Bankrupt Companies Research Articles

Development of fuzzified neural network for enterprise bankruptcy risk estimation

The object of this study is the assessment of the level of enterprise bankruptcy risk. It is a critical component in assessing the financial condition of an enterprise, and also serves as an indicator that allows the management team to reduce potential risks and develop their own strategies to strengthen the financial condition of the enterprise. One of the most challenging aspects of bankruptcy forecasting is the complex financial situations of bankrupt companies. By accurately predicting the risk of bankruptcy, businesses can take preventive measures to mitigate financial difficulties and ensure long-term sustainability. Previous methods, such as Altman's Z-score, are not accurate enough, as presented in the study. The paper investigates a modern approach to bankruptcy prediction based on a neural network with complex neural elements, namely neural arithmetic logic units (NALUs) and a custom phasing layer. This layer can process complex raw numerical values, such as financial indicators relevant to the analysis of a company's bankruptcy. Compared to Altman's Z-score, the developed method demonstrates a better F1 score in bankruptcy classification (48 %). On the raw data, the neural network demonstrates an improvement in the F1 score by about 40 % compared to the classical multilayer perceptron (MLP) with linear layers and nonlinear activation functions. A modern replacement for ReLU called Mish was used, which achieves better generalization. It was also assumed that the addition of new neural elements, which provide the neural network with arithmetic capabilities, contributes to the performance of processing non-normalized input data. This work highlights the importance of using advanced neural network architectures to improve the accuracy and reliability of forecasting in financial risk assessment. Using the parameters presented in the study, managers of enterprises will be able to more accurately assess the risk of bankruptcy.

Airline insolvencies and environmental concerns: a comparative legal perspective

Abstract Airline insolvencies can be economically stressful for the countries concerned. One of the significant challenges associated with such insolvencies is the grounding of the aircraft over an extended period due to the pendency of the legal proceedings. The ease with which the lessors can regain possession of the aircraft, triggered by the default of the insolvency company, determines the efficacy of the regulatory climate. Leaving aside the financial troubles the insolvent company faces, it may be additionally expected to match its environmental obligations. Therefore, the push for ‘green insolvencies’ is slowly gaining momentum within the international community. The Cape Town Convention stipulates a sui generis process of seeking possession of an aircraft without undue delay. The countries incorporating the Convention into their domestic setups stand to gain a tremendous regulatory edge over those that are yet to align their national laws with this international instrument. While focusing on developing an efficient insolvency framework is not enough in light of the Sustainable Development Goals, it is possible to address environmental concerns by incorporating more climate-sensitive insolvency frameworks at the national level. The countries may continue to turn to their domestic environmental norms in this regard. This research study aims to throw light on how airline insolvencies need to be more considerate towards environmental interests. It seeks to fill the gaps in the current literature regarding this interface and explore this rather tenuous relationship from a comparative legal lens. The first section of the paper provides an overview of the study. The second section considers the applicability of the Cape Town Convention and discusses the relevant insolvency frameworks in the USA, the United Kingdom, Canada, India, and Nigeria. The last section of the study provides the concluding remarks and suggestions.

Risk analysis of Spanish companies

AbstractThis paper aims to investigate the determinants of different types of market risk faced by Spanish firms from 2012 to 2019. Using Fama and French's (Journal of Financial Economics, 1993, 33, 3) three‐factor model, we estimate total risk, diversifiable risk, and systematic or non‐diversifiable risk in the three dimensions proposed by these authors: market risk, size risk, and valuation risk. Risk determinants are derived from a series of economic and financial variables obtained from the information contained in financial statements. This information is summarised using a factor analysis that aims to resolve the correlation issues between the proposed measures. The study demonstrates that the systematic risk factors proposed by Fama and French in their 1993 three‐factor model incorporate dimensions of systematic risk that are relevant to investors and that the set of economic and financial variables proposed can explain these risks. Among these variables, profitability and the market to book ratio have the greatest impact in explaining company risk, while factors such as operating and financial leverage, growth, or company insolvency have a much smaller effect as explanatory factors for risk.

