Bankruptcy Forecasting Research Articles

Bankruptcy Forecasting in Enterprises and its Security Using Hybrid Deep Learning Models

In current scenario when economic and risk management sectors need accurate predictions of enterprise bankruptcy, it is very importance issue to research in the field of security of enterprise bankruptcy. In this context, we propose an hybrid deep learning model through the use of convolutional neural network to enhance bankruptcy forecasting models. We address the high-dimensional data and imbalanced problems by introducing feature selection strategically and Synthetic Minority Over-sampling Technique (SMOTE). In a comparative evaluation, the performance of our model is over 81%, which is better than that for Logistic Regression and Support Vector Machines. This leap in accuracy demonstrates the cutting edge unprecedented ability of our model to decrypt complex financial patterns and establishes a new precedent for deep learning applications in the nuanced field of financial analytics.

Ecuadorian Manufacturing Companies in Pandemic: Comparative Analysis of Bankruptcy Forecast

Ecuadorian Manufacturing Companies in Pandemic: Comparative Analysis of Bankruptcy Forecast

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.

Non-numerical Bankruptcy Forecasting Based on Three Trends Values – Increasing, Constant, Decreasing

Non-numerical Bankruptcy Forecasting Based on Three Trends Values – Increasing, Constant, Decreasing

EFFECTIVENESS OF THE BUSINESS FAILURE PREDICTION MODELS IN THE IBERIAN TRANSPORT COMPANIES

Purpose: validate, in the existing models, its effectiveness and ability to provide useful information for decision-making, allowing the choice of one that presented as the best alternative for predicting bankruptcy for companies in the transport economic sector (NACE H) up to 6 years before it occurs. Theoretical Framework: in the last decades, since Beaver's (1966) preliminary work in bankruptcy prediction, followed by Altman (1968) models, numerous authors developed different techniques and models to this purpose. Of all the techniques used and developed in almost 60 years of bankruptcy prediction study, Multivariate Discriminant Analysis (MDA) stands out. Despite its limitations, it best combines management and usage simplicity offering stable levels of efficiency. Design/Methodology/Approach: we selected, Portuguese and Spanish companies, from the transport and storage economic sector, subject to statutory auditing, in a sample of 22 companies, considered healthy, according to the most common criterion in the literature: Equity above zero, during six and that in the seventh were considered bankrupt (Equity below zero) and another, paired with the previous one, by Total Assets and Revenues, with 36 companies that presented Equity above zero throughout all the analyzed period, granting the analyzed models full forecasting potential. Were used 21 multi-sectorial models, for different timelines and geographies, with a greater presence in the literature, or developed by Edward Altman, a unique researcher on this subject, between 1979 and 2014. Findings: As a main conclusion, in addition to the description of models and techniques, the formulations developed by Carvalho das Neves (1998), Lizarraga (1998) and Monelos et al. (2011) were the best predictors of bankruptcy, up to 6 years before it occurs, for Portuguese and Spanish companies in the transport economic sector (NACE H). Research, Practical & Social Implications: This information can be used by any interested entity to improve current conditions, optimize and enhance the usage the bankruptcy forecast models. Originality/Value: The global financial crisis and the growing number of company closures make it crucial to understand the causes of corporate failure, with emphasis on forecasting and anticipating it.

Bankruptcy forecast analysis: empirical study of the manufacturing subsector of Norte de Santander, Colombia

The study \"Bankruptcy Forecast Analysis in the Manufacturing Subsector of Norte de Santander, Colombia (2015 – 2022)\" offers an empirical evaluation of financial stability in this region, using the capital structure theory of Modigliani and Miller and the Altman Z-Score model for bankruptcy risks as a theoretical foundation. Through a quantitative approach that includes time series analysis, the internal and external factors that affect the financial competitiveness of the subsector are analyzed. The research highlights the contributions and limitations of capital structure theory and the Z-Score model in real practices, and discusses the integration of advanced technologies such as artificial intelligence to fine-tune financial risk predictions. Using purposeful sampling and documentary analysis, patterns are identified and business solvency is assessed, providing a detailed view of the sector\'s resilience in the face of economic challenges. This study provides valuable insights for those interested in the financial sustainability of the manufacturing sector, highlighting the importance of adapting risk management strategies to regional particularities. Its contribution to the specialized literature lies in offering an updated perspective on bankruptcy forecasting and financial management adapted to the specific context of the Colombian emerging economy.

