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Predicting SMEs Failure: Logistic Regression vs Artificial Neural Network Models

Author

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  • 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
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    References listed on IDEAS

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    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

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