Search Results (92)

Predicting company bankruptcy is a critical task in financial risk assessment. This study introduces a novel approach using Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to enhance bankruptcy prediction accuracy. By transforming financial statements into grayscale images and leveraging synthetic data generation, we analyze a dataset of 6249 companies, including 3256 active and 2993 bankrupt firms. Our methodology innovates by addressing dataset limitations through GAN-based data augmentation. CNNs are employed to take advantage of their ability to extract hierarchical patterns from financial statement images, providing a new approach to financial analysis, while GANs help mitigate dataset imbalance by generating realistic synthetic data for training. We generate synthetic financial data that closely mimics real-world patterns, expanding the training dataset and potentially improving classifier performance. The CNN model is trained on a combination of real and synthetic data, with strict separation between training/validation and testing. Full article

Insider threats pose significant challenges to organizations, seriously endangering information security and privacy protection. These threats arise when employees with legitimate access to systems and databases misuse their privileges. Such individuals may alter, delete, or insert data into datasets, sell customer or client email addresses, leak strategic company plans, or transfer industrial and intellectual property information. These actions can severely damage a company’s reputation, result in revenue losses and loss of competitive advantage, and, in extreme cases, lead to bankruptcy. This study presents a novel solution that examines how organizational factors such as job satisfaction and security, organizational support, attachment, commitment, involvement in information security, and organizational norms influence employees’ attitudes and intentions, thereby mitigating insider threats. A key strength of this research is its integration of two foundational theories: the Social Bond Theory (SBT) and the Theory of Planned Behavior (TPB). The results reveal that job satisfaction and security, affective and normative commitment, information security training, and personal norms all contribute to reducing insider threats. Furthermore, the findings indicate that employees’ attitudes, perceived behavioral control, and subjective norms significantly influence their intentions to mitigate insider threats. However, organizational support and continuance commitment were not found to have a significant impact. Full article

This study addresses the increasing emphasis on sustainability and the importance of understanding how environmental risk influences business failure, a factor unexplored in traditional financial prediction models. Environmental risk, or environmental financial exposure, refers to the potential percentage of a company’s revenue at risk due to the environmental damage it causes. Previous research has not sufficiently integrated environmental variables into failure prediction models. This study aims to determine whether environmental risk significantly predicts business failure and how it interacts with conventional financial indicators. Utilizing data from 971 Spanish cooperative companies in 2022, including financial ratios, the VADIS bankruptcy propensity indicator, and the TRUCAM environmental risk score, the study employs the Extreme Gradient Boosting (XGBoost) machine learning algorithm, chosen for its robustness in handling multicollinearity and nonlinear relationships. The methodology involves training and validation samples, cross-validation for hyperparameter tuning, and interpretability techniques such as variable importance analysis and partial dependence plots. Results demonstrate that the variable related to environmental risk (TRUCAM) ranks among the top predictors, alongside liquidity, profitability, and labor costs, with higher TRUCAM values correlating positively with failure risk, underscoring the importance of sustainable cost management. These findings suggest that firms facing substantial environmental risk are more prone to financial distress. By incorporating this environmental variable into a machine learning framework, this work contributes to the interaction between sustainability practices and corporate viability. Full article

Predicting corporate bankruptcy is a key task in financial risk management, and selecting a machine learning model with superior generalization performance is crucial for prediction accuracy. This study evaluates the effectiveness of k-fold cross-validation as a model selection strategy for random forest and XGBoost classifiers using a publicly available dataset of Taiwanese listed companies. We employ a nested cross-validation framework to assess the relationship between cross-validation (CV) and out-of-sample (OOS) performance on 40 different train/test data partitions. On average, we find k-fold cross-validation to be a valid selection technique when applied within a model class; however, k-fold cross-validation may fail for specific train/test splits. We find that 67% of model selection regret variability is explained by the particular train/test split, highlighting an irreducible uncertainty real world practitioners must contend with. Our study extensively explores hyperparameter tuning for both classifiers and highlights key insights. Additionally, we investigate practical implementation choices in k-fold cross-validation—such as the value of k or prediction strategies. We conclude that k-fold cross-validation is effective for model selection within a model class and on average, but it can be unreliable in specific cases or when comparing models from different classes—this latter issue warranting further investigation. Full article

Companies operating in the energy market in Poland conduct business activity on the basis of special regulations applicable to this type of entity. However, they are, like any other entrepreneur, exposed to the risk of delays in payments, non-payment, restructuring, or even bankruptcy of their contractor. Appropriate instruments should be used to mitigate these risks. There are many methods available today to deal with trading risks. However, they should be tailored to the individual needs of each entrepreneur based on an in-depth analysis of its contractors. This article analyzes the five largest companies selling electricity in Poland in terms of the risk of late payments in the period 2018–2023. It turned out that in the surveyed companies in the period 2018–2013, the amount of receivables was constantly increasing, and the average recovery term was longer than the average payment term in enterprises in general. The real impact of delayed payments on the profitability of the surveyed companies was also calculated. Then, the available methods of transaction risk mitigation (tangible collateral, personal collateral, form of paying, other legal, banking and insurance instruments) were analyzed and described, and whether and to what extent they are used in the surveyed companies. The conducted research also allowed the author to conclude that, unfortunately, despite the existence of many instruments, they are not used due to the costs and formalities associated with their acquisition. Full article