Methodology for comprehensive assessment of the fisheries complex enterprises' bankruptcy probability

Subject. Enterprises that are actively operating in the market are exposed to factors affecting their financial health. If the nature of the factors is negative, the enterprises will face adverse impact, like a decrease in financial stability and solvency. Objectives. The study aims at development of scientific and practical tools for information and analytical substantiation of bankruptcy probability assessment and monitoring of financial health of organizations of the fisheries complex when making management decisions. Methods. The methodological approach to assessing the probability of bankruptcy of fisheries enterprises relies on the study of multifactor models designed to predict bankruptcy. Results. The paper considers causes of bankruptcy and the influence of factors leading to loss of solvency of the enterprise, defines specific features of bankruptcy of fishing companies. We performed the analysis of financial condition of companies with 3.22.1 "Freshwater Industrial Fish Farming" core activity. On the basis of modern scientific research in the field of assessing insolvency and the probability of bankruptcy, we developed our own methodological approach for fishing companies. Conclusions. If used, the offered methodology will provide the company managers with reliable information on the possible occurrence of company's insolvency.

Insolvency of Companies in Romania

Insolvency of Companies in Romania

The unexpected consequences of company bankruptcy: An investigation into the spillover effect of local economic liquidation

Understanding the impact of corporate failures is crucial, as they not only directly harm the companies involved but also have far-reaching consequences on local economies, leading to increased unemployment, crime rates, and societal problems. The objective of this study is to examine the unanticipated consequences of corporate bankruptcy on regional economies, with a specific emphasis on the ripple effects that go beyond the immediate parties involved. We employed a difference-in-differences model to investigate the effects of corporate liquidation events on 2985 enterprises situated in 276 cities throughout China, using data spanning from 2002 to 2021. The results of our study indicate that towns hosting bankrupt enterprises witnessed a significant decline in their total economic productivity, a rise in unemployment rates, and an increasing dependence on social welfare programs. The impact of these spillover effects was especially noticeable in cities where the insolvent companies had substantial market shares. The findings indicate that company bankruptcies have wide-ranging consequences, requiring comprehensive measures and proactive policies to minimize the effects of future economic disruptions.

МОДЕЛИРОВАНИЕ ВЕРОЯТНОСТИ БАНКРОТСТВА КОМПАНИЙ СТРОИТЕЛЬНОЙ ОТРАСЛИ

Due to the unstable economic situation, companies are facing financial difficulties, so their assessment of the likelihood of bankruptcy in the short term is more than relevant. This article reflects the main results of building a model for predicting the probability of bankruptcy of companies in the construction industry: companies, developers. The model was built using a statistical method – logistic regression. The sample of the study included operating construction companies that operate on the territory of the Russian Federation and bankrupt companies. Initially, the study sample consisted of 132 companies, data on which were collected using the Russian reference and information system SPARK. The total sample of the study was divided into training and test in the ratio of 80% and 20%, respectively. The endogenous variable was a binary one, and the exogenous variables were 10 financial indicators selected by experts based on a literature review. As a result, the following factors turned out to be significant: solvency ratio; coverage ratio; asset turnover ratio. According to the results of the study, the accuracy of predicting bankrupt companies of the author's model is 76,2%, and nonbankrupt 83,3%. The overall predictive power of the logit model is 78%. The obtained model for predicting the probability of bankruptcy of companies in the construction industry, firms that operate in the wholesale and retail trade in building materials and goods for repairs, can be introduced into their scoring system for assessing the reliability of counterparties in order to conduct a more accurate scoring procedure and minimize financial risks.

