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This chapter investigates the effectiveness and the motivation behind the choice of different types of distress resolution strategies in the banking sector. This is a global study that analyzes key financial characteristics of distressed banks that were either acquired by other banks, divested assets, or were subject to government intervention, as well as the change in the financial profile of those distressed institutions from one year pre-deal to three years post-deal. The results show that governments intervene in the (relatively) best performers that only underperform in liquidity ratios, an indication of critical short-term flow problems. Distressed sellers, the underperformers of the three groups, enjoy much improved performance, in particular in cross-border deals. There is some evidence of foreign acquirers “cherry picking” the least distressed banks, though no significant differences in target performance remain post-deal between cross-border and domestic deals. These findings provide some useful guidance for policy makers globally and for future financial crises that impact the banking sector.

This chapter aims at examining financial distress issue by designing a comprehensive model to explain and predict financial distress in Egypt. This comprehensive model incorporates accounting ratios, market-based ratios and macroeconomic ratios. The sample of the existing research includes all the listed firms in two main sectors: basic resources and chemicals. Using logistic regression model, the results showed that adding market ratios and macroeconomic ratios enhances the predictability of the model and accounting information are not sufficient to explain financial distress.

Dynamic predictions of financial distress of the firms have received less attention in finance literature rather than static prediction, specifically in Malaysia. This study, therefore, investigates dynamic symptoms of the financial distress event a few years before it happened to the firms by using neural network method. Cox Proportional Hazard regression models are used to estimate the survival probabilities of Malaysian PN17 and GN3 listed firms. Forecast accuracy is evaluated using receiver operating characteristics curve. From the findings, it shown that the independent directors’ ownership has negative association with the financial distress likelihood. In addition, this study modeled a mix of corporate financial distress predictors for Malaysian firms. The combination of financial and non-financial ratios which pressure-sensitive institutional ownership, independent director ownership, and Earnings Before Interest and Taxes to Total Asset shown a negative relationship with financial distress likelihood specifically one year before the firms being listed in PN 17 and GN 3 status. However, Retained Earnings to Total Asset, Interest Coverage, and Market Value of Debt have positive relationship with firm financial distress likelihood. These research findings also contribute to the policy implications to the Securities Commission and specifically to Bursa Malaysia. Furthermore, one of the initial goals in introducing the PN17 and GN3 status is to alleviate the information asymmetry between distressed firms, the regulators, and investors. Therefore, the regulator would be able to monitor effectively distressed firms, and investors can protect from imprudent investment.

Lai, Li, Conover, and Wu (2010) propose a four-factor financial distress model to explain stock returns in the U.S. and Japanese markets. We examine this model in the stock markets of Australia, and six Asian markets (Hong Kong, Indonesia, Korea, Malaysia, Singapore, and Thailand). We find broad empirical support for the four-factor financial distress risk asset-pricing model in those markets. The four-factor financial distress asset pricing model improves explanatory power beyond the Fama–French (1993) three-factor asset pricing model in six of the seven Asian-Pacific markets (12 of 14 portfolio groupings), while the Carhart (1997) momentum-based asset pricing model only improves explanatory power beyond the Fama–French model in three of the seven markets (4 of 14 portfolio groupings).

We examine explicitly priced financial distress risk in post-1990 equity markets. We add a financial distress risk factor to Fama and French's (1993) three-factor model, based on Griffin and Lemmon's (2002) findings that financial distress is not fully captured by the book-to-market factor. We test three-factor and four-factor capital asset pricing models using both annual buy-and-hold analysis and monthly time series analysis across portfolios adjusted for common book-to-market, size, and financial distress factors. We find empirical support for an Ohlson (1980) O-score-based financial distress risk four-factor asset pricing model in the U.S. and Japanese markets.

Few industries may be better suited to study the effects of financial distress on managerial decision making than the airline industry. Economic recessions, natural catastrophes, and terrorist attacks are just some of the factors that frequently take a particularly heavy toll on the airline industry. Thus, coping with and overcoming financial distress is a critical aspect of airline management.

Starting in March 2020, Indonesia had the COVID-19 pandemic. Furthermore, this situation has decreased the utilization of highways due to complying with the government regulation, including work from home and large-scale social restrictions to reduce the spreading the corona virus. There are three highway companies listed on Indonesia Stock Exchange such as CMNP, META, and JSMR. On the other hand, the research about the financial performance and the financial distress prediction in Highways sector, especially in Indonesia is not available during the COVID-19 pandemic. This research is aimed to evaluate the financial distress by the Zmijewski model with two criterions: bankrupt and non-bankrupt zone and the financial performance by state-owned enterprise (SOE) rating with three criterions: healthy, less healthy, and unhealthy condition. The period of research is Q1 2019 – Q1 2020 as the period before the COVID-19 pandemic and Q2 2020 – Q2 2021 as the period during the COVID-19 pandemic. The study concludes that all highway companies was in non-bankrupt zone by the Zmijewski model for both before and during the COVID-19 pandemic. In addition, based on SOE rating on average for the period before the COVID-19 pandemic, CMNP, META, and JSMR achieved rating consecutively BBB, BBB, and BB. Meanwhile, on average, for the period during the COVID-19 pandemic, CMNP, META, and JSMR achieved ratings consecutively BB, BB, and B.

