide the first empirical analysis on the effects of credit default swaps (CDS) on corporate distress resolution with a focus on debt recovery rate. CDS contracts are settled shortly after the occurrence of credit events such as restructuring or bankruptcy filings and, presumably, should cease to be relevant after the settlements. However, we find that the ultimate debt recovery rates, which are determined in the final resolution plan and long after the CDS settlements, are higher for the bankrupt firms with CDS contracts before bankruptcy filings. This CDS effect on recovery rates is more pronounced for bonds than for loans. The overall evidence is consistent with the view that CDS trigger earlier bankruptcy filings, leaving more valuable assets for bondholders in the bankrupt entities.

Corporate Bankruptcy, Soft Budget Constraints, and Business Group Affiliation: Evidence from Indian Firms

We examine the ex-ante performance of 1185 firms that filed for bankruptcy between 1992 and 2009. Evidence suggests that firm specific poor operating performance and industry wide distress are the principal causes (contributing 42% each for cash flow shortfall) of corporate distress. We observe vitiating investment, operating, and financing performance of the sample firms starting from four years prior to their bankruptcy. Further, we report less severe decline in capital expenditure in the case of state controlled firms and business group affiliated firms, indicating the presence of "soft budget constraints" among the sample firms. Logistic regression, estimating firm level distress probabilities offer indirect evidence for "risk sharing argument" among business group affiliates.

Corporate distress and lobbying: Evidence from the Stimulus Act

The literature on distressed firms has focused on these firms’ investment, capital structure, and labor decisions. This paper investigates a novel aspect of firm behavior in distress: how financial health affects a firm׳s lobbying and, consequently, its relationship with the government. We exploit the shock to nonfinancial firms during the 2008 financial crisis and the availability of the stimulus package in the first quarter of 2009. We find that firms with weaker financial health, as measured by credit default swap spreads, lobbied more. We also show that the amount spent on lobbying was associated with a greater likelihood of receiving stimulus funds.

Financial Crisis Prediction Capability of Financial Ratios

Bankruptcy of a business firm is an event which results substantial losses to creditors and stockholders. A model which is capable of predicting an upcoming business failure will serve as a very useful tool to reduce such losses by providing warning to the interested parties. This was the main motivation for Beaver (1966) and Altman (1968) to construct bankruptcy prediction models based on the financial data (Deakin 1972). This research study also initiated with a great interest on this subject to investigate the predictive capability of financial ratios for forecasting of corporate distress and bankruptcy events. The current global financial climate demands even the best international companies to constantly monitor their financial situation and their related companies with which they cooperate. Globalization process has delivered a complex network of relationships in the business environment. Due to increase in complexity of related business environment, forecasting the financial health of companies nowadays became increasingly important and worthwhile to analyse (Korol 2013). Bankruptcy is a continuous process, which can be distinguished into several stages, starting from the emergence of the first signs of financial crisis, through blindness and ignorance towards the financial and nonfinancial symptoms of crisis in a firm, to inappropriate activities that lead to the final phase of the crisis, which is bankruptcy. The Bankruptcy process cycle may take up to 5–6 years which is not a sudden phenomenon and impossible to predict, however the earlier warning signals can be detected and corrective measures may avoid the ultimate bankruptcy event depending on the preparation and reactions of the management to tackle the bankruptcy (Korol 2013). Due to the recent worldwide corporate financial crisis the need to reform the existing financial architecture has been intensified. Objective of business crisis prediction is to build models that can read the risk factors from the past observations and evaluate business crisis risk of companies with a much broader scope (Lin et al. 2011). Ozkan cited in Lin et al. 2011 mentioned that financial indicators has been reviewed by number of researchers as a major basis for predicting financial distress and some common methodologies including peer group analysis, comprehensive risk assessment systems, and statistical and econometric analysis. Premachandra (2009) argued that bankruptcy prediction is important because corporate failure imposes significant direct and indirect costs on stakeholders. Warner cited in Premachandra (2009), evidence suggests that direct bankruptcy costs (such as court costs, lawyers and accountants fees) may be as low as 5%, or (Altman cited in Premachandra 2009) can shoot up to 28% when both direct and indirect costs (such as lost sales, lost profits, higher cost of credit, inability to issue new securities and lost investment opportunities) are considered. Therefore the early detection of potential bankruptcy is very important due to corporate decision makers make their decisions in a world of dynamic technology development, imperfect knowledge and uncertainty (Premachandra 2009). Niewrzedowski cited in Korol (2013) indicated that as per statistical analysis by Huler-Hermes, the number of potential bankruptcy has been increased in USA by 54%, in Spain by 118% and in the UK by 56%. Therefore the importance of early warning of potential bankruptcy has been increased along with the overall increase of bankruptcy risk in companies around the world.

