Skip to main content
SpringerLink
Log in

Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation

  • S.I. : Innovation and Service-Dominant Logic
  • Published:
Service Business Aims and scope Submit manuscript

Abstract

The importance of industry-specific characteristics in financial distress is widely acknowledged, but often overlooked by researchers studying the hospitality industry. The primary objective of this paper is to investigate the key determinants of US hospitality firms’ financial distress between 1988 and 2010 using ensemble models. The data used in this study come from the Compustat database produced by Standard and Poor’s Institutional Market Services. The data were collected from three hospitality-related segments, 5812 eating Places, 7011 hotels and motels, 7990 amusement and recreation services not elsewhere classified according to Standard Industrial Classification. In the restaurant-stacking model, debt-to-equity ratio, growth in owners’ equity, net profit margin, and stock-price trend were chosen as financial distress predictors. In the hotel stacking model, debt-to-equity ratio, stock-price trend, and account receivable turnover were selected as financial distress predictors. In the amusement and recreation-stacking model, debt-to-equity ratio, growth in owners’ equity, net profit margin, and management practice were defined as significant financial distress predictors. Although many researchers have stressed that an ensemble method, which combines the characteristics and advantages of particular models, may improve the performance or interpretability of predictive methods, few hospitality financial distress prediction studies employed ensemble methods. This study demonstrates its originality in this perspective.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abbass H, Towsey M, Finn G (2001) C-Net: a method for generating non-deterministic and dynamic multivariate decision trees. Knowl Inf Syst 3(2):184–197

    Google Scholar 

  • Acharya V, Bharath S, Srinivasan A (2007) Does industry-wide distress affect defaulted firms? Evidence from creditor recoveries. J Financ Econ 85(3):787–821

    Google Scholar 

  • Alfaro E, Garcia N, Gamez M, Elzondo D (2008) Bankruptcy forecasting: an empirical comparison of AdaBoost and neural networks. Decis Support Syst 45(1):110–122

    Google Scholar 

  • Altman E (1968) Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy. J Financ 23:589–609

    Google Scholar 

  • Altman E (1993) Corporate financial distress and bankruptcy – a complete guide to predicting and avoiding distress and profiting from bankruptcy, 2nd edn. Wiley, New York

    Google Scholar 

  • Altman E, Lavallee M (1981) Business failure classification in Canada. J Bus Adm Summer 12(1):147–164

    Google Scholar 

  • Altman E, Sabato G, Wilson N (2010) The value of non-financial information in small and medium-sized enterprise risk management. J Credit Risk 6(2):95–127

    Google Scholar 

  • Anonymous (2010) Number of U.S. restaurants declines. http://www.upi.com/Business_News/2010/08/21/Number-of-US-restaurants-declines/UPI-80231282400173/. Retrieved 29 April 2011

  • Argenti J (1976) Corporate collapse: the causes and symptoms. Wiley, New York, pp 5–6

    Google Scholar 

  • Assaad M, Bone R, Cardot H (2008) A new boosting algorithm for improved time-series forecasting with recurrent neural networks. Inf Fusion 9:41–55

    Google Scholar 

  • Aziz M, Dar H (2006) Predicting corporate bankruptcy: where we stand? Corp Gov 6(1):18–33

    Google Scholar 

  • Balcaen S, Ooghe H (2006) Thirty-five years of studies on business failure: and overview of the classic statistical methodologies and their related problems. Br Account Rev 38(1):63–93

    Google Scholar 

  • Barreda A, Kageyama Y, Singh D, Zubieta S (2017) Hospitality bankruptcy in United States of America: a multiple discriminant analysis-logit meld comparison. J Qual Assur Hosp Tour 18(1):86–106

    Google Scholar 

  • Beahm J (2011) Rising food prices: more restaurant bankruptcy. http://www.reuters.com/2011/03/10/tagblogsfindawcom2011-freeenterprise-idUS235815727020110310. Retrieved 29 April 2011

