Predict US Restaurant Firm Failures: The Artificial Neural Network Model versus Logistic Regression Model

Abstract

Based on recent years' financial data of US restaurant firms, this study developed failure prediction models using logistic regression and artificial neural networks (ANNs). The findings show that the logistic model is not inferior to the ANNs model in terms of prediction accuracy. For restaurant firms, the logistic model not only provides bankruptcy prediction at an accuracy rate no inferior to that provided by the ANNs model but also indicates how firms can act to reduce the chance of going bankrupt. Therefore, for US restaurant firms the logistic model is recommended as a preferred method for predicting restaurant firm failures.

Keywords

bankruptcy prediction;financial ratios;artificial neural networks;logistic regression;US restaurant firms

Get full access to this article

View all access options for this article.

References

Alfaro

Garcia

Gamez

Elizondo

(2008) Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems 45: 110–122.

Altman

E.I.

(1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23(4): 589–609.

Altman

E.I.

Levallee

M.K.

(1981) Business failure classification in Canada. Journal of Business Administration 12(1): 147–164.

Anandarajan

Lee

Anandarajan

(2001) Bankruptcy prediction of financially stressed firms: An examination of the predictive accuracy of artificial neural networks. International Journal of Intelligent Systems in Accounting, Finance and Management 10(2): 69–81.

Beaver

W.H.

(1966) Financial ratios as prediction of failure. Empirical research in accounting: Selected studies. Journal of Accounting Research 4(3): 71–111.

Bell

T.B.

Ribar

G.S.

Verchio

(1990) Neural nets vs. Logistic regression: A comparison of each model's ability to predict commercial bank failures. Proceedings of the 1990 Deloitte Touche/University of Kansas Symposium on Auditing Problems. Lawrence, Kansas: University of Kansas, pp. 29–53.

Bloom

J.Z.

(2004) Tourist market segmentation with linear and non-linear techniques. Tourism Management 25(6): 723–733.

Boritz

J.E.

Kennedy

D.B.

Albuquerque

(1995) Predicting corporate failure using a neural network approach. Intelligent Systems in Accounting, Finance and Management 4: 95–111.

Brigham

E.F.

Gapenski

L.C.

(1994) Financial Management: Theory and Practice. Orlando, FL: The Dryden Press.

10.

Charitou

Neophytou

Charalambous

(2004) Predicting corporate failure: Empirical evidence for the UK. European Accounting Review 13(3): 465–497.

11.

Cho

(1994) Predicting business failure in the hospitality industry: An application of logit model. PhD dissertation, Virginia Polytechnic Institute and State University.

12.

Darayseh

Waples

Tsoukalas

(2003) Corporate failure for manufacturing industries using firms specifics and economic environment with logit analysis. Managerial Finance 29(8): 23–36.

13.

DeAndrés

Landajo

Lorca

(2005) Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case. European Journal of Operational Research 167(2): 518–542.

14.

Denton

J.W.

Hung

M.S.

Osyk

B.A.

(1990) A neural network approach to the classification problem. Expert Systems with Applications 1(4): 417–424.

15.

Dimitras

A.I.

Zanakis

S.H.

Zopounidis

(1996) A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research 90(3): 487–513.

16.

Etheridge

H.L.

Sriram

R.S.

Hsu

H.Y.K.

(2000) A comparison of selected artificial neural networks that help auditors evaluate client financial viability. Decision Sciences 31 (2): 531–550.

17.

Fitch Ratings. (2009) Fitch 2009 US restaurant outlook: Credit risk escalates as challenges persist, http://www.pr-inside.com/print922971.htm, accessed 11 February 2009.

18.

Goss

E.P.

Ramchandani

(1995) Comparing classification accuracy of neural networks, binary logit regression and discriminant analysis for insolvency prediction of life insurers. Journal of Economics & Finance 19(3): 1–18.

19.

(2002) Analyzing bankruptcy in the restaurant industry: A multiple discriminant model. International Journal of Hospitality Management 21(1): 25–42.

20.

Gao

(2000) A multivariate model for predicting business failures of hospitality firms. Tourism and Hospitality Research: The Surrey Quarterly Review 2(1): 37–50.

21.

Hamer

M.M.

(1983) Failure prediction: Sensitivity of classification accuracy to alternative statistical methods and variable sets. Journal of Accounting and Public Policy 2: 289–307.

22.

Jain

B.A.

Nag

B.N.

(1997) Performance evaluation of neural network decision models. Journal of Management Information Systems 14(2): 201–216.

23.

Jarvis

L.P.

Mayo

E.J.

(1986) Winning the market-share game. Cornell Hotel and Restaurant Administration Quarterly 27(3): 72–79.

