Predicting default of Russian SMEs on the basis of financial and non-financial variables

This article develops two models for predicting the default of Russian Small and Medium-sized Enterprises (SMEs). The most general questions that the article attempts to answer are ‘Can the default risk of Russian SMEs be assessed with a statistical model?’ and ‘Would it sufficiently demonstrate high predictive accuracy?’ The article uses a relatively large data set of financial statements and employs discriminant analysis as a statistical methodology. Default is defined as legal bankruptcy. The basic model contains only financial ratios; it is extended by adding size and age variables. Liquidity and profitability turned out to be the key factors in predicting default. The resulting models have high predictive accuracy and have the potential to be of practical use in Russian SME lending.     

Empirical comparison of hazard models in predicting SMEs failure

This study aims to shed light on the debate concerning the choice between discrete-time and continuous-time hazard models in making bankruptcy or any binary prediction using interval censored data. Building on the theoretical suggestions from various disciplines, we empirically compare widely used discrete-time hazard models (with logit and clog-log links) and the continuous-time Cox Proportional Hazards (CPH) model in predicting bankruptcy and financial distress of the United States Small and Medium-sized Enterprises (SMEs). Consistent with the theoretical arguments, we report that discrete-time hazard models are superior to the continuous-time CPH model in making binary predictions using interval censored data. Moreover, hazard models developed using a failure definition based jointly on bankruptcy laws and firms’ financial health exhibit superior goodness of fit and classification measures, in comparison to models that employ a failure definition based either on bankruptcy laws or firms’ financial health alone.     

德国公司违约概率预测及其对我国信用风险管理的启示

内部信用评级是新巴塞尔资本协议的核心,而违约概率的预测又是内部评级的基础。本文利用具有出色分类功能的非线性支持向量分类(SVC)方法来预测德国公司的违约概率,识别其信用风险。结果显示,SVC模型的预测能力优于基准的logit模型;而且非线性SVC模型能够捕捉线性logit模型所不能识别的影响信用风险的重要变量。本文虽然分析的是德国公司数据,但是同样对我国商业银行和公司构建全面风险管理体系有着直接的指导意义。     

The Resiliency of the High-Yield Bond Market: The LTV Default

This paper investigates the resiliency of the new-issue high-yield bond market by examining the changes in implied default rates of such bonds before and after the largest high-yield bond default, i.e., the LTV bankruptcy. Specifically, the paper compares implied default probabilities of high-yield bonds during the post-LTV period calculated from actual new-issue yields with instrumental default probabilities calculated on the assumption that the default had not occurred. A comparison of these probabilities reveals that the market's perception of default on the high risk segment of the bond market increased significantly after the LTV bankruptcy. However, the effect was transitory, lasting only six months. Thus, the market was resilient to a major default.     

The differential impact of leverage on the default risk of small and large firms

We analyse a sample of 6 million firm-year observations of large corporations and small and medium sized enterprises (SMEs) spanning 6 European countries from 2005 to 2015, to determine the impact of leverage and different sources of funding on default risk. We find that financial leverage has a greater impact on the probability of default of SMEs than of large corporations. The difference in default probability between the top and bottom leverage quartiles is 1.24% for large firms and 2.87% for SMEs. This difference may be explained by the greater exposure of SMEs to short-term debt and their consequently higher refinancing risk. Indeed, we find that SMEs that recover from the state of insolvency may have similar leverage to defaulted SMEs; however their liability structure is significantly altered towards long-term debt and away from short-term debt. Our findings have important implications not only for bank regulators and policy-makers but also for credit risk modelling.     

A simple correction of the WACC discount rate for default risk and bankruptcy costs

Standard discounted cash flow approaches suffer from a rudimental modeling of the possibility of a default, as the main characteristics such as the default probability and potential bankruptcy costs are commonly disregarded. This paper aims at providing a tractable extension of the well-known WACC approach for both default risk and bankruptcy costs. The corrected WACC discount rate reveals that default risk results in a systematically higher WACC because the tax component is scaled by the survivorship probability and an aditional component for bankruptcy costs must be added. This difference between the classical WACC discount rate and the simple modified WACC rate can be remarkable especially for firms from businesses with high bankruptcy costs and a relevant default probability.     

