Indicators Of Financial Distress Research Articles

Evaluasi Metode Deteksi Financial Distress pada Perusahaan LQ45

Objectives: This study aims to evaluate the accuracy of three financial distress prediction models (Springate, Zmijewski, Grover) on LQ45 companies listed on the Indonesia Stock Exchange (IDX) during the 2020–2023 period. The focus is to assess the models' effectiveness in addressing contemporary financial challenges such as debt restructuring and financial distress indicators amidst dynamic global economic conditions.Design/method/approach: This quantitative research utilizes secondary data from 37 LQ45 companies selected through purposive sampling, resulting in 148 financial statements for the 2020–2023 observation period. The analysis employs financial ratios and prediction scores from each model using Microsoft Excel for computation.Results/findings: The Grover model demonstrates the highest accuracy rate (95%) with a type error of 6%, outperforming Zmijewski (accuracy 89%, type error 11%) and Springate (accuracy 59%, type error 41%). This suggests that the Grover model is superior in detecting financial distress.Theoretical contribution: This study identifies the Grover model as the most accurate method for predicting financial distress in Indonesia. It contributes to the development of more relevant financial distress prediction models for the capital market.Practical contribution: The findings provide critical insights for shareholders and stakeholders to detect financial risks in LQ45 companies.Limitations: The study is limited by its reliance on classical prediction models, focus on LQ45 companies, and analysis restricted to the 2020–2023 period. Modern approaches like machine learning, which could enhance prediction accuracy, are not explored.Type: empirical, review (conceptual and viewpoints).

Financial Integration of the European Union Financial Markets. A PCA Approach

Abstract European stock markets are a complex and dynamic terrain, requiring sophisticated methods of analysis to understand their degree of interconnectedness and integration. The paper investigates the financial integration of European Union (EU) financial markets using a dynamic Principal Component Analysis (PCA) approach. By calculating the Financial Integration Index (FII) from daily stock market index returns and volatilities, we provide a comprehensive analysis of the integration trends over the past two decades across three distinct groups: the EU-27, the Eurozone, and the Non-Eurozone emerging economies. Our findings indicate an increased evolution of financial integration for all groups, with the Eurozone and Non-Eurozone groups exhibiting higher levels of financial integration compared to the broader EU-27. The results highlight that major crises significantly influence the dynamics of financial integration. Finally, the study underscores the higher sensitivity of volatility-based FII compared to return-based FII, making it a more effective measure for real-time monitoring of financial market integration. Our findings have several important implications for policymakers, which should monitor volatility closely as an early warning indicator of financial distress, and for portfolio managers, which have to consider both returns and volatility when assessing international portfolio diversification strategies.

Cash flow analysis: Red flag indicators of financial distress in Malaysian companies

The unprecedented coronavirus disease (COVID-19) pandemic devastated the world from early 2020 caused an economic decline impacting numerous companies worldwide, including Malaysia. Thus, companies should foresee financial distress to better prepare and establish mitigating measures for future challenges. One method to examine the financial situation of a company is by investigating the cash flow information. The study aims to investigate the relationship between cash flow patterns and financial distress. Data were gathered from 24 public listed companies in Bursa Malaysia from 2009 to 2019 and the study applied the binary logistic regression and chi-square analysis test for independence. The results indicated that companies with a negative cash flow from operating activities would more likely experience financial distress due to the inability to generate sufficient cash to cover operations, future investment, and long-term debt. Additionally, distressed companies tend to discard fixed assets/investments, or drawdown borrowings to relieve the cash deficit.

