A comparison of alternative bankruptcy prediction models

Early models of bankruptcy prediction employed financial ratios drawn from pre-bankruptcy financial statements and performed well both in-sample and out-of-sample. Since then there has been an ongoing effort in the literature to develop models with even greater predictive performance. A significant innovation in the literature was the introduction into bankruptcy prediction models of capital market data such as excess stock returns and stock return volatility, along with the application of the Black–Scholes–Merton option-pricing model. In this note, we test five key bankruptcy models from the literature using an up-to-date data set and find that they each contain unique information regarding the probability of bankruptcy but that their performance varies over time. We build a new model comprising key variables from each of the five models and add a new variable that proxies for the degree of diversification within the firm. The degree of diversification is shown to be negatively associated with the risk of bankruptcy. This more general model outperforms the existing models in a variety of in-sample and out-of-sample tests.     

Hedging structured credit products during the credit crisis: A horse race of 10 models

Pricing and hedging structured credit products poses major challenges to financial institutions. This paper puts several valuation approaches through a crucial test: How did these models perform in one of the worst periods of economic history, September 2008, when Lehman Brothers went under? Did they produce reasonable hedging strategies? We study several bottom-up and top-down credit portfolio models and compute the resulting delta hedging strategies using either index contracts or a portfolio of single-name CDS contracts as hedging instruments. We compute the profit-and-loss profiles and assess the performances of these hedging strategies. Among all 10 pricing models that we consider the Student-t copula model performs best. The dynamical generalized-Poisson loss model is the best top-down model, but this model class has in general problems to hedge equity tranches. Our major finding is however that single-name and index CDS contracts are not appropriate instruments to hedge CDO tranches.     

Nominal versus indexed debt: A quantitative horse race

The main arguments in favor and against nominal and indexed debts are the incentive to default through inflation versus hedging against unforeseen shocks. We model and calibrate these arguments to assess their quantitative importance. We use a dynamic equilibrium model with tax distortion, government outlays uncertainty, and contingent-debt service. Our framework also recognizes that contingent debt can be associated with incentive problems and lack of commitment. Thus, the benefits of unexpected inflation are tempered by higher interest rates. We obtain that costs from inflation more than offset the benefits from reducing tax distortions. We further discuss sustainability of nominal debt in developing (volatile) countries.     

An empirical comparison of bankruptcy models

Four types of bankruptcy prediction models based on financial statement ratios, cash flows, stock returns, and return standard deviations are compared. Based on a sample of bankruptcies from 1980 to 1991, results indicate that no existing model of bankruptcy adequately captures the data. During the last fiscal year preceding bankruptcy, none of the individual models may be excluded without a loss in explanatory power. If considered in isolation, the cash flow model discriminates most consistently two to three years before bankruptcy. By comparison, the ratio model is the best single model during the year immediately preceding bankruptcy. Quasi-jack-knifing procedures suggest that none of the models can reliably predict bankruptcy more than two years in advance.     

Differences of opinion make a horse race

A model of trading in speculative markets is based on differencesof opinion among traders. Our purpose is to explain some ofthe empirical regularities that have been documented concerningthe relationship between volume and price and the time-seriesproperties of price and volume. We assume that traders sharecommon prior beliefs and receive common information but differin the way in which they interpret this information. Some resultsare that absolute price changes and volume are positively correlated,consecutive price changes exhibit negative serial correlation,and volume is positively autocorrelated.     

Linear models for minimizing misclassification costs in bankruptcy prediction

This paper illustrates how a misclassification cost matrix can be incorporated into an evolutionary classification system for bankruptcy prediction. Most classification systems for predicting bankruptcy have attempted to minimize misclassifications. The minimizing misclassification approach assumes that Type I and Type II error costs for misclassifications are equal. There is evidence that these costs are not equal and incorporating costs into the classification systems can lead to better and more desirable results. In this paper, we use the principles of evolution to develop and test a genetic algorithm (GA) based approach that incorporates the asymmetric Type I and Type II error costs. Using simulated and real-life bankruptcy data, we compare the results of our proposed approach with three linear approaches: statistical linear discriminant analysis (LDA), a goal programming approach, and a GA-based classification approach that does not incorporate the asymmetric misclassification costs. Our results indicate that the proposed approach, incorporating Type I and Type II error costs, results in lower misclassification costs when compared to LDA and GA approaches that do not incorporate misclassification costs. Copyright © 2001 John Wiley & Sons, Ltd.     

The probability of bankruptcy: A comparison of empirical predictions and theoretical models

The last decade has witnessed the development of many empirical models to predict corporate bankruptcy and of several bankruptcy theories. This paper reviews and integrates these two strands of research and finds a substantial amount of overlap. However, the overlap is not perfect. The paper presents a new theory of bankruptcy that appears to fit the data better. The paper also suggests directions for future empirical and theoretical research.     

An effect of hindsight on predicting bankruptcy with accounting information

A decision maker (DM) must make decisions with a given amount of information (called pre-outcome knowledge). Later, if a person (judge) is asked to evaluate the accuracy of those decisions, the judge frequently has an additional piece of information available to him/her that wasn't available to the DM: knowledge of the outcome (hindsight). Hence, the judge's (post-outcome) knowledge includes both pre-outcome knowledge and hindsight. Research in other areas has shown that judges, when attempting to evaluate decisions that were made with only pre-outcome knowledge, are unable to separate their knowledge of the outcome from pre-outcome knowledge. In effect, they infer that knowledge of the outcome should have been apparent from the pre-outcome information. This current research shows that the same phenomenon can exist in the public accounting environment; several implications for accounting are discussed.     