Financial forecasting of bankruptcy of small and medium-sized business entities

Financial forecasting of bankruptcy of small and medium-sized business entities

Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset

Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset

METHODOLOGICAL APPROACHES TO ASSESSING THE LEVEL OF ECONOMIC SECURITY OF THE ENTERPRISE

The article is devoted to the problem of assessing the level of economic security of enterprises in modern conditions. The authors define economic security as an important aspect of the functioning and development of enterprises in a dynamic economic environment. The authors point out the urgency of the problem of finding criteria and tools for assessing this indicator, given the variety of approaches to determining the economic security of an enterprise in the literature. The methodology uses general scientific and special methods, such as analysis, synthesis, logical, terminological, financial, systemic and structural analysis, systematization and generalization, to confirm the reliability of the results obtained. The approaches and methods for assessing the level of economic security of an enterprise depend on the diversity of interpretations of the very concept of «economic security of an enterprise», which leads to the absence of a single indicator for a full assessment of this phenomenon. Different approaches to assessing the economic security of an enterprise are presented through resource-functional, indicator, profit and investment, three-dimensional, program-target, bankruptcy forecasting, economic and mathematical, systemic and economic risk theory. Each of these approaches uses different methods and criteria to determine the level of economic security of an enterprise. It is noted that economic security assessment can be carried out using various methods, such as expert assessments, regression and variance analysis, exponential smoothing, fuzzy systems theory and multivariate statistical analysis. The author emphasizes the importance of a systematic approach that takes into account internal and external relations, interdependence and subordination of the functional components of an enterprise when assessing the level of its economic security. For a comparative analysis of the levels of functional components of the economic security of enterprise, the authors use the calculation of the rates of change of indicators. The general methodology for calculating indicators of the levels of functional components includes analysis and evaluation of the dynamics of indicators, which indicates trends and changes in various areas of economic security of enterprise.

Applicability of bankruptcy prediction models in the western Hungarian food retail sector

The article examines the applicability of existing bankruptcy prediction models in the food retail sector. The industry has been chosen for analysis due to the fact, that according to official statistics, it has been significantly affected by the events of recent years (Covid-19 epidemic and economic crisises). For the study, a database was created with all firms that had ceased to operate in the sector over an 11-year period in 3 counties. The main objectives of the study were: 1) to check whether there are differences in the reliability of the models and which models are most applicable to the selected sector; 2) to investigate the accuracy of the models over the time horizons of bankruptcy; 3) to check the accuracy of forecasting models by type of bankruptcy completion procedure; 4) provide suggestions on the possibilities of applying bankruptcy forecasting models and indicate directions for further research. During the research, it was found that not all considered models are suitable for accurate forecasting. Two of the five models have very low prediction accuracy (Virág-Hajdu model and Tafler model). Assessment of model accuracy across bankruptcy time horizons showed that there is little variation in short-term forecast accuracy, with a slight decrease in the reliability of the models in the long term. Examining the predictive accuracy of the models by completion procedure type revealed that the types of procedures affected the prediction. The most accurate results were obtained for liquidation, while voluntary liquidation was the least accurate in several cases. The use of bankruptcy prediction models is important for an enterprise because they are a key tool for reducing its operational risk and preventing financial problems. The study can provide information for businesses in the food retail sector and the results can be used for further research. There are additional opportunities for expanding the enterprise database, transferring the research to other sectors of the economy, and expanding the number of bankruptcy models to study the suitability of their application to enterprises of various industries

Modern Approaches in Credit Risk Modeling: A Literature Review

Abstract In the financial industry, models are pervasive, and their quantity and complexity continue to increase. Constant advancements are made in econometric and statistical theory, but a fast-developing body of rules and regulations governing their use needs modeling specialists to remain vigilant and adaptable. The tendency of these regulations to be ambiguous necessitates that industry professionals and institutions interpret them independently and jointly. This leads in what is referred to as a “industry standard,” or a set of procedures that are recognized among modeling professionals but not necessarily to those outside of the industry. Non-practitioners in the industry may view the modeling department as a “black box” for these reasons. The accurate evaluation of financial credit risk and the forecasting of bankruptcy are crucial to both the economy and society. In recent years, more and more approaches and algorithms have been advanced for this purpose. At this point, it is of the highest concern to investigate the current credit risk assessment methods. In this paper, we review the traditional statistical models and cutting-edge intelligent methods for forecasting financial distress, with a focus on the greatest advances in the academic literature, as the promising trend in this field. Lastly, the paper will conclude with an overview of the evolution of methodologies and conceptual frameworks in credit risk management research, as well as possible future research directions.

