Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches

Predicting financial distress in TSX-listed firms using machine learning algorithms

Introduction

This study investigates the application of machine learning (ML) algorithms, a subset of artificial intelligence (AI), to predict financial distress in companies. Given the critical need for reliable financial health indicators, this research evaluates the predictive capabilities of various ML techniques on firm-level financial data.

Methods

The dataset comprises financial ratios and firm-specific variables from 464 firms listed on the TSX. Multiple ML models were tested, including decision trees, random forests, support vector machines (SVM), and artificial neural networks (ANN). Recursive feature elimination with cross-validation (RFECV) and bootstrapped CART were also employed to enhance model stability and feature selection.

Results

The findings highlight key predictors of financial distress, such as revenue growth, dividend growth, cash-to-current liabilities, and gross profit margins. Among the models tested, the ANN classifier achieved the highest accuracy at 98%, outperforming other algorithms.

Discussion

The results suggest that ANN provides a robust and reliable method for financial distress prediction. The use of RFECV and bootstrapped CART contributes to the model's stability, underscoring the potential of ML tools in financial health monitoring. These insights carry valuable implications for auditors, regulators, and company management in enhancing practices around financial oversight and fraud detection.

For better or worse: Governing healthcare organisations in times of financial distress

Due to processes of financialisation, financial parties increasingly penetrate the healthcare domain and determine under which conditions care is delivered. Their influence becomes especially visible when healthcare organisations face financial distress. By zooming-in on two of such cases, we come to know more about the considerations, motives and actions of financial parties in healthcare. In this research, we were able to examine the social dynamics between healthcare executives, banks and health insurers involved in a Dutch hospital and mental healthcare organisation on the verge of bankruptcy. Informed by interviews, document analysis and translation theory, we reconstructed the motives and strategies of executives, banks and health insurers and show how they play a crucial role in decision-making processes surrounding the survival or downfall of healthcare organisations. While parties are bound by legislation and company procedures, the outcome of financial distress can still be influenced. Much depends on how executives are perceived by financial stakeholders and how they deal with threats of destabilisation of the network. We further draw attention to the consequences of financialisation processes on the practices of healthcare organisations in financial distress.

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).

Speech emotion recognition and text sentiment analysis for financial distress prediction

In recent years, there has been an increasing interest in text sentiment analysis and speech emotion recognition in finance due to their potential to capture the intentions and opinions of corporate stakeholders, such as managers and investors. A considerable performance improvement in forecasting company financial performance was achieved by taking textual sentiment into account. However, far too little attention has been paid to managerial emotional states and their potential contribution to financial distress prediction. This study seeks to address this problem by proposing a deep learning architecture that uniquely combines managerial emotional states extracted using speech emotion recognition with FinBERT-based sentiment analysis of earnings conference call transcripts. Thus, the obtained information is fused with traditional financial indicators to achieve a more accurate prediction of financial distress. The proposed model is validated using 1278 earnings conference calls of the 40 largest US companies. The findings of this study provide evidence on the essential role of managerial emotions in predicting financial distress, even when compared with sentiment indicators obtained from text. The experimental results also demonstrate the high accuracy of the proposed model compared with state-of-the-art prediction models.

A Hybrid Algorithm-Level Ensemble Model for Imbalanced Credit Default Prediction in the Energy Industry

Credit default prediction for the energy industry is essential to promoting the healthy development of the energy industry in China. While previous studies have constructed various credit default prediction models with brilliant performance, the class-imbalance problem in the credit default dataset cannot be ignored, where the numbers of credit default cases are usually much smaller than the number of non-default ones. To address the class-imbalance problem, we proposed a novel CT-XGBoost model, which adds to XGBoost with two algorithm-level methods for class imbalance, including the cost-sensitive strategy and threshold method. Based on the credit default dataset consisting of energy corporates in western China, which suffers from the class-imbalance problem, the CT-XGBoost model achieves better performance than the conventional models. The results indicate that the proposed model can efficiently alleviate the inherent class-imbalance problem in the credit default dataset. Moreover, we analyze how the prediction performance is influenced by different parameter settings in the cost-sensitive strategy and threshold method. This study can help market investors and regulators precisely assess the credit risk in the energy industry and provides theoretical guidance to solving the class-imbalance problem in credit default prediction.