Comparing the performance of market-based and accounting-based bankruptcy prediction models

Recently developed corporate bankruptcy prediction models adopt a contingent claims valuation approach. However, despite their theoretical appeal, tests of their performance compared with traditional simple accounting-ratio-based approaches are limited in the literature. We find the two approaches capture different aspects of bankruptcy risk, and while there is little difference in their predictive ability in the UK, the z-score approach leads to significantly greater bank profitability in conditions of differential decision error costs and competitive pricing regime.     

Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework

Prediction of corporate failure is one of the major activities in auditing firms risks and uncertainties. The design of reliable models to predict bankruptcy is crucial for many decision making processes. Although a large number of models have been designed to predict bankruptcy, the relative performance evaluation of competing prediction models remains an exercise that is unidimensional in nature, which often leads to reporting conflicting results. In this research, we overcome this methodological issue by proposing an orientation-free super-efficiency data envelopment analysis model as a multi-criteria assessment framework. Furthermore, we perform an exhaustive comparative analysis of the most popular bankruptcy modeling frameworks for UK data including our own models. In addition, we address two important research questions; namely, do some modeling frameworks perform better than others by design? and to what extent the choice and/or the design of explanatory variables and their nature affect the performance of modeling frameworks?, and report on our findings.     

Bayesian estimation of financial models

This paper outlines a general methodology for estimating the parameters of financial models commonly employed in the literature. A numerical Bayesian technique is utilised to obtain the posterior density of model parameters and functions thereof. Unlike maximum likelihood estimation, where inference is only justified in large samples, the Bayesian densities are exact for any sample size. A series of simulation studies are conducted to compare the properties of point estimates, the distribution of option and bond prices, and the power of specification tests under maximum likelihood and Bayesian methods. Results suggest that maximum–likelihood–based asymptotic distributions have poor finite–sampleproperties.     

保险公司破产的国际经验与借鉴

本文结合国外保险公司破产的具体案例,综合分析了保险公司破产的原因,这些原因既有保险公司内部的原因,也有外部的竞争和经济环境因素,而这些破产的历史教训是预防保险公司破产、及时甄别出有破产风险保险公司的宝贵经验。本文还介绍了英国、美国和日本的保险监管措施,以及RBC、IR IS、FAST、动态财务分析四种偿付能力监管系统。最后总结了国外保险公司破产对我国的启示。     

Textual sentiment in finance: A survey of methods and models

We survey the textual sentiment literature, comparing and contrasting the various information sources, content analysis methods, and empirical models that have been used to date. We summarize the important and influential findings about how textual sentiment impacts on individual, firm-level and market-level behavior and performance, and vice versa. We point to what is agreed and what remains controversial. Promising directions for future research are emerging from the availability of more accurate and efficient sentiment measures resulting from increasingly sophisticated textual content analysis coupled with more extensive field-specific dictionaries. This is enabling more wide-ranging studies that use increasingly sophisticated models to help us better understand behavioral finance patterns across individuals, institutions and markets.     

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.     

On the estimation of beta-pricing models

An integrated econometric view of maximum likelihood methodsand more traditional two-pass approaches to estimating beta-pricingmodels is presented. Several aspects of the well-known 'errors-in-variablesproblem' are considered, and an earlier conjecture concerningthe merits of simultaneous estimation of beta and price of riskparameters is evaluated. The traditional inference procedureis found, under standard assumptions, to overstate the precisionof price of risk estimates and an asymptotically valid correctionis derived. Modifications to accommodate serial correlationin market-wide factors are also discussed.     

Expected shortfall estimation for apparently infinite-mean models of operational risk

Statistical analyses on actual data depict operational risk as an extremely heavy-tailed phenomenon, able to generate losses so extreme as to suggest the use of infinite-mean models. But no loss can actually destroy more than the entire value of a bank or of a company, and this upper bound should be considered when dealing with tail-risk assessment. Introducing what we call the dual distribution, we show how to deal with heavy-tailed phenomena with a remote yet finite upper bound. We provide methods to compute relevant tail quantities such as the Expected Shortfall, which is not available under infinite-mean models, allowing adequate provisioning and capital allocation. This also permits a measurement of fragility. The main difference between our approach and a simple truncation is in the smoothness of the transformation between the original and the dual distribution. Our methodology is useful with apparently infinite-mean phenomena, as in the case of operational risk, but it can be applied in all those situations involving extreme fat tails and bounded support.     

美国非自愿破产是债权人联合追讨的好方式

中国信保专家提醒：一旦遭遇严重拖欠,出口企业只有通过专业追偿机构,联合起来采取快速有效的追偿行动才能尽量挽回损失     

A solution to an estimation problem involving models with rational expectations

Recently Flood and Garber (1980) have shown that the use of Fair's (1970) estimation technique in the context of a rational expectations errors in variables model yields inconsistent parameter estimates. In this paper we explain why this happens and suggest two consistent estimation procedures.     

Bayesian range-based estimation of stochastic volatility models

Alizadeh, Brandt, and Diebold [2002. Journal of Finance 57, 1047–1091] propose estimating stochastic volatility models by quasi-maximum likelihood using data on the daily range of the log asset price process. We suggest a related Bayesian procedure that delivers exact likelihood based inferences. Our approach also incorporates data on the daily return and accommodates a nonzero drift. We illustrate through a Monte Carlo experiment that quasi-maximum likelihood using range data alone is remarkably close to exact likelihood based inferences using both range and return data.