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 upto- 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.

Using a Bayesian model for bankruptcy prediction : a comparative approach

The purpose of this study is to examine the impact of the choice of cut-off points, sampling procedures, and the business cycle on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy. To test the impact of the choice of cut-off points and sampling procedures, three bankruptcy prediction models are assessed- Bayesian, Hazard and Mixed Logit. A salient feature of the study is that the analysis includes both parametric and nonparametric bankruptcy prediction models. A sample of firms from Lynn M. LoPucki Bankruptcy Research Database in the U. S. was used to evaluate the relative performance of the three models. The choice of a cut-off point and sampling procedures were found to affect the rankings of the various models. In general, the results indicate that the empirical cut-off point estimated from the training sample resulted in the lowest misclassification costs for all three models. Although the Hazard and Mixed Logit models resulted in lower costs of misclassification in the randomly selected samples, the Mixed Logit model did not perform as well across varying business-cycles. In general, the Hazard model has the highest predictive power. However, the higher predictive power of the Bayesian model, when the ratio of the cost of Type I errors to the cost of Type II errors is high, is relatively consistent across all sampling methods. Such an advantage of the Bayesian model may make it more attractive in the current economic environment. This study extends recent research comparing the performance of bankruptcy prediction models by identifying under what conditions a model performs better. It also allays a range of user groups, including auditors, shareholders, employees, suppliers, rating agencies, and creditors' concerns with respect to assessing failure risk.

Neural Networks in Bankruptcy Prediction - A Comparative Study on the Basis of the First Hungarian Bankruptcy Model

The article attempts to answer the question whether or not the latest bankruptcy prediction techniques are more reliable than traditional mathematical–statistical ones in Hungary. Simulation experiments carried out on the database of the first Hungarian bankruptcy prediction model clearly prove that bankruptcy models built using artificial neural networks have higher classification accuracy than models created in the 1990s based on discriminant analysis and logistic regression analysis. The article presents the main results, analyses the reasons for the differences and presents constructive proposals concerning the further development of Hungarian bankruptcy prediction.

A comprehensive study of bankruptcy prediction: Accounting ratios, market variables, and microstructure

Bankruptcy prediction has been a fruitful area of research. Univariate analysis and discriminant analysis were the first methodologies used. While they perform relatively well at correctly classifying bankrupt and nonbankrupt firms, their predictive ability has come into question over time. Univariate analysis lacks the big picture that financial distress entails. Multivariate discriminant analysis requires stringent assumptions that are violated when dealing with accounting ratios and market variables. This has led to the use of more complex models such as neural networks. While the accuracy of the predictions has improved with the use of more technical models, there is still an important point missing. Accounting ratios are the usual discriminating variables used in bankruptcy prediction. However, accounting ratios are backward-looking variables. At best, they are a current snapshot of the firm. Market variables are forward-looking variables. They are determined by discounting future outcomes. Microstructure variables, such as the bid-ask spread, also contain important information. Insiders are privy to more information that the retail investor, so if any financial distress is looming, the insiders should know before the general public. Therefore, any model in bankruptcy prediction should include market and microstructure variables. That is the focus of this dissertation. The traditional models and the newer, more technical models were tested and compared to the previous literature by employing accounting ratios, market variables, and microstructure variables. Our findings suggest that the more technical models are preferable, and that a mix of accounting and market variables are best at correctly classifying and predicting bankrupt firms. Multi-layer perceptron appears to be the most accurate model following the results. The set of best discriminating variables includes price, standard deviation of price, the bid-ask spread, net income to sale, working capital to total assets, and current liabilities to total assets.

Bankruptcy Situation Model in Small Business: The Case of Restaurant Firms

The purpose of this paper is to describe and discuss the current bankruptcy prediction models. This is done in the context of pros and cons of proposed models to determine the appropriate factors of failure phenomenon in cases involving restaurants that have filed for bankruptcy under Chapter 11. A sample of 11 restaurant companies that filed for bankruptcy between 1993 and 2003 were identified from the Form 8-K reported to the Securities and Exchange Commission (SEC). By applying financial ratios retrieved from the annual reports which contain, income statements, balance sheets, statements of cash flows, and statements of stockholders’ equity (or deficit) to the Atlman’s mode, Springate model, and Fulmer’s model. The study found that Atlman’s model for the non-manufacturing industry provided the most accurate bankruptcy predictions.

