Business failure prediction is a constantly evolving stream of literature. The research field is important because when companies fail, they can have a significantly negative social and financial impact on owners, employees, creditors, clients and other stakeholders of the failed businesses, but also to economies and societies in general (Alaka et al. 2018
; Camacho-Miñano et al. 2015
; Wu 2010
). Business failure as a phenomenon has a broad range of definitions. For example, in their study Dias and Teixeira
(2017, p. 3
) analysed 201 journal articles on the topic and found that business failure is most commonly defined as an event of “bankruptcy, business closure, ownership change, and failure to meet expectations.” In addition, business failure could mean bond default, bank loan default, delisting of a company, government intervention and liquidation (Altman and Narayanan 1997
). The most commonly used definition in failure-prediction studies is bankruptcy; however, it is only one of the many negative events in the business failure process (Balcaen and Ooghe 2006
). Weitzel and Jonsson
) created a stage model for the business failure process, where every stage is seen as some sort of failure. According to the stage model (Weitzel and Jonsson 1989, p. 102
), payment default is connected with the crisis stage, logically seen as a result of factors such as blindness, inaction and faulty actions from the earlier stages. At that stage, effective reorganization might save the company, and thus, the prediction of it is potentially more beneficial for the stakeholders than forecasting bankruptcy. Payment default is among the most serious warning signals that a company is in risk of terminal failure (Balcaen and Ooghe 2006
From a creditor’s point of view, in order to avoid negative consequences it is vital to assess a firm’s probability of failure to achieve sounder credit decisions and to appropriately compensate the risk in expected returns, or to avoid crediting unhealthy firms in the first place (Alaka et al. 2018
; Atiya 2001
; Xu and Zhang 2009
). Many banks and other credit providers have set up an automated system giving early warning signals about potential failure, which provides a necessary window for the stakeholders to take action and try to minimize negative consequences (Laitinen 2008
). Still, the prediction of loan default might be more difficult when compared with terminal failure (e.g., bankruptcy), because negative signals might not be observable through publicly available information.
Classical studies of the research area include univariate (Beaver 1966
) and multivariate (Altman 1968
) failure-prediction models that apply historical accounting data (financial ratios) as predictor variables. While pioneering studies were based on classical statistical techniques, the latest innovations in failure prediction take advantage of artificial intelligence and machine learning tools. It can be concluded that there are numerous techniques applied in hundreds of studies that mostly use financial ratios to create failure-prediction models with high prediction accuracies (for example, see the review by Sun et al. 2014
). Still, there are comparatively very few studies about corporate bank loan default prediction, partly probably because of difficulties with obtaining relevant information publicly. Thus, detailed knowledge is missing from the extant literature, whether variables beneficial for predicting failure definitions positioned further in the timeline (e.g., bankruptcy, involuntary liquidation) are applicable in case of forecasting loan defaults positioned much earlier in the timeline.
Derived from the latter, the paper aims to compare the accuracy of financial ratios, tax arrears and annual report delays in bank loan default prediction. A three-layer analysis (i.e., single variables, all variables from a domain, and finally, a cross-domain approach) is performed by using two methods: logistic regression and neural networks. Such an approach avoids the single-method bias and gives a holistic perspective about the prediction accuracies through the three layers. As noted, in the failure process default is located before permanent insolvency, and thus, default prediction capabilities of financial ratios, which are commonly used in the context of permanent insolvency, could be questionable. The latter is subject to several theoretical considerations elaborated in the literature review (see Section 2.1
for more details). Therefore, two novel domains, namely tax arrears and reporting delays, are included in the current study. Neither of those two has been applied in prior literature in the current setting, i.e., for loan default prediction, although there are a few examples available of their successful implementation in the corporate bankruptcy prediction setting (see Section 2.2
for more details). Thus, the main contribution of the paper to the extant literature is the provision of a novel approach for the prediction of bank loan defaults.
The paper is structured as follows. Literature review consists of two subsections: first, the theoretical background of company failure, and second, an overview of financial and non-financial variables used in the previous research. This is followed by an overview and explanation of data, variables and methods used in the empirical part. Thereupon, the results and their discussion are presented. The paper ends with a conclusion in the last section.