  • Beaver W (1966) Financial ratios as predictors of failure. Empir Res Account Sel Stud 4:71–111

    Google Scholar 

  • Boritz J, Kennedy D, Albuqereque A (1995) Predicting corporate failure suing a neural network approach. Intell Syst Account Financ Manag 4(2):95–111

    Google Scholar 

  • Breiman L (1996a) Bagging predictors. Mach Learn 24(2):123–140

    Google Scholar 

  • Breiman L (1996b) Stacked regressions. Mach Learn 24(1):49–64

    Google Scholar 

  • Chan K, Chen N (1991) Structural and return characteristics of small and large firms. J Financ 46(4):1467–1484

    Google Scholar 

  • Chancharat N, Davy P, McCrae M, Tian G (2007) Firms in financial distress, a survival model analysis. In: 20th Australasian Finance and Banking Conference 2007 Paper, SSRN: http://ssrn.com/abstract=1009385

  • Chava S, Jarrow R (2004) Bankruptcy prediction with industry effects. Rev Financ 8(4):537–569

    Google Scholar 

  • Chen K, Shimerda T (1981) An empirical analysis of useful financial ratios. Financ Manag 10(1):51–60

    Google Scholar 

  • Chen W, Ma C, Ma L (2009) Mining the customer credit using hybrid support vector machine technique. Expert Syst Appl 36(4):7611–7616

    Google Scholar 

  • Cho S, Kim J, Bae J (2009) An integrative model with subject weight based on neural network learning for bankruptcy prediction. Expert Sys with App 36(1):403–410

    Google Scholar 

  • Chow C, McNamee A, Plumlee R (1987) Practitioners’ perceptions of audit step difficulty and criticalness: implications for audit research. Audit J Pract Theory 6(2):123–133

    Google Scholar 

  • Connelly B, Hoskisson R, Tihanyi L, Certo T (2010) Ownership as a form of corporate governance. J Manag Stud 47(8):1561–1589. https://doi.org/10.1111/j.1467-6486.2010.00929.x

    Google Scholar 

  • Cristiamini N, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge University Press, Cambridge

    Google Scholar 

  • Davalos S, Gritta R, Chow G (1999) The application of a neural network approach to predicting bankruptcy risks facing the major US air carriers: 1979-1996. J Air Transp Manag 5:81–86

    Google Scholar 

  • Deakin E (1972) A discriminant analysis of predictors of business failure. J Account Res 10(1):167–179

    Google Scholar 

  • Dietterich T (2000) Experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 40:139–157

    Google Scholar 

  • Dimitras A, Zanakis S, Zopounidis C (1996) A survey of business failure with an emphasis on prediction methods and industrial application. Euro J Oper Res 90:487–513

    Google Scholar 

  • Doran M, Raicu D, Furst J, Settimi R, Schipma M, Chandler D (2007) Oligonucleotide microarray identification of bacillus anthraces strains using support vector machines. Bioinformatics 23(4):487–492

    Google Scholar 

  • Duin R, Tax D (2000) Experiments with classifier combination rules. In Kittler J, Roli F (eds) Multiple classifier systems. First International Workshop, MCS, Cagliari, Italy, 1857 of LNCS. Springer, New York, pp 16–29

  • Dun and Bradstreet (2012) Business failure record. New York

  • Esteban A, Garcia N, Gamez M, Elizondo D (2007) Bankruptcy forecasting: an empirical comparison of AdaBoost and neural networks. Decis Support Syst 45:110–122

    Google Scholar 

  • Etheridge H, Sriram R (1997) A comparison of the relative costs of financial distress models: artificial neural networks, logit and multivariate discriminant analysis. Intell Syst Account Financ Manag 6:235–248

    Google Scholar 

  • Fama E, French K (2004) New lists: fundamentals and survival rates. J Financ Econ 73(2):229–269

    Google Scholar 

  • Flagg J, Giroux G, Wiggins C (1991) Predicting corporate bankruptcy using failing firms. Rev Financ Econ 1(4):67–78