Deep Learning-Based Model for Financial Distress Prediction

Predicting bankruptcies and assessing credit risk are two of the most pressing issues in finance. Therefore, financial distress prediction and credit scoring remain hot research topics in the finance sector. Earlier studies have focused on the design of statistical approaches and machine learning models to predict a company's financial distress. In this study, an adaptive whale optimization algorithm with deep learning (AWOA-DL) technique is used to create a new financial distress prediction model. The goal of the AWOA-DL approach is to determine whether a company is experiencing financial distress or not. A deep neural network (DNN) model called multilayer perceptron based predictive and AWOA-based hyperparameter tuning processes are used in the AWOA-DL method. Primarily, the DNN model receives the financial data as input and predicts financial distress. In addition, the AWOA is applied to tune the DNN model's hyperparameters, thereby raising the predictive outcome. The proposed model is applied in three stages: preprocessing, hyperparameter tuning using AWOA, and the prediction phase. A comprehensive simulation took place on four datasets, and the results pointed out the supremacy of the AWOA-DL method over other compared techniques by achieving an average accuracy of 95.8%, where the average accuracy equals 93.8%, 89.6%, 84.5%, and 78.2% for compared models.

Combining white box models, black box machines and human interventions for interpretable decision strategies

Granting a short-term loan is a critical decision. A great deal of research has concerned the prediction of credit default, notably through Machine Learning (ML) algorithms. However, given that their black-box nature has sometimes led to unwanted outcomes, comprehensibility in ML guided decision-making strategies has become more important. In many domains, transparency and accountability are no longer optional. In this article, instead of opposing white-box against black-box models, we use a multi-step procedure that combines the Fast and Frugal Tree (FFT) methodology of Martignon et al. (2005) and Phillips et al. (2017) with the extraction of post-hoc explainable information from ensemble ML models. New interpretable models are then built thanks to the inclusion of explainable ML outputs chosen by human intervention. Our methodology improves significantly the accuracy of the FFT predictions while preserving their explainable nature. We apply our approach to a dataset of short-term loans granted to borrowers in the UK, and show how complex machine learning can challenge simpler machines and help decision makers.

Financial Distress Prediction of Cooperative Financial Institutions—Evidence for Taiwan Credit Unions

In response to relatively little evidence on the determinants of the financial distress in cooperative financial institutions (e.g., Credit Unions), this paper proposes a distress indicator of Merton Distance to default (Merton DD), which was constructed with a z-score, possessed improved predictive capability, but reducing equity volatility. This model possesses the advantages of both hazard and modified Merton DD model, which could timely reflect market volatility and predict when distress would occur. As a demonstration, we applied this model to forecast the financial distress of credit unions in Taiwan. The results can provide more information to researchers.

Minimalistic Logit Model as an Effective Tool for Predicting the Risk of Financial Distress in the Visegrad Group

Predicting financial distress is one of the most well-known issues in corporate finance. Investors and other stakeholders often use prediction models as relevant tools for identifying weaknesses to eliminate potential threats to business partners. This paper aims to present an effective logistic regression model for a one-year-ahead prediction of financial distress with the minimum set of predictors as a part of risk management. The paper is motivated by various works dealing with the curse of dimensionality phenomenon and the observation that the increasing number of logit-model predictors does not improve the prediction—on the contrary. Monitoring the significance of improvement in the stepwise growth of the predictor set is used to identify the minimal set. Logistic regression with cross-validation is involved in the modelling process. The proposed model is compared with other logit-based models used regionally or globally on the same large dataset, which underlines the model validity and robustness. The proposed logit model contains only two significant predictors and achieves excellent performance metrics compared to other models. The added value of the article lies in a simple application for managers, investors, creditors, financial institutions, and others with a reliable classification of companies into healthy and unhealthy company groups.

Can Corporate Social Responsibility Decrease the Negative Influence of Financial Distress on Accounting Quality?