4. Results and Discussion
This section outlines the results by three layers of analysis and discusses the findings in respect to their importance to scientific literature and risk-management practice (in the banking sector). Descriptive statistics of the financial variables can be seen in Table 5
-values of Welch’s robust ANOVA test being ≤0.05 indicate that most of the ratios’ means are significantly different through the two groups of firms, although ≤0.01 differences become less frequent. The defaulted group indicates worse performance in all main domains analysed, i.e., liquidity, profitability, efficiency and solvency. Large differences of the minimum and maximum values combined with large standard deviations inside both groups’ results indicate that there is no single cause or pathway to failure. The presence of different failure processes is reasoned with the use of a whole-population dataset from an Estonian commercial bank, which in essence makes the dataset quite heterogeneous. The result concerning the presence of different failure processes is also in line with previous failure research (e.g., Lukason and Laitinen 2016
), and because of that, high prediction accuracies might not be achievable with financial ratios. The differences of means are statistically significant for all variables, except for ORA and FREOR (0.693 and 0.07, respectively). Therefore, the ORA variable reflects that in terms of a firm’s efficiency, the means of the defaulted and non-defaulted groups are equal. In their default-based Italian study covering years 1999–2002, Bottazzi et al.
) found that productive efficiency reduced the risk of default; however, its importance decreased over time and was insignificant in the last year before the default, i.e., in principle providing the same result as the current finding, because this study uses data only one year prior to default. The track of significant variables matches well of what has been found in previous literature reviews about failure prediction (see e.g., Dimitras et al. 1996
); thus, financial problems preceding default are generally similar to those occurring before bankruptcy, although there can be differences in their magnitude.
It is clearly visible that the defaulted companies have serious issues with tax arrears, while the non-defaulted have almost none (see Table 6
). The mean value of TCOUNT for the defaulted firms is 5.6, which is well above the three times threshold, i.e., the point, when reached or exceeded, Back
) in his similar payment default setting discovered an important increase in the probability of permanent payment default. Back
) used a 2.5-year horizon; hence, the finding of TCOUNT mean value of 5.6 in current study with only one-year horizon is remarkable. In their bankruptcy-based Estonian study, the respective result by Lukason and Andresson
) for one-year horizon was 7.4. It can be assumed that defaulted firms either try to survive by aggressively evading tax obligations in favour of other creditors (for example, banks and key suppliers), or the firm has been left dormant because of not having perspective of continuing activities, and thus, the unpaid obligations accumulate further (until official insolvency proceedings).
It was confirmed that the mean value of reporting delays was 55 days more for the defaulted firms compared to the non-defaulted, though median values for both groups were zero. It shows that a minority of firms with defaults had problems with timely reporting. Additionally, as the minimum and median values for the defaulted group are zero, it explains that, in general, delays in reporting would not directly indicate an increased risk of a payment default. Lukason
) found that the non-submission of reports in Estonia varies for different insolvency types and was more frequent in cases of a bankruptcy proceeding abatement, i.e., in a situation where the insolvent firm is assetless, and thus, managers have more incentives to hide financial information. The latter case is expectedly not very usual in cases of a commercial bank’s corporate clients, as loans are guaranteed. Thus, in this context, the RDD variable could indicate that as explained in Section 2.1
, a firm that has defaulted might not end up insolvent, so an incentive to hide financial information might also not be present.
Next, the univariate prediction abilities of the applied variables are presented in Table 7
. In terms of financial variables, it can be followed that the best prediction capabilities come from solvency variables (DA and FREA), closely followed by liquidity (CCLA) and profitability (NIA and NIOR) variables. However, almost all non-financial variables outperform every financial variable, the latter of which have very weak prediction abilities, symbolizing more a “coin toss” situation. Reporting delays show slightly lower prediction accuracy than the best-performing financial variables, therefore being individually not useful for bank loan default prediction. Tax arrears have clearly the highest univariate prediction accuracies, specifically the maximum tax arrears variable (TMAX) with 84%. Therefore, the companies that indicate large tax arrears are most likely to default. Lukason and Andresson
) arrived exactly at the same conclusion in their different bankruptcy-oriented setting, where the maximum tax arrears variable had also the best univariate failure (bankruptcy) prediction accuracy (85.9%).
