Bankruptcy Risk Research Articles

Firm biodiversity risk, climate vulnerabilities, and bankruptcy risk

This study examines the relationship between firms’ biodiversity risk, climate susceptibility, and bankruptcy risk. The findings indicate that firm exposure to biodiversity risk increases the likelihood of financial distress. Furthermore, we document that firms’ susceptibility to climate risk increases the likelihood of bankruptcy risk. We also demonstrate that financial constraints, growth opportunities, and membership in carbon-intensive industries can worsen or alleviate the bankruptcy implications of climate-related risk. Firms’ continent of operation is also an important consideration. The findings imply that severe climate-related vulnerabilities and firm biodiversity risk have profound consequences for corporate outcomes. This study sheds more light on how corporate financial outlook is impacted by ecological degradation and climate-related vulnerabilities.

The Signalling Effect of Corporate Social Responsibility Reporting: Evidence From Short Selling

Using exogenous regulatory changes that have gradually removed short‐sale restrictions in China's stock exchanges, we examine how such deregulation influences firms’ corporate social responsibility (CSR) reporting. Our findings indicate a significant improvement in the calibre of CSR reporting among firms designated as qualified for short selling in a deregulation pilot program (pilot firms) compared to non‐pilot firms. Moreover, our empirical evidence shows that the improvement in CSR reporting practices is greater for pilot firms experiencing stronger downward price pressure, negative earning news, higher bankruptcy risk, greater ownership concentration, and those classified as state‐owned enterprises. These results demonstrate that firms susceptible to price declines from short selling utilize CSR reporting as a visible and credible signal to safeguard and enhance corporate reputation, garnering increased stakeholder support.

Studying the impact of profitability, bankruptcy risk, and pandemic on narrative tone in annual reports in an emerging market in the East

The purpose of this paper is to investigate whether the narrative tone of annual reports is influenced by profitability, bankruptcy risk, and pandemic in the context of Vietnam. The study applies the necessary regression analysis steps such as ordinary least squares (OLS), random effects model (REM), fixed effects model (FEM), and feasible generalized least squares (FGLS). Bootstrapping and System Generalized Method of Moments (SGMM) methods are used to test the robustness and address endogeneity and dynamic relationships in the data. The findings show that net tones increase while negative tones decrease in companies with high profitability. Companies at risk of bankruptcy use more negative tone and less net tone than companies that are not at risk of bankruptcy. The study also affirms that bankruptcy risk moderates the relationship between profitability and negative tone. During the COVID-19 pandemic, companies used more positive and negative tones than they did before the pandemic. After the COVID-19 situation stabilized, positive tones were used more, and negative tones were used less than during the outbreak. The research results also recognized that companies do not intend to hide information through impression management when faced with difficult economic conditions. This study has practical implications for investors and information users when considering management disclosures through the narrative tone of annual reports. To our knowledge, this is the first study (1) in an emerging market in the East—where there are fundamental cultural and linguistic differences compared to Western countries, (2) to build a list of Vietnamese words and phrases expressing emotional nuances used in finance and accounting, and (3) refers to the narrative tone of annual reports across different groups of companies.

Personal Bankruptcy Prediction Using Logistic Regression Model

According to the Insolvency Department of Malaysia, as of December 2023, 233,483 Malaysians are currently involved in bankruptcy cases due to their defaults on hire purchase loans, credit card loans, personal loans, housing loans, and business loans. This is indeed a critical issue because the growing number of personal bankruptcy cases will hurt the Malaysian economy as well as society. From an individual's economic perspective, bankruptcy minimizes their chances of getting a job. Apart from that, their accounts will be frozen, they will lose control of their properties and assets, and they will not be allowed to start any business or be a part of any company's Board of Directors. Bankrupts also will be rejected from any loan application. This paper examines this problem by developing a personal bankruptcy prediction model using the logistic regression technique. This paper defines "bankrupt" as terminated members who failed to settle their loans. The sample comprised 24,546 cases with 17% settled cases and 83% terminated cases. The data included a dependent variable, i.e., bankruptcy status (Y=1(bankrupt), Y=0(non-bankrupt)), and 12 predictors. Upon completion, this paper succeeds in coming out with a reliable personal bankruptcy prediction model and significant variables of personal bankruptcy. The findings of this paper are very beneficial and significant to creditors, banks, the Malaysia Department of Insolvency, potential borrowers, members of AKPK, and society in general in raising awareness of personal bankruptcy risks and such information may help them to take preventive measures in minimizing the number of personal bankruptcy cases.