    Google Scholar 

  • Foster B, Ward T (1997) Using cash flow trends to identify risks of bankruptcy. CPA J 67:60–61

    Google Scholar 

  • Freund Y, Schapire R (1996) Experiments with a new boosting algorithm. In: Proceedings of the eleventh international joint conference on artificial intelligence, Detroit, MI, pp 788–793

  • Gepp A, Kumar K, Bhattacharya S (2010) Business failure prediction using decision trees. J Forec 29(6):536–555

    Google Scholar 

  • Gerena G (2012) Club membership deposits in bankruptcy. Hospitality Law Check-in July 2, 2012. http://www.gtlaw-hospitalitylawcheckin.com/clubs/club-membership-deposits-in-bankruptcy/. Retrieved 10 February 2017

  • Goh A (1995) Back-propagation neural networks for modeling complex systems. Artif Intell Eng 9(3):143–151

    Google Scholar 

  • Grice J (2000) Bankruptcy prediction models and going concern audit opinions before and after SAS No. 59. Business Quest: A J App Topics Bus Econ :1-11

  • Grice S, Dugan M (2001) The limitations of bankruptcy prediction models: some cautions for the researcher. Rev Quant Financ Account 17(2):151–166

    Google Scholar 

  • Grice S, Dugan M (2003) Re-estimations of the Zmijewski and Ohlson bankruptcy prediction models. Adv Account 20:77–93

    Google Scholar 

  • Gu Z (2002) Analyzing bankruptcy in the restaurant industry: a multiple discriminant model. Hosp Manag 21:25–42

    Google Scholar 

  • Hambrick D, D’Aveni R (1988) Large corporate failures as downward spirals. Adm Sci Q 33:1–23

    Google Scholar 

  • Hanson R (2003) A study of Altman’s revised four-variable Z-score bankruptcy prediction model as it applies to the service industry. Unpublished doctoral dissertation, Nova Southeastern University, United States

  • Haskins M, Williams D (1990) A continent model of intra-big eight auditor changes. Auditing J Pract Theory 9(3):55–74

    Google Scholar 

  • Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning: data mining, inference, and prediction. Springer series in statistics. Springer, New York

  • He Y, Kamath R, Meier H (2005) An empirical evaluation of bankruptcy prediction models for small firms: an over-the-counter (OTC) market experience. Acad Account Financ Stud J 9(1):1. http://www.freepatentsonline.com/article/Academy-Accounting-Financial-Studies-Journal/182468597.html. Retrieved 11 April 2014

  • He Q, Chong T, Li L, Zhang J (2010) A competing risks analysis of corporate survival. Financial Manag 39(4):1697–1718

    Google Scholar 

  • Hou O, Chuang S (2007) Examining financially distressed companies in Taiwan: application of survival analysis. Bus Rev 8(2):231–242

    Google Scholar 

  • Hua Z, Wang Y, Xu X, Zhang B, Liang L (2007) Prediction corporate financial distress based on integration of support vector machine and logistic regression. Expert Syst Appl 33:434–440

    Google Scholar 

  • Huang Z, Chen H, Hsu C, Chen W, Wu S (2004) Credit rating analysis with support vector machines and neural networks: a market comparative study. Decis Support Syst 37(4):543–558

    Google Scholar 

  • Hung C, Chen J (2009) A selective ensemble based on expected probabilities for bankruptcy prediction. Expert Syst Appl 36(3):5297–5303

    Google Scholar 

  • Israel D (2008) Data analysis in business research: a step-by-step nonparametric approach. SAGE Publications India Pvt Ltd, New Delhi. https://doi.org/10.4135/9788132108405

    Google Scholar 

  • Jhee W, Lee J (1993) Performance of neural networks in managerial forecasting. Intell Syst Account Financ Manag 2:55–71

    Google Scholar 

  • Jo H, Han I (1996) Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction. Expert Sys Appl 11(4):415–422

    Google Scholar 

  • Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. In: Proceedings of European conference on machine learning. Springer, Berlin, pp. 137–142