AN ACCOUNTING INSIGHT ON ROMANIAN SOCIAL ECONOMY ENTITIES SUSTAINABILITY

Social economy has a core importance in creating jobs and delivering goods and services for the disadvantages groups members. Even if no searching for profit, the Social Economy Entities (SEEs) have to maintain a financial stability allowing the continuously fulfilling their social mission. Sustainability is crucial in delivering to the disadvantaged. Romania financed during the years 2020 and 2021 around 2800 SEEs, but not very much is widely presented about their activity. Financial information is still publicly available, so the SEEs economic behaviour may be monitor and even predicted as a trend, using forensic accounting techniques: reviewing the existing public information on public and private sites, analysing the summary of financial statements, as it may be found on the Ministry of Finance online database, and interpreting the evidence so gathered. Within this paper, the sustainability of a sample consisting on 172 SEEs incorporated in Maramureș County, Romania is analysed using publicly available data, including by the use of the indicator of financial distress (Z- Altman score). It reveals that in actual conditions, about 80% of the sample population are in high risk of bankruptcy, only 6% of them being financial sustainable. This research may be useful for national deciders and local social economy facilitators in focusing the aid and support actions, and also for the SEEs initiators, in preparing the best ways of managing the assumed risks.

Understanding Vulnerability to the Poverty Premium: An Analysis of Factors Influencing Use of High-Cost Credit Among Low-Income Individuals

Introduction & BackgroundAccess to affordable credit is essential for individuals living on low incomes to participate in society fully. However, due to their limited credit history and volatile incomes, many cannot access mainstream sources of credit, such as credit cards and personal loans. As a result, they often must rely on more expensive sources of credit such as payday loans, doorstep loans or rent-to-own loans. This leads to them paying more for credit, also referred to as the poverty premium. Incurring the poverty premium exacerbates the financial challenges faced by already vulnerable individuals and can lead to a cycle of financial distress. Identifying the behaviours and factors that lead to a need for high-cost credit can help in identifying individuals who are most vulnerable to incurring the poverty premium. Objectives & ApproachTo achieve this, we rely on anonymized Open Banking transaction data from 100,000 individuals provided by a UK-based social lender.Given the latent nature of vulnerability, we identify indicators of vulnerability to poverty premium which include frequency of overdraft use, previous debt problems, low financial resilience, and indebtedness. We use a copula-based approach to create an index of vulnerability to poverty premium. This is based on weighting the individual indicators using their Spearman rank correlation coefficient. We use a fixed effects model to identify the factors that contribute to this vulnerability, where the index of vulnerability is the dependent variable. Relevance to Digital FootprintsWe use a rich and granular dataset on individual financial transactions to address a key social issue. The findings from this study can inform policy and industry efforts to promote greater credit affordability for these vulnerable individuals. This is particularly important due to the renewed concerns regarding the increased use of high-cost credit by individuals living on low incomes due to COVID-19 and the increased cost of living. ResultsOur findings show that variables related to the financial profile of an individual are important driving factors of the vulnerability to poverty premium. These include the number of salary sources, frequency of salary receipt, benefit receipt and savings frequency. Other variables related to spending behaviour such as gambling, volatility in fixed expenses and high transaction counts all have positive relationships with this vulnerability. Conclusions & ImplicationsThis study is a first step towards examining the determinants of vulnerability to poverty premium by analyzing an Open Banking transaction data set. The innovative feature of this work is the creation of an index of vulnerability to poverty premiums based on various indicators of financial distress and high-cost credit use. Our findings on the relationships between the individual's financial profile and the vulnerability to poverty premium suggest that policymakers should consider targeting interventions for individuals with specific profiles to alleviate the need for high-cost credit. For example, programs to promote regular savings or financial education for gamblers and people with volatile spending behaviour can help assist these individuals manage their finances better.

Influence of Capital Adequacy Ratio, Credit Risk, Market Risk and Financial Distress Indicator towards Stock Return

The research objective is to the influence of capital adequacy ratios, credit risk, market risk, and financial distress indicators on stock returns. The sample uses a saturated sampling method. The data used is secondary data collected using the documentation method. Independent variables include indicators of capital adequacy ratio, credit risk, market risk, and financial distress with the dependent variable being stock returns. The analysis uses multiple regression. This research found that: capital adequacy ratio, credit risk, market risk, and financial distress have no effect on stock returns.