Cost of Debt and Corporate Profitability

This paper examines the relationship between the cost of debt and corporate profitability using a sample of 3,556 Italian unlisted firms between 2007 and 2011. On the basis of the logistic regression model, we find that the cost of debt, measured by the interest expense to financial debt ratio, is negatively correlated to various proxies of firm profitability. These findings are consistent with previous research on the relevance of indirect costs of corporate distress. However, although the analysis found evidence that unprofitable firms are highly leveraged in accordance with the Pecking order theory, we also observed that the cost of debt for the firms included in the sample is inversely dependent on the amount of financial debt.

Rethinking the Role of the Law of Corporate Distress in the Twenty-First Century

Thomas Jackson famously described the role of all bankruptcy law as reducing the incentive for individual enforcement against the assets of a distressed company. Although scholars have debated other aspects of Jackson’s thesis, most have continued to identify with this as the central tenet of bankruptcy law. This paper proposes a new taxonomy: the law of corporate distress comprised of insolvency law and restructuring law. It argues that Thomas Jackson’s description remains apt for part of that taxonomy but draws a distinction between the constituent parts. It reframes the unifying aim of the law of corporate distress as the facilitation of the reallocation of resource in the economy to best use and draws a distinction between insolvency law’s role in reducing the incentive for individual enforcement and restructuring law’s role in providing a deadlock resolution procedure. Adopting a comparative Anglo-American approach it examines the implications of this distinction for insolvency law and restructuring law in the twenty-first century.

Corporate Distress and Lobbying: Evidence from the Stimulus Act

The literature on distressed firms has focused on these firms' investment, capital structure, and labor decisions. This paper investigates a novel aspect of firm behavior in distress: how financial health affects a firm's lobbying and, consequently, its relationship with the government. We exploit the shock to non-financial firms during the 2008 financial crisis and the availability of the stimulus package in the first quarter of 2009. We find that firms with weaker financial health, as measured by credit default swap spreads, lobbied more. We also show that the amount spent on lobbying was associated with a greater likelihood of receiving stimulus funds.

Predicting corporate distress in the Nigerian stock market: Neural network versus multiple discriminant analysis

The objective of the paper is to assess the quality of neural networks in predicting distress as against discriminant analysis and its applications in enhancing managers’ decision. Forty four firms listed on the Nigerian Stock Market between 1987 and 2006 are used for the study. The performance of neural network is then compared with the more familiar discriminant analysis statistical technique, and the performance of both is further with a performance obtainable by mere guesswork. The results show that, while both the neural network and the discriminant analysis techniques performed better than guess work, the neural network out performs the discriminant analysis technique. The outstanding performance of neural network underscores its importance as an invaluable tool in the business decision-making process. The study suggests that neural networks could aid managers in decision making to reverse the present down trend of the Nigerian Stock Market. Key words: Stock market, neural networks, firm’s distress, business decision, pay-out policy, discriminant analysis.

Financial distress, earnings management and market pricing of accruals during the global financial crisis

