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1 – 10 of over 49000This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the…
Abstract
Purpose
This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.
Design/methodology/approach
This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.
Findings
There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.
Originality/value
The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.
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David Veganzones and Eric Severin
Corporate failure remains a critical financial concern, with implications for both firms and financial institutions; this paper aims to review the literature that proposes…
Abstract
Purpose
Corporate failure remains a critical financial concern, with implications for both firms and financial institutions; this paper aims to review the literature that proposes corporate failure prediction models for the twenty-first century.
Design/methodology/approach
This paper gathers information from 106 published articles that contain corporate failure prediction models. The focus of the analysis is on the elements needed to design corporate failure prediction models (definition of failure, sample approach, prediction methods, variables and evaluation metrics and performance). The in-depth review creates a synthesis of current trends, from the view of those elements.
Findings
Both consensus and divergences emerge regarding the design of corporate failure prediction models. On the one hand, authors agree about the use of bankruptcy as a definition of failure and that at least two evaluation metrics are needed to examine model performance for each class, individually and in general. On the other hand, they disagree about data collection procedures. Although several explanatory variables have been considered, all of them serve as complements for the primarily used financial information. Finally, the selection of prediction methods depends entirely on the research objective. These discrepancies suggest fundamental advances in discovery and establish valuable ideas for further research.
Originality/value
This paper reveals some caveats and provides extensive, comprehensible guidelines for corporate failure prediction, which researchers can leverage as they continue to investigate this critical financial subject. It also suggests fruitful directions to develop further experiments.
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Nirosh Kuruppu, Fawzi Laswad and Peter Oyelere
The purpose of this paper is to ascertain the practical efficacy of statistical corporate failure models in improving auditors' going concern assessment. It also aims to examine…
Abstract
Purpose
The purpose of this paper is to ascertain the practical efficacy of statistical corporate failure models in improving auditors' going concern assessment. It also aims to examine auditors' perceptions of corporate failure models as an analytical procedure in this context.
Design/methodology/approach
The paper utilises a survey questionnaire with a case study component to evaluate the practical value of corporate failure models for assessing going concern, and to examine auditors' perceptions of such models as an analytical procedure for assessing going concern.
Findings
The results indicate that corporate failure models facilitate the formation of more appropriate going concern opinions and increase judgment consensus. Auditors perceive such models as useful in obtaining relevant evidential matter and in mitigating some of the subjectivity involved in assessing going concern. However, the results also indicate that corporate failure models are perceived to be more effective in the planning stages than at the final stages of the audit. Furthermore, auditors are seeking more explicit guidance in auditing standards on the use of corporate failure models for assessing going concern.
Originality/value
The study extends previous research by examining the practical efficacy of corporate failure models for assisting auditors to assess going concern in light of human information processing limitations. Further, it examines auditors' perceptions of corporate failure models as an analytical procedure, and the guidance that auditors seek on the use of such models in auditing standards.
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Kosmas Kosmidis and Antonios Stavropoulos
The main purposes of this paper are to provide evidence about corporate failure diagnosis in SMEs, identify the predictor variables that enhance the accuracy of the corporate…
Abstract
Purpose
The main purposes of this paper are to provide evidence about corporate failure diagnosis in SMEs, identify the predictor variables that enhance the accuracy of the corporate failure diagnosis models, and perform comparative analysis of the proposed models with the existing literature. The paper supports the proposition that the majority of the proposed corporate failure diagnosis models in the literature exhibit an endogenous drawback since their construction is based on large entities or listed corporations' samples.
Design/methodology/approach
The present study employs multiple discriminant analysis, logit analysis, and probit analysis to construct corporate failure diagnosis models based on SMEs longitudinal data from Greece.
Findings
The paper provides evidence that the contribution of human capital is immensely more important to the viability of SMEs than to the viability of large corporations. Moreover, this study identifies interactions among seemingly insignificant variables that exhibit incremental information content and attribute massive discriminant power to the proposed corporate failure diagnosis models.
Practical implications
The results of this study encourage regulatory authorities to adopt enhancements to the Basel II framework and financial institutions as regards to constructing their corporate failure diagnosis models. The models is based upon internal default experience and mapping to external data incorporating both quantitative and qualitative variables.
Originality/value
The contribution of this paper is the proposition of new value-relevant variables that enhance the accuracy of existing corporate failure diagnosis models for SMEs.
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Kingsley Opoku Appiah, Amon Chizema and Joseph Arthur
This paper aims to review the existing literature systematically so as to contribute towards a better understanding of methodological problems of the classical statistical…
Abstract
Purpose
This paper aims to review the existing literature systematically so as to contribute towards a better understanding of methodological problems of the classical statistical techniques, artificially intelligent expert systems and theoretical approaches to solve the corporate failure syndrome.
Design/methodology/approach
This paper presented a systematic review of 83 articles reporting 137 prediction failure models published within 1966-2012 in scholarly reviewed journals in four main disciplines, namely, accounting, finance, banking and economics. The authors performed the systematic literature review with five main sources, namely, Science Direct, Google Scholar, Wiley Interscience, Metalib, Web of Science and Business Source Complete of the Social Sciences. The review modified the approaches used by Aziz and Dar (2006), Ravi and Ravi (2007) and Balcaen and Ooghe (2006).
Findings
The results indicate significant body of prior literature on prediction of corporate failure, but a theoretically sound, highly accurate, simple and widely used corporate failure prediction model for stakeholders has yet to be developed.
