Predicting bankruptcy using the discrete-time semiparametric hazard model

The usual bankruptcy prediction models are based on single-period data from firms. These models ignore the fact that the characteristics of firms change through time, and thus they may suffer from a loss of predictive power. In recent years, a discrete-time parametric hazard model has been proposed for bankruptcy prediction using panel data from firms. This model has been demonstrated by many examples to be more powerful than the traditional models. In this paper, we propose an extension of this approach allowing for a more flexible choice of hazard function. The new method does not require the assumption of a parametric model for the hazard function. In addition, it also provides a tool for checking the adequacy of the parametric model, if necessary. We use real panel datasets to illustrate the proposed method. The empirical results confirm that the new model compares favorably with the well-known discrete-time parametric hazard model.     

The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model

This paper investigates the extent to which the size affects the SME probabilities of bankruptcy. Using a dataset of (11,117) US non-financial firms, of which (465) filed for insolvency under chapters 7/11 between 1980 and 2013. We forecast the bankruptcy probabilities by developing four discrete-time duration-dependent hazard models for SMEs, Micro, Small, and Medium firms. A comparison of the default prediction models for medium firms and SMEs suggests that an almost identical set of explanatory variables affect the default probabilities leading us to believe that treating each of these groups separately has no material impact on the decision making process. However, comparisons between the micro and small firms with the SMEs firms strongly suggest that these categories need to be considered separately when modelling their credit risk.     

Bankruptcy Prediction with Industry Effects

This paper investigates the forecasting accuracy of bankruptcy hazard rate models for U.S. companies over the time period 1962–1999 using both yearly and monthly observation intervals. The contribution of this paper is multiple-fold. One, using an expanded bankruptcy database we validate the superior forecasting performance of Shumway's (2001) model as opposed to Altman (1968) and Zmijewski (1984). Two, we demonstrate the importance of including industry effects in hazard rate estimation. Industry groupings are shown to significantly affect both the intercept and slope coefficients in the forecasting equations. Three, we extend the hazard rate model to apply to financial firms and monthly observation intervals. Due to data limitations, most of the existing literature employs only yearly observations. We show that bankruptcy prediction is markedly improved using monthly observation intervals. Fourth, consistent with the notion of market efficiency with respect to publicly available information, we demonstrate that accounting variables add little predictive power when market variables are already included in the bankruptcy model.     

Corporate bankruptcy prediction in Japan

The purpose of this study is to highlight the financial characteristics of failed firms in Japan, and to construct corporate bankruptcy prediction models with greater prediction accuracy. Our principal component analysis indicated that failed firms in Japan could be classified into two groups: a group having negative financial structures and a group having a declining flow of funds. Additionally, they can be classified into two other different categories of groups: one whose financial position during three years before shows a ‘V’ shape and another group that shows a ‘XXX’ shape.Our discriminant analysis indicated that improved prediction accuracy could be obtained by using, as predictor variables, both ratios and absolute amounts based on cash base financial statement data three years before failure. This data was adjusted to properly reflect the exceptions, reservations, and qualifications appearing in the audit reports and those based on accrual base financial statement data.     

Innovate or die: Corporate innovation and bankruptcy forecasts

We investigate the relationship between a firm’s innovation performance and its probability of bankruptcy. Estimating the discrete hazard model with a comprehensive set of bankruptcies spanning the period of 1980–2009, we find several previously neglected innovation-based variables are important determinants of bankruptcy probability, especially for firms belonging to technology-intensive industries. R&D productivity demonstrates persistent significance across different prediction horizons while the predictive power of patent count becomes larger and more significant at longer prediction horizons. We also find that a firm’s organization capital intensity correlates positively with future bankruptcy.     

Institutional arrangements and debt financing

I find that institutional arrangements have an impact on the real economy by affecting firms’ choice between private and public debt and the subsequent financing costs. Using new debt issued by firms in 26 non-US countries, I find, after controlling for firm characteristics predicted by debt agency and information asymmetry theories, that the level of financial market development, the efficiency of bankruptcy procedure, the integrity and enforceability of laws, and the transparency of financial information have significant impacts not only on firms’ debt choice and yield to maturity in domestic debt market, but also their issuance choice in the international debt market.     

Dynamic prediction of hedge fund survival in crisis-prone financial markets

This study focuses on dynamic changes in survival probabilities over the lifetimes of hedge funds. To model such probabilities, a mixed Cox proportional hazards (CPH) model-specifically, a survival/hazard model with time-varying covariates and fixed covariates- is employed. Resulting dynamic survival probabilities show that the mixed CPH model provides significantly higher accuracy in predicting hedge fund failure than other models in the literature, including fixed covariate CPH models and discrete logit models. Our results are useful to investors and regulators of hedge funds in crisis-prone financial markets.     

