Distressed Firms Research Articles

“Extraordinary Administration” for Business Rescue in Italy: Admission Requirements and Value Creation

Large Italian firms in financial distress are admitted to the business rescue procedure called “Extraordinary Administration” with a view to preserving the business as a going concern when two objective requirements are met: at least two hundred employees and debts not less than two-thirds both of total assets and revenues. This study examines whether these selection criteria are adequate to identify large firms in terms of value creation. The analysis is motivated by the idea that social utility in the rescue of large firms should not be justified only by the number of employees, but also by the worth of the goods and services created by the firms. The sample is made up of 1,581 Italian manufacturing firms and four subsamples were analyzed for the three year period 2015-2017 using a set of logistic regression models. Research findings show that highly leveraged firms eligible to go into “Extraordinary Administration” do not select large firms as measured by proxy variables that take into account value creation, such as total assets and/or revenues. On the other hand, hypothetical alternative selection criteria based on total assets and revenues identify large firms in terms of value creation but no statistical evidence was found to show how these firms are leveraged.

Can board diversity predict the risk of financial distress?

PurposeThe purpose of this study is to explore whether different board diversity attributes (corporate governance aspect) can be used to predict financial distress. This study also aims to identify what type of prediction models are more applicable to capture board diversity along with conventional predictors.Design/methodology/approachThis study used Chinese A-listed companies during 2007–2016. Board diversity dimensions of gender, age, education, expertise and independence are categorized into three broad categories; relation-oriented diversity (age and gender), task-oriented diversity (expertise and education) and structural diversity (independence). The data is divided into test and validation sets. Six statistical and machine learning models that included logistic regression, dynamic hazard, K-nearest neighbor, random forest (RF), bagging and boosting were compared on Type I errors, Type II errors, accuracy and area under the curve.FindingsThe results indicate that board diversity attributes can significantly predict the financial distress of firms. Overall, the machine learning models perform better and the best model in terms of Type I error and accuracy is RF.Practical implicationsThis study not only highlights symptoms but also causes of financial distress, which are deeply rooted in weak corporate governance. The result of the study can be used in future credit risk assessment by incorporating board diversity attributes. The study has implications for academicians, practitioners and nomination committees.Originality/valueTo the best of the authors’ knowledge, this study is the first to comprehensively investigate how different attributes of diversity can predict financial distress in Chinese firms. Further, this study also explores, which financial distress prediction models can show better predictive power.

Does board committee independence affect financial distress likelihood? A comparison of China with the UK

This study explores the relationship between board committees’ independence and the financial distress of firms in China and the UK. Akin to previous literature, we estimate this relationship between 2007 and 2016 using a conditional logit model on a sample of matched pair firms. For China, the results indicate a positive and statistically significant association between the percentage of independent audit committee members and the financial distress of firms. In contrast, the opposite relationship was found for independent compensation and nomination committee members. For the UK, the relationships are the same, but the results are significant only for nomination committee independence. Our overall results suggest that independent audit committee members are not favorable for firm survival. In contrast, the independent compensation and nomination committee members are beneficial for the financial health of firms, as they are responsible for designing fair compensation packages and for selecting the most suitable persons to decide the strategic moves of the firm, respectively. A robustness test demonstrates that the results are robust to the assumed functional form imposed by the conditional logit model and confirms our core findings for both Chinese and UK firms.

Financial Distress and Forewarning Bankruptcy: An Empirical Analysis of Textile Sector in Pakistan

This study aims to measure the financial distress and forewarn bankruptcy in Textile Sector of Pakistan using popular statistical measures i.e., Z-Score, O-Score, Probit and D-Score. First, applicable financial ratios (profitability, liquidity, leverage, market ratios) and scores (Z-Score, O-Score, Probit and D-Score) of all 77 textile companies were calculated then estimated scores were compared with cut-off point of respective model. Based on findings, models are categorized in two groups: (a) Group-I (Z-Score and O-Score), (b) Group-II (Probit and D-Score). Results indicate that some of the textile firms are about to face financial distress in near future, which could ultimately lead those firms to bankruptcy. The findings of Group-I indicate that about 43% - 44% companies in the textile sector are in the phase of financial distress; whereas the results of Group-II reveal that about 8% - 16% companies are in financial distress phase. Thus, we could draw two conclusions: (1) the two models (Z-Score and O-Score) in Group-I were found to be robust for assessing financial distress and (2) the two models (Probit and D-Score) in Group-II were found to be less rigorous in forecasting financial distress. The previous studies attempted to compare the prediction accuracy of various models by examining the data of both financially distress firms and financially stable firms. But this study is aimed to foretell bankruptcy using comprehensive models (Z-Score, O-Score, Probit and D-Score), to compare the consistency of results across all four models of the study and to categorize financially stable and financially distress companies under each model. The findings of the study are expected to be beneficial at coutry level, firm level and indiviual level such as government and regulatory bodies of Pakistan can intervene to avert bankruptcy rate, management can devise appropriate strategies to reduce financial distress. Moreover. investors can safeguard their investment by making right decissions based on the findings.