This study aimed to test how corporate social responsibility (CSR) can affect the impact of corporate financial distress on earnings management. Based on the existing literature, distressed firms tend to hide their financial crises through earnings manipulation. However, as CSR can positively affect companies in terms of performance, risk reduction, and market response, the better a firm’s CSR is the less managers will attempt earnings management even if they experience temporary distress. Consistent with the literature, test results using Korean-listed companies show that distress increased earnings management, and we confirmed that CSR weakened the positive effect of distress on earnings management. After testing each of the CSR subcategories, significant results were found mainly on environmental performance, reflecting the globally increasing interest in environmental issues. This study contributes to the literature on distress and earnings management, which rarely considers CSR as a moderating factor.

Explanatory Factors of Business Failure: Literature Review and Global Trends

This study aims to provide a bibliometric analysis of business failure research, recognise the main existing research topics and establish future research challenges. The results, based on a sample of 588 articles, show that the number of published papers and citations has grown steadily, especially in the last 14 years. The most productive and relevant journals, countries, institutions and authors are presented using bibliometric performance indicators. In addition, through the graphical mapping of strategic diagrams, this study identifies the most significant research trends and proposes several directions for future research. The results of this research may be helpful for beginner researchers and experts in business failure, as they contribute to bringing clarity to this line of investigation. These results reveal all the aspects involved in business failure research, analysing its temporal and methodological characterisation, and the most prolific authors who have participated in its study (see, i.e., H. Li), leading journals (see, i.e., Expert Systems with Applications) or academic institutions that have headed the scientific analysis of this business phenomenon. Likewise, it has been possible to identify three main areas in which the research on business failure has been focused: business, management and accounting; economics, econometrics and finance; and social sciences. In addition, a complete, synthesised and organised summary of the various definitions, perspectives and research trends are presented.

Impact of External Environmental Dimensions on Financial Performance of Major Teaching Hospitals in the U.S

Teaching hospitals have a unique mission to not only deliver graduate medical education but to also provide both inpatient and ambulatory care and to conduct clinical medical research; therefore, they are under constant financial pressure, and it is important to explore what types of external environmental components affect their financial performance. This study examined if there is an association between the short-term and long-term financial performance of major teaching hospitals in the United States and the external environmental dimensions, as measured by the Resource Dependence Theory. Data for 226 major teaching hospitals spanning 46 states were analyzed. The dependent variable for short-term financial performance was days cash on hand, and dependent variable for long-term financial performance was return on assets, both an average of most recently available 4-year data (2014-2017). Utilizing linear regression model, results showed significance between outpatient revenue and days cash on hand as well as significant relationship between population of the metropolitan statistical area, unemployment rate of the metropolitan statistical area, and teaching hospital's return on assets. Additionally, system membership, type of ownership/control, and teaching intensity also showed significant association with return on assets. By comprehensively examining all major teaching hospitals in the U.S. and analyzing the association between their short-term and long-term financial performance and external environmental dimensions, based upon Resource Dependence Theory, we found that by offering diverse outpatient services and novel delivery options, administrators of teaching hospitals may be able to increase organizational liquidity.

A Decision Support System for Corporate Tax Arrears Prediction

This paper proposes a decision support system to predict corporate tax arrears by using tax arrears in the preceding 12 months. Despite the economic importance of ensuring tax compliance, studies on predicting corporate tax arrears have so far been scarce and with modest accuracies. Four machine learning methods (decision tree, random forest, k-nearest neighbors and multilayer perceptron) were used for building models with monthly tax arrears and different variables constructed from them. Data consisted of tax arrears of all Estonian SMEs from 2011 to 2018, totaling over two million firm-month observations. The best performing decision support system, yielding 95.3% accuracy, was a hybrid based on the random forest method for observations with previous tax arrears in at least two months and a logical rule for the rest of the observations.

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.

