Early warning of bank failure : A logit regression approach

Handbook of Quantitative Finance and Risk Management, 2010

This article introduces definitions of the terms bankruptcy, corporate failure, insolvency, as well as the methods of bankruptcy, and popular economic failure prediction models. We will show that a firm filing for corporate insolvency does not necessarily fail to pay off its financial obligations as they mature. Moreover, we will assume an appropriate risk monitoring system centered by well-developed failure prediction models, which is crucial to various parties in the investment world as a means to look after the financial future of their clients or themselves.

Journal of Business Finance & Accounting, 2006

This paper examines whether the characteristics of banks and borrowers are associated with banks' decisions to waive violations of debt covenants. The findings suggest that banks possess sufficient private information about firms, and they use this information in their waiver decisions. Banks' decisions to waive violations vary with the borrowers' agency costs, debt features, the banks' characteristics and regulatory circumstances, and the bank-firm business relationship. There is no evidence that syndicated loans, bank structure, and adverse economic conditions are significant determinants of the waiver decision. Research findings offer valuable insight into the theoretical and practical implications of debt covenants and agency costs.

Agricultural Finance Review, 2013

Purpose -The late 2000s Great Recession led to a surge of bank failures in the USA with nearly 300 banks failing from 2009 to 2010. Recalling the farm crises of the 1980s where the farm sector was pinpointed as one of the major precursors of such crises, this study is an attempt to validate if the agricultural sector can once again be considered as a major instigator of the current financial crises. Design/methodology/approach -An early warning model is developed based on factors that may cause bank failures, with special attention given to the role of the agricultural lending portfolios of commercial banks. The model will have several time period versions that will determine the length of time prior to the actual bank bankruptcy declarations that early warning signals could be detected. Findings -The empirical results indicate that credit exposure to the farm sector does not necessarily enhance a bank's tendency to fail or its probability of success or survival. This lends support to the reality that agricultural loan delinquency rates are consistently below the banks' overall loan delinquency rates, thus confirming that agricultural lenders are in relatively stronger financial health. This study instead finds that costly funding arrangements, increasing interest rate risk, and declining asset quality can be possible early warning signals that can be detected as far back as two or three years before eventual bank failure. Originality/value -This study differentiates itself from previous studies by its special focus on the role of the agricultural finance industry in the ensuing economic crises. This study's early warning model also presents an extended version of previous empirical models as it accounts for measures of capital adequacy, asset quality, management risk, profitability, liquidity risk, loan portfolio composition and risk, funding arrangement, structural and macroeconomic variables.

International Journal of Managerial Finance, 2011

Lecture Notes in Computer Science, 2009

Prediction of financial distress of companies is analyzed with several machine learning approaches. We used DIANE, a large database containing financial records of small and medium size French companies from the year of 2002 up to 2007. It is shown that inclusion of historical data, up to 3 years priori to the analysis, increase the prediction accuracy. In particular, fluctuations of some financial ratios are found to be crucial. Due to the inclusion of a large amount of inputs particular attention is given to feature selection. An accuracy of up to 94% is achieved with the best models.

Journal of Business Finance & Accounting, 1991

Economic Change and Restructuring, 2011

Sustainability

The main purpose of this study is to conceptualize a sustainable banking model for Islamic banking by blending three essential business aspects namely financial performance, Islamic corporate governance, and sustainability practices dimension. In the case of Islamic banking, evidence shows that a Shariah-based bankruptcy prediction model for apprehending the true bankruptcy prediction is over-sighted. This study offers an efficient Shariah-based bankruptcy prediction model by first, reviewing the previously applied conventional bankruptcy prediction models; secondly, by developing and proposing a robust, multidimensional model for predicting bankruptcy in Islamic banking. This framework may have profound implications on the existing bankruptcy evaluation structure of the Islamic banking industry and may provide a strong sustainability management guideline to the global Islamic banking industry.