  • John K, Larry H, Lang P, Netter J (1992) The voluntary restructuring of large firms in response to performance decline. J Financ 47(3):891–917

    Google Scholar 

  • Kasler D (2016) Sinatra’s old Tahoe casino in bankruptcy. Business Real Estate. 5 August 2016. http://www.sacbee.com/news/business/article94017057.html. Retrieved 10 February 2017

  • Kim K (2003) Financial time series forecasting using support vector machines. Neurocomputing 55(1/2):307–319

    Google Scholar 

  • Kim S (2008) Hotel management contracts: impact on performance in the Korean hotel sector. Serv Ind J 28(5):701–718

    Google Scholar 

  • Kim S (2011) Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression, and multivariate discriminant analysis. Serv Ind J 31(3–4):441–468

    Google Scholar 

  • Kim H, Noh H (1997) Predictability of interest rates using data mining tools: a comparative analysis of Korea and the US. Working Paper, KAIST

  • Kim S, Upneja A (2014) Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models. Econ Model 36(January):354–362

    Google Scholar 

  • Kirkos E, Spathis C, Manolopoulos Y (2008) Support vector machines, decision trees and neural networks for auditor selection. J Comput Method Sci Eng 8(3):213–224

    Google Scholar 

  • Kirkos E, Spathis C, Manolopoulos Y (2010) Audit-firm group appointment: an artificial intelligence approach. Intel Syst Account Financ Manag 17(1):1–17

    Google Scholar 

  • Kittler J (1998) Combining classifiers: a theoretical framework. Pattern Anal Appl 1(1):18–27

    Google Scholar 

  • Kittler J (2000) A framework for classifier fusion: Is it still needed? LNCS 1876, pp 45–56

  • Koh H (2004) Going concern prediction using data mining techniques. Manag Audit J 19(3):462–476

    Google Scholar 

  • Kumar K, Singh Y, Sanyal S (2009) Hybrid approach using case-based reasoning and rule-based reasoning for domain dependent clinical decision support in ICU. Expert Syst Appl 36(1):65–71

    Google Scholar 

  • Kurane S (2015) Friendly’s franchisee J&B files for bankruptcy protection. http://www.reuters.com/article/2015/01/07/jb-bankruptcy-idUSL3N0UM1NS20150107. Retrieved 17 August 2015

  • Lahiri R (2006) Comparison of data mining and statistical techniques for classification model. PhD dissertation in information systems and decision science, Jadavpur University, India

  • Laitinen E (1991) Financial ratios and different failure processes. J Bus Financ Account 18(5):649–673

    Google Scholar 

  • Laitinen E (2005) Survival analysis and financial distress prediction: finnish Evidence. Rev Account Financ 4(4):76–90

    Google Scholar 

  • Lau A (1987) A five-star financial distress prediction model. J Account Res 25(1):127–138

    Google Scholar 

  • Ledezma A, Aler R, Sanchis A, Borrajo D (2010) GA-Stacking: evolutionary stacked generalization. Intell Data Anal 14(1):89–119

    Google Scholar 

  • Lee K, Han I, Kwon Y (1996) Hybrid neural network models for bankruptcy prediction. Decis Support Syst 18:63–72

    Google Scholar 

  • Lee T, Chiu C, Chou Y, Lu C (2006) Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Compu Stat Data Anal 50(4):1113–1130

    Google Scholar 

  • Li M, Sun J, Sun B (2009) Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors. Expert Syst Appl 36(1):643–659

    Google Scholar 

  • Lin F, McClean S (2001) A data mining approach to the prediction of corporate failure. Knowl Based Syst 14:189–195

    Google Scholar 

  • Lincoln M (1984) An empirical study of the usefulness of accounting ratios to describe levels of insolvency risk. J Bank Financ 8(2):321–340

    Google Scholar 

  • Lipovatz D, Mandaaraka M, Mourelatos A (2000) Multivariate analysis for the assessment of factors affecting industrial competiveness: the case of Greek food and beverage industries. Appl Stoch Models Bus Ind 16(2):85–98