Bankruptcy Prediction with the Use of Data Envelopment Analysis: An Empirical Study of Slovak Businesses

The paper deals with methods of predicting bankruptcy of a business with the aim of choosing a prediction method which will have exact results. Existing bankruptcy prediction models are a suitable tool for predicting the financial difficulties of businesses. However, such tools are based on strictly defined financial indicators. Therefore, the Data Envelopment Analysis (DEA) method has been applied, as it allows for the free choice of financial indicators. The research sample consisted of 343 businesses active in the heating industry in Slovakia. Analysed businesses have a significant relatively stable position in the given industry. The research was based on several studies which also used the DEA method to predict future financial difficulties and bankruptcies of studied businesses. The estimation accuracy of the Additive DEA model (ADD model) was compared with the Logit model to determine the reliability of the DEA method. Also, an optimal cut-off point for the ADD model and Logit model was determined. The main conclusion is that the DEA method is a suitable alternative for predicting the failure of the analysed sample of businesses. In contrast to the Logit model, its results are independent of any assumptions. The paper identified the key indicators of the future success of businesses in the analysed sample. These results can help businesses to improve their financial health and competitiveness.

One-Dimensional Convolutional Neural Networks with Feature Selection for Highly Concise Rule Extraction from Credit Scoring Datasets with Heterogeneous Attributes

Convolution neural networks (CNNs) have proven effectiveness, but they are not applicable to all datasets, such as those with heterogeneous attributes, which are often used in the finance and banking industries. Such datasets are difficult to classify, and to date, existing high-accuracy classifiers and rule-extraction methods have not been able to achieve sufficiently high classification accuracies or concise classification rules. This study aims to provide a new approach for achieving transparency and conciseness in credit scoring datasets with heterogeneous attributes by using a one-dimensional (1D) fully-connected layer first CNN combined with the Recursive-Rule Extraction (Re-RX) algorithm with a J48graft decision tree (hereafter 1D FCLF-CNN). Based on a comparison between the proposed 1D FCLF-CNN and existing rule extraction methods, our architecture enabled the extraction of the most concise rules (6.2) and achieved the best accuracy (73.10%), i.e., the highest interpretability–priority rule extraction. These results suggest that the 1D FCLF-CNN with Re-RX with J48graft is very effective for extracting highly concise rules for heterogeneous credit scoring datasets. Although it does not completely overcome the accuracy–interpretability dilemma for deep learning, it does appear to resolve this issue for credit scoring datasets with heterogeneous attributes, and thus, could lead to a new era in the financial industry.

Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry

Using logistic regression technique and Deep Recurrent Convolutional Neural Network, this study seeks to improve the capacity of existing bankruptcy prediction models for the restaurant industry. In addition, we have verified, in the review of existing literature, the gap in the research of restaurant bankruptcy models with sufficient time in advance and that only companies in the restaurant sector in the same country are considered. Our goal is to build a restaurant bankruptcy prediction model that provides high accuracy, using information distant from the bankruptcy situation. We had a sample of Spanish restaurants corresponding to the 2008–2017 period, composed of 460 solvent and bankrupt companies, for which a total of 28 variables were analyzed, including some of a non-financial nature, such as age of restaurant, quality, and belonging to a chain. The results indicate that the best bankruptcy predictors are financial variables related to profitability and indebtedness and that Deep Recurrent Convolutional Neural Network exceeds logistic regression in predictive capacity.

Forecasting the Environmental, Social, and Governance Rating of Firms by Using Corporate Financial Performance Variables: A Rough Set Approach

The environmental, social, and governance (ESG) rating of firms is a useful tool for stakeholders and investment decision-makers. This paper develops a rough set model to relate ESG scores to popular corporate financial performance measures. This methodology permits handling with information in an uncertain, ambiguous, and imperfect context. A large database was gathered, including ESG scores, as well as industry sector and financial variables for publicly traded European companies during the period 2013–2018. We carried out 500 simulations of the rough set model for different values in the discretization parameter and different grouping scenarios of firms regarding ESG scores. The results suggest that the variables considered are useful in the prediction of ESG rank when firms are clustered in three or four equally balanced groups. However, the prediction power vanishes when a larger number of groups is computed. This would suggest that industry sector and financial variables serve to find big differences across firms regarding ESG, but the significance of the model drops when small differences in ESG performance are scrutinized.

