Persistence of collective memory of corporate bankruptcy events discussed on X (Twitter) is influenced by pre-bankruptcy public attention.

Collective attention and memory involving significant events can be quantitatively studied via social media data. Previous studies analyzed user attention to discrete events that do not change post-event, and assume universal public attention patterns. However, dynamic events with ongoing updates are common, yielding varied individual attention patterns. We explore memory of U.S. companies filing Chapter 11 bankruptcy and being mentioned on X (formerly Twitter). Unlike discrete events, Chapter 11 entails ongoing financial changes as the company typically remains operational, influencing post-event attention dynamics. We collected 248,936 X mentions for 74 companies before and after each bankruptcy. Attention surged after bankruptcy, with distinct Low and High persistence levels compared with pre-bankruptcy attention. The two tweeting patterns were modeled using biexponential models, successfully predicting (F1-score: 0.81) post-bankruptcy attention persistence. Studying bankruptcy events on social media reveals diverse attention patterns, demonstrates how pre-bankruptcy attention affects post-bankruptcy recollection, and provides insights into memory of dynamic events.

River water quality management using an integrated multi-objective optimization-simulation approach based on bankruptcy rules.

The aim of this research is to allocate the river's self-purification (acceptance capacity of pollution) fairly between the beneficiaries (pollutant sources) using bankruptcy theory. For this purpose, four bankruptcy rules (CAE, CEL, P, and TAL) were called using the link of the water quality simulation model (QULA2Kw) to an evolutionary optimization algorithm (multi-objective imperialist competition algorithm (MOICA)). The objective functions were reducing polluters' wastewater treatment costs and preventing biochemical oxygen demand (BOD) violations of the standard level along the river. The applicability of the approach is demonstrated by the case study that was carried out on the Dez River in Iran. According to the results, the CEL scenario is the most effective method for the Dez River when taking into account the most optimal state for both objective functions (selecting the best compromise solution from the Pareto front). This is because it has the lowest violation value of the standard level for BOD along the river when compared to other scenarios. Alternatively, when considering Solution 20, which focuses on the maximum cost of treating the polluters while staying within the acceptable level of pollution in the river, the results indicated that the CEA rule emerged as the most favorable option. This is due to its lower treatment cost (156.9 (1000$)) and higher pollution discharge to the river (681.91 g/s).

Company scaling and its deviations: New indicators for enterprise evaluation and bankruptcy prediction.

Many studies have shown that scaling laws widely exist in various complex systems, such as living organisms, cities, and online communities. In this research, we found that scaling laws also hold for companies. The macroscopic variables of companies, such as incomes, expenses, or total liability, all have power-law relationships with respect to the sizes of companies, which can be measured by sales, total assets, or the total number of employees. What is more, we also found the power law exponents always deviate from 1. That means large companies naturally have certain advantages, but the widely used financial indicators based on total volume or ratio may not reflect the company's status well because they are also size-dependent. To tackle this problem, this paper proposes a new set of evaluation indices based on the deviations of the macroscopic variables from the scaling law to eliminate the size-dependent effect. We found that the indicators based on deviations can give more reasonable evaluations for companies and can outperform other conventional indicators to predict the financial distress of companies.

A new fuzzy approach and bankruptcy theory in risk estimation in Waste Load Allocation.

In this paper, we developed a simulator-optimizer model based on risk analysis to determine Waste Load Allocation (WLA). A new Fuzzy index as Fuzzy Risk Index (FRI) was linked with multi-objective optimization to minimize FRI for the environmental stakeholder and the total cost of sewage treatment for the polluting industries as the other collective stakeholder. Afterwards, the conflict was resolved with the help of Nash bargaining and bankruptcy approach (Constrained Equal Awards Rule). The model was run using quantitative/qualitative data for the KhoramAbad River. To check the efficiency of FRI, the process followed for WLA was reimplemented by the Monte Carlo simulation (MCS). A comparison between the two approaches revealed that the outcomes derived from Fuzzy arithmetic across all aspects, encompassing river qualitative simulation, nondominated curve, Nash bargaining's agreed point, and bankruptcy output, closely mirrored the results of MCS. The notable distinction lies in the drastic reduction of the model's execution time by a factor of 450.

