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Predicting Business Failure for Malaysia SMEs in the Hospitality Industry

机译：在酒店业中马来西亚中小企业预测业务失败

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摘要

SMEs are an important segment of the Malaysian economy and contribute significantly to the country's economic growth. Nonetheless, SMEs are riskier and associated with a high failure rate. Hence, the aim of this study is to develop a failure prediction model for SMEs in the hospitality industry by using the logit and artificial neural network (ANN) approach for 82 SMEs over the period 2000 to 2016. The findings show that the ANN model predicts better than the logit model in both the estimation and holdout sample with a predictive accuracy rate of 98.2% and 92%, respectively, while the logit model provides overall accuracy rates of 86% and 80%, respectively. This study also finds that both models identify return on assets and board size as an important signal of business failure. The models could be used to assist investors, creditors and lenders to screen out failing SMEs, and the authorities could decide on policies to improve SMEs in the hospitality industry.
机译：中小企业是马来西亚经济的一个重要部分，对该国的经济增长大大贡献。 尽管如此，中小企业的风险较高，失败率高。 因此，本研究的目的是在2000年至2016年期间使用82中小企业的Logit和人工神经网络（ANN）方法，在招待所行业中开发中小企业的失败预测模型。该研究结果表明ANN模型预测 比估计和阻出样品中的Logit模型更好，预测精度分别为98.2％和92％，而Logit模型分别提供86％和80％的总体精度率。 本研究还发现，两种模型都识别资产回报和电路板尺寸作为业务失败的重要信号。 该模型可用于协助投资者，债权人和贷方筛选失败的中小企业，当局可以决定改善酒店业中小企业的政策。

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