24.

Jones

F.L.

(1987) Current techniques in bankruptcy prediction. Journal of Accounting Literature 6: 131–164.

25.

Kim

(2006a) A logistic regression analysis for predicting bankruptcy in the hospitality industry. The Journal of Hospitality Financial Management 14(1): 17–34.

26.

Kim

(2006b) Predicting restaurant bankruptcy: A logit model in comparison with a discriminant model. Journal of Hospitality & Tourism Research 30(4): 474–493.

27.

Kuo

Reitsch

(1995) Neural networks vs. conventional methods of forecasting. The Journal of Business Forecasting Methods & Systems 14(4): 17–22.

28.

Lacher

R.C.

Coats

P.K.

Sharma

S.C.

Fant

L.F.

(1995) A neural network for classifying the financial health of a firm. European Journal of Operational Research 85(1): 53–65.

29.

Lee

Booth

Alam

(2005) A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms. Expert Systems with Applications 29(1): 1–16.

30.

Luther

R.K.

(1998) An artificial neural network approach to predicting the outcome of chapter 11 bankruptcy. The Journal of Business and Economic Studies 4(1): 57–73.

31.

Metaxiotis

Psarras

(2004) The contribution of neural networks and genetic algorithms to business decision support: Academic myth or practical solution? Management Decision 42(2): 229–242.

32.

National Restaurant Association. (2008) Healthy kids' meals, local produce, mini desserts among hottest menu trends for 2009, http://www.restaurant.org/pressroom/pressrelease.cfm?ID=1708, accessed 1 April 2009.

33.

Neophytou

Molinero

C.M.

(2004) Predicting corporate failure in the UK: A multidimentional scaling approach. Journal of Business Finance and Accounting 31(5/6): 677–710.

34.

O'Connor

(2002) Bolstering cash flow with strategic revenue management. Business Credit 104(8): 42–44.

35.

Palmer

Montaño

J.J.

Franconetti

F.J.

(2008) Sensitivity analysis applied to artificial neural networks for forecasting time series. European Journal of Research Methods for the Behavioral and Social Sciences 4(2): 80–86.

36.

Palmer

Montaño

J.J.

Sesé

(2006) Designing an artificial neural network for forecasting tourism time series. Tourism Management 27(5): 781–790.

37.

Kumar

Ravi P.

Ravi

(2007) Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review. European Journal of Operational Research 180(1): 1–28.

38.

Robinson

(2008) United States: As restaurants falter, planning for restructuring, bankruptcy is crucial, http://www.mondaq.com/article.asp?articleid=69280, accessed 11 February 2009.

39.

Sirakaya

Choi

(2005) Forecasting gaming referenda. Annals of Tourism Research 32(1): 127–149.

40.

SPSS. (2007) SPSS Neural Networks 16.0. Chicago, IL.: SPSS Inc.

41.

Standard and Poor's. (2008) Standard & poor's industry surveys. S&P NetAdvantage database, accessed 11 February 2009.

42.

Storey

Keasey

Watson

Wynarczyk

(1990) The Performance of Small Firms: Profits, Jobs, and Failures. London: Routledge Small Business Series.

43.

Tam

K.Y.

Kiang

M.Y.

(1992) Managerial applications of neural networks: The case of bank failure prediction. Management Science 38(7): 926–947.

44.

Trigueiros

Taffler

(1996) Neural networks and empirical research in accounting. Accounting & Business Research 26(4): 347–355.

45.

U.S. Courts. (2010) Bankruptcy basics, http://www.uscourts.gov/bankruptcycourts/bankruptcybasics/process.html, accessed 14 February 2010.

46.

Wang

Zeng

Gui

(2007) Application of artificial neural network supported by BP and Particle Swarm Optimization Algorithm for evaluating the criticality class of spare parts. Proceedings of the Third International Conference on Natural Computation; August 2007. Haikou, China; New York: Institute of Electrical and Electronics Engineers, pp. 24–27.

47.

C.Y.

(2004) Using non-financial information to predict bankruptcy: A study of public companies in Taiwan. International Journal of Management 21(2): 194–201.

48.

Lee

V.C.S.

Tan

T.Y.

(2006) Data preprocessing and data parsimony in corporate failure forecast models: Evidence from Australian materials industry. Accounting and Finance 46(2): 327–345.

49.

Youn

H.W.

(2010) Predicting Korean lodging firm failures: An artificial neural network model along with a logistic regression model. International Journal of Hospitality Management 29: 120–127.

50.

Zhang

M.Y.

Patuwo

B.E.

Indro

D.C.

(1999) Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of (Operational Research 116(1): 16–32.