Predicting bankruptcy in the retail sector: an examination of the validity of key measures of performance

doi:10.1016/S0969-6989(97)00029-5

Journal of Retailing and Consumer Services

Volume 5, Issue 2, April 1998, Pages 105-117

https://doi.org/10.1016/S0969-6989(97)00029-5 Get rights and content

Abstract

The large number and size of bankruptcies in the increasingly competitive retail environment necessitates a review of our ability to signal financial distress in retailing companies. The paper shows that it is still possible to predict upcoming retail failure up to six years in advance. Thus bankruptcy prediction models can be an important diagnoostic tool for managers, wishing to identify problems early and take corrective action. Our study attempts to reconcile the contradictions in the extant literature concerning the reliability of different measures used to predict bankruptcy for the retail sector. Past problems in the ability to predict financial distress in retailers using general models can be explained by capitalizing lease and rental payments made by lessees. Once the tendency of retailers to rent rather than own is accommodated, models predict successfully. Contrary to popular belief, variables that are purported to be particularly relevant to retailers such as cash flow and inventory turnover do not improve the warning signals available from conventional bankruptcy models.

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Inventory management helps ensure internal operational leanness and is essential for maintaining the pace and rhythm of retail operations in a new venture. Bhargava, Dubelaar, and Scott (1998) in a sample of COMPUSTAT firms find that “inventory turnover do[es] not improve the warning signals available from conventional bankruptcy models” (p. 105). Counter to their findings, in line with inventory growth generally increases the odds of bankruptcy (Gaur, Kesavan, and Raman 2014; McGurr and DeVaney 1998), we find that inventory turnover increases the odds of survival.
While the factors related to the survival of established retail firms are well researched, current understanding of drivers of new retail venture survival is limited. We assess the influence of retail operations characteristics on the survival of new retail ventures. Based on data from 15,901 Portuguese retail ventures that were founded between 2006 and 2010 and followed until 2014, the new retail ventures with faster inventory turnover or higher staff expenses per employee had a higher likelihood of survival while higher investment in intangible assets had a negative but negligible effect on survival. The implications of these findings for entrepreneurs of new retailing ventures are discussed.
The influence of related and unrelated industry diversity on retail firm failure
2016, Journal of Retailing and Consumer Services
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A contribution of the paper is the inclusion of both internal resources and external local conditions in explaining the likelihood of failure. Retail firm-level analyses that include both the internal- and external perspective and that examine heterogeneity within the sector are limited compared to the number of firm-level studies that focus on assessing the reliability of firm performance measures in predicting retail firm failure (Altman 1968; Bhargava, Dubelaar and Scott, 1998; McGurr and DeVaney, 1998). The analysis in this paper and its contribution is made possible having access to detailed and geocoded firm-level data that identify a sample of 48,953 retail firms in Sweden during 2002–2010.
The purpose of this paper is to analyse the influence of related and unrelated industry diversity on retail firm failure with a focus on Swedish retailers. The paper develops competing hypotheses from organizational theory and the economics of agglomeration concerning the survival chances of retail firms located in geographic proximity. Hypotheses are tested using a hazard model and a sample of 48,953 retail firms observed during 2002–2010. Key findings show that increases in the local share of similar retail firms is positively related to the risk of failure while there is a negative relation between increases in local industry diversity and the risk of failure. These results indicate that knowledge transfer from a diverse set of industries are important in lowering the failure risk. Differentiating among small and specialized retail firms indicate that there is significant intra-industry heterogeneity in the influence of local industrial composition on the likelihood of failure.
A hybrid device for the solution of sampling bias problems in the forecasting of firms' bankruptcy
2012, Expert Systems with Applications
Citation Excerpt :
Nevertheless, there are several papers examining the bankruptcy in sectors other than manufacturing. For example, telecommunications industry (Foreman, 2003); restaurant industry (Gu, 2002; Kim & Gu, 2006; Youn & Gu, 2010); air carriers (Davalos, Gritta, & Chow, 1999); nursing facility industry (Knox, Blankmeyer, Trinidad, & Stutzman, 2009); oil companies (Sena & Williams, 1998); retail sector (Bhargava, Dubelaar, & Scott, 1998); construction industry (Sueyoshi & Goto, 2009b). Therefore, a database with Spanish construction firms was drawn up.
This paper proposes a new approach to the forecasting of firms’ bankruptcy. Our proposal is a hybrid method in which sound companies are divided in clusters using Self Organized Maps (SOM) and then each cluster is replaced by a director vector which summarizes all of them. Once the companies in clusters have been replaced by director vectors, we estimate a classification model through Multivariate Adaptive Regression Splines (MARS). For the test of the model we considered a real setting of Spanish enterprises from the construction sector. With this procedure we intend to overcome the sampling-bias problems that matched-pairs models often suffer. We estimated two benchmark models: a back propagation neural network and a simple MARS model. Our results show that the proposed hybrid approach is much more accurate than the benchmark techniques for the identification of the bankrupt companies.
Mining financial distress trend data using penalty guided support vector machines based on hybrid of particle swarm optimization and artificial bee colony algorithm
2012, Neurocomputing