Financial distress poses a significant risk to companies worldwide, irrespective of their nature or size. It refers to a situation where a company is unable to meet its financial obligations on time, potentially leading to bankruptcy and liquidation. Predicting distress has become a crucial application in business classification, employing both Statistical approaches and Artificial Intelligence techniques. Researchers often compare the prediction performance of different techniques on specific datasets, but no consistent results exist to establish one model as superior to others. Each technique has its own advantages and drawbacks, depending on the dataset. Recent studies suggest that combining multiple classifiers can significantly enhance prediction performance. However, such ensemble methods inherit both the strengths and weaknesses of the constituent classifiers. This study focuses on analyzing and comparing the financial status of Indian automobile manufacturing companies. Data from a sample of 100 automobile companies between 2013 and 2019 were used. A novel Firm-Feature-Wise three-step missing value imputation algorithm was implemented to handle missing financial data effectively. This study evaluates the performance of 11 individual baseline classifiers and all the 11 baseline algorithm’s combinations by using ensemble method. A manual ranking-based approach was used to evaluate the performance of 2047 models. The results of each combination are inputted to hard majority voting mechanism algorithm for predicting a company’s financial distress. Eleven baseline models are trained and assessed, with Gradient Boosting exhibiting the highest accuracy. Hyperparameter tuning is then applied to enhance individual baseline classifier performance. The majority voting mechanism with hyperparameter-tuned baseline classifiers achieve high accuracy. The robustness of the model is tested through k-fold Cross-Validation, demonstrating its generalizability. After fine-tuning the hyperparameters, the experimental investigation yielded an accuracy of 99.52%, surpassing the performance of previous studies. Furthermore, it results in the absence of Type-I errors. Full article

As corporate sector stability is crucial for economic resilience and growth, machine learning has become a widely used tool for constructing early warning systems (EWS) to detect financial vulnerabilities more accurately. While most existing EWS research focuses on bankruptcy prediction models, bankruptcy signals often emerge too late and provide limited early-stage insights. This study employs a random forest approach to systematically examine whether a company’s insolvency status can serve as an effective multi-stage financial distress EWS. Additionally, we analyze how the financial characteristics of insolvent companies differ from those of active and bankrupt firms. Our empirical findings indicate that highly accurate insolvency models can be developed to detect status transitions from active to insolvent and from insolvent to bankrupt. Furthermore, our analysis reveals that the financial determinants of these transitions differ significantly. The shift from active to insolvent is primarily driven by structural and operational ratios, whereas the transition from insolvent to bankrupt is largely influenced by further financial distress in operational and profitability ratios. Full article

Efficient management of bankruptcy risk requires treating distant-to-default (DD) stochastically as long as historical stock prices move randomly and, thus, do not guarantee that history may repeat itself. Using long-term data that date back to 1952–2023, including the nonfinancial companies listed in the Dow Jones Industrial Average and National Association of Securities Dealers Automated Quotations indexes, this study estimates the historical and stochastic DDs via the geometric Brownian motion (GBM). The results show that (a) the association between the debt-to-equity ratio and the stochastic DD can be used as an indicator of excessive debt financing; (b) debt tax savings have a positive effect on stochastic DD; (c) bankruptcy costs have negative effects on stochastic DD; (d) in terms of the size of the company being proxied by sales revenue and the equity market value of the company, the DD is a reliable measure of bankruptcy costs; (e) in terms of macroeconomic influences, increases in the percentage change in manufacturing output are associated with lower observed and stochastic DD; and (f) in terms of the influences of industry, the stochastic DD is affected by the industry average retail inventory to sales. This paper contributes to related studies in terms of focusing on the indicators that a company’s management can focus on to address the stochastic patterns inherent in the estimation of the DD. Full article

Hospital and health system bankruptcies and closures continue to rise in the United States. They are troubling news not only for patients and communities but also for insurance companies. Hospital closures often lead to higher costs for insurers due to increased claim denials, delayed payments, reduced provider network and access to care, higher out-of-network costs, and a disruption of our healthcare system. These factors ultimately impact the health insurance companies’ bottom lines as well as their ability to manage patient care effectively with the risk of causing customer/patient dissatisfaction. Insurance companies can help prevent hospital closures, especially in rural areas, by implementing some of the following mechanisms: timely and adequate payments; improved patient-centric payment systems; and standby capacity payments to cover minimum fixed costs. Such early strategic investments have the potential to offset the higher costs for insurance companies associated with hospital closures and improve the sustainability of the U.S. healthcare system. Full article