Bankruptcy prediction using machine learning models with the text-based communicative value of annual reports

We investigate whether including the text-based communicative value of annual report increases the predictive power of four machine learning models (Logistic regression, Random Forest, XGBoost, and Support Vector Machine) for corporate bankruptcy prediction using U.S. firm observations from 1994 to 2018. We find that the overall prediction effectiveness of these four models (e.g. accuracy, F1-score, AUCs) significantly improves, especially true in the performance of XGBoost and Random Forest models. In addition, we find that annual report text-based communicative value variables significantly reduce models’ Type II error and keep the Type I error at a relatively small level, especially for the short-term bankruptcy forecast. The results reveal that annual report text-based communicative value effectively mitigates the model misidentification of a non-bankrupt firm as a bankrupt firm. Our results also suggest that annual report text-based communicative value is helpful for bank’s corporate loan underwriting decisions. Finally, our findings still hold when considering different testing periods and random state settings, replacing by another publicly available bankruptcy dataset, and introducing neural network models.

Fintech tools for predicting bankruptcy: integration of traditional and innovative approaches

Methods. The theoretical and methodological basis of the study is the scientific developments of domestic and foreign scholars related to the issues of determining the role and features of using traditional discriminant models of bankruptcy forecasting and modern FinTech tools. The results of the study were obtained using the methods of abstraction; general and particular – in establishing the unity of existing traditional methods of bankruptcy forecasting and innovative FinTech technologies; the systematic method was used to systematise a wide range of FinTech tools specifically designed to predict the bankruptcy of enterprises, the method of generalisation – in identifying common digital elements to create analytical capabilities for managing the financial activities of an enterprise and risk management. Methods of theoretical and empirical research, including abstraction and comparison, were also used to identify the specifics of applying various forecasting models and the capabilities and advantages of FinTech tools. Graphical analysis methods were used to visualise the results of the study. Results. The results of the study revealed the potential of integrating discriminant models and FinTech tools in bankruptcy forecasting, which will help improve financial analysis and risk management in the context of economic changes. The article shows that the use of discriminant models for predicting bankruptcy has the potential to improve financial analysis and effective risk management. These models allow to take into account various financial indicators and factors that affect the probability of bankruptcy of an enterprise. They provide an opportunity to assess a company's financial stability and ability to repay its liabilities. If used correctly, these models can help to ensure early detection of financial problems and take effective measures to prevent bankruptcy. Novelty. In the course of the study of bankruptcy forecasting models, it is established that the optimal combination of discriminant models and FinTech tools provides an opportunity to obtain accurate forecasts of the further functioning and development of enterprises and opens up new prospects for financial analysis to avoid risks and implement effective innovative solutions in today's changing environment. Practical value. It is the ability to integrate traditional and innovative approaches to solving specific problems related to bankruptcy forecasting and the development of enterprises in an unstable environment.

The Capacity to Forecast Enterprise Insolvency on the Polish Market Using the Precursory Altman Z-Score Model (1968)

Theoretical background: Many years have passed since the publication of the first multidimensional model (Z-score) of early bankruptcy warning by E.I. Altman in 1968. New models have since emerged in different countries. In this research stream, the share of econometric modelling based on the assumptions of discriminant analysis, i.e. MDA – Multiple Discriminant Analysis – one of the multidimensional classification methods categorised as empirical-induction methods, plays a particularly important role. Purpose of the article: The article assesses the capacity of the Altman Z-Score to forecast (1 year ahead) insolvency of enterprises on the Polish market. Research methods: Due to the sample size (i.e. over 2,700 bankrupt entities in Poland), the study constitutes one of the broadest studies carried out so far on the assessment of the predictive capacity of early warning models in Polish economy. Main findings: The authors, conducting research in this area, put forward a hypothesis that the variables that were well identified and introduced to discriminatory models in one economy work equally well in other countries. Despite the economic differences in these countries and the phases of the business cycle, the financial-analysis indicators that allow effective forecasting of enterprise bankruptcy are valid for different economies. The discriminant-function variability ranges that are characteristic of failing and healthy economic entities change but the variables themselves are good predictors.