Market risk, financial distress and firm performance in Vietnam.

In 2021, when the Covid-19 pandemic had a severe impact on the economy, a significant number of enterprises in Vietnam temporarily suspended doing business. Previous studies have focused on either model for predicting bankruptcy and financial distress or measuring market risk during extreme events. The effects of market risk and financial distress on a firm's performance have largely been ignored in the literature, particularly in Vietnam. This study examines the effects of market risk, measured using the conditional value-at-risk technique and financial distress proxied by the interest coverage ratio (ICR) on firm performance for 500 nonfinancial listed firms in Vietnam from 2012 to 2021. We also estimate the optimal ICR for Vietnam's listed firms. Two estimation techniques are used: dynamic panel models (two-step difference-and system-generalized method of moments) and panel threshold regression. We find that increased market risk reduces firm performance. However, a higher ICR (lower financial distress) also improves a firm's performance. With increased market risk, the financial performance of firms with a high ICR deteriorates significantly.

Does the bankrupt cheat? Impact of accounting manipulations on the effectiveness of a bankruptcy prediction.

The aim of this article is to answer the question whether the unreliability of the Altman bankruptcy prediction model may be caused by manipulations in financial statements. Our study was carried out on a group of 369 bankrupt Polish companies, with the research period covering the years 2011-2020. In the study, we divided the companies into two groups: those correctly classified by Altman's model as at risk of bankruptcy, and companies for which the model did not indicate a significant bankruptcy risk. Using a logit model, we tested whether the probability of companies being correctly classified as failed depends on the risk of a manipulation of financial statements. We use Benford's law to measure the risk of a manipulation of financial statements. We also repeated our study using panel data models. Our analyses show that the manipulation of financial statements is not the cause of the inaccurate predictions of the Altman model. On the contrary, the results of the analyses indicate that manipulations occurs for companies with a lower Z-score and therefore a worse financial situation. This means that a deterioration in the quality of financial statements can be a signal of an increasing probability of bankruptcy.

China's practical wisdom: Assumption of liability for endangering public health in bankruptcy proceedings-A case study of the Changchun Changsheng Biotechnology vaccine incident and the Johnson & Johnson baby powder incident.

The assumption of liability for endangering public health has always been a legislative challenge in bankruptcy proceedings. Although it has been theoretically proven that the tort creditor should hold a position higher than that of unsecured creditors in bankruptcy proceedings, both legislation and judicial practice have been found wanting in many countries. China has witnessed large-scale domestic public health incidents where the tort debtor has entered bankruptcy proceedings while the tort claims were being settled. In Changchun Changsheng Biotechnology vaccine incident, to maintain social order and protect the rights and interests of the tort creditor, the Chinese government required the tort debtor to set up a special compensation fund of RMB 500 million and hand it over to a third party for management. This approach was mainly adopted because tort creditors can only participate in the bankruptcy distribution as an unsecured creditor, according to the Enterprise Bankruptcy Law of China, and as a result, their rights and interests cannot be guaranteed. In the context of the Enterprise Bankruptcy Law of China, this approach face predicaments of legitimacy and effectiveness. Moreover, even if the legislators follow scholars' advice and grant the tort creditor priority in bankruptcy proceedings, that would still not be enough to protect the rights and interests of the tort creditor, not to mention the possibility that the tort debtor might follow the example of Johnson & Johnson to avoid liability in practice. In fact, the Chinese government's approach is similar to that of Johnson & Johnson's, but more advisable. The Enterprise Bankruptcy Law of China (Bill of Amendment) will be submitted to the Standing Committee of the National People's Congress for preliminary deliberation this year, and the Chinese government's approach to the Changchun Changsheng vaccine case is very likely to be codified. This will resolve the predicaments of legitimacy and effectiveness that the government's current approach is facing and serve as a point of reference for the future revision of U.S. bankruptcy law and the handling of related cases.

The Nexus between Smart Sensors and the Bankruptcy Protection of SMEs.

