[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/mfa/journl/v28y2020i2p29-41.html
   My bibliography  Save this article

Predicting SMEs Failure: Logistic Regression vs Artificial Neural Network Models

Author

Listed:
  • Juraini Zainol Abidin

    (College of Business, Universiti Utara Malaysia, Malaysia.)

  • Nur Adiana Hiau Abdullah

    (College of Business, Universiti Utara Malaysia, Malaysia.)

  • Karren Lee-Hwei Khaw

    (Faculty of Business and Accountancy, University of Malaya, Malaysia.)

Abstract
Research questions: This study compares the power of logit and artificial neural network (ANN) models in predicting the failure of SMEs in the hospitality industry and identifies the predictors that are significant in determining business failure. Motivation: SMEs are an important segment of the Malaysian economy and contribute significantly to the country’s economic growth. However, SMEs are riskier and associated with a high failure rate. In Malaysia, around 3.5% of the SMEs in the hospitality industry fail within the first two years and 54% of them cease operations within four. Idea: The use of ANN to model business failure, particularly in the hospitality industry, is relatively unexplored in the emerging markets. Based on the literature, this study hypothesizes that ANN models outperform logit models because of less stringent model assumptions. Data: Excluding missing information, a matched sample of 41 failed and 41 non-failed SMEs in the hospitality industry was identified from the year 2000 to 2016. The accounting ratios, firm-specific characteristics and governance variables are selected as potential predictors of SMEs failure in the hospitality industry. Method/Tools: Stepwise logit regression and multilayer perceptron ANN models were used to determine significant predictors to predict business failure. Each model’s predictive power was compared. Findings: The ANN model was found to consistently outperform the logit model in classifying the failed and non-failed SMEs in the hospitality industry. Furthermore, the ANN model ranked liquidity as the most important predictor, followed by profitability and leverage, in determining business failure. Board size was also found to be a significant predictor in addition to the financial variables. The stepwise logit model also suggests a significant relationship between board size and the failure of SMEs. Therefore, in addition to financial predictors, a firm’s governance is also key to business survival. Contributions: The findings of this study contribute to the limited literature on SMEs in the hospitality industry by providing empirical evidence from an emerging market perspective. The failure prediction model can be utilized to warn of potential business failure so that strategic measures can be taken to mitigate the risk of failure.

Suggested Citation

  • Juraini Zainol Abidin & Nur Adiana Hiau Abdullah & Karren Lee-Hwei Khaw, 2020. "Predicting SMEs Failure: Logistic Regression vs Artificial Neural Network Models," Capital Markets Review, Malaysian Finance Association, vol. 28(2), pages 29-41.
    • Handle: RePEc:mfa:journl:v:28:y:2020:i:2:p:29-41
    as

