The Performance of Insolvency Prediction and Credit Risk Models in the UK: A Comparative Study

Theoretically-driven, market-based contingent claims models have recently been applied to the field of corporate insolvency prediction in an attempt to provide the art with a theoretical methodology that has been lacking in the past. Limited studies have been carried out in order directly to compare the performance of these models with that of their accounting number-based counterparts. We use receiver operating characteristic curves to assess the efficacy of thirteen selected models using, for the first time, post-IFRS UK data; and investigate the distributional properties of model efficacy. We find that the efficacy of the models is generally less than that reported in the prior literature; but that the contingent claims models outperform models which use accounting numbers. We also obtain the counter-intuitive finding that predictions based on a single variable can be as efficient as those which are based on models which are far more complicated – in terms of variable variety and mathematical construction. Finally, we develop and test a naive version of the down-and-out-call barrier option model for insolvency prediction and find that, despite its simple formulation, it performs favourably compared alongside other contingent claims models.

[1]  Kaisa Sere,et al.  Choosing Bankruptcy Predictors Using Discriminant Analysis, Logit Analysis, and Genetic Algorithms , 1996 .

[2]  Roger M. Stein Benchmarking default prediction models: pitfalls and remedies in model validation , 2007 .

[3]  Erkki K. Laitinen,et al.  Survival analysis as a tool for company failure prediction , 1991 .

[4]  W. Beaver Financial Ratios As Predictors Of Failure , 1966 .

[5]  James E. Walter Determination of Technical Solvency , 1957 .

[6]  E. Altman PREDICTING FINANCIAL DISTRESS OF COMPANIES: REVISITING THE Z-SCORE AND ZETA ® MODELS , 2013 .

[7]  Paul Newbold,et al.  CLASSIFYING BANKRUPT FIRMS WITH FUNDS FLOW COMPONENTS , 1985 .

[8]  M. Gönen Receiver Operating Characteristic (ROC) Curves , 2006 .

[9]  D. McFadden Conditional logit analysis of qualitative choice behavior , 1972 .

[10]  James G. MacKinnon,et al.  Convenient Specification Tests for Logit and Probit Models , 1984 .

[11]  R. Moyer Forecasting Financial Failure: A Re-Examination , 1977 .

[12]  H. S. Diamond,et al.  Pattern recognition and the detection of corporate failure , 1976 .

[13]  Jessica Lowell Neural Network , 2001 .

[14]  Elton Scott,et al.  On the Financial Application of Discriminant Analysis: Comment , 1978 .

[15]  R. C. Merton,et al.  On the Pricing of Corporate Debt: The Risk Structure of Interest Rates , 1974, World Scientific Reference on Contingent Claims Analysis in Corporate Finance.

[16]  Kevin Swingler,et al.  Applying neural networks - a practical guide , 1996 .

[17]  Huong Giang Nguyen,et al.  USING NEUTRAL WORK IN PREDICTING CORPORATE FAILURE , 2005 .

[18]  Nadine Meskens,et al.  Business failure prediction : A review and Analysis of the Literature , 2002 .

[19]  Richard Morris,et al.  Early Warning Indicators of Corporate Failure , 1998 .

[20]  Robert A. Eisenbeis,et al.  PITFALLS IN THE APPLICATION OF DISCRIMINANT ANALYSIS IN BUSINESS, FINANCE, AND ECONOMICS , 1977 .

[21]  C. Charalambous,et al.  Predicting Corporate Failure: Empirical Evidence for the UK by , 2001 .

[22]  Vineet Agarwal,et al.  Twenty‐five years of the Taffler z‐score model: Does it really have predictive ability? , 2007 .

[23]  E. Altman The success of business failure prediction models: An international survey , 1984 .

[24]  Paul Newbold,et al.  Funds Flow Components, Financial Ratios, and Bankruptcy , 1987 .

[25]  J. Parkinson,et al.  The Combined Code on Corporate Governance , 1999 .

[26]  Y. Eom,et al.  Failure Prediction: Evidence from Korea , 1995 .

[27]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[28]  R. Geske The Valuation of Corporate Liabilities as Compound Options , 1977, Journal of Financial and Quantitative Analysis.

[29]  Yuhang Xing,et al.  Default Risk in Equity Returns , 2004 .

[30]  Phillip E. Pfeifer,et al.  Predicting Corporate Bankruptcy , 2009, SSRN Electronic Journal.

[31]  P. Lachenbruch An almost unbiased method of obtaining confidence intervals for the probability of misclassification in discriminant analysis. , 1967, Biometrics.