Transportation, logistics, storage, and many other sectors provide a wide space for applying Industry 4.0. This era, with its components, represents the equipment necessary to obtain a unique competitive advantage. Being smart through sensors, big data, and digitalization corresponds not only to evolution but also provides protection for businesses in the face of depression. The COVID-19 pandemic caused collapses and defects for very large enterprises and large enterprises, especially for small and medium-sized enterprises (SMEs). This article focuses on SMEs and their profits from using smart sensors. Thus, the aim was to expose the striking effect of Industry 4.0 on earnings during the crisis in the Visegrad Four. The Mann-Kendall trend was used to map the consequences contrasting the period of 2016-2021. The investigation involved samples from 1221 Slovak, 259 Czech, 855 Polish, and 2156 Hungarian enterprises. The results showed that more than 80% of businesses did not have a negative trend in how their earnings changed over time. This fact was confirmed by a z-test for the comparison of one proportion for each analyzed country. The adaptation to Industry 4.0 strengthened the muscle for bankruptcy resilience during the crisis. In addition, it may encourage enterprises to be smart in the same or different sectors.

How does the leniency of personal bankruptcy law affect entrepreneurship in EU countries?

Several studies examined how some characteristics of personal bankruptcy laws influenced entrepreneurial developments during the last two decades. Our main objective is to analyze the association between self-employment and the leniency of the personal bankruptcy systems in 24 EU countries. Unlike previous studies, we measure differences and changes in the leniency of the regulations with a composite index that incorporates 35 variables. Based on a cross-country database of self-employment ratios and various control variables spanning the years 2000 to 2019, we apply a panel regression model. We find that the implementation of new regulations and reforms in personal bankruptcy legislation in more lenient directions positively correlates with entrepreneurial developments measured by self-employment rates. This is more significant in the group of countries where the eligibility criteria for entrepreneurs are not constrained. We find a one-year negative time-lag effect and conclude that strong anticipation of the law for a more lenient system can immediately change the risk-reward profile, and thereby influence entrepreneurship before implementing the actual reform. An important policy implication is that a major reform in regulation or the first implementation of conservative legislation has the same order of magnitude of effect on promoting entrepreneurship as other public policy reforms of similar purpose.

Managing basin-wide ecosystem services using the bankruptcy theory.

Bankrupt ecosystems are those that cannot appropriately provide all their ecosystem services. In this paper, a novel bankruptcy-based methodology is developed to manage ecosystem services. To test the applicability of the developed methodology, it is used in the Zarrinehrud river basin in Iran. First, an integrated framework is used to assess regulating, supporting, provisioning, and cultural ecosystem services of the study area under three climate change scenarios of Representative Concentration Pathway (RCP) 4.5, 6.0, and 8.5. Then, for each ecosystem service, an aggregated utility is calculated that takes into account the stakeholders' different opinions toward ecosystem services. The utilities of the ecosystem services show that the Zarrinehrud river basin is bankrupt. To manage this ecosystem, six bankruptcy methods of Adjusted Proportional, Constrained Equal Loss, Constrained Equal Award, Piniles, Talmud, and Hybrid are developed and used in the study area. In this study, the summation of ecosystem services' aggregated utilities under each management scenario is considered as an asset, and all mentioned bankruptcy methods are used to redistribute these assets to different ecosystem services. Considering aggregated utilities, redistributed utilities, and each ecosystem service's claim, two different Root Mean Square Error-based approaches are developed to find the most applicable management scenario in a bankruptcy condition. Using the mentioned approaches, management scenario 128, which is comprised of all management packages, is chosen as the best option under all climate change scenarios. This scenario includes projects such as improving cropping patterns, allocating water to the lake from new water resources, and rehabilitating irrigation and draining systems. Moreover, analyzing the results derived from different bankruptcy methods shows that the Talmud, Hybrid, and Constrained Equal Loss methods have the best performance.

Security Evaluation of Financial and Insurance and Ruin Probability Analysis Integrating Deep Learning Models.