Perbedaan Nilai Pasar Dan Kinerja Keuangan Perbankan Pada Saat Sebelum Dan Sesudah Covid 19

This research aims to analyze differences in market value and financial performance before and after covid 19, as well as their impact on stock returns. The research applies the associative method. the sample was determined using a saturated sampling method. research data uses secondary data in the form of financial reports. The data is presented in the form of financial reports and annual reports for banks that use basel III and IV between 2015 and 2022, sourced from the idx.co.id website. as independent variables, financial difficulties, credit risk, market risk, and capital adequacy ratio serve as proxies for financial performance. stock returns function as the dependent variable. multiple linear regression and paired sample t test were used to test the data. this research found that it was proven that there were differences in capital adequacy ratios and financial distress before and after covid 19, but the market performance of credit risk, market risk and stock returns was proven to be no different before and after covid 19. the research results also found: (1) the capital adequacy ratio has an impact on stock returns; (2) credit risk has no impact on stock returns; (3) market risk has an impact on stock returns; and (4) financial distress indicators have an impact on stock returns at banks included in book 3 banks and book 4 banks that implement basel in indonesia. Keywords: Capital Adequacy Ratio, Credit Risk, Market Risk, Financial Distress, Stock Returns, Basel Jel Classification: G21

Enhancing Financial Stability in Ghana's Universal Banking Sector: A Governance Perspective and Practical Insights

Purpose: This study investigates the impact of corporate governance structures on financial distress within the universal banking sector in Ghana.
 Methodology: The research systematically gathers data from 19 banks spanning 10-year period. Financial distress indicators were extracted from financial statements, while corporate governance measures were obtained from annual reports. The data was analysed through regression analysis and counterfactual analysis through an endogenous switching regression model.
 Findings: Key findings reveal that profitability, liquidity, and effective risk management are critical factors influencing financial distress in both strong and weak governance environments. The results also showed that improving the strength of Corporate Governance has a positive influence on Altman Z Scores, contributing to a potential reduction in financial distress.
 Unique contributor to theory, policy and practice: The study provides valuable insights for policymakers, regulators, and banking institutions in Ghana, emphasizing the need to prioritize and strengthen governance practices for a resilient banking sector. Limitations include data unavailability, reliance on unbalanced panel data, and a narrow focus on board characteristics.

Bankruptcy Prediction using Diophantine Neutrosophic Number for Enterprise Resource Planning on Value of Accounting Information

Enterprise Resource Planning (ERP) is paramount in modern business, integrating many fundamental processes such as human resources, economics, customer relationship management, and supply chain management into a comprehensive infrastructure. Leveraging the wide-ranging data apprehended by ERP techniques, an organization could improve its financial analysis abilities, involving bankruptcy prediction. By using analytics methods like predictive modeling and machine learning, the ERP system could examine market trends, historical financial information, key performance indicators, and other related factors to evaluate the financial stability and health of the company. This prediction insight empowers businesses to vigorously detect advanced indicators of financial distress, alleviate risks, and make informed strategic decisions to avoid bankruptcy. Integrating bankruptcy prediction techniques within the ERP system allows organizations to reinforce contingency strategies, financial planning, and risk management, protecting long-term competitiveness and sustainability in a dynamic business environment. This study introduces a Bankruptcy Prediction using the Diophantine Neutrosophic Number for Enterprise Resource Planning (BPDNN-ERP) technique on the value of accounting information. The BPDNN-ERP technique begins with a harmony search algorithm (HSA) for electing feature subsets. In addition, the BPDNN-ERP technique applies the DNN model for the prediction of bankruptcies. To increase the performance of the DNN model, the manta ray foraging optimization (MRFO) model can be used. The experimental study demonstrated the enhanced performance of the BPDNN-ERP algorithm equated to existing forecasting methods

재무건전성지수의 기업부실예측 검증 : 상장폐지기업의 Z점수를 중심으로

재무건전성지수의 기업부실예측 검증 : 상장폐지기업의 Z점수를 중심으로

Accounting‐based variables as an early warning indicator of financial distress in crisis and non‐crisis periods