Distress risk anomaly and misvaluation

This paper examines the effects of misvaluation on the well-documented negative relation between distress risk and stock returns (distress risk anomaly). Findings indicate that distress risk is negatively related to subsequent stock returns only in the subset of the most overvalued stocks, which is consistent with mispricing explanations provided by prior studies. The distress anomaly disappears after controlling for mispricing effects. Further analysis reveals earnings management to be one possible cause for the overvaluation of highly distressed firms. The results are robust to alternative specifications of distress risk and mispricing measures.

Turnaround Model Prediction with Content Dimension on Financial Distress Firms

This article aims to synthesize and test empirically turnaround model by content dimension that is an integration of the organization change framework into turnaround research to measure some activities done to avoid failure in turnaround. This study on the Indonesian Stock Exchange (BEI) by using Ordinary Least Square technique. The results of the analysis from content dimension show that the CEO expertise variable has a significant relationship to turnaround, since companies in Indonesia are dominated by family companies, so maintaining the incumbent CEO is more appropriate, because it is a family member. In addition, free assets show a significant relationship with turnaround, while leverage and growth of sales do not have a significant relationship with turnaround. Keywords: Financial Distress, Turnaround Model, CEO Expertise, Free Assets, Leverage and Growth of Sales,

Does compliance with corporate governance codes help to mitigate financial distress?

This paper analyzes whether the compliance with corporate governance codes helps to mitigate the financial distress of firms. We examine three different levels of compliance: overall compliance, the compliance with the recommendations regarding the board of directors and the compliance with the recommendations on board subcommittees. Our results reveal that only the fulfillment with the recommendations about the board of directors leads to a reduction in the likelihood of financial distress. These findings extend the academic debate concerning the role of governance codes and their impact on firm outcomes, and have practical implications for both professionals and firms. Moreover, our findings emphasize the need to distinguish between the different types of recommendations to investigate the effects of these codes. In addition, the results can be useful for policymakers in the configuration of new requirements and recommendations regarding corporate governance structures. Furthermore, our results contribute to the literature, delving into the determinants of the financial distress of firms.

A new multivariate approach for assessing corporate financial risk using balance sheets

Several indicators and univariate ratios can be used to measure the soundness of firms as reflected in their balance sheets (leverage, profitability, liquidity ratio, etc.). However, each indicator alone cannot measure a firm's overall financial risk or financial distress level. In this study, we measure the financial strength of the real sector firms listed on the Borsa Istanbul (BIST) by producing a composite index score that combines several different corporate finance ratios. In the first section, we conduct a multiple discriminant analysis of the variables used in Altman's z-score (1968), which is the most prevalent composite index used to measure firms' financial risk in the literature. In the second section, we introduce a new index, called the multivariate firm assessment (MFA) score, which uses the ratios that best explain the characteristics of companies listed on the BIST. The Tailored version of the Altman z-score and our new index have predictive power of around 90 percent. Furthermore, the MFA score reflects the impact of macroeconomic developments on firms' balance sheets and thus serves as an early warning of financial distress for Turkish firms. Our analyses using the MFA score suggest that non-exporting firms and firms with an open foreign exchange position have weaker balance sheets.

Marking to Market Corporate Debt

Models of capital structure and credit risk make predictions about market valuations of debt, but are routinely tested on the basis of book debt from common data sources. In this paper, we propose to close this gap. We construct a rich data set on firm level debt market valuations by carefully matching data on corporate bond and loan secondary market transactions. We document significant discrepancies between market and book values, especially for distressed firms. We use our dataset to i) provide novel rules of thumb on how to adjust leverage and unlever returns using standard datasets, and ii) to revisit a number of prominent empirical patterns involving corporate debt. Using a market-based measure of Tobin's Q, we find little evidence for investment cash-flow sensitivity in our data. We find that using market debt values significantly improves default prediction, and do not detect a credit spread puzzle. In asset pricing tests, we find a leverage premium, but no evidence for a value premium after controlling for market leverage. Moreover, a novel measure of financial distress, namely market-to-book debt, predicts stock returns positively in the cross-section, inconsistent with a financial distress puzzle.