Cognitive Model Based on The Dl Random Forest Method for Profi t Forecasting and Fuzzy Algorithm for Assessing Company Sustainability in Conditions of Uncertainty

The article is devoted to the problem of supporting management decision-making on choosing a strategic partner whose activities would be effective and sustainable. During the study, based on the results of the work of enterprises in the domestic confectionery industry, a dataset was generated, which was subsequently used for the deep learning model DL-model "Random Forest" in order to calculate the predicted values of the net profi t of enterprises in the industry. Assessing companies for the purpose of selecting a strategic partner, using models such as: deep learning model "Random Forest" (DL Random Forest), VaR, Z-Altman, Hurwitz matrix, Fuzzy algorithm in modern conditions has great practical significance. The relevance of the study lies in the fact that in conditions of increasing market uncertainty, approaches to ensuring the sustainable development of an organization based on AI systems are increasingly being used. The scientific novelty lies in the fact that the study used a set of models that made it possible to assess the actual sustainability of companies and, based on the calculated forecast values of net profi t, as well as business efficiency indicators ROE and ROS, to make a decision on the choice of potential business partners. During the study the following were developed: a VaR model, which made it possible to obtain an assessment of fi nancial risk, a Z-Altman model for assessing the risk of bankruptcy of an enterprise. In addition, based on the calculated parameters of the regression equation, a Hurwitz matrix was formed, which made it possible to draw a conclusion about the sustainability of each enterprise as a system. The use of the Fuzzy algorithm made it possible to obtain a decision on choosing a partner enterprise.

BANKRUPTCY RISK AND SHARE PRICES: A CASE STUDY ON ENERGY COMPANIES IN KUWAIT

<p>This study aims to investigate how investors' perceptions of risk impact the stock prices of energy companies listed on the Kuwait Stock Exchange (KSE) from 2016 to 2023. Investors' belief in the future financial stability of the companies they invest in influences their risk perception. Therefore, if investors hold pessimistic views, they will decrease their investment in these companies, causing share prices to drop, and vice versa. The Zmijewski x-score model is utilized in this study as a measure of risk to assess the financial stability of Kuwaiti energy companies and its impact on their stock prices. The study employs ordinary least square regression (OLS) to analyze the correlation between the x-score and share prices. Results from this research indicated that there was no statistically significant relation between Zmijewski's x-score and stock price and that investors look at other factors when choosing their investments. This is evident because the company with the greatest bankruptcy risk in the following two years also had the second-highest share price.</p><p> </p><p><strong>JEL: </strong>G1, G4, M21</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0699/a.php" alt="Hit counter" /></p>

How artificial intelligence incidents affect banks and financial services firms? A study of five firms

We investigate the impact of AI incidents on banking and the financial industries. By analyzing five U.S. banks and financial services firms, we find that the average short-term Cumulative Abnormal Returns (CARs) loss of AI incidents is -21.04 % and the negative impact can spread out to the financial industry with a three-day loss of -0.13 %. Compared to firms without AI incidents, banks and financial services firms with AI incidents have higher bankruptcy risk and lower operational cash flows. To our knowledge, this is the first study analyzing the AI incident impact on the performance of banks and financial services firms.