This study focuses on and describes only the highest prediction accuracies achieved by using either of the two methods. Financial ratios show modest prediction capabilities because only a 65.9% accuracy was reached. In a comparable setting, Back
) achieved 72%. It also confirms an important finding in prior failure literature, because several previous studies (e.g., Altman and Sabato 2007
; Ciampi 2015
) have shown that failure prediction models that are based on financial variables and perform well on large public firms tend to show poor prediction accuracies for SMEs. Additionally, financial reports often fail to indicate problems in the SMEs’ financial health even one year before bankruptcy (Lukason and Laitinen 2019
), while the moment of default occurs much earlier. Thus, the first main finding of the paper is that at least in cases of quality loan portfolios, financial ratios are not useful to predict loan defaults.
The reporting delay variable shows almost no prediction capability for the defaulted group with only 29.5%. Thus, this variable is not suitable for payment default prediction either. Although, for example, in their study Lukason and Camacho-Miñano
) showed that delays in reporting indeed do show increased risk of failure, the differences in the risk for (non-)delayers are not substantial enough to enable high-precision prediction. Probably the usage of this variable (i.e., RDD) would make more sense in cases of applying a more severe failure definition, e.g., start of insolvency proceedings or declaration of bankruptcy, or when focusing on firms that do not use bank loans, as banks might monitor the fulfillment of annual report submission obligations.
The tax arrears domain combining TMAX, TMEDIAN and TCOUNT variables strongly outperforms all other domains (see Table 8
). Prediction accuracy for the defaulted group is 80.9% and 83.5% overall. The result confirms initial findings of univariate prediction accuracies and is comparable with the Back
) study using a similar dependent variable, where 86.3% default prediction accuracy was achieved by using only non-financial variables. Lukason and Andresson
) reached an even higher accuracy (89.5%) by using bankruptcy as the dependent variable and the same independent variables as in the current study (tax arrears for 12 months prior to event date). Still, the latter accuracy is not surprising because the declaration of insolvency at court can only occur in the circumstances of unpaid claims and the only pending questions would be: (a) how many firms go bankrupt because of unpaid tax claims, (b) what is the proportion of healthy firms with (episodic) tax arrears.
Finally, a multivariate model was constructed that included all the aforementioned three domains. Prediction accuracy was 88.6% for the defaulted group and 89.1% overall. As can be seen from Table 8
, in terms of methods used in the study, the modern machine learning method (NN) outperformed the classical statistical method (LR). For NN, the most important predictors of default were TCOUNT (100% normalized importance rate), FREA (98.2%) and TMED (75.6%) (see Appendix A Table A2
). Due to high multicollinearity between the variables (see the correlation table in Appendix A Table A3
), logistic regression models are not presented, because the variables’ estimations would be biased.
The main theoretical implication of the paper is that tax arrears offer high predictive performance when forecasting bank loan payment defaults. Such implication is important because there is no prior literature where tax arrears would be used to predict loan defaults. Tax arrears outperform the most common financial ratios previously used in the failure prediction literature. It is important to note that one year prior to bankruptcy, as shown by Lukason and Andresson
), financial ratios had a 79.5% prediction accuracy; while in the context of defaults in this paper, the accuracy was only 65.9%. This indicates that companies default rather unexpectedly, i.e., it will not be seen coming from the companies’ financial reports. A potential explanation for this finding was given by Laitinen and Lukason
(2014, p. 827
), who found that Estonian firms generally lacked financial flexibility to withstand external shocks and other specific external events. The present study’s conclusion in general confirms previous research, as the inclusion of non-financial information does greatly enhance failure-prediction accuracy. In addition, the paper clearly shows that debtors first leave unpaid the obligations that are less vital for them, i.e., a clear pecking-order behaviour is present concerning satisfying different types of claims in the failure process.