К вопросам обращения взыскания по налоговой задолженности в исполнительном производстве

The positions of various authors on the issues of enforcement measures and actions used by bailiffs and tax officials to collect tax debts are considered. The possibility of collecting tax debt from a debtor-citizen for his economic activities in a liquidated, bankrupt company or in the absence of its property, when the citizen appropriated financial amounts for himself without paying taxes on them, is touched upon. It is proposed to transfer the powers to foreclose on tax debts and compile statistical reports from the Federal Tax Service to the Federal Bailiffs Service, leaving the function of “seizure” of the debtor’s property at the claim stage of settling tax debts to the Federal Tax Service, so that the debtor would subsequently be unable to avoid its collection by a bailiff. In this regard, it is expected to be quite logical to reduce the number of staff in the Federal Tax Service and increase it in the Federal Bailiffs Service. The introduction of a ban on registration of shell companies is justified by the insufficiency of insurance financial and (or) property assets. The position of the inexpediency of judicial collection of funds by a bailiff due to a debtor from a third party under judicial acts is substantiated, if there is confirmation of financial or property funds due to the debtor by documentary evidence collected at the request of a bailiff or provided by the recoverer – the Federal Tax Service, since foreclosure by a bailiff on these funds is possible and without legal recourse.

Early Insolvency Prediction as a Key for Sustainable Business Growth

This research aimed to determine whether and how financial analysis combined with machine learning can support decision-making for sustainable business growth. This study was conducted using a sample of 100 Serbian companies whose bankruptcies were initiated between 2019 and 2021 to identify key factors that distinguish solvent from insolvent companies. Two neural networks (NNs) were trained and tested to predict these discriminating factors one year (Y-1) and two years (Y-2) before bankruptcy initiation. Initially, a total of 37 predictor variables were included, but prior to modeling, variable reduction was performed through VIF analysis and t-tests. The training dataset comprised 70% of the sample, while the remaining 30% was used for testing. Both NNs utilized a softmax activation function for the output layer and a hyperbolic tangent for the hidden layers. Two hidden layers were included, and training was conducted over 2000 epochs using the gradient descent algorithm for optimization. The research results indicate that poor cash management is the first sign of possible insolvency one year in advance. Additionally, the findings reveal that retained earnings management can serve as a reliable bankruptcy predictor two years in advance. The overall predictive accuracy of the NN models is 80.0% (Y-1) and 73.3% (Y-2) for the testing dataset. These findings demonstrate how selected ratios can support bankruptcy prediction, providing valuable insights for company proprietors, management, and external stakeholders.

Business bankruptcy prediction: the multisectorial models’ efficiency in the Old Blue Economy Iberian companies’

In the risk evaluation by the stakeholders it’s crucial to understand the causes of bankruptcy or breach in corporate sustainability, as well as more efficient ways of predicting it. These techniques, mainly supported by financial analysis, over time, have given rise to a diversity of approaches in particular to the Multivariate Discriminant Analysis. We selected, 54 iberian companies from that sector, divided them in two samples, one of the bankrupt companies and another of the healthy ones, matched by size and business' volume. Were applied 21 multisectoral models from a diversity of countries abd time horizons, with greater presence in the literature. As conclusions, continuing Peres and Antão (2019) the work, were identified the best bankruptcy or sustainability predictors, to the Old Blue Economy Iberian industries’, up to 5 years in advance.

Legal protection of employee wage rights in bankrupt companies: evidence from China

This study aims to identify the factors contributing to the legal protection of workers' wage rights in bankrupt companies in China, focusing on aspects that have not been grounded in social justice. This research is also to formulate and form a legal protection model for workers' wage rights in bankrupt companies based on social justice in Indonesia and China. This research is empirical. The research data is in the form of primary and secondary data (primary and secondary legal materials). The study results show that the factors that cause legal protection of workers' wage rights in bankrupt companies are not based on social justice and are the legal substance factor, namely the legal norms governing workers' wage rights and other rights. When a company goes bankrupt, there is non-uniformity in the priority hierarchy of creditors in the Bankruptcy Law, General Provisions on Tax Implementation Procedures, and Labor Laws. Another factor is the legal and structural factor, which only involves the trustee and supervisory judges in the settlement of bankrupt assets. The last factor is the legal culture, where participation in the payment of bankruptcy assets is minimal and the current economic system tends to be capitalist. Indonesia must take the Chinese government as an example; China places a premium on modifying labour standards to safeguard the rights and interests of workers in all circumstances and promote economic and social development.