AbstractFinancial integration in the Association of Southeast Asian Nations (ASEAN) region is a key focus of the ASEAN Economic Community. Whereas many studies focus on modelling corporate default, this paper identifies early warning indicators of financial distress before a default, using multiple discriminant analysis (MDA) models with a sample of listed and delisted companies in the ASEAN region. The analysis examines 720 companies in 10 different industries across six ASEAN countries from 1997 to 2016. The study constructs individual models for each country as well as an overall model for the entire region, using both in‐sample and out‐of‐sample approaches. This overall model could be useful for an integrated banking system. To ensure robustness, the study also separately examines the predictive performance of the MDA models across different economic crises: the Asian financial crisis (AFC) from 1997 to 2000, the global financial crisis (GFC) from 2007 to 2009 and their pre‐ and post‐crisis periods. We find that profitability ratios are the best indicators of financial distress in the ASEAN region, followed by liquidity and leverage ratios. In addition, our findings reveal common indicators that can be used to predict financial distress across ASEAN countries. The single model performs reasonably well in predicting financial distress 1 year ahead. In addition, the model is extended to incorporate a market‐based indicator into the MDA models, the distance to default. However, the inclusion of this indicator does not significantly improve the accuracy of the models in predicting financial distress at listed firms in the ASEAN region.

Age and Sex Differences in the Associations Among Socioeconomic Status, Affective Reactivity to Daily Stressors, and Physical Health in the MIDUS Study.

Low socioeconomic status (SES) is robustly associated with increased risks of morbidity and mortality. Affective reactivity to daily stressors has been proposed to be a mediator for this association. However, few longitudinal studies have empirically tested the indirect effect of SES on health through affective reactivity to daily stressors. This study aimed to test the indirect effect of SES on physical health via affective reactivity to daily stressors over a 10-year period and to explore age and sex differences in such indirect effect. Data were drawn from a subsample of 1,522 middle-aged and older adults (34-83 years of age, 57.2% female, 83.5% White) from the Midlife in the United States study. SES (i.e., education, household income, indicators of financial distress) was assessed in 2004-2006. Affective reactivity to daily stressors was computed using data collected during the 8-day daily stress assessment in 2004-2009. Self-reported physical health conditions were assessed in 2004-2006 and 2013-2014. There was a significant indirect effect of lower SES on more physical health conditions via elevated negative affective reactivity to daily stressors among women but not men. The indirect effect of SES on physical health conditions via negative affective reactivity to daily stressors was consistent across the middle and older adulthood. Our findings suggest that negative affective reactivity to daily stressors might be a key intermediate process contributing to persistent SES disparities in physical health, particularly among women.

Signs of Stress: Identifying Indicators of Financial Distress in the Chinese Securities Market

This conceptual paper aims to explore the signs of stress and identify key indicators of financial distress within the Chinese securities market. With China's rapidly growing economy and evolving financial landscape, understanding the signals of potential market downturns and identifying early warning signs of financial distress is crucial for investors, policymakers, and market regulators. By analyzing a comprehensive range of financial and macroeconomic variables, this study aims to uncover robust indicators of financial distress specific to the Chinese securities market context. The findings of this research can inform the development of proactive risk management strategies and contribute to the overall stability and resilience of the Chinese securities market.

Predictive Modelling for Financial Distress amongst Manufacturing Companies in India

This study develops a model predicting financial distress amongst manufacturing companies in India using logistic regression. 18 financial ratios of 574 companies from 34 industries in manufacturing sector were examined for the period 2005 – 2019 to develop and validate the model. The study can be considered as one of the very few which has examined the financial distress indicators of manufacturing sector in India. EBIT margin, Interest coverage, quick ratio, Cash flow from operations to Sales, Debtors Turnover, Working Capital to Total Assets, Fixed Assets to Total Assets are important determinants of financial health of a business. This study provides useful insights to business managers and lenders to review and monitor financial soundness of business. The findings can also help policy makers to design policies and programs to support distressed industries in India. This study also addresses the urgent need for a country specific model for distress prediction. The model developed shows high predictive ability.
 Key words: Financial distress, Indian manufacturing sector, distress prediction, logistic regression, distress indicators.