Balancing customer and employee orientations: impact on financial distress and firm value

PurposeThis paper investigates the effect of balanced orientation (BO) toward customers and employees on financial distress and firm valuation. Rooted in stakeholder theory and the attention-based theory of firms, the study aims to understand how a balanced synthesis of employee orientation (EO) and customer orientation (CO) influences the long-term market performance and survivability of firms, particularly in the context of emerging markets.Design/methodology/approachA panel dataset of the top 500 firms listed on the National Stock Exchange of India was analyzed using fixed-effect panel models with robust estimators. The Heckman procedure was employed to address selection bias. Financial data were sourced from the CMIE Prowess database and balanced orientation data were extracted from the annual letters to shareholders.FindingsThe study finds that a balanced approach to EO and CO significantly lowers bankruptcy risk and increases firm valuation. Contrary to the traditional dichotomy of EO and CO, BO emerges as a key driver in reducing financial distress and enhancing long-term market performance. Firms embracing multiple strategic orientations outperform those solely focused on customer orientation.Practical implicationsFor emerging market firms facing resource constraints, developing BO is challenging but crucial. This study suggests adopting a sequential or alternating approach to developing EO and CO, which can alleviate bankruptcy risk and improve market valuation even in competitive environments.Originality/valueThis paper contributes to the existing literature by empirically demonstrating the significance of BO in emerging markets. It offers a novel perspective on strategic management, highlighting the importance of balancing customer and employee needs, especially in resource-constrained environments.

Averting bankruptcies among the software development houses in Lahore, Pakistan

Pakistan, as the 4th largest technological market globally, is home to over 2000 registered software development houses. However, the industry is not without its challenges, and hundreds of these houses close yearly owing to insolvency, and even more crop up. To understand the financial difficulties these houses face, a sample of 20 small-size and 20 medium-sized software houses has been taken from the hundreds in Arfa Software Technology Park in Lahore, Pakistan. Financial Blueprints of 2 projects per house per year for five years, i.e., 2018-2022, have been obtained. These case studies have been contrasted with those written and published in the developed world. It is found that there is a dearth of costing and pricing mechanisms in software-related projects. This results in under/over-costing the cost and pricing of the projects, which is a leading cause of financial bankruptcy for most software houses. It is proposed that activity-based costing, as the costing technique, and competitive pricing, as the pricing mechanism, may be adopted as the best practice in the software development industry. These solutions, if implemented, could significantly reduce the risk of financial bankruptcy among software houses, potentially transforming the industry's financial landscape.

머신러닝을 활용한 해운기업 파산 예측에 관한 연구

Purpose: This study aims to enhance bankruptcy prediction accuracy in the shipping industry by using advanced machine learning models, specifically XGBoost and LSTM, comparing the predictive factors between large enterprises and SMEs. Research design, data, and methodology: Utilizing financial data from 2001 to 2023 for the Korean shipping industry, the study analyzes key financial ratios and macroeconomic indicators. XGBoost and LSTM models are employed to develop customized bankruptcy prediction models for large enterprises and SMEs. Results: The XGBoost model outperforms others, effectively handling complex financial and macroeconomic variables. Large enterprises are more influenced by internal financial factors, while SMEs are sensitive to external economic conditions like a shipping index. Conclusions: The study highlights the need for tailored prediction models to improve bankruptcy risk management in the shipping industry, especially focusing on the size of the company. Future research should explore model applicability across different regions, and should consider integrating real-time data for enhanced accuracy.

Exploring the predictive ability of cost asymmetry on bankruptcy

This study explores whether cost asymmetry affects bankruptcy predictions. Cost asymmetry, driven by adjustment costs, empire-building behaviours, and managerial optimism, can reduce resource availability, liquidity, and earnings predictability, thereby increasing the risk of liquidity issues and potential bankruptcy. High cost asymmetry implies additional financing needs due to idle resources, which are less likely (or more costly) to be covered by retained earnings or capital market funds. Furthermore, elevated cost stickiness driven by intense empire-building consumes valuable resources and signals weaker governance and auditing efficiency, while increased managerial optimism heightens the risk to future operating performance. Using a sample of US publicly listed firms over the period 1990–2020, we provide empirical evidence that the level of cost asymmetry is incrementally useful for bankruptcy prediction. The fundamental factors of cost asymmetry, including adjustment costs and managerial incentives, reinforce its predictive ability to corporate bankruptcy. Additional robustness tests confirm our empirical results across (i) fluctuations in sales revenue, (ii) the effects of financial constraints on cost asymmetry, (iii) managerial and firm-specific characteristics, and (iv) propensity score-based partitioned samples.