Do forecasts of bankruptcy cause bankruptcy? A machine learning sensitivity analysis

It is widely speculated that auditors’ public forecasts of bankruptcy are, at least in part, self-fulfilling prophecies in the sense that they actually cause bankruptcies that would not have otherwise occurred. This conjecture is hard to prove, however, because the strong association between bankruptcies and bankruptcy forecasts could simply indicate that auditors are skillful forecasters with unique access to highly predictive covariates. In this paper we investigate the causal effect of bankruptcy forecasts on bankruptcy using nonparametric sensitivity analysis. We contrast our analysis with two alternative approaches: a linear bivariate probit model with an endogenous regressor and a recently developed bound on risk ratios called E-values. Additionally, our machine learning approach incorporates a monotonicity constraint corresponding to the assumption that bankruptcy forecasts do not make bankruptcies less likely. Finally, a tree-based posterior summary of the treatment effect estimates allows us to explore which observable firm characteristics moderate the inducement effect.

RESEARCH OF THE EPISTEOLOGY AND DEVELOPMENT OF FINANCIAL RISK MANAGEMENT: ORIGIN, CONCEPTS, PROSPECTIVE DIRECTIONS

Goal – research of financial rhysicology from the standpoint of modern theoretical and practical requirements, identification of existing problems in the theory and practice of financial risk management and determination of directions for its further development in order to increase the effectiveness of practical financial management of enterprises in conditions of increased uncertainty of the environment in which they (enterprises) operate. Method. To achieve the goal, analytical studies were performed and scientific methods were used, in particular: content analysis of modern views and positions of domestic and foreign scientists regarding the definition and classification of entrepreneurial, economic and financial risks; the method of system analysis and portfolio factor analysis for structuring and determining the hierarchy in the risk system, a systematic approach in substantiating the unity of strategic, tactical and operational risk management; assessment of quantitative and qualitative methods of financial risk analysis, as well as methods of behavioral economics. The results. Considering the relevance of the topic, the main attention is paid to the analysis of financial rhysicology with an in-depth study of the complex of theoretical, scientific and practical aspects of this topic. The objective prerequisites for the formation and history of the development of financial rhysicology and its scientific connections with other related fields of scientific knowledge have been studied. The content analysis of the publications of domestic and foreign scientists in the field of financial risk management and those related to it made it possible to identify the main problems and unresolved issues that are currently inhibiting the development of financial management. It has been proven that such obstacles include, in particular: the vague definition of the specifics of financial risks and the lack of research into their systemic synergistic relationships with other risks; a certain disregard of the objective relationship between strategic, tactical and operational financial risk management and the absence of indicators of strategic, tactical and operational financial risks adapted to the conditions and features of a specific industry, enterprise, situation, etc.; insufficient development of financial risk assessment methods for the most risky types of activities or financial transactions; the need to develop and apply the manager's risk appetite in financial risk management; inadequate use of outdated, including foreign, methods and models in the methods of financial analysis and bankruptcy forecasting; excessive, sometimes unjustified, use of complex mathematical methods and models in financial risk management, which reduce the effectiveness of risk management. Scientific novelty. The conducted research has a scientific result: a system of portfolios of modern economic and economic risks that affect financial risks; substantiating the combination of strategic, tactical and operational financial risk management as a complete system – the triad of financial management; pragmatically priming transition from classical to neoclassical rhysicology; justification of changes in the methods of financial analysis and bankruptcy forecasting and the principled rejection of the use of outdated and therefore inadequate methods and models; substantiation of the application of elements of behavioral economics, in particular – the risk appetite of the financial manager, when making management decisions, which in the complex made it possible to form a list of the most relevant areas of further scientific research. Practical significance. The results of the study are aimed at improving the practice of making financial decisions and carrying out financial transactions taking into account the existing economic, economic and financial risks; should ensure the validity and improve the effectiveness of the relevant management actions. Key words: risk, classical and neoclassical rhysicology, content analysis, financial risk, financial management, system, financial analysis, risk appetite.

Analyzing acquisitions using bankruptcy forecasting models

Merger and acquisition (M&A) transactions are often associated with increasing market share, improved operational efficiency and the ability to create value. Nevertheless, many studies have presented the drawbacks of M&A transactions, including an increase in the risk of bankruptcy. A significant number of companies go bankrupt in Hungary every year, even in non-recession periods. The goal of our analysis is to examine how the risks of companies, especially their bankruptcy risks, were affected by acquisitions in Hungary between 2008 and 2017. The results of our study with a focus on Hungarian companies confirm the research published in the international literature, according to which M&A transactions are unsuccessful in many cases, and the transactions are often followed by adverse effects. Our research results may change the perspectives associated with the allegedly well-known benefits and expectations of acquisitions. Based on our research, we recommend that companies planning to merge or acquire should also consider the possible failure of such transactions.