    Google Scholar 

  • Lu M, Tzeng G, Cheng H, Hsu C (2015) Exploring mobile banking services for user behavior in intention adoption: using new hybrid MADM model. Serv Bus 9(3):541–565

    Google Scholar 

  • Malhotra R, Jain A (2011) Software effort prediction using statistical and machine learning methods. Int J Adv Comput Sci Appl 2(1):145–152

    Google Scholar 

  • Masulli F, Valentini G (2001) Dependence among codeword bits errors in ECOC learning machines: an experimental analysis. In: Kittler J, Roli F (eds) Multiple classifier systems. MCS 2001. Lecture notes in computer science, vol 2096. Springer, Berlin, pp 158–167

    Google Scholar 

  • Maqsood I, Khan M, Abraham A (2004) An ensemble of neural networks for weather forecasting. Neural Comput Appl 13:112–122

    Google Scholar 

  • Maux J, Morin D (2011) Black and white and red all over: Lehman Brother’s inevitable bankruptcy splashed across its financial statements. Int J Bus Soc Sci 2(20):39–65

    Google Scholar 

  • McKee TE, Lensberg T (2002) Genetic programming and rough sets: a hybrid approach to bankruptcy classification. Euro J Oper Res 138:436–451

    Google Scholar 

  • Meyer M, Rogsby J, Boone J (2007) The impact of auditor-client relationships on the reversal of first-time audit qualifications. Manag Audit J 22(1):53–70

    Google Scholar 

  • Migueis V, Camanho A, Borges J (2017) Predicting direct marketing response in banking: comparison of class imbalance methods. Serv Bus 11(4):831–849

    Google Scholar 

  • Min J, Lee Y (2005) Bankruptcy prediction using support vector machine with optimal choice of kernel-function parameters. Expert Syst Appl 28(4):603–614

    Google Scholar 

  • Min S, Lee J, Han I (2006) Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Syst Appl 31(3):652–660

    Google Scholar 

  • Morrow J, Johnson R, Busenitz L (2004) The effects of cost and asset retrenchment on firm performance: the overlooked role of a firm’s competitive environment. J Manag 30(2):189–208

    Google Scholar 

  • Moses D, Liao S (1987) On developing models for failure prediction. J Commer Bank Lend 69(7):27–38

    Google Scholar 

  • Munsif V, Raghunandan D, Rama V, Singhvi M (2011) Audit fees after remediation of internal control weaknesses. Account Horiz 25(1):87–105

    Google Scholar 

  • National Restaurant Association (2008) 2009 restaurant industry forecast. http://www.restaurant.org. Retrieved 20 April 2010

  • Noronha G, Singal V (2004) Financial health and airline safety. Manag Decis Econ 25(1):1–16

    Google Scholar 

  • O’Leary D (1998) Using neural networks to predict corporate failure. Int J Intell Syst Account Financ Manag 7(3):187–197

    Google Scholar 

  • O’Neill J, Carlback M (2011) Do brands matter? A comparison of branded and independent hotels’ performance during a full economic cycle. Int J Hosp Manag 30(3):515–521

    Google Scholar 

  • Odom M, Sharda R (1993) A neural network model for bankruptcy prediction. In: Trippi RR, Turban E (eds) Neural networks in finance and investing. Probus, Chicago, pp 177–185

    Google Scholar 

  • Ohlson J (1980) Financial ratios and the probabilistic prediction of bankruptcy. J Account Res 18:109–131

    Google Scholar 

  • Olmeda I, Fernandez E (1997) Hybrid classifiers for financial multicriteria decision making: the case of bankruptcy prediction. Comput Econ 10(4):317–335

    Google Scholar 

  • Optiz D, Maclin R (1999) Popular ensemble methods: an empirical study. J Artif Intell Res 11:169–198

    Google Scholar 

  • Outecheva N (2007) Corporate financial distress: an empirical analysis of distress risk. Unpublished doctoral dissertation, University of St Gullen, Switzerland