[32]  D. Cassidy Maximizing shareholder value: the risks to employees, customers and the community , 2003 .

[33]  Marcos Dipinto,et al.  Discriminant analysis , 2020, Predictive Analytics.

[34]  Amir F. Atiya,et al.  Bankruptcy prediction for credit risk using neural networks: A survey and new results , 2001, IEEE Trans. Neural Networks.

[35]  Ramesh Sharda,et al.  A neural network model for bankruptcy prediction , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[36]  F. Black,et al.  VALUING CORPORATE SECURITIES: SOME EFFECTS OF BOND INDENTURE PROVISIONS , 1976 .

[37]  George E. Pinches,et al.  Factors influencing classification results from multiple discriminant analysis , 1980 .

[38]  J. E. Boritz,et al.  Predicting Corporate Failure Using a Neural Network Approach , 1995 .

[39]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[40]  Andrew W. Lo,et al.  Logit versus discriminant analysis : A specification test and application to corporate bankruptcies , 1986 .

[41]  Lea V. Carty,et al.  Riskcalc for Private Companies: Moody's Default Model , 2000 .

[42]  D. G. Morrison,et al.  Bias in Multiple Discriminant Analysis , 1965 .

[43]  P. Schmidt,et al.  Limited-Dependent and Qualitative Variables in Econometrics. , 1984 .

[44]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[45]  Marc Blum FAILING COMPANY DISCRIMINANT-ANALYSIS , 1974 .

[46]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[47]  J. G. Rolph,et al.  Ratio Analysis of Financial Statements. , 1928 .

[48]  H. Guterman,et al.  Knowledge extraction from artificial neural network models , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[49]  Charles L. Merwin,et al.  Financing Small Corporations in Five Manufacturing Industries, 1926-36. , 1944 .

[50]  L. Bauwens,et al.  Econometrics , 2005 .

[51]  E. Altman,et al.  Assessing Potential Financial Problems for Firms in Brazil , 1979 .

[52]  Sharon A. DeVaney,et al.  Predicting Business Failure of Retail Firms: An Analysis Using Mixed Industry Models , 1998 .

[53]  J. Sinkey A MULTIVARIATE STATISTICAL ANALYSIS OF THE CHARACTERISTICS OF PROBLEM BANKS , 1975 .

[54]  S. Shapiro,et al.  An Approximate Analysis of Variance Test for Normality , 1972 .

[55]  William H. Beaver,et al.  Market Prices, Financial Ratios, And Prediction Of Failure , 1968 .

[56]  Desmond Fletcher,et al.  Forecasting with neural networks: An application using bankruptcy data , 1993, Inf. Manag..

[57]  E. Altman,et al.  ZETATM analysis A new model to identify bankruptcy risk of corporations , 1977 .

[58]  W. E. Silver,et al.  Economics and Information Theory , 1967 .

[59]  Espen Gaarder Haug,et al.  Why We Have Never Used the Black-Scholes-Merton Option Pricing Formula , 2008 .

[60]  Marjorie B. Platt,et al.  DEVELOPMENT OF A CLASS OF STABLE PREDICTIVE VARIABLES: THE CASE OF BANKRUPTCY PREDICTION , 1990 .

[61]  R. Fisher THE STATISTICAL UTILIZATION OF MULTIPLE MEASUREMENTS , 1938 .

[62]  C. Zavgren ASSESSING THE VULNERABILITY TO FAILURE OF AMERICAN INDUSTRIAL FIRMS: A LOGISTIC ANALYSIS , 1985 .

[63]  D-Score : Bankruptcy Prediction Model for Middle Market Public Firms , 2004 .

[64]  E. Mine Cinar,et al.  Neural Networks: A New Tool for Predicting Thrift Failures , 1992 .

[65]  Tyler Shumway Forecasting Bankruptcy More Accurately: A Simple Hazard Model , 1999 .

[66]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[67]  Ho-cheong. Chan,et al.  Financial ratios, discriminant analysis and the prediction of corporate financial distress in Hong Kong , 1985 .

[68]  C. Zavgren,et al.  The prediction of corporate failure: The state of the art , 1983 .

[69]  Gary King,et al.  Logistic Regression in Rare Events Data , 2001, Political Analysis.

[70]  Feller William,et al.  An Introduction To Probability Theory And Its Applications , 1950 .

[71]  S. Kaplan The Physiology of Thought , 1950 .

[72]  Miklós Virág,et al.  NEURAL NETWORKS IN BANKRUPTCY PREDICTION - A COMPARATIVE STUDY ON THE BASIS OF THE FIRST HUNGARIAN BANKRUPTCY MODEL* , 2005 .