Company Bankruptcy Prediction with SMOTE

Abstract: Effective bankruptcy forecasting is essential for financial firms to make sound loan decisions. In general, the two most essential aspects influencing prediction performance are input variables (or features) such as financial ratios and prediction approaches such as statistical and machine learning techniques. While numerous relevant publications have presented innovative prediction algorithms, relatively few have examined the discriminating potential of bankruptcy prediction variables. In addition to financial ratios (FRs), corporate governance indicators (CGIs) have been identified as an important form of input variable in the literature. The prediction performance produced by merging CGIs with FRs, however, has not been thoroughly investigated. Only a subset of CGIs and FRs were employed in related investigations, and the characteristics used may vary from study to study. As a result, the goal of this work is to evaluate the prediction performance obtained by combining seven distinct FR categories and five different CGI categories. Based on a real-world dataset from Taiwan, the experimental results reveal that the FR categories of solvency and profitability, as well as the CGI categories of board structure and ownership structure, are the most critical variables in bankruptcy prediction. The best prediction model performance is determined by combining prediction accuracy, Type I/II errors, ROC curve, and misclassification cost. However, these findings may not be applicable in certain countries, such as China, where the definition of distressed enterprises is vague and the features of corporate governance measures are not explicit.

Applying, updating and comparing bankruptcy forecasting models. The case of Greece

Research Question: This study examines whether bankruptcy prediction models work well during recessionary periods, on an advanced economy, and how their results can be improved, via a methodological approach to change the coefficients of their variables. Motivation: This is the first study to follow a methodological approach of a simultaneous comparison-update-comparison task, during a recessionary period, for an advanced economy. Idea: The paper explores, updates and compares the effectiveness of five of the most common bankruptcy prediction models on the listed companies of an advanced economy (Greece), covering the recessionary period of 2010-2019. Data: The study sample consists of Greek companies, listed in the Athens Stock Exchange, covering the period 2010-2019, classified into viable and non-viable, based on specific criteria. The final sample consists of fifty-two (52) companies, listed in the Athens Stock Exchange during the period from 2010 to 2019. Tools: We follow a two-stage analysis. First, we apply the original five bankruptcy prediction models of Altman (2000) and Grammatikos and Gloubos (1984), MDA and LPM models, Taffler (1983) and Dimitras et al. (1999) Next, we recalculate their coefficients, keeping the variables stable, and we again apply them to the same sample and compare them again. Findings: We find that the original models are significantly biased against viable companies, but predict with almost perfect accuracy non-viable companies’ bankruptcy. Once we update the variables’ coefficients, we get significantly improved results as regards correctly predicting viable companies, at the expense of slightly decreased, but still high, non-viable companies’ bankruptcy prediction rates. We suggest a similar methodology to be applied in other similar economies, to increase models’ accuracy. Contribution: The contribution of the paper is threefold. First, we show how we can develop highly accurate bankruptcy prediction models that can be applied in the economic environment of a developed economy. Second, we show that these models work well during recessionary periods as well, and can also be improved when their coefficients are changed. Third, we suggest a methodology of applying, comparing and updating such models, thus showing in detail this improvement process per model.

Identifying Symptoms of Bankruptcy Risk Based on Bankruptcy Prediction Models—A Case Study of Poland

The article presents selected Polish early warning models (logit and discriminant models) that allow the assessment of the risk of bankruptcy of a company, and the purpose of the considerations is to indicate their prognostic effectiveness in predicting susceptible Polish companies one year before their declarations of bankruptcy. The limitations of these methods were also indicated in unpredictable situations, such as the outbreak of an economic crisis, e.g., caused by a humanitarian crisis—the COVID-19 pandemic. Another aim chosen in the article is a methodological critical assessment of the phenomenon of widespread use of foreign models (including the common Altman method) in the study of the risk of bankruptcy of Polish enterprises. Models developed on a sample of foreign enterprises without prior adaptation to domestic conditions are used all over the world, so the conclusions of the article are applicable internationally. The research was based on a query of Polish and foreign literature in the field of economic and legal aspects of bankruptcy and financial analysis, including, in particular, bankruptcy forecasting. The empirical research analyzes the financial data of 50 Polish enterprises from 2017 to 2018. The effectiveness of the selected bankruptcy forecasting models in identifying enterprises from section C of the Polish economy (industrial processing) that filed for bankruptcy in 2018 and 2019 was tested. The time frame fully allows for the identification and the assessment of the effectiveness of early warning models a year before bankruptcy.