Journal of Banking Regulation

Decision Sciences, 2000

This study compares the performance of three artificial neural network (ANN) approaches-backpropagation, categorical learning, and probabilistic neural networkas classification tools to assist and support auditor's judgment about a client's continued financial viability into the future (going concern status). ANN performance is compared on the basis of overall error rates and estimated relative costs of misclassification (incorrectly classifying an insolvent firm as solvent versus classifying a solvent firm as insolvent). When only the overall error rate is considered, the probabilistic neural network is the most reliable in classification, followed by backpropagation and categorical learning network. When the estimated relative costs of misclassification are considered, the categorical learning network is the least costly, followed by backpropagation and probabilistic neural network.

Journal of Risk and Financial Management

This paper proposes a conceptual modeling framework based on category theory that serves as a tool to study common structures underlying diverse approaches to modeling credit default that at first sight may appear to have nothing in common. The framework forms the basis for an entropy-based stacking model to address issues of inconsistency and bias in classification performance. Based on the Lending Club’s peer-to-peer loans dataset and Taiwanese credit card clients dataset, relative to individual base models, the proposed entropy-based stacking model provides more consistent performance across multiple data environments and less biased performance in terms of default classification. The process itself is agnostic to the base models selected and its performance superior, regardless of the models selected.

Journal of Risk and Financial Management

Prediction insolvency is one of the most important issues during creditworthiness assessment, especially in the turmoil environment. That is why the problem of insolvency and bankruptcy prediction has been the subject of numerous studies focused on its causes, consequences, and prediction. The main goal of the study was to develop a prediction model that can be effectively used in practice to analyze and signal the risk of insolvency and bankruptcy of a construction firms. Also, the research must identify the key factors that would allow for early identification of the symptoms of the upcoming financial failure of companies from a construction sector. To reach the goal of the study discriminant analysis, logistic regression and classification trees were used. The final estimated models included nine variables related to the profitability; revenues; liquidity; asset’s structure; and dynamics of own and foreign capitals, some of which referred to the industry and market situation in a...

2014

Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy). The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier.

International Journal of Financial Engineering

Financial distress and companies’ failure have always been a complicated and intriguing problem for businesses. Because of the unfavorable impacts of financial distress on companies and societies, accounting and finance researchers around the world are thinking of ways to anticipate corporate financial distress. Several models are provided in the literature for predicting financial distress. This research develops nonlinear decision tree and linear discriminant analysis models to predict financial distress of companies listed in Iranian Stock Exchange during 2010 to 2015. The drivers are firms’ financial ratios, intellectual capital and performance indicators. According to the results, intellectual capital and financial performance indices have no informational content in decision tree model. Comparing the result show that both models predict financial distress with 90.9% and 81.8% accuracy, respectively. Moreover, the difference between the accuracy of the models however is not mea...

Revista Contabilidade & Finanças

This study aims to propose an early warning model for predicting financial distress events in Brazilian banking institutions. Initially, a set of economic-financial indicators is evaluated, suggested by the risk management literature for identifying situations of bank insolvency and exclusively taking public information into account. For this, multivariate logistic regressions are performed, using as independent variables financial indicators involving capital adequacy, asset quality, management quality, earnings, and liquidity. The empirical analysis was based on a sample of 142 financial institutions, including privately and publicly held and state-owned companies, using monthly data from 2006 to 2014, which resulted in panel data with 12,136 observations. In the sample window there were nine cases of Brazilian Central Bank intervention or mergers and acquisitions motivated by financial distress. The results were evaluated based on the estimation of the in-sample parameters, out-o...

Sustainability

The role of a relatively small cadre of high-tech startup firms in driving innovation and economic growth has been well known and amply celebrated in recent history. At the same time, it is well recognized that, while the overall contribution of startups is crucial, the high-risk and high-reward strategy followed by these startups leads to significant failure rates and a low ratio of successful startups. So, it is curious to notice that literature tends to focus on successful startups and on quantitative studies looking for determinants of success while neglecting the numerous lessons that can be drawn by examining the stories of startups that failed. This paper aims to fill this gap and to contribute to the literature by providing a repeatable and scalable methodology that can be applied to databases of unstructured post-mortem documents deriving startup failure patterns. A further and related contribution is the analysis carried out with this methodology to a large database of 214 startup post-mortem reports. Descriptive statistics show how the lack of a structured Business Development strategy emerges as a key determinant of startup failure in the majority of cases.