PurposeThe aim of this paper is to examine empirically the managerial earnings management practices of financially distressed firms, and to consider whether these practices changed during the recent global financial crisis. Although corporate distress has been a topic of research interest for many years, earnings manipulation by distressed firms has received relatively little attention.Design/methodology/approachThe paper uses three measures of distress, and discretionary accruals, a popular proxy for earnings management, to investigate the impact of distress on earnings management.FindingsThe paper finds that managers of distressed firms engage more in income‐decreasing earnings management practices compared to their healthy firm counterparts. The paper also finds some evidence of the effect of the global financial crisis on the association between financial distress and earnings management. Finally, the paper shows some evidence of positive market pricing of discretionary accruals in the non‐crisis period, but a substantial reduction in pricing coefficients during the global financial crisis period.Practical implicationsFinancial distress experienced by firms provides incentives to managers for earnings manipulation. However, the direction of the earnings management could be income‐increasing or income‐decreasing. The findings from this study will allow investors to make better investment decisions for firms that are experiencing financial difficulties.Originality/valueThis paper is the first in New Zealand to investigate the association between firm distress and managerial earnings management decisions. Recently, New Zealand experienced a spate of finance company collapses that somewhat contributes, indirectly, to financial distress experienced by firms. The New Zealand reporting environment is characterized by concentrated ownership, relaxed monitoring by regulatory authorities, and a very low litigation threat that provides an interesting setting to examine the research question. This paper is also the first to test the market pricing of earnings components in New Zealand.

Systematizing the Empirical Research on Corporate Distress. What Board Features Affect Survival?

Over the years, understanding the possible antecedents of corporate distress has received considerable interest among the scholars and practitioners of management worldwide. Also, much of the evolving empirical research around this topic has been increasingly devoted to studying what board features can increase or decrease the survival of distressed firms. Although still fragmented, the advancements within this kind of research have demonstrated that particular board features count. Thus, this article aims at providing both the management scholars and practitioners with the systematization of these advancements. The findings from the present review show that board independence tends to prevent distress situations and increase the survival of those firms that initiate their restructuring. The board heterogeneity and low tenure can also count. Finally, it is evidenced that the heterogeneity of the statistical methods employed by the extant literature has increased proportionally with the time and journal ranking of the reviewed publications.

Corporate Defaults, Workouts and the Rise of the Distressed Asset Investment Industry

This paper examines corporate distress episodes for the United States, Western Europe, and Asia during the late twentieth century, and charts the emergence and growth of new actors in the corporate restructuring process – distressed asset investment firms. We analyse the formation and growth of these firms; including their investment focus, geographic and product expansion strategies. We find that the distressed asset investment industry is dominated by firms based in the United States, is relatively concentrated in terms of number of firms and assets under management, due in large part to early movers developing distinctive investment capabilities through participation in landmark transactions, relationship-specific resources, and scale effects. We examine empirically how the rise of distressed asset investment firms has altered the pricing dynamics in the corporate distressed market. We model the demand and supply of distressed debt over a thirty year period using historical data on loan issuances, corporate defaults and recovery rates. We find that the demand for distressed asset investment has become more responsive to supply over time with falling supply-demand ratios, implying lower investment returns. We conjecture that the emergence and growth of distressed asset investment firms has resulted in private sector workouts becoming more competitive and more efficient, especially in the United States.

The Prediction of Corporate Financial Distress in Tunisia

The ability to accurately predict potential corporate distress and to provide early warnings has become of interest not only to managers but also to external stakeholders of a company. In this way, and since there are very distinct differences in the accounting procedures and the quality of financial statements between the firms in Tunisia and those in the other countries, it may not be rational to adopt models developed elsewhere to forecast the potential distressed situations. Hence, the purpose of this study is to examine the effect of logistic regression on bankruptcy prediction accuracy of the Tunisian firms. Applying this statistical technique to a sample of 214 distressed companies, and 1550 healthy we determine those financial ratios that establish the differences between these groups, and thus predict possible financial distress. The results demonstrate that profitability, solvency and debt ratios are among the most significant ones for the wholesale and retail companies. For the manufacturing companies, profitability and leverage ratios are the most significant ones.

Boarding a Sinking Ship? An Investigation of Job Applications to Financially Distressed Firms

We use novel data from a leading online job search platform to examine the impact of corporate distress on firms’ ability to attract job applicants. Survey responses suggest that job seekers accurately perceive firms’ financial condition, as measured by companies’ credit default swap prices and accounting data. Analyzing responses to job postings by major financial firms during the Great Recession, we find that an increase in an employer’s distress results in fewer and lower quality applicants. These effects are particularly evident when the social safety net provides workers with weak protection against unemployment and for positions requiring a college education.