Los conceptos de salud financiera, riesgo y epidemiología en los estados financieros de las compañías del sector de extracción de petróleo crudo y gas natural en Colombia

El presente proyecto tiene como objeto identificar cuáles son los conceptos de salud, enfermedad, epidemiología y riesgo aplicables a las empresas del sector de extracción de petróleo y gas natural en Colombia. Dado, el bajo nivel de predicción de los análisis financieros tradicionales y su insuficiencia, en términos de inversión y toma de decisiones a largo plazo, además de no considerar variables como el riesgo y las expectativas de futuro, surge la necesidad de abordar diferentes perspectivas y modelos integradores. Esta apreciación es pertinente dentro del sector de extracción de petróleo y gas natural, debido a la creciente inversión extranjera que ha reportado, US$2.862 millones en el 2010, cifra mayor a diez veces su valor en el año 2003. Así pues, se podrían desarrollar modelos multi-dimensional, con base en los conceptos de salud financiera, epidemiológicos y estadísticos. El termino de salud y su adopción en el sector empresarial, resulta útil y mantiene una coherencia conceptual, evidenciando una presencia de diferentes subsistemas o factores interactuantes e interconectados. Es necesario mencionar también, que un modelo multidimensional (multi-stage) debe tener en cuenta el riesgo y el análisis epidemiológico ha demostrado ser útil al momento de determinarlo e integrarlo en el sistema junto a otros conceptos, como la razón de riesgo y riesgo relativo. Esto se analizará mediante un estudio teórico-conceptual, que complementa un estudio previo, para contribuir al proyecto de finanzas corporativas de la línea de investigación en Gerencia.

Incremento de la salud financiera mediante la utilización de la relación estratégica comunitaria y marketing.

Debido a las crisis mundiales, la perdurabilidad empresarial se ha convertido en la primera preocupación de las organizaciones, puesto que los problemas económicos en otros países pueden generar un efecto negativo en las condiciones del mercado doméstico, que junto con el entorno empresarial complejo y dinámico en el que se deben desempeñar las empresas hoy en día gracias a la globalización, sumado al aumento en la competitividad nacional e internacional, la perdurabilidad de las empresas se está viendo seriamente comprometida. Lo anterior, ha llevado a las empresas a buscar nuevas formas de mejorar su salud financiera. Para medir la salud financiera empresarial, se pueden usar distintos indicadores como lo es el flujo de caja que está asociado con la rentabilidad, el patrimonio que está asociado a las dificultades financieras, entre otros, o a través de varios modelos de bancarrota, los cuales, por medio de un conjunto de ratios financieros, reflejan el estado actual de la organización y su probabilidad de fracaso en el futuro. Las estrategias comunitarias y el marketing permiten incrementar la salud financiera de las empresas a través de la orientación al cliente y el establecimiento de relaciones gana-gana a largo plazo con las diferentes comunidades con las que se relaciona la organización.

A dynamic ratio-based model for signalling corporate collapse

The recognition of behavioural elements in finance has caused major shifts in the analytic framework pertaining to ratio-based modeling of corporate collapse. The modeling approach so far has been based on the classical rational theory in behavioural economics, which assumes that the financial ratios (i.e., the predictors of collapse) are static over time. The paper argues that, in the absence of rational economic theory, a static model is flawed, and that a suitable model instead is one that reflects the heuristic behavioural framework, which is what characterises behavioural attributes of company directors and in turn influences the accounting numbers used in calculating the financial ratios. This calls for a dynamic model: dynamic in the sense that it does not rely on a coherent assortment of financial ratios for signaling corporate collapse over multiple time periods. This paper provides empirical evidence, using a data set of Australian publicly listed companies, to demonstrate that a dynamic model consistently outperforms its static counterpart in signaling the event of collapse. On average, the overall predictive power of the dynamic model is 86.83% compared to an average overall predictive power of 69.35% for the static model.

Connection between commonly used financial ratios and misrepresentation of financial statements of collapsed corporations

This paper unravels dynamic and intriguing shifts in the use of financial ratios in signaling corporate collapse. An empirical examination of the anecdotal evidences from notable recent corporate collapses coupled with the short-lived usefulness of financial ratios in various prediction models suggest that companies(1) that deliberately misrepresent their financial statements may have taken cues from the ratios that are commonly investigated. This proposition is supported by an extensive examination of over 50 studies conducted between 1968 and 2002. The erosion in the reliability of numbers in financial statements has led to significant distortions in the predictive power of financial ratios when used in signaling corporate collapse. Recent collapses such as Parmalat in Europe, Enron and WorldCom in the U.S. and HIH in Australia, present yet another reminder that financial statement items are being misrepresented. These are all large corporations with well-established household names, and are for sure closely monitored by financial communities around the globe. Nevertheless, a common thread seems to link the collapse of these companies: none of these collapses were foreseen by credit rating agencies or foretold by the widely accepted bankruptcy prediction models. Why? This paper attempts to use some anecdotal evidence in order to provide logical explanations to the existence of such a common thread. It argues that there appears to be anecdotal evidence to suggest that directors of publicly listed companies that have collapsed may have deliberately misrepresented financial statement items.