The recurrence of financial distress: A survival analysis

Companies often suffer periods of financial distress before filing for bankruptcy. Unlike one-off bankruptcies, financial distress can occur repeatedly within the same individual firm. This paper is focused on the recurrence of financial distress and studies the Chinese stock market, where Special Treatment – an official indicator of financial distress – can be repeatedly applied to a listed company. We employ a stratified hazard model to predict the probability of subsequent distress with variables, including duration dependency, event-based factors, institutional variables, financial ratios, market-based variables and macroeconomic conditions. Our empirical results show that accounting and market-based variables have limited power in predicting the recurrence of distress, whereas the duration of recovery, restructuring events and their interaction terms with the accounting and macroeconomic factors affect the recurrent risk significantly. Tested on out-of-time samples, our proposed hazard models show a robust performance in the prediction of recurrent risk over time.

Corporate Default Prediction Model: Evidence from the Indian Industrial Sector

The unprecedented pandemic COVID-19 has impacted businesses across the globe. A significant jump in the credit default risk is expected. Credit default is an indicator of financial distress experienced by the business. Credit default often leads to bankruptcy filing against the defaulting company. In India, the Insolvency and Bankruptcy Code (IBC) is the law that governs insolvency and bankruptcy. As reported by the Insolvency and Bankruptcy Board of India (IBBI), the number of companies filing for bankruptcy under IBC is on a rise, and the industrial sector has witnessed the maximum number of bankruptcy filings. The present article attempts to develop a credit default prediction model for the Indian industrial sector based on a sample of 164 companies comprising an equal number of defaulting and nondefaulting companies. A total of 120 companies are used as training samples and 44 companies as the testing samples. Binary logistic regression analysis is employed to develop the model. The diagnostic ability of the model is tested using receiver operating characteristic curve, area under the curve and annual accuracy. According to the study, return on assets, current ratio, debt to total assets ratio, sales to working capital ratio and cash flow to total assets ratio is statistically significant in predicting default. The findings of the study have significant implications in lending and investment decisions.

The Financial Ratio Analysis in Predicting the Conditions of Financial Distress

Financial distress is a stage of decline in a company's financial condition that occurs before bankruptcy or liquidation. The Indicators of financial distress from results of the test scores using financial ratios, financial ratios are figures obtained from results comparisons between one financial statement item and another that have a relevant and significant relationship. The purpose of this study is to examine the effect of financial ratios to predict financial distress on manufacturing companies in Indonesia Stock Exchange. The research population was all manufacturing companies listed on the Indonesia stock exchange, period 2015-2019. The research sample used the purposive sampling technique. The data analysis method used logistic regression analysis. The results showed liquidity, leverage, and activity profitability, respectively simultaneously affect financial distress. Partially profitability has a positive effect on financial distress. Liquidity, leverage, and activity have negatively affected financial distress in manufacturing companies listed on IDX from 2015 to 2019.

The impact of corporate social responsibility on financial distress: empirical evidence

