Accurate business failure prediction represents an advantage for market players and is important for risk management. The purpose of this study is to develop a more accurate and stable business failure prediction model by using a majority voting ensemble method with a decision tree (DT) with experimental data on US restaurant between 1980 and 2017. According to the diversity principle and individual optimized principle, DT and logit were selected as basic learning algorithms for the voting ensemble of business failure prediction. Three models, including an entire period (EP) model, an economic downturn (ED) model, and an economic expansion (EE) model, were developed by using WEKA 3.9. The prediction accuracy of the models were 88.02% for the EP model, 80.81% for the ED model, and 87.02 % for the EE model. While the EE model revealed the market capitalization, operating cash flow after interest and dividends (OCFAID), cash conversion cycle (CCC), return on capital employed (ROCE), accumulated retained earnings, stock price, and Tobin’s Q as significant variables, the ED model exposed quite different variables such as OCFAID, KZ index, stock price, and CCC. The EP model combined most of the variables from two sub-divided models except for Tobin’s Q, stock price, and debt to equity (D/E) ratio. The contribution of the paper is twofold. First, this is the first study to comprehensively evaluate the financial and market-driven variables in the context of predicting restaurant failure, especially during economic recessions. This research has employed several accounting-based measures, market-based variables, and a macro-economic factor to improve the relevance and effectiveness of prediction models. And second, by using an ensemble model with a DT, it has improved both the interpretability of the results and the prediction accuracy.

准确的业务失败预测对于市场参与者而言是一项优势，对于风险管理也很重要。这项研究的目的是通过使用具有决策树（DT）的多数投票集成方法和1980年至2017年间美国餐厅的实验数据，来开发更准确，更稳定的业务失败预测模型。原则上，选择DT和logit作为业务失败预测的投票合奏的基本学习算法。使用WEKA 3.9开发了三个模型，包括整个时期（EP）模型，经济衰退（ED）模型和经济扩张（EE）模型。该模型的预测精度对于EP模型为88.02％，对于ED模型为80.81％，对于EE模型为87.02％。尽管EE模型显示出市值，扣除利息和股息后的经营现金流（OCFAID），现金转换周期（CCC），已用资本回报率（ROCE），累计保留收益，股价和托宾Q值，但ED模型暴露了完全不同的变量，例如OCFAID，KZ指数，股票价格和CCC。EP模型将来自两个细分模型的大多数变量组合在一起，除了Tobin的Q，股价和债务对权益（D / E）比率。论文的贡献是双重的。首先，这是第一项在预测饭店倒闭（尤其是在经济衰退期间）的背景下全面评估财务和市场驱动变量的研究。这项研究采用了几种基于会计的方法，基于市场的变量，还有一个宏观经济因素可以改善预测模型的相关性和有效性。其次，通过将整体模型与DT结合使用，可以提高结果的可解释性和预测准确性。