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Purpose: This study analyzed the effect of financial ratios, corporate governance, and macroeconomic variables on financial distress. This research was conducted during the covid-19 pandemic when many companies experienced difficulties due to activity restrictions during the pandemic. Theoretical framework: Prolonged financial difficulty can lead to the company's insolvency. As a result, understanding the company's health status is critical. Internal company factors, such as the firm's financial situation and company management, as reflected in corporate governance, and external company factors, such as macroeconomic conditions, can all influence the occurrence of financial hardship in the company. Design/Methodology/Approach: This study uses a sample of property and real estate sector companies listed on the Indonesia stock exchange. The model used was 270 observations. This research is a quantitative approach using a logistic regression test. The unit of analysis in this study is property, real estate, and building construction sector companies listed on the Indonesia Stock Exchange, where each company is undoubtedly influenced by internal company factors, namely financial ratios and corporate governance, and external factors, namely macroeconomics. Findings: The results showed that the relevant financial ratios are sales total assets and retained earnings to total assets. At the same time, the corporate governance included in the model is director size and macroeconomic variables in the form of inflation entering the financial distress model. The accuracy of the model in classifying its observations is 84.1%. Research, practical & social implications: The implication of this study's results is that financial ratios, governance, and macroeconomic indicators can be used as a benchmark to detect the possibility of financial distress. Originality/Value: This research was conducted in the real estate industry during the Covid-19 pandemic when many companies experienced financial difficulties. This research combines financial ratios, corporate governance, and macroeconomics that impact the possibility of financial distress. This research is different because it combines financial ratios that reflect company performance, governance practices, and macroeconomic indicators to predict bankruptcy.

Financial Ratios, Corporate Governance, and Macroeconomic Indicators in Predicting Financial Distress

Purpose: This study analyzed the effect of financial ratios, corporate governance, and macroeconomic variables on financial distress. This research was conducted during the covid-19 pandemic when many companies experienced difficulties due to activity restrictions during the pandemic.
 
 Theoretical framework: Prolonged financial difficulty can lead to the company's insolvency. As a result, understanding the company's health status is critical. Internal company factors, such as the firm's financial situation and company management, as reflected in corporate governance, and external company factors, such as macroeconomic conditions, can all influence the occurrence of financial hardship in the company.
 
 Design/Methodology/Approach: This study uses a sample of property and real estate sector companies listed on the Indonesia stock exchange. The model used was 270 observations. This research is a quantitative approach using a logistic regression test. The unit of analysis in this study is property, real estate, and building construction sector companies listed on the Indonesia Stock Exchange, where each company is undoubtedly influenced by internal company factors, namely financial ratios and corporate governance, and external factors, namely macroeconomics.
 
 Findings: The results showed that the relevant financial ratios are sales total assets and retained earnings to total assets. At the same time, the corporate governance included in the model is director size and macroeconomic variables in the form of inflation entering the financial distress model. The accuracy of the model in classifying its observations is 84.1%.
 
 Research, practical & social implications: The implication of this study's results is that financial ratios, governance, and macroeconomic indicators can be used as a benchmark to detect the possibility of financial distress.
 
 Originality/Value: This research was conducted in the real estate industry during the Covid-19 pandemic when many companies experienced financial difficulties. This research combines financial ratios, corporate governance, and macroeconomics that impact the possibility of financial distress. This research is different because it combines financial ratios that reflect company performance, governance practices, and macroeconomic indicators to predict bankruptcy.

Analisis Kebangkrutan Pada Perusahaan Sektor Pariwisata, Restoran, dan Hotel

The purpose was to test the bankruptcy potential and accuracy of the three models used to analyze company insolvency using the Altman Z-Score, Springate S-Score, and Zmijewski X-Score methods. Each model is calculated using ratio analysis of the elements of assets, liabilities, equity, and retained earnings. This study used secondary data that had been processed. The samples are tourism, restaurant and hotel companies listed on the Indonesia Stock Exchange (IDX) in 2018-2022. Using the purposive sampling method, predetermined criteria so that 26 companies with a total of 130 data. The analysis technique is to calculate financial ratios, then compare the results of the three bankruptcy prediction models and test their accuracy. As a result, the Springate S-Score model with the highest accuracy of 80.77% is considered more accurate in predicting the possibility of bankruptcy which supported by the existence of companies suspended by the IDX due to going concern issues.