The Sources of Financing Constraints

Which financial frictions drive firms’ financing constraints? We structurally estimate dynamic firm financing models embedding many financial frictions, on panels of public firms and private firms. We focus on limited enforcement, moral hazard, and trade-off models and assess which models rationalize best observed corporate policies across various samples. Our tests, based on empirical policy function benchmarks, favor trade-off models for larger public firms, limited commitment models for smaller public firms, and moral hazard models for Private firms. Our estimates suggest significant financing constraints due to agency frictions and highlight the importance of identifying their sources for firm valuation.     

Forecasting forward defaults: a simple hazard model with competing risks

A forward default prediction method based on the discrete-time competing risk hazard model (DCRHM) is proposed. The proposed model is developed from the discrete-time hazard model (DHM) by replacing the binary response data in DHM with the multinomial response data, and thus allowing the firms exiting public markets for different causes to have different effects on forward default prediction. We show that DCRHM is a reliable and efficient model for forward default prediction through maximum likelihood analysis. We use actual panel data-sets to illustrate the proposed methodology. Using an expanding rolling window approach, our empirical results statistically confirm that DCRHM has better and more robust out-of-sample performance than DHM, in the sense of yielding more accurate predicted number of forward defaults. Thus, DCRHM is a useful alternative for studying forward default losses on portfolios.     

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.     

Firms’ performance under different bankruptcy systems: a Europe–USA empirical analysis

This study empirically analyses the effect that the bankruptcy law has on firms’ performance based on its financial situation. To do this, we considered the different types of efficiency and their influence on firms’ value. The study was carried out for Germany, Spain, the United States, France and the United Kingdom. We applied System‐GMM estimation to dynamic panel data. The main results show that under creditor‐oriented systems, there is a decrease in the value of both financially distressed firms and those filing for bankruptcy.     

An Empirical Investigation of Firm Longevity: A Model of the Ex Ante Predictors of Financial Distress

Empirical models of a potential failure process that incorporate distress states between the extremes of corporate health and bankruptcy are uncommon. We depict financial distress as a series of financial events that reflect varied stages of corporate adversity. Our intent is to provide information regarding the influence of certain risk dimensions and firm-specific attributes on distressed firm survival over time. Within a theorized distress framework, we utilize the techniques of survival analysis to longitudinally track firms, grouped a priori according to an initial decline in operating cash flows. We find that the event of default has a significant positive association with business failure. Further, we document that the significant accounting covariates tend to change conditional on a firm having progressed through the diverse stages of distress. These findings accentuate the heterogeneous nature of financial distress and potential business failure.     

Impaired bank health and default risk

Empirical studies in corporate finance have long been focused on the role of banks in reducing the costs of financial distress. The environment and events in Japan provide a “natural experiment” that allows such empirical studies. The number of bankruptcies steadily increased throughout the 1990s, and peaked in 2000. During this period, Japan's banking sector, in contrast, faced considerable problems regarding the disposal of their bad loans. The purpose of this paper is to investigate how various measures of bank health and how defaults of major trading partners affected the probability of bankruptcy among medium-size firms in Japan. Using probit models, we examine the causes of bankruptcy for unlisted Japanese companies in the late 1990s and early 2000s. We find that several measures of bank-specific financial health have had significant impacts on a borrower's probability of bankruptcy, even when observable characteristics relating to these borrower's financial variables are controlled. In particular, a close bank–firm relationship—which usually reduces the probability of bankruptcy—exacerbates the impacts of a financial crisis, which substantially damages other bank health measures as well.     

Cash Management Behavior and Failure Prediction

The purpose of this study is to evaluate the information contained in static and dynamic inventory cash management models to predict failure in a sample of 41 small and middle-sized Finnish bankrupt firms and their nonbankrupt counterparts. The results indicate that the estimates of the (scale) elasticity of cash balance with respect to the volume of transactions (approximated by net sales) is significantly lower for the failed firms. Furthermore, only the scale elasticity appears to be a statistically significant discriminating variable, and only in the first year before bankruptcy. This estimate remarkably increased the Lachenbruch validated classification accuracy based on traditional financial variables.     

Effect of cash flow risk on corporate failures,and the moderating role of earnings management and abnormal compensation

In this study, we find that United States firms' average cash flow risk (CFR) shows a significantly increasing trend over the past four decades or so. This does not portend well considering the significance of cash flows in maintaining a firm's financial health and going concern status. The CFR also increases dramatically for firms approaching financial distress or bankruptcy, suggesting its important role in predicting a firm's failure. Empirically, we find that CFR has a strong positive effect on a firm's financial distress likelihood. We also find that the association between CFR and financial distress is negatively moderated in firms with high earnings management and abnormal compensation. The results suggest that managers in firms with high CFR are more likely to use heuristics in form of earnings management. Thus, supporting the upper echelons theory related to managers under performance pressure. Meanwhile, consistent with the notion in the agency theory that financial incentives serve as effective monitoring mechanisms, compensation packages can incentivize better risk management practices and decrease the likelihood of a firm's failure. Our findings are also robust to alternative definitions of a firm's failure: financial constraints, presumed debt covenant violation and legal bankruptcy filings.     