Originality/value
This paper contributes towards a systematic understanding of the methodological problems associated with the statistical, artificially intelligent expert systems and theoretical approaches to solve the corporate failure prediction problems faced by firms in 11 countries.
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Malcolm Smith, Yun Ren and Yinan Dong
The purpose of this paper is to examine the extent to which “corporate governance” and “conservatism” variables can contribute to the predictive ability of corporate financial…
Abstract
Purpose
The purpose of this paper is to examine the extent to which “corporate governance” and “conservatism” variables can contribute to the predictive ability of corporate financial disclosures.
Design/methodology/approach
Multiple discriminant analysis is used to differentiate between good and poor companies in Australian manufacturing industry on the basis of their 2009 performance. A classification model including size, governance and conservatism variables, together with financial ratio data is constructed based on 2008 data, and used to predict 2009 performance.
Findings
A model with conservatism, total debt/total assets, company size, and “percentage of shareholdings held by non‐executive directors” (representing corporate governance) as its independent variables, has a classification accuracy of 80.6 percent, and a predictive accuracy of 62.2 percent.
Research limitations/implications
The relatively small sample size, for Australian manufacturing companies, limits both the predictive ability of the model and its generalisability elsewhere.
Practical implications
The findings of the paper demonstrate the importance of both “conservatism” and “corporate governance” measures in determining corporate financial performance.
Originality/value
The paper uses familiar discriminant methods in an unfamiliar context – focusing on surviving companies exhibiting extremes of financial performance.
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M.L. Nasir, R.I. John, S.C. Bennett, D.M. Russell and A Patel
An appropriate use of neural computing techniques is to apply them to corporate bankruptcy prediction, where conventional solutions can be hard to obtain. Having said that…
Abstract
An appropriate use of neural computing techniques is to apply them to corporate bankruptcy prediction, where conventional solutions can be hard to obtain. Having said that, choosing an appropriate Artificial Neural Network topology (ANN) for predicting corporate bankruptcy would remain a daunting prospect. The context of the problem is that there are no fixed rules in determining the ANN structure or its parameter values, a large number of ANN topologies may have to be constructed with different structures and parameters before determining an acceptable model. The trial‐and‐error process can be tedious, and the experience of the ANN user in constructing the topologies is invaluable in the search for a good model. Yet, a permanent solution does not exist. This paper identifies a non trivial novel approach for implementing artificial neural networks for the prediction of corporate bankruptcy by applying inter‐connected neural networks. The proposed approach is to produce a neural network architecture that captures the underlying characteristics of the problem domain. The research primarily employed financial data sets from the London Stock Exchange and Jordans financial database of major public and private British companies. Early results indicate that an ANN appears to outperform the traditional approach in forecasting corporate bankruptcy.
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Kingsley Opoku Appiah and Joshua Abor
The purpose of this paper is to use relevant financial information of private medium‐sized failed and non‐failed manufacturing firms in the UK, during the period 1994‐2004 to…
Abstract
Purpose
The purpose of this paper is to use relevant financial information of private medium‐sized failed and non‐failed manufacturing firms in the UK, during the period 1994‐2004 to determine whether corporate failure can be predicted by developing a Z‐score model.
Design/methodology/approach
Multiple discriminant analysis is used to develop the Z‐score to support the notion that Z‐score is an innovation to overcome the numerous difficulties associated with using single ratios to measure companies' health or risk of failure.
Findings
This paper advances the notion that the net profit margin is superior to the gross profit margin in discriminating between failed and non‐failed UK manufacturing companies in terms of its significant contribution to the Z‐score, though the latter exceeds the former slightly using the univariate analysis.
Originality/value
This research contributes to the area of benchmarking by providing a method to more accurately predict corporate failure.
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Nirosh Kuruppu, Fawzi Laswad and Peter Oyelere
Recent research questions whether bankruptcy is the best proxy for assessing going concern since filing for bankruptcy is not synonymous with the invalidity of the going concern…
Abstract
Recent research questions whether bankruptcy is the best proxy for assessing going concern since filing for bankruptcy is not synonymous with the invalidity of the going concern assumption. Furthermore, in contrast to debtor‐oriented countries such as the USA, liquidation is the most likely outcome of corporate insolvency in creditor‐oriented countries such as the UK, Germany, Australia and New Zealand. This suggests that bankruptcy prediction models have limited use for assessing going concern in creditor‐oriented countries. This study examines the efficacy of a corporate liquidation model and a benchmark bankruptcy prediction model for assessing company liquidation. It finds that the former is more accurate in predicting company liquidations in comparison with the latter. Most importantly, Type 1 errors for the liquidation prediction model are significantly lower than for the bankruptcy prediction model, which indicates its greater efficacy as an analytical tool for assessing going concern. The results also suggest that bankruptcy prediction models might not be appropriate for assessing going concern in countries where the insolvency code is creditor‐oriented.
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Provides a comprehensive, critical review of failure prediction with cash flow information since Beaver (1966); and tabulates the methods and cash flow variables used, and the…
Abstract
Provides a comprehensive, critical review of failure prediction with cash flow information since Beaver (1966); and tabulates the methods and cash flow variables used, and the results produced. Describes the literature as “inconsistent and inconclusive” and discusses possible reasons why, e.g. the measurement and diversity of cash flows, lack of model validation, multicollinearity etc. Points out the importance of cash to solvency and dividend payouts; and the limitations it places on creative accounting. Summarizes the reasons for previous inconsistencies and considers possibilities for further research.
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