PurposeThe present study aims to explore the role of corporate social responsibility (CSR) on the likelihood of financial distress for a sample of 139 Pakistan Stock Exchange (PSX) listed firms throughout 2008–2019.Design/methodology/approachPanel logistic regression (PLR) and the dynamic generalized method of moments (GMM) estimator are used to examine the impact of CSR on financial distress. The investment in CSR measures through a multidimensional financial approach which comprises the sum of the contribution made by the company in the form of charitable donation, employees’ welfare and research and development, whereas the Altman Z-score and ZM-Score are used as an indicator of financial distress. The higher the Z-score lower will be the probability of financial distress, whereas the higher ZM score shows a greater probability of financial distress risk.FindingsThe authors find a significant negative impact of CSR on financial distress in both PLR and GMM models. This finding is consistent with the stakeholder view of CSR, as an investment in CSR not only aligns the interest between shareholders and stakeholders but also mitigates the risk of financial distress as well.Research limitations/implicationsLike other studies, the present study is not free from limitations. First, financial firms skipped from the sample, although literature witnesses a lot of studies highlight the financial firms' commitment to achieving CSR goals. Second, financial distress occurs in different stages, the authors fail to establish linkage CSR engagements at different stages of CSR. In the future, researchers can make a valuable addition by covering these missing links in present studies.Practical implicationsThe findings of this study provide more insight to corporate managers and investors about the association between the quality of investment in CSR and the degree of financial distress, concerning Pakistani firms. Furthermore, this study contributes to the existing literature by adding new evidence from developing countries such as Pakistan which are helpful for regulatory bodies and policymakers in the formulation of long-term CSR strategies to manage financial distress.Originality/valueThe study extends the body of existing literature on CSR and the likelihood of financial distress in Pakistan. The results suggest that policymakers may pay special attention to the quality of CSR while predicting corporate financial distress.

UNTYING EMPIRICAL KNOTS: DETERIORATION OF PROFITABILITY, ECONOMIC FAILURE, AND FINANCIAL INSOLVENCY IN THE PHILIPPINES BEFORE COVID-19 OUTBREAK

Corporate bankruptcy has enormous economic ramifications, particularly for investors and creditors of publicly listed companies (PLC). Prior to a corporate collapse, a company's financial status is frequently in jeopardy, and its performance either affirms progress or predicts failure. As a result, management is interested in a technique of determining a company's financial distress. Financial accounting analyses were performed to determine the solvency, liquidity, profitability, and gearing capacity of 136 firms, with 680 economic entries, before CoVid-19 Outbreak. To scrutinise financial distress, the Altman Z-scores and financial zone of discriminations were generated through GB bankruptcy, and PLC bankruptcy model. The link between declining profitability, economic failure, and financial insolvency as indicators of financial distress was examined through panel regression with random factors. Prior to the COVID-19 outbreak, there were no signs of declining profitability, economic collapse, or financial insolvency in the Philippines, according to the findings of the study. Individual components of financial distress and the overall z-score have no statistically meaningful association with financial performance and position markers. As a result, the solvency ratio has little predictive value in forecasting financial distress. The fact that a company has a higher solvency ratio does not also imply that it is less likely to go bankrupt. The findings go counter to classic accounting perspectives and pure managerial research that claim the solvency ratio is always a reliable predictor of financial distress. Finally, the paper examined the financial health of firms and untangled the knots of financial distress.

A cross-sectional study of financial distress in persons with multimorbidity.

BackgroundFinancial distress among persons with multimorbidity is an important topic which has been inadequately addressed to date.ObjectiveWe examined the extent of financial distress among persons with multimorbidity, using data from the 2017 Behavioral Risk Factor Surveillance System (BRFSS).DesignCross-sectional, population-based study.ParticipantsAdults ages ≥ 18 years with multimorbidity.Main measuresLow income and selected social determinants of health that are indicators of financial distress.Key resultsMultimorbidity was more common among those with a household income of less than $15,000 per year (P < 0.001) and among those who were 65 years of age or older (P < 0.001). There was an approximately linear increase in the percentage of individuals who had a household income of less than $15,000 or $25,000 per year with increasing number of morbidities. About one-quarter of individuals who had five or more morbidities had a household income of less than $15,000 per year as compared with 4.49% of individuals with no morbidities (P < 0.001). For all of the social determinants of health examined (Couldn’t pay bills, didn’t have money for food, didn’t have money for balanced meals, didn’t have enough money to make ends meet, and felt this kind of stress), there was an approximately linear increase in the percentage of individuals with an indicator of financial distress with increasing number of morbidities. Further research is needed examining the prevalence and correlates of financial distress in this population as well effective strategies for ameliorating its impact on the health and wellbeing of these persons.