Comparison of the Results of a Data Envelopment Analysis Model and Logit Model in Assessing Business Financial Health

This paper focuses on business financial health evaluation with the use of selected mathematical and statistical methods. The issue of financial health assessment and prediction of business failure is a widely discussed topic across various industries in Slovakia and abroad. The aim of this paper was to formulate a data envelopment analysis (DEA) model and to verify the estimation accuracy of this model in comparison with the logit model. The research was carried out on a sample of companies operating in the field of heat supply in Slovakia. For this sample of businesses, we selected appropriate financial indicators as determinants of bankruptcy. The indicators were selected using related empirical studies, a univariate logit model, and a correlation matrix. In this paper, we applied two main models: the BCC DEA model, processed in DEAFrontier software; and the logit model, processed in Statistica software. We compared the estimation accuracy of the constructed models using error type I and error type II. The main conclusion of the paper is that the DEA method is a suitable alternative in assessing the financial health of businesses from the analyzed sample. In contrast to the logit model, the results of this method are independent of any assumptions.

On Sampling Techniques for Corporate Credit Scoring

The imbalanced dataset is a crucial problem found in many real-world applications. Classifiers trained on these datasets tend to overfit toward the majority class, and this problem severely affects classifier accuracy. This ultimately triggers a large cost to cover the error in terms of misclassifying the minority class especially in credit-granting decision when the minority class is the bad loan applications. By comparing the industry standard with well-known machine learning and ensemble models under imbalance treatment approaches, this study shows the potential performance of these models towards the industry standard in credit scoring. More importantly, diverse performance measurements reveal different weaknesses in various aspects of a scoring model. Employing class balancing strategies can mitigate classifier errors, and both homogeneous and heterogeneous ensemble approaches yield the best significant improvement on credit scoring.

Tax Arrears Versus Financial Ratios in Bankruptcy Prediction

This paper aims to compare the usefulness of tax arrears and financial ratios in bankruptcy prediction. The analysis is based on the whole population of Estonian bankrupted and survived SMEs from 2013 to 2017. Logistic regression and multilayer perceptron are used as the prediction methods. The results indicate that closer to bankruptcy, tax arrears’ information yields a higher prediction accuracy than financial ratios. A combined model of tax arrears and financial ratios is more useful than the individual models. The results enable us to outline several theoretical and practical implications.

Dynamic Bankruptcy Prediction Models for European Enterprises

This manuscript is devoted to the issue of forecasting corporate bankruptcy. Determining a firm’s bankruptcy risk is one of the most interesting topics for investors and decision-makers. The aim of the paper is to develop and to evaluate dynamic bankruptcy prediction models for European enterprises. To conduct this objective, four forecasting models are developed with the use of four different methods—fuzzy sets, recurrent and multilayer artificial neural network, and decision trees. Such a research approach will answer the question of whether changes in indicators are relevant predictors of a company’s coming financial crisis because declines or increases in values do not immediately indicate that the company’s economic situation is deteriorating. The research relies on two samples of firms—the learning sample of 50 bankrupt and 50 non-bankrupt enterprises and the testing sample of 250 bankrupt and 250 non-bankrupt firms.

Corporate governance and failure risk: evidence from Estonian SME population

Purpose

The purpose of this study is to find out how corporate governance is interconnected with failure risk in case of small- and medium-sized enterprises (SMEs).

Design/methodology/approach

The study is based on Estonian whole population of SMEs, in total 67,058 observations, and data are obtained from Estonian Business Register. Failure risk (FR) is portrayed with a well-known Altman et al. (2017) model, while seven variables reflecting corporate governance (CG) based on previous studies have been selected. As the method, logistic regression (LR) is applied with FR in the binary form as a dependent variable and seven CG variables as independent. The effect of firm size and age is studied with two separate LR models.

Findings

The results indicate that with the growth in manager’s age and the presence of managerial ownership, failure risk reduces. In turn, the presence of larger boards and managers having directorships in other firms leads to higher failure risk. Gender heterogeneity in the board, board tenure length and ownership concentration by means of having a majority owner are not associated with failure risk. The obtained results vary with firm size and age.