Modeling default risk with support vector machines

Predicting default risk is important for firms and banks to operate successfully. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so-called Support Vector Machine (SVM) to predict the default risk of German firms. Our analysis is based on the Creditreform database. In all tests performed in this paper the nonlinear model classified by SVM exceeds the benchmark logit model, based on the same predictors, in terms of the performance metric, AR. The empirical evidence is in favor of the SVM for classification, especially in the linear non-separable case. The sensitivity investigation and a corresponding visualization tool reveal that the classifying ability of SVM appears to be superior over a wide range of SVM parameters. In terms of the empirical results obtained by SVM, the eight most important predictors related to bankruptcy for these German firms belong to the ratios of activity, profitability, liquidity, leverage and the percentage of incremental inventories. Some of the financial ratios selected by the SVM model are new because they have a strong nonlinear dependence on the default risk but a weak linear dependence that therefore cannot be captured by the usual linear models such as the DA and logit models.     

Bankruptcy probability changes and the differential informativeness of bond upgrades and downgrades

Prior studies have found that stock returns around announcements of bond upgrades are insignificant, but that stock prices respond negatively to announcements of bond downgrades. This asymmetric stock market reaction suggests either that bond downgrades are timelier than upgrades, or that voluntary disclosures by managers preempt upgrades but not downgrades. This study investigates these conjectures by examining changes in firms’ probabilities of bankruptcy (assessed using bankruptcy prediction models) and voluntary disclosure activity around rating change announcements. The results indicate that the assessed probability of bankruptcy decreases before bond upgrades, but not after. By contrast, the assessed probability of bankruptcy increases both before and after bond downgrades. We also find that controlling for potential wealth-transfer related rating actions, which can impact stock returns differently, does not alter our results. Tests of press releases and earnings forecasts issued by firms suggest that the differential informativeness of upgrades and downgrades is not caused by differences in pre-rating change voluntary disclosures by upgraded and downgraded firms. The results support the hypothesis that downgrades are timelier than upgrades.     

非上市中小企业贷款违约率的定量分析——基于判别分析法和决策树模型的分析

本文运用判别分析法和决策树模型对非上市中小企业违约风险进行了分析,并将两种方法的分析结果进行了对比。分析结果表明,两种方法均能较好地判别企业违约的可能性,而采用决策树模型的最大的好处是,除了能够较好地判断企业的违约率之外,它还能够找出影响企业违约的关键性因素。从我们的分析样本来看,商业银行在判断企业的信用水平时,现金流量/总债务的比例、流动资产/流动负债比例是两个非常重要的考察因素。如果银行花精力对它们进行调查、核实,保证其准确性,将大大提高对企业违约率判断的准确性。     

The effect of credit guarantees on credit availability and delinquency rates

We use new data to examine whether credit guarantees affect economic incentives and whether they affect the credit available to small- and medium-size enterprises (SMEs). We find that firms that have both guaranteed and non-guaranteed loans are 1.67% more likely to miss payments on their guaranteed loans, but are not more likely to default on these loans. These findings suggest that guarantees affect firms’ incentives to repay loans but not their long-term performance. We also find that firms selected into the guarantee programs are 1.17% more likely to default on their loans compared with similar firms that borrow without guarantees. Since we find evidence that long-term performance is not affected by guarantees, the higher default rates among firms selected into the guarantee programs must be the consequence of adverse selection. We also find that credit guarantees increase the aggregated amount of credit; in particular, one additional dollar of guarantees increases the total credit for SMEs by US$ 0.65.     