2021

This paper develops an early warning model (EWM) for a micro-macro analysis of individual and aggregated bank vulnerabilities in Ukraine. We applied a stepwise logit for predicting defaults at Ukrainian banks based on a panel bank and macro-level data from Q1 2009 to Q3 2019. Next, we aggregated individual bank default probabilities to provide policymakers with information about the general state of the financial system with a particular focus on generating a signal for countercyclical capital buffer (CCB) activation. Our key findings suggest that the probability of default exceeding 11% could signal about a vulnerable state in a bank and, in the aggregated model, in a financial system in general. The aggregated model successfully issues an out-of-sample signal of a systemic crisis four periods ahead of the start of the 2014-2015 turmoil.

Journal of Risk and Financial Management

This paper proposes a new approach toward understanding the financial performance dynamics in the EU retail sector (pre-pandemic); we focus on the connection between indebtedness and solvency risk and other areas of corporate performance (e.g., liquidity, assets efficiency, and profitability). Its contribution resides in identifying the drivers behind solvency risk in a sector that went through significant transformations in recent decades, as well as the links between the various areas of performance of retailers, and their impacts on solvency risk, using the machine-learning random forest methodology. The results indicate a declining trend for solvency risk of EU food retailers after the global financial crisis and up until the beginning of the pandemic, which may reflect their maturity on the market, but also an adjustment to legal changes in the EU, meant to equalize the tax advantages of debt versus equity financing. Solvency risk accompanied by liquidity risk is a mark of the ...

INQUIRY: The Journal of Health Care Organization, Provision, and Financing

This article uses a modified Altman Z-score to predict financial distress within the nursing home industry. The modified Altman Z-score model uses multiple discriminant analysis (MDA) to examine multiple financial ratios simultaneously to assess a firm’s financial distress. This study utilized data from Medicare Cost Reports, LTCFocus, and the Area Resource File. Our sample consisted of 167 268 nursing home-year observations, or an average of 10 454 facilities per year, in the United States from 2000 through 2015. The independent financial variables, liquidity, profitability, efficiency, and net worth were entered stepwise into the MDA model. All of the financial variables, with the exception of net worth, significantly contributed to the discriminating power of the model. K-means clustering was used to classify the latent variable into 3 categorical groups: distressed, risk-of-financial distress, and healthy. These findings will provide policy makers and practitioners another tool ...

Accounting & Finance, 2013

This study uses survival analysis to determine how early the indications of bank failure can be observed. We find that banks with high loan to asset and high personal loan to assets ratios are more likely to survive. Older banks and banks with high real estate and agricultural loans, loan loss allowance, loan charges off and non-performing loans to assets ratio are more likely to fail. It is possible to predict survival functions of <50% for failed banks, 3 years or less before failure. Moreover, we find that most of the variables present a behaviour that departs from Benford's Law.

Économie internationale, 2009

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Journal of Risk and Financial Management, 2019

We investigate the default probability, recovery rates and loss distribution of a portfolio of securitised loans granted to Italian small and medium enterprises (SMEs). To this end, we use loan level data information provided by the European DataWarehouse platform and employ a logistic regression to estimate the company default probability. We include loan-level default probabilities and recovery rates to estimate the loss distribution of the underlying assets. We find that bank securitised loans are less risky, compared to the average bank lending to small and medium enterprises.