Originality/value

Unlike this study, research published on this topic earlier has used a much narrower definition of failure, mostly focused on large and listed companies, been sample based and information about corporate governance variables has often been obtained through questionnaires. All these limitations are relaxed in this population level study.

Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine

Accurate forecasts of corporate failure in the Chinese energy sector are drivers for both operational excellence in the national energy systems and sustainable investment of the energy sector. This paper proposes a novel integrated model (NIM) for corporate failure forecasting in the Chinese energy sector by considering textual data and numerical data simultaneously. Given the feature of textual data and numerical data, convolutional neural network oriented deep learning (CNN-DL) and support vector machine (SVM) are employed as the base classifiers to forecast using textual data and numerical data, respectively. Subsequently, soft set (SS) theory is applied to integrate outputs of CNN-DL and SVM. Hence, NIM inherits advantages and avoids disadvantages of CNN-DL, SVM, and SS. It is able to improve the forecasting performance by taking full use of textual data and numerical data. For verification, NIM is applied to the real data of Chinese listed energy firms. Empirical results indicate that, compared with benchmarks, NIM demonstrates superior performance of corporate failure forecasting in the Chinese energy sector.

Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?

Purpose: This study aims to compare the prediction accuracy of traditional distress prediction models for the firms which are at an early and advanced stage of distress in an emerging market, Pakistan, during 2001–2015. Design/methodology/approach: The methodology involves constructing model scores for financially distressed and stable firms and then comparing the prediction accuracy of the models with the original position. In addition to the testing for the whole sample period, comparison of the accuracy of the distress prediction models before, during, and after the financial crisis was also done. Findings: The results indicate that the three-variable probit model has the highest overall prediction accuracy for our sample, while the Z-score model more accurately predicts insolvency for both types of firms, i.e., those that are at an early stage as well as those that are at an advanced stage of financial distress. Furthermore, the study concludes that the predictive ability of all the traditional financial distress prediction models declines during the period of the financial crisis. Originality/value: An important contribution is the widening of the definition of financially distressed firms to consider the early warning signs related to failure in dividend/bonus declaration, quotation of face value, annual general meeting, and listing fee. Further, the results suggest that there is a need to develop a model by identifying variables which will have a higher impact on the financial distress of firms operating in both developed and developing markets.

Peer-to-Peer Lending Platform Selection Using Intuitionistic Fuzzy Soft Set and D-S Theory of Evidence

Peer-to-Peer (P2P) lending brings many benefits for both lenders and borrowers, as well as risk for them, especially for the lenders. To help the lenders select a reliable P2P lending platform with high return and low risk, a more comprehensive selection method is necessary. However, vast information with fluctuant, fuzzy and subjective data on the P2P lending platform make it more difficult for lenders to make the right choice. This paper intends to propose a selection method by using intuitionistic fuzzy soft set to organize the data, and using D-S theory of evidence to integrate the intuitionistic fuzzy values. Besides, the paper analyses the critical factors affecting the lenders’ decision, and presents some indicators for evaluating. Finally, an empirical experiment was given by choosing several P2P lending platforms with real data, and the results are compared with the intuitionistic fuzzy weighted average and Development Index from Wangdaizhijia, that verify the validity and superiority of the proposed method.

Prediction of Business Failure with Fuzzy Models

This paper extends the theory of fuzzy diseases predictions in order to detect the causes of business failure. This extension is justified through the advantages of the reference model and its originality. Moreover, the fuzzy model is completed by this proposal and some parts of it have been published in isolated articles. For this purpose, the fuzzy theory is combined with the OWA operators to identify the factors that generate problems in firms. Also, a goodness index to validate its functionality and prediction capacity is introduced. The model estimates a matrix of economic- financial knowledge based on matrices of causes and symptoms. Knowing the symptoms makes it possible to estimate the causes, and managing them properly, allows monitoring and improving the company’s financial situation and forecasting its future. Also with this extension, the model can be useful to develop suitable computer systems for monitoring companies’ problems, warning of failures and facilitating decision-making.