IMPLEMENTATION OF THE SEVERANCE PAYMENT AGREEMENT FOR EMPLOYEES OF PT. GADING BHAKTI

PT. Gading Bhakti, a private company and subsidiary of PT. Mapoli Raya, operates in the palm oil plantation and processing industry. In 2021, PT. Mopli Raya, its parent company, was declared bankrupt under Decree Number 17/pdt-SUS-PKPU/2020/PN.Niaga Medan and Law No. 13 of 2003, Employment Article 95 paragraph 4, mandating debt settlement as a priority for bankrupt companies. Despite this, PT. Gading Bhakti failed to fulfill debt payments to 25 retired employees, resulting in the non-payment of their post-employment benefits. The research aims to investigate the implementation of the severance payment agreement by PT. Gading Bhakti and the company's measures to meet its obligations toward employee severance. The research methodology employed was the empirical juridical method, observing ongoing events and directly examining PT. Gading Bhakti's implementation of the severance payment agreement. Furthermore, the study explores the company's attempts to fulfill the severance payment for its employees. Regrettably, the implementation of the agreement resulted in a breach of contract, with severance payments not being duly honored. Various efforts were made, including rescheduling the agreement and submitting severance payment documents. On the other hand, the employees sought resolution through verbal warnings, mediation, media involvement, and seeking assistance from relevant authorities concerning the unclear situation of severance payments for PT. Mapoli Raya's ex-employees (PT. Gading Bhakti being its subsidiary). In conclusion, the study highlights the challenges faced by retired employees in receiving their post-employment benefits and the need for PT. Gading Bhakti to effectively fulfill its obligations regarding severance payments.

RESPONSIBILITY OF INSURANCE COMPANIES DECLARED BANKRUPT FOR THE REPAYMENT OF POLICYHOLDERS' RECEIVABLES

Insurance companies, as legal entities engaging in business activities, may not always maintain good financial standing. A bankrupt company is typically in a state of insolvency where its assets fall short of its outstanding obligations. The insolvency of an Insurance Company leads to setbacks and limitations in its operations, with the Policyholder being the most affected party. This study aims to explore how dividends from bankrupt insurance companies are distributed and how such companies are accountable for settling Policyholders' receivables. The research employs a normative research method, utilizing a statutory approach and legal concept analysis. The findings indicate that the distribution of bankrupt insurance companies' assets is based on the priority order of creditors. The Policyholder, as the Preferred Creditor, receives payment of their receivables first and assumes the position of Separatist Creditor. In case of bankruptcy, the Insurance Company bears full responsibility for settling Policyholders' receivables. If there are any unpaid receivables, the Insurance Company is obligated to pay the remaining amount to the Policyholder.

Strategies for Preventing Bankruptcy: Adopting Insolvency Tests from the United States Perspective to Indonesia

Companies don't always make a profit and taking loans from banks or other companies to ensure smooth business operations can put a company's existence in danger. Failure to repay loans can lead to bankruptcy, declared by the competent commercial court upon request of the creditor or debtor. The ease with which bankruptcy requirements are regulated under Indonesian law can lead to economic problems. This study seeks to examine the differences between bankruptcy laws in Indonesia and the United States, the challenges in implementing an insolvency test in Indonesia, and the benefits of such a test. The research method used is normative-juridical, employing a conceptual and statutory approach to assess the benefits and obstacles of implementing insolvency tests in Indonesia. The findings suggest that applying insolvency tests can have a positive impact on economic growth, including reducing the number of bankrupt companies and decreasing unemployment, which contributes to development. As a result, it's necessary to modify Law Number 37 of 2004 concerning Bankruptcy and Suspension of Debt Payment Obligations to enable the use of insolvency tests in bankruptcy applications in Indonesia.