To ensure safe development of the financial and insurance industry and promote the continuous growth of the social economy, the theory and its role of deep learning are firstly analyzed. Secondly, the security of financial and insurance and bankruptcy probability are discussed. Finally, an analytical model of the security bankruptcy probability of financial and insurance is designed through a deep learning model, and the model is evaluated comprehensively. The research results manifest that first, the designed security evaluation of the financial and insurance industry based on the deep learning and bankruptcy probability analysis model not only has strong learning ability but also can effectively reduce its own calculation error through short-time learning. Then, by comparing with other models, it is found that the designed model has a stronger ability to control various errors than other models, and the overall error rate of the model can be reduced to about 20%. At last, the data training indicates that the model designed by the deep learning method can accurately and effectively predict the basic situation of the financial and insurance industry, the minimum error can reach 0, and the highest is only about 3. The research provides a technical reference for the development of the financial and insurance industry and contributes to the prosperity of the social economy.

An integrated data framework for policy guidance during the coronavirus pandemic: Towards real-time decision support for economic policymakers.

Usually, official and survey-based statistics guide policymakers in their choice of response instruments to economic crises. However, in an early phase, after a sudden and unforeseen shock has caused unexpected and fast-changing dynamics, data from traditional statistics are only available with non-negligible time delays. This leaves policymakers uncertain about how to most effectively manage their economic countermeasures to support businesses, especially when they need to respond quickly, as in the COVID-19 pandemic. Given this information deficit, we propose a framework that guided policymakers throughout all stages of this unforeseen economic shock by providing timely and reliable sources of firm-level data as a basis to make informed policy decisions. We do so by combining early stage 'ad hoc' web analyses, 'follow-up' business surveys, and 'retrospective' analyses of firm outcomes. A particular focus of our framework is on assessing the early effects of the pandemic, using highly dynamic and large-scale data from corporate websites. Most notably, we show that textual references to the coronavirus pandemic published on a large sample of company websites and state-of-the-art text analysis methods allowed to capture the heterogeneity of the pandemic's effects at a very early stage and entailed a leading indication on later movements in firm credit ratings. While the proposed framework is specific to the COVID-19 pandemic, the integration of results obtained from real-time online sources in the design of subsequent surveys and their value in forecasting firm-level outcomes typically targeted by policy measures, is a first step towards a more timely and holistic approach for policy guidance in times of economic shocks.

A cooperative game model with bankruptcy theory for water allocation: a case study in China Tarim River Basin.

China Tarim River Basin is located in an arid area, whose rapid socioeconomic development intensifies the current water resources shortage. To allocate water resources reasonably, this paper introduces the bankruptcy theory into the cooperative game model to contract a linear function describing the degree of satisfaction of each region's declared water demand. Bankruptcy theory solves the problem of insufficient information about stakeholders in the cooperative game. From the perspective of the cooperative game's stability, the bankruptcy allocation stability index (BASI) is used to evaluate and compare water resource allocation results in the Tarim River Basin in 2025 and 2030 under different scenarios. Moreover, this paper uses the improved TOPSIS model to build the harmony index of water-economy-environment (HWEE) to evaluate the harmony of water resources, economy, and environment in each region. The results show that the model is more suitable for the actual water allocation game and has a good application value than the classical bankruptcy theory. Moreover, the stability index and HWEE proposed in this paper also have better applicability, and the allocation scheme with the same game weight in each region is more stable.

Determinants of financial distress in the European air transport industry: The moderating effect of being a flag-carrier.

Due to the COVID-induced global collapse in demand for air travel, the year 2020 was a catastrophic one for the aviation industry. A dramatic drop in operating revenues along with continuing fixed expenses drained the cash reserves of airlines, with consequent risks of financial distress and, potentially, even of bankruptcy. Flag-carriers are a special group in the airline business-they are considered to have privileges in terms of the support given by governments while, on the other hand, are often viewed as having low efficiency and performance. This study aims to estimate for European airlines the interaction effect of being a flag-carrier (flagship) with the relationship between leverage, liquidity, profitability, and the degree of financial distress. Findings obtained from analysing 99 European airlines over a period of ten years, indicate that the negative influence of leverage on financial stability is higher in the case of flag carriers (flagship). The impact of liquidity and profitability on financial health is more positive for flagship than for non-flagship carriers. These findings are not limited to contributing to the existing literature, but also have significant practical implications for executives, managers, and policy makers in the European air transport sector.