Annals of Operations Research, 2022

For decades, the prediction of bank failure has been a popular topic in credit risk and banking studies. Statistical and machine learning methods have been working well in predicting the probability of bankruptcy for different time horizons prior to the failure. In recent years, bank efficiency has attracted much interest from academic circles, where low productivity or efficiency in banks has been regarded as a potential reason for failure. It is generally believed that low efficiency implies low-quality management of the organisation, which may lead to bad performance in the competitive financial markets. Previous papers linking efficiency measures calculated by Data Envelopment Analysis (DEA) to bank failure prediction have been limited to cross sectional analyses. A dynamic analysis with the updated samples is therefore recommended for bankruptcy prediction. This paper proposes a nonparametric method, Malmquist DEA with Worst Practice Frontier, to dynamically assess the bankruptcy risk of banks over multiple periods. A total sample of 4426 US banks over a period of 15 years (2002-2016), covering the subprime financial crisis, is used to empirically test the model. A static model is used as the benchmark, and we introduce more extensions for comparisons of predictive performance. Results of the comparisons and robustness tests show that Malmquist DEA is a useful tool not only for estimating productivity growth but also to give early warnings of the potential collapse of banks. The extended DEA models with various reference sets and orientations also show strong predictive power.

Journal of Intelligent Manufacturing, 2020

Operations involving gas–liquid agitated vessels are common in the biochemical and chemical industry; ensuring good contact between the two phases is essential to process performance. In this work, a methodology to compute acoustic emission data, recorded using a piezoelectric sensor, to evaluate the gas–liquid mixing regime within gas–liquid and gas–solid–liquid mixtures was developed. The system was a 3L stirred tank equipped with a Rushton Turbine and a ring sparger. Whilst moving up through the vessel, gas bubbles collapse, break or coalesce generating sound waves transmitted through the wall to the acoustic transmitter. The system was operated in different flow regimes (non-gassed condition, loaded and complete dispersion) achieved by varying impeller speed and gas flow rate, with the objective to feed machine learning algorithms with the acoustic spectrum to univocally identify the different conditions. The developed method allowed to successfully recognise the operating regim...

Computational Economics, 2012

The strong relationship between bank failure and economic growth attaches far more importance to the predictability of bank failures. Consequently, numerous statistical prediction models exist in the literature focusing on this particular subject. Besides, artificial intelligence techniques began to attain an increasing level of importance in the literature due to their predictive success. This study distinguishes itself from the similar ones in the sense that it presents a comparison of three different artificial intelligence methods, namely support vector machines (SVMs), radial basis function neural network (RBF-NN) and multilayer perceptrons (MLPs); in addition to subjecting the explanatory variables to principal component analysis (PCA). The extent of this study encompasses 37 privately owned commercial banks (17 failed, 20 non-failed) that were operating in Turkey for the period of 1997-2001. The main conclusions drawn from the study can be summarized as follows: (i) PCA does not appear to be an effective method with respect to the improvement of predictive power; (ii) SVMs and RBF demonstrated similar levels of predictive power; albeit SVMs was found to be the best model in terms of total predictive power; (iii) MLPs method stood out among the SVMs and RBF methods in a negative sense and exhibits the lowest predictive power. Keywords Bank failure • Support vector machines • Multilayer perceptrons • Radial basis function neural network • Principal component analysis Electronic supplementary material The online version of this article

Computational Economics, 2016

The prediction of bankruptcy for financial companies, especially banks, has been extensively researched area and creditors, auditors, stockholders and senior managers are all interested in bank bankruptcy prediction. In this paper, three common machine learning models namely Logistic, J48 and Voted Perceptron are used as the base learners. In addition, an attribute-base ensemble learning method namely Random Subspaces and two instance-base ensemble learning methods namely Bagging and Multi-Boosting are employed to enhance the prediction accuracy of conventional machine learning models for bank failure prediction. The models are grouped in the following families of approaches: (i) conventional machine learning models, (ii) ensemble learning models and (iii) hybrid ensemble learning models. Experimental results indicate a clear outperformance of hybrid ensemble machine learning models over conventional base and ensemble models. These results indicate that hybrid ensemble learning models can be used as a reliable predicting model for bank failures. Keywords Financial crisis • Bank failure • Bagging • Hybrid classifier ensembles • Logistic regression • J48 • Multi-boosting • Random sub-spaces • Voted perceptron JEL Classification C11 • C13 • E37 • E44 B Aykut Ekinci

Information, 2020

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 ty...

Journal of Risk and Financial Management, 2020

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 Logi...

Annals of Operations Research, 2018

Citation Mousavi MM and Quenniche J (2018) Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions. Annals of Operations Research.