Modelling Credit Risk for Innovative SMEs: the Role of Innovation Measures

Small-medium enterprises (SMEs) encounter financial constraints when they try to obtain credit from banks. These constraints are particularly severe for innovative SMEs. Thus, developing models for innovative SMEs that provide reliable estimates of their probabilities of default (PD) is important because the PDs can also serve as ratings. We examine the role of innovative assets such as patents in credit risk modelling due to their signaling value. Specifically, we add to a logit model two innovation-related variables in order to account for both the dimension and the value of the patent portfolio. Based on a unique data set of innovative SMEs with default years of 2005–2008, we show that, although the value of the patent portfolio always reduces the PD, its dimension reduces the firm’s riskiness only if coupled with an appropriate equity level.     

The Impact of Corporate Governance and Audit Quality on the Cost of Private Loans*

The objective of this paper is to examine whether banks discriminate between firms on the basis of their financial condition when assessing the credit default risk, and to what extent corporate governance and auditor quality mitigate such risks in the pricing of new bank loans. The results indicate that, depending on the probability of bankruptcy, banks rely on different monitoring devices. For firms with a low probability of bankruptcy, banks do not rely on the quality of corporate governance or the auditor's industry specialization. However, auditor tenure and a change in auditor affect the spread. For firms with a high probability of bankruptcy, the spread is adjusted for the quality of corporate governance and the auditor's specialization. These results are robust to alternative specifications and measures.     

Financial literacy,financial development,and leverage of small firms

This study examines the relationship between financial literacy and the leverage of small firms, specifically of the legal forms, sole proprietorships, and partnerships. Using a cross-section of 73,302 firms in 22 countries, we find that the leverage of small firms is negatively associated with financial literacy. Further, we explore the role of financial development, bankruptcy and transaction costs, and information asymmetry, in moderating the relationship. We find that the negative relationship is less pronounced in countries with lower financial development, high bankruptcy and transaction costs, and high information asymmetry environments, respectively. We contribute to the understanding of small firms' leverage and the literature on financial literacy, SMEs, finance, and entrepreneurship. This study offers policy implications for economies that promote SMEs for entrepreneurship training and development.     

Using extreme value theory to estimate the likelihood of banking sector failure

Abstract

The growing interest in management of credit risk and estimation of default probabilities has given rise to a range of more or less elaborate credit risk models. While these models work well for non-financial firms they are usually not very successful in capturing the financial strength of banks. As an answer to this, Hall and Miles suggest a simple approach of estimating bank failure probabilities based solely on their stock prices. This paper suggests an extension to the Hall and Miles model using extreme value theory and applies the extended model to the Swedish banking sector around the banking crisis of the early 1990s. The extended model captures very well the increased likelihood of a systemic banking sector failure around the peak of the crisis and it produces default probabilities that are more stable, more realistic and more consistent with Moody’s and Fitch rating implied default rates than probabilities from the original Hall and Miles model.     

Corporate liquidity and the contingent nature of bank credit lines: Evidence on the costs and consequences of bank default

I study the impact of Lehman Brothers' bankruptcy and resultant inability to honor its obligations as a lender under committed credit lines. Firms that lost access to a credit line committed by Lehman Brothers experienced abnormal stock returns of − 3%, on average, on the day of and day after Lehman's bankruptcy filing, amounting to roughly $5.7 billion in aggregate, risk-adjusted losses. These losses were significantly larger for firms that were more financially constrained, firms with less cash, firms for whom Lehman was a lead-bank, and firms that lost access to larger amounts of committed credit. During the four quarters immediately following Lehman's collapse, firms that lost access to a credit line cut their investment spending significantly while simultaneously hoarding more cash than comparable firms. Overall, these findings indicate that firms that lost access to a credit line incurred economically significant costs and real-side consequences as a result of Lehman's default on its loan commitments.     