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.

Using the Altman’s Z-score formula to assess the bankruptcy risk of state - owned construction enterprises in Vietnam

In the current economic situation affected by epidemics, wars, recessions, etc., construction enterprises in Vietnam in general and state – owned construction enterprises in particular face many difficulties to maintain performance efficiency. On the other hand, the policies of restructuring and equitization of state-owned enterprises also have a strong impact on the financial management of construction enterprises in Vietnam. This study uses data from financial statements of 22 state-owned construction enterprises in Hanoi from 2017 to 2021 to calculate the Altman Z-score formula. The Altman Z-score is a linear combination of four or five common business ratios, weighted by coefficients. Z-scores are used to predict corporate defaults and an easy-to-calculate control measure for the financial distress status of enterprises with high reliability. The research results show that a few enterprises are in the "safe" zone and "grey" one while most are in "distress" zone. The cause of inefficient business performance is that the revenue and profit ratio to total assets of state-owned construction enterprises to decrease sharply, the ratio of working capital to total assets is low. Moreover, the ratio of equity to total debt is the most important reason for the low Z-score, which means predicting a high risk of bankruptcy in state-owned construction enterprises. The evidence base from the research results is used to propose 5 solutions to reduce the risk of bankruptcy and improve the efficiency of financial management of enterprises.

Bankruptcy Analysis Using the Altman Z-Score Model (Study on Retail Companies Listed on the Indonesia Stock Exchange)

Bankruptcy is a legal or economic event that often results in the complete cessation of all business activities and operations. This bankruptcy usually starts with liquidity problems, but is closely linked to situations where a company's debt exceeds its total assets. The objective of this study is to evaluate the effectiveness of the Altman Z-score model as a tool to predict bankruptcy. The main focus of this study is retail companies listed on the Indonesia Stock Exchange in 2021-2023. 31 companies participated in the study, supported by 93 financial statements. The researchers used purposive sampling method for the sampling process. The study uses the modified Altman Z-score method (1995) to analyze the obtained data. The analysis revealed that 17 companies (54.8%) could be classified as healthy. 3 companies (9.7%) were classified as weak, and 11 companies (36.7%) were expected to go bankrupt. These results provide insight into the financial health of the retail companies surveyed and highlight the importance of using predictive models to determine bankruptcy risk.

Asset Allocation and Bank Resilience: China Insights

This research is based on the panel data from the annual reports of Chinese commercial banks for the years 2010-2020. A panel data model is constructed to empirically investigate the influence of commercial banks' asset allocation on financial stability. The study innovatively explores the differences between large-scale banks and general-scale banks in this context, as well as the interaction effects between large-scale banks and their methods of capital application. The findings indicate that: firstly, the higher the proportion of corporate loans in commercial banks, the stronger the stability. With the support of national policies, the operating environment for emerging industries has improved, leading to a reduction in bankruptcy risks and further decreasing the credit risk of related banks, which contributes to the enhancement of banks' financial stability. Additionally, compared to securities investments affected by market fluctuations, corporate loans are less susceptible to significant capital changes, and their risks are primarily determined by the operating conditions of the related enterprises, making them relatively controllable. Secondly, the higher the proportion of personal loans in commercial banks, the stronger the stability. The largest part of personal loans consists of housing mortgage loans, which have a low non-performing loan rate and high quality in China. Therefore, an increase in personal loans helps to enhance the financial stability of banks. Thirdly, large-scale banks exhibit lower financial stability. Due to the redundancy in management personnel in large-scale banks, management costs, adjustment costs, and transaction costs are relatively high. Furthermore, it is worth noting that because large-scale banks hold a significant amount of surplus funds, they are prone to excessive securities investments. Fluctuations in the securities market increase external risks and decrease financial stability.