Modelling corporate collapse: definitional issues of the collapse event

This paper investigates problems associated with interpretations of corporate collapse, and argues for a unified legal, rather than financial, definition of the event. In the absence of a formal definition of the event of corporate collapse, the integrity of sample selection becomes questionable; moreover, comparisons between empirical studies becomes less useful, if not altogether futile, due to the lack of a common ground in the basic building block. Upon close examination of 84 studies on ratio-based modeling of corporate collapse, between 1968 and 2004, this paper finds evidence in favor of a legal interpretation of the event of corporate collapse. Specifically, studies that adopted a legal definition are five times as many as those that opted for a financial explanation.

An empirical evaluation of sampling controversies in ratio-based modelling of corporate collapse

Empirical investigations regarding ratio-based modelling of corporate collapse have been on going for decades. With any study of an empirical nature, a data sample is a necessary prerequisite. It allows testing the performance of the prediction model, thereby establishing its practical relevance. However, it is necessary to first ensure that the data sample used satisfies certain conditions, and these have lead to some choice controversies. This paper considers the controversial issues that arise in data sampling, provides a critical evaluation of these issues, and makes choice recommendations on the controversial aspects, by empirically examining the literature.

Connection between commonly used financial ratios and misrepresentation of financial statements of collapsed corporations

This paper unravels dynamic and intriguing shifts in the use of financial ratios in signaling corporate collapse. An empirical examination of the anecdotal evidences from notable recent corporate collapses coupled with the short-lived usefulness of financial ratios in various prediction models suggest that companies(1) that deliberately misrepresent their financial statements may have taken cues from the ratios that are commonly investigated. This proposition is supported by an extensive examination of over 50 studies conducted between 1968 and 2002. The erosion in the reliability of numbers in financial statements has led to significant distortions in the predictive power of financial ratios when used in signaling corporate collapse. Recent collapses such as Parmalat in Europe, Enron and WorldCom in the U.S. and HIH in Australia, present yet another reminder that financial statement items are being misrepresented. These are all large corporations with well-established household names, and are for sure closely monitored by financial communities around the globe. Nevertheless, a common thread seems to link the collapse of these companies: none of these collapses were foreseen by credit rating agencies or foretold by the widely accepted bankruptcy prediction models. Why? This paper attempts to use some anecdotal evidence in order to provide logical explanations to the existence of such a common thread. It argues that there appears to be anecdotal evidence to suggest that directors of publicly listed companies that have collapsed may have deliberately misrepresented financial statement items.

A brief review of recent research trends on applications of computational and statistical techniques in financial and business intelligence

Artificial neural networks and statistical techniques like decision trees, discriminant analysis, logistic regression and survival analysis play a crucial role in Business Intelligence. These predictive analytical tools exploit patterns found in historical data to make predictions about future events. In this paper we have shown some recent developments of a few of these techniques in financial and business intelligence applications like fraud detection, bankruptcy prediction and credit rating scoring.

Riscos de quebra de continuidade e o papel do auditor

Mestrado em Auditoria

Aplicaciones de los modelos de predicción bio-inspirados en la administración

Las organizaciones y sus entornos son sistemas complejos. Tales sistemas son difíciles de comprender y predecir. Pese a ello, la predicción es una tarea fundamental para la gestión empresarial y para la toma de decisiones que implica siempre un riesgo. Los métodos clásicos de predicción (entre los cuales están: la regresión lineal, la Autoregresive Moving Average y el exponential smoothing) establecen supuestos como la linealidad, la estabilidad para ser matemática y computacionalmente tratables. Por diferentes medios, sin embargo, se han demostrado las limitaciones de tales métodos. Pues bien, en las últimas décadas nuevos métodos de predicción han surgido con el fin de abarcar la complejidad de los sistemas organizacionales y sus entornos, antes que evitarla. Entre ellos, los más promisorios son los métodos de predicción bio-inspirados (ej. redes neuronales, algoritmos genéticos /evolutivos y sistemas inmunes artificiales). Este artículo pretende establecer un estado situacional de las aplicaciones actuales y potenciales de los métodos bio-inspirados de predicción en la administración.