The 2008 financial tsunami had a serious impact on the economic development of many countries, including Taiwan. Thus, the ability to predict financial failure and their trends is crucial and attracts public and professional attention when the world enters a period of economic depression. We examined the predictive ability of the proposed support vector machines (SVM) method that uses the characteristics of a penalty function to generate predictions more efficiently. To include the properties of particle swarm optimization (PSO), an evolutionary artificial bee colony (EABC) algorithm was presented; each bee was given a velocity and flying direction to optimize the proposed penalty guided support vector machines (PGSVM). EABC-PGSVM was used to construct a reliable prediction model for public industrial firms in Taiwan. To demonstrate the advantages of EABC and the penalty function, EABC-PGSVM was compared with back-propagation neural network (BPNN), classic SVM optimized by the ABC algorithm (BSVM), and the PGSVM optimized by the ABC algorithm (BPGSVM). Two matched datasets of sample firms that were financially sound or financially distressed during 1999–2006 and 2000–2007 were selected from among the public industrial firms of Taiwan. The final model was validated using within-sample and out-of- the-sample tests. The results demonstrate that the proposed method is promising and can help corporations to prevent failure.
Variables and models for the identification and prediction of business failure: Revision of recent empirical research advances
2012, Revista de Contabilidad-Spanish Accounting Review
Este trabajo analiza la evolución en el tiempo de los estudios sobre fracaso empresarial. Con carácter general, partimos de la revisión crítica realizada en la literatura previa, y aportamos un análisis de la evidencia empírica adicional, con especial atención a la obtenida durante la última década. Pero además, para subsanar algunas deficiencias detectadas en las revisiones anteriores, nos ocupamos de tres aspectos, que pueden considerarse la principal contribución de este trabajo: primero, analizamos la evolución en las últimas décadas del concepto de fracaso empresarial o fallido, detectando cierta evolución desde la identificación hacia la predicción; segundo, analizamos las variables empleadas en los modelos, aportando un estudio de los rasgos empresariales que se representan con las variables (frente al tradicional análisis de frecuencia de las propias variables individuales), siendo los resultados más acordes con los planteamientos y desarrollos teóricos clásicos sobre el fracaso empresarial; y, finalmente, destacamos los puntos fuertes y débiles de las metodologías que, por su reciente aparición, no habían sido analizadas o muy poco por revisiones anteriores: las técnicas de inteligencia artificial y el análisis envolvente de datos (DEA). Adicionalmente, integramos en la revisión el numeroso grupo de trabajos empíricos publicados en España sobre la cuestión, y que no aparecían en ninguna de las revisiones previas analizadas.
This work analyzes the evolution of business failure literature. In it, we consider previous critical revisions, contributing with the analysis of additional empirical evidence, paying special attention to the last decade. In order to make up for some deficiencies detected in previous revisions, we deal with three aspects that can be considered the main contribution of this work. First, we analyze the business failure concept during the last decades, detecting, from identification to prediction, certain evolution. Second, we analyze the variables used in the different models, adding –to the traditional frequency analysis of the individual variables– a study of the business features proxied by the variables, obtaining rankings more in line with the classical theoretical approaches and developments on business failure. Finally, we illustrate the salient strengths and weaknesses of the recently, and scarcely analyzed methodologies, such as artificial intelligence techniques and data envelopment analyses (DEA). In addition, we incorporate a large group of empirical works on this matter published in Spain, missing in the previous revision works examined.
Deconstructing the notion of blame in corporate failure
2011, Journal of Business Research
Citation Excerpt :
The use of synonyms such as collapse, decline, breakdown and demise confuses this task. Researchers in the finance area suggest failure equates to bankruptcy or insolvency (Altman, 1968; Altman et al., 1977; Beaver, 1966; Bhargava et al., 1998; McGurr and DeVaney, 1998b; Sharma and Mahajan, 1980). By contrast, Morris (1997, p. 2) defines failure as embracing “various types of financial distress, ranging from bankruptcy at one extreme to decline in profitability at the other”.
Corporate failure is the subject of considerable academic debate since the 1960s. Failure in the retail sector receives less attention however. This paper addresses the notion of blame in corporate failure. Reference to A Goldberg and Sons, a failed retailer, exemplifies the discussion. Prior to bankruptcy in 1990, this firm was a successful Scottish department store and clothing retailer. The study takes a historical approach, using in-depth interviews, archival material, and other secondary data sources. Findings reveal that, despite warning signs from various key performance indicators and external reviews, the company's board failed to act appropriately. A series of bad strategic decisions contributed to the company's failure. In line with theories of blame attribution, through their (in)actions, the board's negligence played a major role in the firm's demise.

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PaperPredicting bankruptcy in the retail sector: an examination of the validity of key measures of performance

Abstract

Journal of Banking and Finance

Journal of Business Research

Financial ratios, discriminant analysis and the prediction of corporate bankruptcy

Journal of Finance

Corporate Financial Distress and Bankruptcy: A Complete Guide to Predicting and Avoiding Distress and Profiting from Bunkruptcy

Business failure classification in Canada

Journal of Business Administration

The engine of success in retailing

The McKinsey Quarterly

Regression Diagnostics

The incremental information content of accrual versus cash flows

The Accounting Review

Cash flow — it's not the bottom line

Harvard Business Review