The Effect of Income Diversification on Default Risk in Conventional Banking Companies in Indonesia

This study aims to identify variables that affect the risk of bankruptcy in banks listed on the Indonesia Stock Exchange. The research method includes collecting data from 36 listed banks on the Indonesia Stock Exchange over a 5-year period (2018-2022) and using the panel data regression analysis method. The addition of novelty variables in this study, namely bank size, net interest margin, and deposit to total assets that have a negative effect on bankruptcy risk, while the diversification index and gross domestic product variables have no effect on bankruptcy risk. the implication for bank managers is as a reference and consideration in implementing income diversification strategies. this research also provides information that is expected to be used as reference material and expand information and insight in developing research in the banking sector.

Unravelling the CSR-financial distress link: The mediating role of capital structure in Jordan’s financial sector

This study investigates the relationship between corporate social responsibility (CSR), capital structure, and financial distress in Jordan’s financial services sector. It tests the mediating effect of capital structure on the CSR-distress linkage. Utilizing a panel data regression approach, the analysis examines a sample of 35 Jordanian banks and insurance firms from 2015–2020. CSR is evaluated through content analysis of sustainability disclosures. Financial distress is measured using Altman’s Z-score model. The findings reveal an insignificant association between aggregated CSR engagement and bankruptcy risk. However, capital structure significantly mediates the impact of CSR on financial distress. Specifically, enhanced CSR enables higher leverage capacity, subsequently escalating distress risk. The results advance academic literature on the nuanced pathways linking CSR to financial vulnerability. For practitioners, optimally balancing CSR and financial sustainability is recommended to strengthen resilience. This study provides novel empirical evidence on the contingent nature of CSR financial impacts within Jordan’s understudied financial services sector. The conclusions offer timely insights to inform policies aimed at achieving sustainable and stable financial sector development.

Virtual Cloud Computing Lab: A Way for MSMEs in Increasing Firm Performance

This research aims to develop a business diversification strategy through a virtual cloud computing lab to reduce the risk of bankruptcy of SMEs. The method used in this research is mixed research in collaboration with quantitative and qualitative approaches to develop products and find out how much people accept the products developed. To see the acceptance of the developed website, the researcher used the technology acceptance model (TAM model). After forming a good model, a partial test (t-test) is carried out for decision-making. Based on the findings of data analysis, it was found that existing micro, small, and medium enterprises (MSMEs) lacked information and media to assist them in diversifying their business. Based on these needs, a website for MSMEs has been developed that has been validated and obtained results in the "good" category. Researchers conducted tests for the acceptance of websites developed with structural perceived usefulness, perceived ease of use, intention to use, and actual use with results showing the "good" category. From these results, it can be concluded that the website developed has a good reception in supporting MSMEs to reduce the risk of bankruptcy.

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.

The Probability of Hospital Bankruptcy: A Stochastic Approach

Healthcare leaders are faced with many financial challenges in the contemporary environment, leading to financial distress and notable instances of bankruptcies in recent years. What is not well understood are the specific conditions that may lead to organizational economic failure. Though there are various models that predict financial distress, existing regression methods may be inadequate, especially when the finance variables follow a nonnormal frequency pattern. Furthermore, the regression approach encounters difficulties due to multicollinearity. Therefore, an alternate stochastic approach for predicting the probability of hospital bankruptcy is needed. The new method we propose involves several key steps to better assess financial health in hospitals. First, we compute and interpret the relationship between the hospital’s revenues and expenses for bivariate lognormal data. Next, we estimate the risk of bankruptcy due to the mismatch between revenues and expenses. We also determine the likelihood of a hospital’s expenses exceeding the state’s median expenses level. Lastly, we evaluate the hospital’s financial memory level to understand its level of financial stability. We believe that our novel approach to anticipating hospital bankruptcy may be useful for both hospital leaders and policymakers in making informed decisions and proactively managing risks to ensure the sustainability and stability of their institutions.