The results also indicate the prevalence of a quick failure process among firms qualified to obtain a bank loan. Such a failure process has been proposed in earlier studies (D’Aveni 1989
; Laitinen 1991
) and found to be especially frequent in the SME segment (Lukason et al. 2016
; Lukason and Laitinen 2019
). The main characteristic of a given failure process is that even the last annual report, which is submitted in a timely manner, does not indicate financial problems. A possible solution for the latter problem would be quarterly (or even more frequent) reports, although even these might not capture some of the root causes of liquidity problems (e.g., uncollectable receivables) quickly enough. Therefore, the presence of tax arrears serves as a valuable real-time proxy of poor liquidity (management).
The main practical implication of this study is that from a creditor’s point of view, earlier payment disturbances, namely tax arrears, are a clear sign of increased default risk. This information should be considered by banks when granting credit to borrowers and also in the context of an existing loan portfolio, i.e., to take necessary measures in a timely manner in order to minimize potential losses. Evidently, the larger the tax arrears and the more frequent they are, the higher the risk of a loan payment default. Because tax arrears’ information is publicly available on a daily or monthly basis in many countries, it offers high practical value for creditors. It should help in decision-making when financial reporting is delayed or opaque, both of which are especially inherent to companies with increased failure risk (especially in the SME segment). The results of the models in this study indicate that even the sole usage of tax arrears variables without other types of predictors provides a sufficiently high accuracy for practical use. As of today, established financial institutions have already implemented a previous-payment behaviour component in their credit-scoring models, albeit mostly in the form of data about disturbances originating from their own organization. The present study confirms why it is essential to include tax arrears information into credit-scoring models as well. Concerning the latter, the practical application could be much more enhanced than the academic approach presented in this paper, for example, accounting for real-time information and big data about tax payment behaviour and tax arrears combined with other information sources (see e.g., Chang and Ramachandran 2017
; Chang et al. 2020
The study aimed to compare the accuracy of financial ratios, tax arrears and annual report submission delays for predicting bank-loan defaults. For the analysis, logistic regression and neural networks methods were applied on the whole-population dataset consisting of defaulted and non-defaulted companies originating from an Estonian commercial bank.
The results showed that by including non-financial variables, the accuracy of loan-default prediction increases remarkably. The study provided several implications. As for the theoretical implication, it was discovered that tax arrears provide high prediction accuracy to foresee bank-loan defaults. At the same time, even though prior research has found that occurrences of reporting delays can effectively indicate an increased failure risk, the phenomenon does not suit to predict loan defaults. The latter problem is also inherent to financial ratios, which otherwise have been applied with high accuracy in bankruptcy prediction settings. As for the practical implication, the findings should help lenders to consider the role of previous payment history, in the form of tax arrears, in loan defaults’ prediction. Incorporating tax arrears’ information to forecasts would enable the lender to take timely actions to minimize potential financial losses.
As with each study, this paper is not free from limitations. The results of the study are mainly transferrable to commercial banks, which have high-quality portfolios, because the defaults accounted for only 1.2% of the population analysed. The latter was an aggregate figure summarized over a six-year period, leading to an average annual rate of 0.2%. In the case of creditors lending more liberally and having worse portfolio qualities, financial ratios as predictors might play a more important role as in this study. In addition, tax regulations and their enforcement, but also insolvency legislation, can largely vary through different jurisdictions, which should be accounted when applying tax arrears variables. For instance, in some environments the regulations might hinder the emergence of tax arrears before defaulting on debt from the private sector, one example of which is the bank-loan default. Lastly, this study applied financial information from the annual reports, which are officially published with a one-year step. Probably the availability of up-to-date financial reports, for instance with quarterly or even monthly frequency, would to some extent enhance the predictive power of financial ratios. Although in the SME segment such reports are missing, they can be demanded by setting relevant terms in the loan contracts, but their reliability as being non-audited always remains questionable.
This study has filled a gap in modern firm-failure research by analysing tax arrears in the context of loan-payment defaults. Future research could elaborate on the prior-payment concept to test how payment history inside the credit institution itself would compare with or accompany tax arrears to predict failure. In addition, information about other types of defaults could enhance the prediction accuracies even further. Lastly, managerial background could be a crucial predictor in the SME segment (e.g., Back 2005
; Liang et al. 2016
; Süsi and Lukason 2019