A Note on Credit Risk of Vertical <Emphasis Type="Italic">Keiretsu</Emphasis> Firms: Preliminary Evidence from the Japanese Automobile Industry

This paper empirically examines the relationship between the credit risk of Toyota, Nissan and Honda keiretsu-affiliated firms and the credit risk of the respective parent company. As credit spread data for keiretsu-affiliated firms were not available we create a keiretsu default index, as a proxy, using expected default probabilities obtained from the KMV and Leland and Toft (J. Finance 51, 987–1019, 1996) option pricing models. We find parent credit spreads do not Granger cause our keiretsu default index and vice versa in a bivariate vector autoregressive (VAR) framework.JEL classification: G3, L62     

A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: A binary quantile regression approach

While using the binary quantile regression (BQR) model, we establish a hybrid bankruptcy prediction model with dynamic loadings for both the accounting-ratio-based and market-based information. Using the proposed model, we conduct an empirical study on a dataset comprising of default events during the period from 1996 to 2006. In this study, those firms experienced bankruptcy/liquidation events as defined by the Compustat database are classified as “defaulted” firms, whereas all other firms listed in the Fortune 500 with over a B-rating during the same time period are identified as “survived” firms. The empirical findings of this study are consistent with the following notions. The distance-to-default (DD) variable derived from the market-based model is statistically significant in explaining the observed default events, particularly of those firms with relatively poor credit quality (i.e., high credit risk). Conversely, the z-score obtained with the accounting-ratio-based approach is statistically significant in predicting bankruptcies of firms of relatively good credit quality (i.e., low credit risk). In-sample and out-of-sample bankruptcy prediction tests demonstrated the superior performance of utilizing dynamic loadings rather than constant loadings derived by the conventional logit model.     

The Bankruptcy Decision and Debt Contract Renegotiations

We consider the bankruptcy law and workout practices in the United States and model bankruptcy as a strategic decision. We analyze a firm's choice between liquidation under Chapter 7, renegotiation of the debt contract in a workout, and reorganization under Chapter 11 of the bankruptcy code. Our premise is that a financially distressed firm chooses its action in order to minimize the loss in value caused by the well-known over- and under-investment problems. We show that the firm initiates a workout when it faces under-investment, and commences Chapter 11 when it faces over-investment. Some of the results are: (i) in default, total firm value and equity value increase upon the announcement of a workout and decrease upon the announcement of Chapter 11; (ii) firms with shorter maturity of debt are more likely to reorganize in a workout; (iii) among the firms that renegotiate their debt contract, the proportion of firms entering Chapter 11 is higher for firms in mature industries than for firms in growth industries.     

A Note on Selecting a Response Measure for Financial Distress

Since 1966, researchers have examined financial distress prediction models to determine the usefulness of accounting information to lenders. These researchers primarily used legal bankruptcy as the response variable for economic financial distress, or included legal bankruptcy with other events in dichotomous prediction models. However, theoretical models of financial distress normally define financial distress as an economic event, the inability to pay debts when due (insolvency). This study uses a loan default/accommodation response variable as a proxy for the inability to pay debts when due. The purpose of this note is to empirically test whether or not using the inability of a firm to pay debts when due, loan default/accommodation, as a response measure produces different results than using legal bankruptcy as the response measure. The study's empirical results show that legal bankruptcy and loan default/accommodation financial distress prediction models produce different statistical results, thus suggesting that the responses measure different constructs. A loan default/accommodation model also fits the data better than a bankrupt model. Our results suggest that a loan default/accommodation response may be a more appropriate measure to determine which accounting information is most useful to lenders in evaluating a firm's credit risk.     

A note on board characteristics,ownership structure and default risk in Taiwan

This study applies dynamic generalized method of moments estimation to examine the influences of ownership structure and board characteristics on default risk for a full samples and two subsamples (high‐tech and conventional) of publicly listed firms in Taiwan. Our findings reveal that certain characteristics of corporate governance have explanatory power for default probability, but the impact is not straightforward. In particular, the impact of internal and external governance structures on default risk is industry dependent. Accordingly, governance proposals that encourage higher ownership among directors and large block shareholdings in high‐tech firms or reduce managerial ownership in conventional companies can have a counterproductive effect on corporate governance and result in higher bankruptcy possibility.