    Download full text from publisher

    File URL: https://www.mfa.com.my/wp-content/uploads/2020/11/v28_i2_a3_pg29-41.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    2. Jackson, Richard H.G. & Wood, Anthony, 2013. "The performance of insolvency prediction and credit risk models in the UK: A comparative study," The British Accounting Review, Elsevier, vol. 45(3), pages 183-202.
    3. Andreas Charitou & Evi Neophytou & Chris Charalambous, 2004. "Predicting corporate failure: empirical evidence for the UK," European Accounting Review, Taylor & Francis Journals, vol. 13(3), pages 465-497.
    4. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    5. Nur Adiana Hiau Abdullah & Muhammad M. Ma'aji & Karren Lee-Hwei Khaw, 2016. "The Value of Governance Variables in Predicting Financial Distress Among Small and Medium-Sized Enterprises in Malaysia," Asian Academy of Management Journal of Accounting and Finance (AAMJAF), Penerbit Universiti Sains Malaysia, vol. 12(Suppl. 1), pages 1-77–91.
    6. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    7. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    8. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    9. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    10. Soo Young Kim, 2018. "Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation," Service Business, Springer;Pan-Pacific Business Association, vol. 12(3), pages 483-503, September.
    11. Birajit Mohanty & Shweta Mehrotra, 2018. "Relationship between Liquidity and Profitability: An Exploratory Study of SMEs in India," Emerging Economy Studies, International Management Institute, vol. 4(2), pages 169-181, November.
    12. Soo-Seon Park & Murat Hancer, 2012. "A Comparative Study of Logit and Artificial Neural Networks in Predicting Bankruptcy in the Hospitality Industry," Tourism Economics, , vol. 18(2), pages 311-338, April.
    13. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    14. Nur Adiana Hiau Abdullah & Abd Halim Ahmad & Nasruddin Zainudin & Rohani Md Rus, 2016. "Modelling small and medium-sized enterprises' failure in Malaysia," International Journal of Entrepreneurship and Small Business, Inderscience Enterprises Ltd, vol. 28(1), pages 101-116.
    15. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    16. Ciampi, Francesco, 2015. "Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms," Journal of Business Research, Elsevier, vol. 68(5), pages 1012-1025.
    17. Audretsch, David B & Mahmood, Talat, 1995. "New Firm Survival: New Results Using a Hazard Function," The Review of Economics and Statistics, MIT Press, vol. 77(1), pages 97-103, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Agnieszka Ma³kowska & Ma³gorzata Uhruska, 2022. "Factors affecting SMEs growth: the case of the real estate valuation service industry," Oeconomia Copernicana, Institute of Economic Research, vol. 13(1), pages 79-108, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    2. Hamid Waqas & Rohani Md-Rus, 2018. "Predicting financial distress: Applicability of O-score model for Pakistani firms," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(2), pages 389-401, April.
    3. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    4. Fayçal Mraihi, 2016. "Distressed Company Prediction Using Logistic Regression: Tunisian’s Case," Quarterly Journal of Business Studies, Research Academy of Social Sciences, vol. 2(1), pages 34-54.
    5. repec:ctc:sdimse:dime21_01 is not listed on IDEAS
    6. Adler Haymans Manurung & Derwin Suhartono & Benny Hutahayan & Noptovius Halimawan, 2023. "Probability Bankruptcy Using Support Vector Regression Machines," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(1), pages 1-3.
    7. Jairaj Gupta & Nicholas Wilson & Andros Gregoriou & Jerome Healy, 2014. "The value of operating cash flow in modelling credit risk for SMEs," Applied Financial Economics, Taylor & Francis Journals, vol. 24(9), pages 649-660, May.
    8. Scalzer, Rodrigo S. & Rodrigues, Adriano & Macedo, Marcelo Álvaro da S. & Wanke, Peter, 2019. "Financial distress in electricity distributors from the perspective of Brazilian regulation," Energy Policy, Elsevier, vol. 125(C), pages 250-259.
    9. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    10. Alessandro Bitetto & Stefano Filomeni & Michele Modina, 2021. "Understanding corporate default using Random Forest: The role of accounting and market information," DEM Working Papers Series 205, University of Pavia, Department of Economics and Management.
    11. Ilyes Abid & Rim Ayadi & Khaled Guesmi & Farid Mkaouar, 2022. "A new approach to deal with variable selection in neural networks: an application to bankruptcy prediction," Annals of Operations Research, Springer, vol. 313(2), pages 605-623, June.
    12. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    13. Simone Poli & Marco Gatti, 2024. "The relevance of cash flow information in predicting corporate bankruptcy in Italian private companies," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2024(1), pages 179-202.
    14. Alberto Tron & Maurizio Dallocchio & Salvatore Ferri & Federico Colantoni, 2023. "Corporate governance and financial distress: lessons learned from an unconventional approach," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 425-456, June.
    15. Fayçal Mraihi & Inane Kanzari & Mohamed Tahar Rajhi, 2015. "Development of a Prediction Model of Failure in Tunisian Companies: Comparison between Logistic Regression and Support Vector Machines," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(3), pages 184-205.
    16. George Giannopoulos & Sophia Ali Sardar & Rebecca Salti & Nicos Sykianakis, 2022. "Analyzing Insolvency Prediction Models in the Period Before and After the Financial Crisis: A Case Study on the Example of US Firms," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 12(3), pages 23-45.
    17. Liang, Deron & Tsai, Chih-Fong & Lu, Hung-Yuan (Richard) & Chang, Li-Shin, 2020. "Combining corporate governance indicators with stacking ensembles for financial distress prediction," Journal of Business Research, Elsevier, vol. 120(C), pages 137-146.
    18. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    19. Modina, Michele & Pietrovito, Filomena & Gallucci, Carmen & Formisano, Vincenzo, 2023. "Predicting SMEs’ default risk: Evidence from bank-firm relationship data," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 254-268.
    20. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    21. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.

    More about this item

    Keywords

    Artificial neural network; business failure; hospitality industry; logistic regression; SMEs;
    All these keywords.

    JEL classification:

    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mfa:journl:v:28:y:2020:i:2:p:29-41. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Capital Market Review (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.