Anticrisis management: warning signals before failure

Critical situations in the operations of companies, both evitable and inevitable, usually have a certain pattern and development trend but are different as to the duration, sector or region. Many of them ask for some legal procedure, the most critical ones lead to a bankruptcy process. The purpose of the research is the rapid increase in number of bankruptcies in Slovakia in recent years. The main aim of this paper is to analyse the evolution of the bankruptcy as a type of critical situation in Slovak companies and specify it according to the regional and sectoral perspective, including economic conditions prior to a bankruptcy. The paper utilizes secondary data obtained from available databases. The initial analysis is focused on the group of all companies entering the bankruptcy process in the period from 2009 until 2019. Firstly, the full sample is considered, regardless of the legal form and data and the outcome of the analysis is used for mapping of industrial and regional intensity of bankruptcies. The second stage of the research is focused on research sample after the irrelevant subjects were ousted from it. As irrelevant subjects we consider sole traders without business data and companies with doubtful data or unclear bankruptcy start. Through the available indicators are identified the early warning signals from the financial perspective. Indicators are split into two categories – absolute and relative ones and they are investigated three, two and one years before the start of bankruptcy. Four hypotheses were formulated before the research, the length of bankruptcy process was quantified and specified for SK NACE sectors and regions. The paper presents the results of empirical analysis, which showed that the dynamic change in number of bankruptcies was brought with significant amending of bankruptcy legislation. The longest bankruptcy process is found in accommodation and food services (4 years), while IT companies generally went bankrupt within a year. The economic situation in bankrupting companies significantly worsen in case of sales and equity (the number of companies with negative equity doubled), development of profit/loss fluctuate a bit due to the sale of assets which helped in later stages. There is no statistically significant difference in the length of bankruptcy among the industries and regions. In case of legislative rules the lower cash ratio seems to be the dominant reason while companies enter the bankruptcy. The research results can be useful for further and more detailed analysis, mostly in connection with bankruptcy development (what happen when bankruptcy started) and liabilities compensation and through non-financial bankruptcy factors in individual industries.

Predicting Financial Distress in Acute Care Hospitals

Hospitals continue to face financial pressures from healthcare reform and heightened competition. In this study, our objective was to quantify the financial distress in acute care hospitals in Texas, applying multivariate logistic regression in a four-year longitudinal analysis. Of the 310 acute care hospitals, 50 (16.1%) were in financial distress in the most recent year, up considerably year over year. Distressed hospitals had fewer beds, lower patient acuity, and less outpatient revenues than those in good financial condition. Administrators should identify business turnaround strategies for combating distress to avoid potential closure.

Predictors of corporate survival in the US and Australia: an exploratory case study

Purpose

The purpose of this paper is to investigate the relationship of financial ratios and non-financial factors of successful and failed corporations in the USA. Specifically, the authors provide evidence on whether financial ratios and non-financial factors can be jointly included as indicators to improve the predictive capacity of organisational success or failure in different countries and sectors.

Design/methodology/approach

The paper utilises a mixed method exploratory case study focussing on listed corporations in the US and Australian manufacturing, agriculture, finance and property sectors.

Findings

The financial ratio findings demonstrate that (with the exception of the failed Australian manufacturing sector) the integrated multi-measure (IMM) ratio approach consistently provides a higher classification rate for the failed and successful groups than those provided by an individual measure. In all cases the IMM method scored higher for US companies (with the exception of the failed Australian property sector). The findings also show that irrespective of the country location or sector, non-financial factors such as board composition and managements’ involvement in organisational strategy impact on a corporation’s success or failure.

Practical implications

The findings reveal that non-financial factors occur prior to financial ratios when attempting to predict organisational success or failure and the IMM approach enables a more thorough examination of the predictive ability of financial ratios for US and Australian organisations. This intuitively indicates that when combined with financial ratios, non-financial factors may be a useful predictor of corporate success or failure across countries and sectors.

Originality/value

Sound internal processes and the identification of both financial ratios and non-financial factors can be utilised to improve the reliability of financial failure models, enable corrective and preventative steps to be implemented by management and potentially reduce the costs of failure for US and Australian organisations.

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.