  • Pal M, Mather M (2004) Assessment of the effectiveness of support vector machines for hyper spectral data. Future Gener Comput Syst 20(7):1215–1225

    Google Scholar 

  • Parry W (2016) Atlantic City’s Trump Taj Mahal casino emerged from bankruptcy Friday and was taken over by billionaire investor Carl Icahn, who now owns two of the city’s largest casinos. Feb. 26.2016. The associated press. http://www.usnews.com/news/business/articles/2016-02-26/taj-mahal-casino-out-of-bankruptcy-into-icahns-hands. Retrieved 09 Feb 2017

  • Parsa H, Self J, Njite D, King T (2005) Why restaurant fail. Cornell Hotel Restaur Adm Q 46(3):304–322

    Google Scholar 

  • Parsa H, Gregory A, Terry M (2015) Why do restaurants fail? Part III: an analysis of macro and micro factors. https://hospitality.ucf.edu/files/2011/08/DPI-Why-Restaurants-Fail.pdf. Retrieved 17 Oct 2017

  • Platt H, Platt M (1990) Development of a class of stable predictive variables: the case of bankruptcy prediction. J Bus Finance Account 17(1):31–51

    Google Scholar 

  • Platt H, Platt M (2008) Financial distress comparison across three global regions. J Risk Financ Manag 1(1):129–162

    Google Scholar 

  • Pretorius M (2009) Defining business decline, failure and turnaround: a content analysis. S Afr J Entrepreneurship Small Bus Manag 2(1):1–16

    Google Scholar 

  • Raghupathi W, Schkade L, Raju B (1991) A neural network approach to bankruptcy prediction. In: Proceedings of the IEEE 24th annual Hawaii international conference on systems sciences, vol 4, pp 147–155

  • Ravi Kumar R, Ravi V (2007) Bankruptcy prediction in banks and firms via statistical and intelligent techniques-a review. Euro J Oper Res 180(1):1–28

    Google Scholar 

  • Saunders A, Steffen S (2011) The costs of being private: evidence from the loan market. Rev Financ Stud 24(12):4091–4122

    Google Scholar 

  • Schapire R (1990) The strength of weak learnability. Mach Learn 5(2):197–227

    Google Scholar 

  • Schmitz G, Aldrich C, Gouws F (1999) ANN-DT: an algorithm for extraction of decision tress from artificial neural networks. IEEE Trans Neural Netw 10(6):1392–1401

    Google Scholar 

  • Schmuck M (2012) Financial distress and corporate turnaround: an empr anal of the automotive supplier ind. Springer Gabler, DE, pp 28–60

  • Shachumurove Y (2002) Applying artificial neural networks to business, economics and finance (Penn CARESS Working Papers). UCLA Department of Economics, Los Angeles, CA

  • Sharma S, Mahajan V (1980) Early warning indicators of business failure. J Mark 44:80–89

    Google Scholar 

  • Shin K, Lee T, Kim H (2005) An application of support vector machines in bankruptcy prediction model. Expert Syst Appl 28(1):127–135

    Google Scholar 

  • Sollich P, Krogh A (1996) Learning with ensembles: How over-fitting can be useful. In: Touretsky D, Mozer M, Hasselmo M (eds) Advances in neural information processing systems, vol 7. MIT Press, Cambridge, pp 417–526

    Google Scholar 

  • Sung T, Chang N, Lee G (1999) Dynamics of Modeling in data mining: interpretive approach to bankruptcy prediction. J Manag Inf Syst 16(1):63–85

    Google Scholar 

  • Surkan A, Singleton C (1990) Neural networks for bond ratings improved by multiple hidden layers. In: Proceedings of IEEE international conference on neural networks, pp 162–168

  • Swicegood P, Clark J (2001) Off-site monitoring systems for predicting bank underperformance: a comparison of neural networks, discriminant analysis, and professional human judgment. Int J Intell Syst Account Financ Manag 10:169–186