Journal of the Royal Statistical Society Series A: Statistics in Society, 2012

Summary Financial institutions and regulatory agencies direct much effort and expertise towards estimating probabilities of bankruptcy. By comparison, the techniques that are used to evaluate probability estimates have attracted little attention and remain somewhat ad hoc. The most common approach is to count misclassifications, based on an arbitrary classification threshold. Some use is made of conventional probability score functions, such as the Brier score (from meteorology) but these are not standard practice. Our purpose is to introduce a family of economic probability score functions designed to capture the utility obtained by a user, with a specified utility function, who uses the estimated probabilities to make hypothetical bets against a rival forecaster or model. The conceptual appeal of these score functions is that probability forecasts are evaluated neither in abstract, nor in isolation, but by whether they would hypothetically have ‘made money’ for a given user, with ...

Studia Universitatis Babes-Bolyai Oeconomica

This study explores the relation between capital, market risks and banks’ liquidity conditions. In estimating the SVAR regression model, Granger causality, impulse-response functions and forecast error variance decomposition were employed and used for estimation of the results. The data sample comprised of commercial banks over the 2009 to 2018 period. The empirical results showed that liquidity shocks are caused by a combination of structural shocks. The Granger causality, impulse-response functions and forecast error variance decomposition documented that sensitivity to market risk is the key factor affecting liquidity conditions in the banking sector in the long run. In addition, the empirical results showed that capital adequacy has minimal impact on liquidity conditions in the short run. The reforming rate to sensitivity to market risk policies, capital adequacy policies and liquidity policy measures can be valuable policy tools to minimize liquidity shortages and avoid insolve...

The Journal of Risk Finance, 2007

PurposeThe purpose of this research is to investigate the effect of a speculative attack on the Turkish Lira in the North Cyprus banking sector during the period 1984‐2002.Design/methodology/approachA mutivariate logit model is the empirical methodology employed in this analysis that allows us to identify the determinants of the probability of bank failure. In the model, the existence of contagious currency crises is constructed as an index of exchange market pressure, which is a weighted average of changes in interest rates, international reserves and the nominal exchange rate.FindingsThe empirical result reveals that the a speculative attack on the Turkish Lira in 1994 and 2001 put stress on banks operating in North Cyprus and led to banking sector distress. The findings also suggest that bank‐specific weaknesses, high interest rates, high credit, low trade and the fixed exchange rate policy significantly increased the bank fragility.Research implications/limitationsFor further re...

Review of Managerial Science

This paper analyses the impact of sustainability through ESG factors on the default risk. The sample consists of 990 non-financial firms in the Eurozone over the period 2004–2020. The results show that ESG factors influence default risk, although this relationship could be influenced by the economic cycle. Also, the results highlight a significant interaction effect between firm size and ESG which affects default risk. Considering firm size by terciles, the evidence obtained shows that smaller and medium-sized firms have a positive net effect of a high ESG score on their default risk, while the opposite effect was found among larger firms.

Journal of Business Research, 1984

Logit estimation is applied to predicting the probability that a givenfirm will be a merger target. Care is taken to accountfor variation in industry characteristics. The statistical performance of the model and its predictive power in a holdout sample support the approach used.

International Journal of Financial Studies, 2018

In developed countries, the first studies on forecasting bankruptcy date to the early 20th century. In Central and Eastern Europe, due to, among other factors, the geopolitical situation and the introduced economic system, this issue became the subject of researcher interest only in the 1990s. Therefore, it is worthwhile to analyze whether these countries conduct bankruptcy risk assessments and what their level of advancement is. The main objective of the article is the review and assessment of the level of advancement of bankruptcy prediction research in countries of the former Eastern Bloc, in comparison to the latest global research trends in this area. For this purpose, the method of analyzing scientific literature was applied. The publications chosen as the basis for the research were mainly based on information from the Google Scholar and ResearchGate databases during the period Q4 2016-Q3 2017. According to the author's knowledge, this is the first such large-scale study involving the countries of the former Eastern Bloc-which includes the following states: Poland,