BANKRUPTCY PREDICTION ON FIRM PERFORMANCE IN PRE – DURING COVID -19 PANDEMIC: SPECIAL REFERENCE TO LISTED MAINBOARD HOTEL COMPANIES IN SRI LANKA

The influence of corporate bankruptcy on the economy is considerable as it encompasses shareholders, financial lenders, operational lenders, and the government. It is necessary to do a bankruptcy evaluation so the businesses can receive early bankruptcy warning signs. The earlier signs of insolvency are identified, the better for management because they can take immediate action to correct the issue. This paper aims to investigate the impact of bankruptcy prediction on firm performance with involving COVID -19 pandemic with a focus on listed mainboard-hotel companies in Sri Lanka while referring current Sri Lanka’s economic crisis. When there is a financial crisis, it is crucial to choose a model for bankruptcy prediction. The study uses semi-annual secondary data to examine a sample of 12 mainboard-hotel companies listed on the Colombo Stock Exchange from year 2016 to 2022. Altman's and Kida Z-scores are the bankruptcy prediction models used to measure bankruptcy. According to the findings, there are seven safe zone hotel companies, three hotel companies are in grey zone and two are recognized as distressed. Return on equity, return on assets and employee productivity were used to construct independent variables. The study also discovered that the profitability and liquidity ratios could foretell the insolvency of mainboard-hotel companies listed on the Colombo Stock Exchange. The findings of the study examined the comparison between model estimations of the study and the actual status of the firms. The study showed the Altman's and Kida Z-scores classification models have 89.6% and 69.5% accuracy for the average predictive ability of business discontinuation, respectively. Overall Altman’s Z-score is better than the Kida model as most of the hypotheses have been proved and prediction rates are in a good position. The variables have a statistically significant association between bankruptcy risk. Therefore, all objectives have been achieved in the study.

Default prediction of small and medium enterprises: Portuguese construction sector

PurposeThis paper aims to study the default risk of small and medium-sized enterprises in the construction sector.Design/methodology/approachAn unbalanced sample of 2,754 Portuguese companies from the construction sector, from 2008 to 2020, is analysed. Companies are classified in default or compliant following an ex-ante criterion. Then, using the stepwise analysis, the most relevant variables are selected, which are later used in the logit model. To verify the robustness of the results, a sample of legally insolvent companies is added (mixed criterion) and the initial sample is split into two subperiods.FindingsFinancial variables are the most relevant to predict the pattern for this sample. The main conclusions show that smaller and older companies, more indebted, with more liquidity and with higher EBIT have a higher probability of default. These conclusions are confirmed using a mixed criterion to classify companies as default or compliant and including a macroeconomic dummy.Practical implicationsThis work not only contributes to enlarging the literature review but also makes relevant contributions to practice. Companies from the construction sector can understand which indicators must control to avoid financial problems. The government also has relevant information that can help in adapting or creating regulations for recovering or revitalizing companies.Originality/valueThis study proposed an ex-ante criterion that can be used for all types of companies. Most works use a legal or a mixed criterion that does not allow for detecting signs of financial problems in advance. Moreover, the sample used is almost unexplored – SMEs from a sector with great mortality rate.

Bankruptcy prediction using ensemble of autoencoders optimized by genetic algorithm.

The prediction of imminent bankruptcy for a company is important to banks, government agencies, business owners, and different business stakeholders. Bankruptcy is influenced by many global and local aspects, so it can hardly be anticipated without deeper analysis and economic modeling knowledge. To make this problem even more challenging, the available bankruptcy datasets are usually imbalanced since even in times of financial crisis, bankrupt companies constitute only a fraction of all operating businesses. In this article, we propose a novel bankruptcy prediction approach based on a shallow autoencoder ensemble that is optimized by a genetic algorithm. The goal of the autoencoders is to learn the distribution of the majority class: going concern businesses. Then, the bankrupt companies are represented by higher autoencoder reconstruction errors. The choice of the optimal threshold value for the reconstruction error, which is used to differentiate between bankrupt and nonbankrupt companies, is crucial and determines the final classification decision. In our approach, the threshold for each autoencoder is determined by a genetic algorithm. We evaluate the proposed method on four different datasets containing small and medium-sized enterprises. The results show that the autoencoder ensemble is able to identify bankrupt companies with geometric mean scores ranging from 71% to 93.7%, (depending on the industry and evaluation year).