Return Premium of Financial Distress and Negative Book Value: Emerging Market Case

The purpose of this paper is to examine a financial distress premium in the emerging market. A risk-return trade-off of negative book equity (NBE) and distress firms is empirically analyzed using data from the Stock Exchange of Thailand. This research employs Ohlson’s (1980) bankruptcy model as a measurement of distress risk. The results indicate that distress firms outperform solvent firms in the Thai market and deny distress anomaly often found in the developed market. Fama-Frech (1993) three-factor model and Carhart (1997) four-factor model verify the existence of a distress premium in the Thai capital market. Risk-seeking investors demand greater compensation for bearing risks of distress firms’ going concern. This paper provides fresh evidence that default risk is a significant explanatory factor in pricing stocks in the emerging market. Also, this study sheds light on the role of NBE firms in asset pricing. Most studies eliminate NBE firms from their sample. However, NBE firms yield superior average cross-sectional returns, albeit with higher volatility. Investors are rewarded with distress risks associated with NBE firms. The outperformance of NBE firms is statistically significant when compared to the overall market. The NBE premium disappears when factoring size, value, and momentum in time-series analysis.

Machine Learning for Zombie Hunting. Firms' Failures and Financial Constraints.

In this contribution, we exploit machine learning techniques to predict the risk of failure of firms. Then, we propose an empirical definition of zombies as firms that persist in a status of high risk, beyond the highest decile, after which we observe that the chances to transit to lower risk are minimal. We implement a Bayesian Additive Regression Tree with Missing Incorporated in Attributes (BART-MIA), which is specifically useful in our setting as we provide evidence that patterns of undisclosed accounts correlate with firms failures. After training our algorithm on 304,906 firms active in Italy in the period 2008-2017, we show how it outperforms proxy models like the Z-scores and the Distance-to-Default, traditional econometric methods, and other widely used machine learning techniques. We document that zombies are on average 21% less productive, 76% smaller, and they increased in times of financial crisis. In general, we argue that our application helps in the design of evidence-based policies in the presence of market failures, for example optimal bankruptcy laws. We believe our framework can help to inform the design of support programs for highly distressed firms after the recent pandemic crisis.

Expectativas semi-adaptativas en los modelos macroeconómicos multi-agentes: Una aplicación al análisis de la fragilidad financiera empresarial

Este artículo propone una nueva clase de expectativas para los modelos macroeconómicos multi-agentes. Se modifican las expectativas adaptativas, las cuales constituyen la norma en la modelación macroeconómica multi-agentes, para convertirlas en expectativas semi-adaptativas. Este nuevo mecanismo de expectativas se caracterizada por una volatilidad en relación con la influencia de los sentimientos de optimismo/pesimismo en la cognición de los agentes. Entonces, las expectativas semi-adaptativas son integradas en un modelo macroeconómico multi-agentes para ilustrar su impacto en la fragilidad financiera de las empresas. Entre los resultados obtenidos, se evidencia que las empresas pueden limitar la magnitud de su fragilidad financiera si formulan sus expectativas de ingresos con un grado máximo de volatilidad alrededor de una expectativa inicial que sirve de casi-referencia de largo plazo.

The Influence of Corporate Governance Practices on Financial Distress of Firms Listed at the Nairobi Securities Exchange: Moderating Influence of Financial Leverage

The Influence of Corporate Governance Practices on Financial Distress of Firms Listed at the Nairobi Securities Exchange: Moderating Influence of Financial Leverage

Creditor Rights, Technology Adoption, and Productivity: Plant-Level Evidence

AbstractI use U.S. Census microdata to analyze the effect of stronger creditor rights on productivity. Following the adoption of antirecharacterization laws that give lenders greater access to the collateral of firms in financial distress, treated plants’ total factor productivity increases by 2.6%. This effect is concentrated among plants belonging to financially constrained firms. I explore the underlying mechanism and find that treated plants change the composition of their investments and their workforce toward newer capital and skilled labor. My results suggest that stronger creditor rights relax borrowing constraints and help firms adopt more efficient production technologies.