With the frequent occurrence of various emergencies, the stable operation of enterprises has been seriously affected, and the research of resilience has received more and more attention in various fields. Enterprise resilience (En_RES) is not only related to corporate survival but is also the key to determining whether a company can realize long-term development. To explore the impact of En_RES on enterprise sustainable development (En_SD), this paper conducts an empirical test using panel regression models based on the data of A-share listed companies in China from 2004 to 2022. It is found that En_RES has a significant positive contribution to En_SD, which is more obvious when the degree of environmental uncertainty is lower, the degree of information sharing is higher, and the degree of business complexity is higher. The mechanism test analysis finds that En_RES can further contribute to En_SD by reducing the bankruptcy risk, improving credit availability, and optimizing resource allocation efficiency. This paper innovatively analyzes and verifies the impact of En_RES on En_SD and its functioning mechanism from the perspective of microenterprises, which not only enrich the theoretical relationship between En_RES and En_SD but also provide important references for enterprises to pay attention to and develop resilience in practice, which can help enterprises better cope with challenges, grasp opportunities, and make contributions to the sustainable development of enterprises. Full article

Insurance companies are at risk of bankruptcy when their surplus becomes negative, making it necessary to observe the evolution of the surplus over time. In this study, the surplus evolution is assumed to be discrete, based on the fiscal year period. This study examines the surplus model in sharia insurance schemes, focusing on identifying shortcomings in existing models and developing a framework based on community needs. Specifically, this study highlights the potential integration of productive waqf with welfare benefits for fishermen. A systematic review was conducted by collecting scientific works from the Scopus, ScienceDirect, Dimensions, and Google Scholar databases, selected using the PRISMA approach. The results indicate that existing surplus models in sharia insurance schemes remain general and do not address specific community needs, such as fishermen’s welfare. This study provides insights into developing more inclusive and innovative surplus models by integrating productive waqf. These findings are expected to encourage the development of sharia insurance schemes oriented toward sustainability and improving fishermen’s welfare. Full article

Forecasting using historical time series data has become increasingly important in today’s world. This paper aims to assess the potential for stable positive development within the wholesale and retail trade sector (SK NACE Section G) and the operations of HORTI, Ltd.( Košice, Slovakia), a company within this industry (SK NACE 46.31—wholesale of fruit and vegetables) by predicting three financial indicators: costs, revenues, and earnings before taxes (EBT) (or earnings after taxes (EAT)). We analyze quarterly data from Q1 2009 to Q4 2023 taken from the sector and monthly data from January 2013 to December 2022 for HORTI, Ltd. Through time series analysis, we aim to identify the most suitable model for forecasting the trends in these financial indicators. The study demonstrates that simple legacy forecasting methods, such as exponential smoothing and Box–Jenkins methodology, are sufficient for accurately predicting financial indicators. These models were selected for their simplicity, interpretability, and efficiency in capturing stable trends, and seasonality, especially in sectors with relatively stable financial behavior. The results confirm that traditional Holt–Winters’ and Autoregressive Integrated Moving Average (ARIMA) models can provide reliable forecasts without the need for more complex approaches. While advanced methods, such as GARCH or machine learning, could improve predictions in volatile conditions, the traditional models offer robust, interpretable results that support managerial decision-making. The findings can help managers estimate the financial health of the company and assess risks such as bankruptcy or insolvency, while also acknowledging the limitations of these models in predicting large shifts due to external factors or market disruptions. Full article

The aim of this study is to explore the influence of environmental, social, and governance (ESG) factors on business failure in Brazil by employing advanced machine learning techniques. We collected data from 235 companies and conducted principal component analysis (PCA) on 40 variables already used in the bankruptcy failure literature, resulting in the formation of seven variables that predict business failure. The results indicate that ESG factors significantly predict business failure in Brazil. This study has implications for investors, policymakers, and business leaders, offering a more precise tool for risk assessment and strategic decision-making. Full article

The aspiration to reach the net zero carbon target has initiated new ideas for the sustainable development of the world economy. However, it has also accelerated the formation of stranded assets in high-carbon-emitting companies. Taking a Chinese thermal power company as an example, this paper proposes a model to estimate the degree of impairment loss for thermal power companies by integrating the net present value model with forward-looking carbon emission pathways under different policy intervention scenarios. The results show that under the low-carbon transition scenario with different policy interventions, the percentage of impairment loss of thermal power companies reaches up to 64.09%. Furthermore, impairment losses formed by stranded assets in the thermal power sector impose a severe shock on the national economy, as most of the impairment losses will ultimately be borne by the state treasury. Compared with conventional thermal power generation, new-energy power generation has a weak performance in delaying company bankruptcy caused by stranded assets. Therefore, in the process of a low-carbon transition, governmental departments should focus on the impairment loss of thermal power companies caused by stranded assets and should further integrate “green support” and “brown punishment” policies to effectively promote the low-carbon transition of thermal power companies. Full article

This paper systematically reviews the behavior of numerical indicators in predicting future bankruptcy of companies through statistical analysis models. Following the PRISMA standard, ten primary studies were included in the review. The obtained results underline (1) the ability of numerical indicators, through simple statistical analysis models, to forecast the bankruptcy of businesses and companies and (2) the reliability of cash flows in predicting financial distress through statistical analysis, and (3) models are built with indicators from a specific economy; it is impossible to consider them stable and unchanging, as changes in a country’s economic conditions can potentially impact their predictive accuracy. Full article