This study investigates the role of opportunistic behavior in earnings management. Using listed firms in the Indonesia Stock Exchange as the object of study, our examination shows that profitability's opportunistic behavior affects earnings management significantly. The higher the profitability, the higher the earnings management will be. Financial distress also affects the tendency of earnings management. The more severe financial distress, the higher the earnings management is. Another important finding is that bigger firms tend to perform more earnings management activities. This study contributes to earnings management and agency problems research in the context of go public firms in emerging markets since opportunistic earnings management will prevent investments, which will hamper the country's economic growth. This study also contributes to entrepreneurial studies. The manager is considered an entrepreneur CEO, so all the management strategies affect company value, including how the manager communicates the earnings information to accounting information users.

This study aims to study the impact of corporate governance (CG) versus ethical investment on the firm performance. It takes into account the firms of Bangladesh, India, and Pakistan for the purpose of the study. A composite variable of CG index and environmental, social, and governance (ESG) index is used to test the impact on the firm performance. Separate country wise and overall analysis is obtained. Regression analysis is used to obtain the results. Two measures of performance are used, one is return on assets (ROA) and other is Tobin Q. The findings of the study reveal that there is an impact of corporate governance index (CGI) on firm performance (overall and country wise) whereas ethical investment (EI) has an impact on firm performance when tested overall and no impact when checked for country wise results. The results further show that on country level, increase in CG measures may lead to positive results, but at the macro level, it may lower the performance. On the other hand, at the micro level, ethical finance may not show its impact; however, at the macro level, it has an impact. The study has implications for the investors and policymakers.

The overall objective of this research was to elucidate the ecosystem of women’s health social enterprises (WHSEs) based in the United States. The Aim I was to conduct a secondary data analysis of a random national sample of non-profit WHSEs based in the United States regarding their characteristics and areas of intervention. Aim II was to conduct a qualitative assessment of a sample of WHSEs based in the United States regarding their perspectives on the ecosystem of WHSEs. Aim I utilized the GuideStar database and assessed enterprise size, geographic location, financial distress, health intervention area, and health activity category using descriptive statistics, statistical tests, and multivariable regression analysis via SPSS. Aim II utilized in-depth interviewing and grounded theory analysis via MAXQDA 2018 to identify novel themes and core categories while using an established framework for mapping social enterprise ecosystems as a scaffold.

Aim I findings suggest that WHSE activity is more predominant in the south region of the United States but not geographically concentrated around cities previously identified as social enterprise hubs. WHSEs take a comprehensive approach to women’s health, often simultaneously focusing on multiple areas of health interventions. Although most WHSEs demonstrate a risk for financial distress, very few exhibited severe risk. Risk for financial distress was not significantly associated with any of the measured enterprise characteristics. Aim II generated four core categories of findings that describe the ecosystem of WHSE: (1) comprehensive, community-based, and culturally adaptive care; (2) interdependent innovation in systems, finances, and communication; (3) interdisciplinary, cross-enterprise collaboration; and (4) women’s health as the foundation for family and population health. These findings are consistent with the three-failures theory for non-profit organizations, particularly that WHSEs address government failure by focusing on the unmet women’s health needs of the underserved populations (in contrast to the supply of services supported by the median voter) and address the market failure of over exclusion through strategies such as cross-subsidization and price discrimination. While WHSEs operate with levels of financial risk and are subject to the voluntary sector failure of philanthropic insufficiency, the data also show that they act to remediate other threats of voluntary failure.

Aim I findings highlight the importance of understanding financial performance of WHSEs. Also, lack of significant associations between our assessed enterprise characteristics and their financial risk suggests need for additional research to identify factors that influence financial performance of WHSE. Aim II findings show that WHSEs are currently engaged in complex care coordination and comprehensive biopsychosocial care for women and their families, suggesting that these enterprises may serve as a model for improving women’s health and health care. The community-oriented and interdisciplinary nature of WHSE as highlighted by our study may also serve as a unique approach for research and education purposes. Additional research on the ecosystem of WHSE is needed in order to better inform generalizability of our findings and to elucidate how WHSE interventions may be integrated into policies and practices to improve women’s health.