Strategic Bankruptcies and Price Reactions to Bankruptcy Filings

This study investigates whether the stock market differentiates between firms that file bankruptcy petitions for strategic reasons and firms that file bankruptcy petitions for financial reasons. We perform both univariate and regression tests on a sample of 245 firms that filed Chapter 11 bankruptcy petitions between 1981 and 1996. After controlling for bankruptcy outcome, probability of bankruptcy, firm financial condition, and firm size, we find that, in the period around bankruptcy filing, firms that file bankruptcy petitions for financial reasons have significantly larger stock price declines than firms that file bankruptcy petitions for strategic reasons.     

OPERATING LEVERAGE AND UNDERINVESTMENT

Using a contingent claims model, we examine the impacts of both operating leverage and financial leverage on a firm's investment decisions in the context of capacity expansion. Our model shows that quasi‐fixed operating costs could significantly mitigate the underinvestment problem for debt‐financed firms. The existing debt induces equity holders to delay equity‐financed expansion because the expanded earnings base will also benefit the debt holders by lowering the bankruptcy risk. The operating costs decrease this type of wealth transfer from equity holders to debt holders by magnifying the bankruptcy risk of the existing debt upon investment. By applying the Cox proportional hazard model on a large sample of publicly traded U.S. firms over 1966–2016, we offer empirical support for the theoretical predictions. The results are robust to various measures of operating leverage.     

Stiffing the creditor: Asset verifiability and bankruptcy

Evidence suggests that asset pledgeability, debt complexity, and control rights of dispersed debt influence financial distress resolution. We model how courts’ imperfect verifiability of assets and valuable control of misaligned creditors shape firms’ debt structure and create coordination problems that determine distress outcomes and financing. A key result is that an increase in verifiability allows financially constrained firms to fund projects by pledging more assets to misaligned creditors, making contract renegotiation in distress times more difficult and increasing the probability of bankruptcy. Since equity receives less in the event of distress, constrained firms choose riskier projects with higher returns. Consistent with our model, bankruptcy filings increase after the U.S. Supreme Court decision imposing a “market test” to assess the value of stockholders’ interest in debtor proposals. The effect is stronger for firms with low asset verifiability. These firms also experienced an increase in recovery rates, debt capacity, and risk-taking. Our findings suggest that reforms improving the verifiability of assets substantially impact credit access. However, our results also point out that improving asset verifiability may be insufficient for constrained firms with aligned creditors. Therefore, complementary reforms that facilitate firms’ access to creditors from different market segments may be necessary.     

A horse race of models and estimation methods for predicting bankruptcy

We use a comprehensive set of performance metrics to analyze the improvement in the classification power and prediction accuracy of various bankruptcy prediction models after adding governance variables and/or varying the estimation method used. In a sample covering bankruptcies of U.S. public firms in the period 2000 to 2015, we find that the addition of governance variables significantly improves the performance of all bankruptcy prediction models. We also find that the additional explanatory power provided by governance measures improves the further the firm is from bankruptcy, which suggests that governance variables may provide earlier and more accurate warning of the firm's bankruptcy potential. Our findings show that the performance of any bankruptcy prediction model is significantly affected by the estimation method used. We find that regardless of the bankruptcy model, hazard analysis provides the best classification and out-of-sample forecast accuracy among the parametric methods. Furthermore, non-parametric methods such as neural networks, data envelopment analysis or classification and regression trees appear to provide comparable and sometimes superior classification accuracy to hazard analysis. Lastly, we use the dynamic panel generalized methods of moments model to address concerns raised in prior studies about the susceptibility of similar studies to endogeneity issues and find that our findings continue to hold.     

Empirical performance of Gaussian affine dynamic term structure models in the presence of autocorrelation misspecification bias

Recently, several authors have documented the presence of estimation bias in Gaussian affine dynamic term structure models (GADTSM). However, only a few applications involving its impact on the empirical performance of GADTSM exist in the extant literature, and these studies focus solely on discrete-time vector autoregressive (VAR) based GADTSM and concentrate on issues of small-sample bias and persistence. In this paper, we provide a comprehensive investigation of this issue that includes the estimation of both discrete-time VAR based GADTSM and continuous-time GADTSM at multiple data frequencies through a unique empirical design and two Monte Carlo simulation experiments, within which we construct estimation bias from the serial correlation in yield pricing errors. Our findings show that, although, empirical performance of all studied GADTSM are severely impacted by estimation bias, discrete-time GADTSM are more severely impacted by estimation bias than continuous-time GADTSM. Building on theoretical arguments developed in previous works, we attribute this finding to the strong dependence of discrete-time VAR based GADTSM on the ordinary least squares econometric technique relative to the continuous-time GADTSM for which general maximum likelihood estimation is more suitable.