An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction

Edmister, Robert O.

doi:10.2307/2329929

Cited by 555 publications

(328 citation statements)

References 10 publications

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“…9 The original Z-score model (Altman, 1968) For many years thereafter, MDA was the prevalent statistical technique applied to the default prediction models. It was used by many authors (Deakin (1972), Edmister (1972), Blum (1974), Eisenbeis (1977), Taffler and Tisshaw (1977), Altman et al (1977), Bilderbeek (1979), Micha (1984), Gombola et al (1987), Lussier (1995), Altman et al (1995)). However, in most of these studies, authors pointed out that two basic assumptions of MDA are often violated when applied to the default prediction problems 10 .…”

Section: Default Prediction Studiesmentioning

confidence: 99%

“…One motivation is to show the significant importance for banks of modeling credit risk for SMEs separately from large corporates. The only study that we are aware of that focused on modeling credit risk specifically for SMEs is a fairly distant article by Edmister (1972). He analyzed 19 financial ratios and, using multivariate discriminant analysis, developed a model to predict small business defaults.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Credit Risk for SMEs: Evidence from the US Market

Altman

Sabato²

2005

SSRN Journal

124

217

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Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyze its effectiveness compared to a generic corporate model. The behaviour of financial measures for SMEs is analyzed and the most significant variables in predicting the entities' credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on panel data of over 2,000 US firms (with sales less than $65 million) over the period 1994-2002, we develop a one-year default prediction model. This model has an out-of-sample prediction power which is almost 30 percent higher than a generic corporate model. An associated objective is to observe our model's ability to lower bank capital requirements considering the new Basel Capital Accord's rules for SMEs. JEL classification: G21; G28

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Section: Default Prediction Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Credit Risk for SMEs: Evidence from the US Market

Altman

Sabato²

2005

SSRN Journal

124

217

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“…Among the most widespread definitions in the mainstream studies we can consider the following ones: − A firm's formal declaration of bankruptcy or another legal proceeding (Altman, 1968;Taffler, 1982;Laffarga et al, 1985;Ohlson, 1980;Zmijewski, 1984;Peel et al, 1986;Pina, 1989;Theodossiou, 1991;Odom and Sharda, 1992;Dimitras et al, 1999;Charitou et al, 2004); − Failure in the sense of insolvency, as the inability of a firm to pay debts as they fall due (Edmister, 1972;Laitinen, 1991), or − A group of different situations, as well as the two previous ones (Altman et al, 1994;Laitinen and Laitinen, 1998), such as an overdrawn account and the nonpayment of a preferred stock dividend (Beaver, 1966;Deakin, 1972) or an explicit agreement with creditors to reduce debts (Blum, 1974;Elam, 1975).…”

Section: Definition Of Business Failurementioning

confidence: 99%

Análisis del fracaso empresarial por sectores: factores diferenciadores = Cross-industry analysis of business failure: differential factors

Quintana

Gallego

Pascual

2012

Pecvnia

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ResumenEl objetivo de este trabajo se centra en el análisis del fracaso empresarial por sectores, a fin de identificar los factores explicativos y predictivos de este fenómeno que son diferentes en tres de los principales sectores que se distinguen en toda economía: industria, construcción y servicios. Para cada uno de estos sectores, seguimos el mismo procedimiento. En primer lugar, aplicamos un análisis de componentes principales con el que identificamos los factores explicativos del fracaso empresarial en los tres sectores. A continuación, consideramos dichos factores como variables independientes en un análisis discriminante, que aplicamos para predecir el fracaso de una muestra de empresas, utilizando no sólo información financiera en forma de ratios, sino también otras variables no financieras relativas a las empresas, así como información externa a las mismas que refleja las condiciones macroeconómicas bajo las que desarrollan su actividad.Palabras clave: Fracaso empresarial; Ratios financieros; Información no financiera; Análisis discriminante; Sectores. AbstractThis paper focuses on a cross-industry analysis of business failure, in order to identify the For each one of these industries, the same procedure is followed. First, a principal components analysis is applied in order to identify the explanatory factors of business failure in the three industries. Next, these factors are considered as independent variables in a discriminant analysis, so as to predict the firms' failure, using not only financial information expressed by ratios, but also other non-financial variables related to the firms, as well as external information that reflects macroeconomic conditions under which they develop their activity.Keywords: Business failure; Financial ratios; Non-financial information; Discriminant analysis; Industries. INTRODUCCIÓNLa predicción del fracaso empresarial es un importante campo de investigación dentro de la literatura financiera, que en los últimos tiempos ha adquirido gran trascendencia, debido a la severa crisis económica y financiera que está afectando a muchos países en Europa y el resto del mundo.En este contexto de incertidumbre, las empresas, cuya actividad es la base para el desarrollo de las economías, sufren tales consecuencias que les llevan a su fracaso o crisis o, incluso, a su desaparición. Esto tiene importantes efectos en los agentes con los que la empresa se relaciona, tales como accionistas, empleados, clientes y proveedores, pero también en otros negocios que actúan en el sistema económico (Lang y Stulz, 1992), lo que puede provocar un impacto general de gran magnitud en las economías. Por tanto, se hace necesario disponer de herramientas que permitan anticipar el posible fracaso de una empresa y, por consiguiente, evitar los efectos que el fracaso empresarial tiene sobre los agentes económicos que participan en el sistema.

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“…For example, Beaver (1966) and Edminister (1972) accepted nonperforming financial liabilities in due time, unpaid debts apart from those leading to bankruptcy, bounced checks, not distributing profit to privileged shareholders and so on as measures for financial failure.…”

Section: Introductionmentioning

confidence: 99%

Predicting Financial Failure Using Decision Tree Algorithms: An Empirical Test on the Manufacturing Industry at Borsa Istanbul

Ocal¹,

Ercan²,

Kadioglu³

2015

IJEF

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This study aims to develop a model using C5.0 and CHAID decision tree algorithms to estimate the financial failure and/or success of a given manufacturing company. Within the scope of this study, 35 financial ratios are used as independent variables calculated on the grounds of both company's annual financial statements and notes from 2007 to 2013. The dependent variable is the successful or unsuccessful status in terms of financial capability of 206 manufacturing firms listed on the Borsa Istanbul. Qualitative criteria are used to categorize the companies as successful or unsuccessful. The rates of accurate classification for both models are found to be at acceptable levels. Although the CHAID algorithm's general rate of accuracy and its rate for successful companies are greater than the rates obtained from the C5.0 algorithm for the same observations, the CHAID algorithm yielded much lower results than the C5.0 algorithm in predicting unsuccessful companies.

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An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction

Cited by 555 publications

References 10 publications

Modeling Credit Risk for SMEs: Evidence from the US Market

Modeling Credit Risk for SMEs: Evidence from the US Market

Análisis del fracaso empresarial por sectores: factores diferenciadores = Cross-industry analysis of business failure: differential factors

Predicting Financial Failure Using Decision Tree Algorithms: An Empirical Test on the Manufacturing Industry at Borsa Istanbul

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