Unemployment, Bankruptcies, and Deaths From Multiple Causes in the COVID-19 Recession Compared With the 2000‒2018 Great Recession Impact.

Objectives. To determine whether unemployment and bankruptcy rates are related to increased excess deaths during the COVID-19 recession and to examine whether the current recession-based mortality rate not only is dependent on COVID-19 but also continues the pattern of recessions, especially the Great Recession, in relation to chronic disease mortality rates and mental health disturbances (e.g., including suicide) from 2000 to 2018. Methods. This study used pooled cross-sectional time series analysis to determine the impact of unemployment and bankruptcy rates on excess deaths from February to November 2020 for US states. The study used a second pooled cross-sectional time series analysis to determine whether the COVID-19‒ era recessional mortality continues the impact of prepandemic recessions (2000-2018) on multiple causes of mortality. Results. Ten percent unemployment was associated with approximately 48[thin space]149 excess deaths, while, jointly with bankruptcies, their combined effect produced 35 700 and 144 483 excess deaths, for unemployment and bankruptcies, respectively. These health-damaging COVID-19‒recessional findings suggest a reiteration of the significantly increased major cause‒specific mortality during 2000 to 2018, mitigated by the size of the health care workforce. Conclusions. Minimization of deaths attributable to the COVID-19 recession requires ample funding for the unemployed and underemployed, especially Black and Hispanic communities, along with significant investments in the health workforce. (Am J Public Health. 2021;111(11):1950-1959. https://doi.org/10.2105/AJPH.2021.306490).

Undersampling bankruptcy prediction: Taiwan bankruptcy data.

Machine learning models have increasingly been used in bankruptcy prediction. However, the observed historical data of bankrupt companies are often affected by data imbalance, which causes incorrect prediction, resulting in substantial economic losses. Many studies have proposed the insolvency imbalance problem, but little attention has been paid to the effect of the undersampling technology. Therefore, a framework is used to spot-check algorithms quickly and combine which undersampling method and classification model performs best. The results show that Naive Bayes (NB) after Edited Nearest Neighbors (ENN) has the best performance, with an F2-measure of 0.423. In addition, by changing the undersampling rate of the cluster centroid-based method, we find that the performance of the Linear Discriminant Analysis (LDA) and Naive Bayes (NB) are affected by the undersampling rate. Neither of them is uniformly declining, and LDA has higher performance when the undersampling rate is 30%. This study accordingly provides another perspective and a guide for future design.

Breast Cancer-Related Employment Disruption and Financial Hardship in the Sister Study.

Background: More than one-half of breast cancer cases are diagnosed among women aged younger than 62 years, which may result in employment challenges. This study examined whether cancer-related employment disruption was associated with increased financial hardship in a national US study of women with breast cancer. Methods: Women with breast cancer who were enrolled in the Sister or Two Sister Studies completed a survivorship survey in 2012. Employment disruption was defined as stopping work completely or working fewer hours after diagnosis. Financial hardship was defined as: 1) experiencing financial problems paying for cancer care, 2) borrowing money or incurring debt, or 3) filing for bankruptcy because of cancer. Prevalence ratios and 95% confidence intervals for the association between employment disruption and financial hardship were estimated using multivariable Poisson regression with robust variance. Results: We analyzed data from women employed at diagnosis (n = 1628). Women were a median age of 48 years at diagnosis and 5.6 years from diagnosis at survey completion. Overall, 27.3% of women reported employment disruption (15.4% stopped working; 11.9% reduced hours), and 21.0% experienced financial hardship (16.0% had difficulty paying for care; 12.6% borrowed money or incurred debt; 1.8% filed for bankruptcy). In adjusted analysis, employment disruption was associated with nearly twice the prevalence of financial hardship (prevalence ratio = 1.93, 95% confidence interval = 1.58 to 2.35). Conclusions: Women experiencing employment disruptions after breast cancer may be more vulnerable to financial hardship. Findings highlight the need to target risk factors for employment disruption, facilitate return to work or ongoing employment, and mitigate financial consequences after cancer.