[73]  K. Kraay,et al.  The failure , 2020, Trust in Divided Societies.

[74]  R.H.A. Hennawy,et al.  THE SIGNIFICANCE OF BASE YEAR IN DEVELOPING FAILURE PREDICTION MODELS , 1983 .

[75]  Gregory J. Wolff,et al.  Optimal Brain Surgeon and general network pruning , 1993, IEEE International Conference on Neural Networks.

[76]  M. Zmijewski METHODOLOGICAL ISSUES RELATED TO THE ESTIMATION OF FINANCIAL DISTRESS PREDICTION MODELS , 1984 .

[77]  R. Taffler,et al.  Forecasting Company Failure in the Uk Using Discriminant Analysis and Financial Ratio Data , 1982 .

[78]  P. Geroski,et al.  Coping with Recession: UK Company Performance in Adversity , 1997 .

[79]  R. C. West A factor-analytic approach to bank condition , 1985 .

[80]  Kar Yan Tam,et al.  Neural network models and the prediction of bank bankruptcy , 1991 .

[81]  Edward I. Altman,et al.  Financial Applications of Discriminant Analysis: A Clarification , 1978 .

[82]  Kevin Keasey,et al.  The Prediction of Small Company Failure: Some Behavioural Evidence for the UK , 1986 .

[83]  Li-Chiu Chi,et al.  Neural networks analysis in business failure prediction of Chinese importers: A between-countries approach , 2005, Expert Syst. Appl..

[84]  Jenifer Piesse,et al.  The Information Value of Mda Based Financial Indicators , 1987 .

[85]  Lode Li,et al.  Moody''''s Public Firm Risk Model: A Hybrid Approach To Modeling Short Term Default Risk , 2000 .

[86]  J. Piatt,et al.  Receiver-operating characteristic curves. , 2001, Journal of neurosurgery.

[87]  Vineet Agarwal,et al.  Comparing the Performance of Market-Based and Accounting-Based Bankruptcy Prediction Models , 2006 .

[88]  Sreedhar T. Bharath,et al.  Forecasting Default with the Kmv-Merton Model , 2004 .

[89]  Sofie Balcaen,et al.  Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Alternative Methodologies in Studies on Business Failure: Do They Produce Better Results than the Classical Statistical Methods? , 2022 .

[90]  Ramesh Sharda,et al.  Bankruptcy prediction using neural networks , 1994, Decis. Support Syst..

[91]  A Prediction Model for Convertible Debentures , 1971 .

[92]  David R. Cox,et al.  The Theory of Stochastic Processes , 1967, The Mathematical Gazette.

[93]  Sidney S. Alexander,et al.  The Effect of Size of Manufacturing Corporation on the Distribution of the Rate of Return , 1949 .

[94]  Harry J. Turtle,et al.  A barrier option framework for corporate security valuation , 2003 .

[95]  Philip Sedgwick,et al.  Receiver operating characteristic curves , 2011, BMJ : British Medical Journal.

[96]  E. Deakin Discriminant Analysis Of Predictors Of Business Failure , 1972 .

[97]  Anja Vogler,et al.  An Introduction to Multivariate Statistical Analysis , 2004 .

[98]  Susan G. Watts,et al.  Bankruptcy classification errors in the 1980s: An empirical analysis of Altman's and Ohlson's models , 1996 .

[99]  O. Maurice Joy,et al.  OF FINANCIAL AND QUANTITATIVE ANALYSIS DECEMBER 1975 ON THE FINANCIAL APPLICATIONS OF DISCRIMINANT ANALYSIS , 2009 .

[100]  A. Abdel-khalik,et al.  The Effect of Aggregating Accounting Reports on the Quality of the Lending Decision: An Empirical Investigation , 1973 .

[101]  E. Capaldi,et al.  The organization of behavior. , 1992, Journal of applied behavior analysis.

[102]  Garry Young,et al.  A Merton-Model Approach to Assessing the Default Risk of UK Public Companies , 2003 .

[103]  H. Koh Model Predictions and Auditor Assessments of Going Concern Status , 1991 .

[104]  Sofie Balcaen,et al.  35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems , 2006 .

[105]  Sofie Balcaen,et al.  Are failure prediction models transferable from one country to another? An empirical study using financial statements , 2002 .

[106]  Nahum D. Melumad,et al.  On Auditors And The Courts In An Adverse Selection Setting , 1990 .

[107]  T. Ward,et al.  A Note on Selecting a Response Measure for Financial Distress , 1997 .