Firms in financial distress: evidence from inter-firm payment networks with volatility driven by ‘animal spirits’

This paper elaborates an agent-based model of a pure market economy to provide theoretical evidence on how volatility-induced changes in inter-firm payment networks affect the financial distress of firms. This volatility is driven by ‘animal spirits’ in that it arises from the feelings of optimism/pessimism independently of rational decision-making, and influences the liquidity available to each firm through the inter-firm payment network; consequently, some firms may enter financial distress. The model first determines the inter-firm payment network. Then, a mean-reverting square-root process introduces volatility into the inter-firm payment network through firms’ propensity to pay suppliers according to the payments that firms expect to receive from customers. The model is calibrated for compatibility with relevant macro- and microeconomic stylized facts. According to computational experiments, financial distress in the business sector is minimized when feelings of optimism/pessimism generate the lowest volatility in firms’ propensity to pay suppliers. In addition, this volatility must materialize around an intermediate value of firms’ propensity to pay suppliers, and firms must keep this intermediate value over time.

Corporate distress prediction using random forest and tree net for india

The paper deals with the Random Forest, a popular classification machine learning algorithm to predict bankruptcy (distress) for Indian firms. Random Forest orders firms according to their propensity to default or their likelihood to become distressed. This is also useful to explain the association between the tendency of firm failure and its features. The results are analyzed vis-à-vis Tree Net. Both in-sample and out of sample estimations have been performed to compare Random Forest with Tree Net, which is a cutting edge data mining tool known to provide satisfactory estimation results. An exhaustive data set comprising companies from varied sectors have been included in the analysis. It is found that Tree Net procedure provides improved classification and predictive performance vis-à-vis Random Forest methodology consistently that may be utilized further by industry analysts and researchers alike for predictive purposes.

Is Financial Distress Value Relevant? – Implications for Multiple-Based Valuation

AbstractIn relative valuation peer groups of comparable companies are essential to derive the value of the firm. Valuing a target firm that is in financial distress by using a set of healthy peer group firms probably leads to an overvaluation. We examine whether the financial distress risk has an influence on a company’s value and quantify the discount through financial distress. We identify financial distress by Standard and Poor’s long-term issuer ratings and Altman’s z″-score. We then match the identified firms in financial distress with healthy counterparts that are comparable in value relevant characteristics, i. e. profitability, risk, and growth, to estimate the percentage difference in valuation multiples. Using rating information, in every year almost half of the companies are in financial distress whereas by Altman’s z″-score about 20% of the companies in the sample are in financial distress. We find that the discount caused by financial distress makes up about 4–7% of firm value. The discount increases for lower rating classes and lower z″-scores. Besides the degree of financial distress, market downturns as the financial crisis affect the distress discount.

Debt-Equity Swap, Subsidy and Optimal Government Policy

In 2016, the Chinese government proposed a one-time debt-equity swap (DES) program to assist firms in financial distress and to reduce the leverage ratio of non-financial firms. We use a dynamic capital structure model to study the effects of this program on firms with and without subsidy. A DES can increase a firm’s value by lowering its leverage ratio and reducing its expected bankruptcy cost. The subsidized firms initially take more debt and are thus more incentivized to undertake a DES. Notably, a firm’s undertaking a DES can have opposite effects on government value: on the one hand, it can increase government value from the tax-benefit channel, but on the other hand, it can reduce government value by extending the period of tax shields and subsidies granted to a firm. Consequently, we show that the optimal government policy depends on the characteristics of firms and the ultimate goals of government. In particular, the optimal policy for subsidized weak firms is a DES with subsidy removal, which introduces a tradeoff between a DES and a subsidy.

The Life and Death of Zombies – Evidence from Government Subsidies to Firms

We analyze the demographics of zombie firms and durations of zombie spells as well as their determinants, including an application on public subsidies using firm level population panel data from Finland. Firm-level analysis of firm demographics reveals that zombie-firms, as commonly defined in the literature, are often not truly distressed firms but rather companies with temporarily low revenues relative to interest payments. More importantly, we find that roughly a third of these firms are in fact growing companies and two thirds recover from the zombie status to become healthy firms. We also show that the increase of zombie firms over the past 15 years has mainly been driven by cyclical factors, as opposed to a secular trend. In our policy application on government subsidies to firms, estimation results strongly suggest that subsidy-receiving firms are less likely to die, regardless of the type of subsidy. However, with regard to recovery there is heterogeneity in the effects depending on the type of firm and the type of subsidy received. Thus, we do not find a robust positive association of subsidies with zombie recovery.

Takeover Protections and Stock Returns

We argue that takeover protections increase equity risk and stock returns by removing a valuable put option to sell equity when firms approach financial distress. We investigate these claims empirically by looking at the risk and return dynamics of distressed firms around the enactment of anti-takeover laws, both domestically and internationally. In line with our predictions, we find that distressed firms experience a significant increase in returns and market betas after the passage of anti-takeover laws. We find no such effects in the full sample of firms.