    Google Scholar 

  • Tam K, Kiang M (1992) Managerial applications of neural networks: the case of bank failure predictions. Manag Sci 38(7):926–947

    Google Scholar 

  • Ulas M (2009) Incremental construction of cost-conscious ensembles using multiple learners and representations in machine learning. PhD. Dissertation for computer engineering, Bogazici University, Istanbul, Turkey

  • Upneja A, Dalbor M (2001) An examination of capital structure in the restaurant industry. Int J Contemp Hosp Manag 13(2):54–59

    Google Scholar 

  • Valentini G, Masulli F (2002) Ensembles of learning machines. Neural Nets WIRN Vietri-02, Series Lecture Notes in Computer Science 2486. Springer, Heidelberg, pp 3–19

  • Vapnik N (1995) The nature of statistical learning theory. Springer, New York

    Google Scholar 

  • Vapnik N (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  • Verwoerd T, Hunt R (2002) Intrusion detection techniques and approaches. Comput Commun 25:1356–1365

    Google Scholar 

  • Wang H, Zhao T, Tan H, Zhang S (2008) Biomedical named entity recognition based on classifiers ensemble. Int J Comput Sci Appl 5(2):1–11

    Google Scholar 

  • Wang G, Hao J, Ma J, Jiang H (2011) A comparative assessment of ensemble learning for credit scoring. Expert Syst Appl 38(1):223–230

    Google Scholar 

  • West D, Dellana S, Qian J (2005) Neural network ensemble strategies for financial decision applications. Comput Oper Res 32:2543–2559

    Google Scholar 

  • Whitaker R (1999) The early stage of financial distress. J Econ Financ 23(2):123–133

    Google Scholar 

  • White MC (2009) More hotels facing an uncertain future. April 13, B7, The New York Times. http://travel.nytimes.com/2009/04/14/business/14hotels.html?partner=rssnyt&emc=rss. Retrieved 09 February 2017

  • Wolpert D (1992) Stacked generalization. Neural Netw 5:241–259

    Google Scholar 

  • Wong B, Bondnovich T, Yakup S (1997) Neural network applications in business: a review and analysis of the literature (1988-95). Decis Support Syst 19:301–320

    Google Scholar 

  • Wu C (2004) Using non-financial information to predict bankruptcy: a study of public companies in Taiwan. Int J Manag 21(2):194–201

    Google Scholar 

  • Wu Q (2011) Hybrid model based on wavelet support vector machine and modified genetic algorithm penalizing Gaussian noises for power load forecasts. Expert Syst Appl 38(1):379–385

    Google Scholar 

  • Wu C, Tzeng G, Goo Y, Fang W (2007) A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Syst Appl 32(2):397–408

    Google Scholar 

  • Yang S, Browne A (2004) Neural network ensembles: combining multiple models for enhanced performance suing a multistate approach. Expert Syst 21(5):279–288

    Google Scholar 

  • Yim J, Mitchelle H (2002) A comparison of Australian financial service failure models: Hybrid neural networks, logit models and discriminant analysis. RMIT working paper, ISSN 1038-7448:1-35

  • Yoon Y, Guimaraes T, Swales G (1994) Integrating artificial neural networks with rule-based expert systems. Decis Support Syst 11(5):497–507

    Google Scholar 

  • Yu R (2009) More hotels are facing foreclosure, bankruptcy. March 5, USA Today; http://www.usatoday.com/travel/hotels/2009-03-05-foreclosed-hotels_N.htm. Retrieved 09 February 2017

  • Zmijewski M (1984) Methodological issues related to the estimation of financial distress prediction models. J Account Res 22(1):59–86

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Hotel and Tourism Management, Sejong Cyber University, Seoul, Republic of Korea

    Soo Young Kim

Authors
  1. Soo Young Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soo Young Kim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, S.Y. Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation. Serv Bus 12, 483–503 (2018). https://doi.org/10.1007/s11628-018-0365-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11628-018-0365-x

Keywords

Search

Navigation