[108]  S. J. Press,et al.  Choosing between Logistic Regression and Discriminant Analysis , 1978 .

[109]  Claudia Perlich,et al.  A Market-Based Framework for Bankruptcy Prediction , 2004 .

[110]  Thomas Kida AN INVESTIGATION INTO AUDITORS CONTINUITY AND RELATED QUALIFICATION JUDGMENTS , 1980 .

[111]  Laurence R. Paquette,et al.  Using a bankruptcy model in the auditing course: The evaluation of a company as a going concern , 1996 .

[112]  M. Aziz,et al.  Predicting corporate bankruptcy: where we stand? , 2006 .

[113]  Kevin Keasey,et al.  THE FAILURE OF UK INDUSTRIAL FIRMS FOR THE PERIOD 1976–1984, LOGISTIC ANALYSIS AND ENTROPY MEASURES , 1990 .

[114]  Godwin J. Udo,et al.  Neural network performance on the bankruptcy classification problem , 1993 .

[115]  R. Foreman,et al.  A logistic analysis of bankruptcy within the US local telecommunications industry , 2003 .

[116]  F. Black,et al.  The Pricing of Options and Corporate Liabilities , 1973, Journal of Political Economy.

[117]  Michelle M. Hamer Failure prediction: Sensitivity of classification accuracy to alternative statistical methods and variable sets , 1983 .

[118]  R. Taffler,et al.  The Assessment of Company Solvency and Performance using a Statistical Model , 1983 .

[119]  R. Libby Accounting Ratios and the Prediction of Failure: Some Behavioral Evidence , 1975 .

[120]  I. G. Dambolena,et al.  Ratio Stability and Corporate Failure , 1980 .

[121]  Erkki K. Laitinen,et al.  Traditional versus operating cash flow in failure prediction , 1994 .

[122]  TamKar Yan,et al.  Managerial Applications of Neural Networks , 1992 .

[123]  Clive S. Lennox,et al.  Identifying failing companies: a re-evaluation of the logit, probit and DA approaches , 1999 .

[124]  O. Maurice Joy,et al.  OF FINANCIAL AND QUANTITATIVE ANALYSIS March 1978 SOME CLARIFYING COMMENTS ON DISCRIMINANT ANALYSIS 0 , 2009 .

[125]  Kimmo Kiviluoto,et al.  Predicting bankruptcies with the self-organizing map , 1998, Neurocomputing.

[126]  James A. Ohlson FINANCIAL RATIOS AND THE PROBABILISTIC PREDICTION OF BANKRUPTCY , 1980 .

[127]  R. O. Edmister,et al.  JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS March 1972 AN EMPIRICAL TEST OF FINANCIAL RATIO ANALYSIS FOR SMALL BUSINESS FAILURE PREDICTION , 2009 .

[128]  J. D. Vinso,et al.  Estimating the probability of failure for commercial banks and the banking system , 1977 .

[129]  Yann LeCun,et al.  Optimal Brain Damage , 1989, NIPS.

[130]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[131]  Teija Laitinen,et al.  Comparative analysis of failure prediction methods: the Finnish case , 1999 .

[132]  James A. Koziol,et al.  A class of invariant procedures for assessing multivariate normality , 1982 .

[133]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[134]  Sreedhar T. Bharath,et al.  Forecasting Default with the Merton Distance to Default Model , 2008 .

[135]  Christine Jubb,et al.  Predicting corporate failure , 1997 .

[136]  W. B. Hickman,et al.  Corporate Bond Quality and Investor Experience. , 1958 .

[137]  Juliana Yim,et al.  A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis , 2005 .

[138]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[139]  Constantin Zopounidis,et al.  A comparison of nearest neighbours, discriminant and logit models for auditing decisions , 2007, Intell. Syst. Account. Finance Manag..

[140]  James H. Scott The probability of bankruptcy: A comparison of empirical predictions and theoretical models , 1981 .

[141]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[142]  Pamela K. Coats,et al.  Recognizing Financial Distress Patterns Using a Neural Network Tool , 1993 .

[143]  Rudy Setiono,et al.  Use of a quasi-Newton method in a feedforward neural network construction algorithm , 1995, IEEE Trans. Neural Networks.

[144]  Edward I. Altman,et al.  Corporate Financial Distress and Bankruptcy , 1993 .

[145]  Comparing the Performance of Market-Based and Accounting-Based Bankruptcy Prediction Models , 2006 .

[146]  P. Chalos Financial Distress - A Comparative-Study Of Individual, Model, And Committee Assessments , 1985 .