计算机科学 ›› 2022, Vol. 49 ›› Issue (11): 197-205.doi: 10.11896/jsjkx.210900195
宋美琦1,2,3, 傅湘玲1,2,3, 闫晨巍1,2,3, 仵伟强3, 任芸3
SONG Mei-qi1,2,3, FU Xiang-ling1,2,3, YAN Chen-wei1,2,3, WU Wei-qiang3, REN Yun3
摘要： 传统的风险管理方法专注于识别、预测和评估可能发生的潜在风险,但当企业面临突发的、不可预期的风险时,往往束手无策。因此,学术界逐渐将风险管理的视角由预测并规避风险转变为提升企业自身对风险的承受能力和从风险中恢复的能力,也就是企业的弹性能力。文中提出了基于时序特征数据的企业弹性能力预测方法,使用Bi-LSTM对时序特征数据进行双向编码,获得企业的特征表示,并通过softmax分类器得到弹性能力分类结果。模型在中国上市公司的真实数据集中进行实验,macro-F1值达到89.0%,与RF,XGBoost和LightGBM等未使用时序特征数据的模型相比有一定提升。此外,进一步探讨了企业弹性能力的多种影响因素及其重要程度,并首次将机器学习方法应用到企业弹性能力的评估预测中,为企业应对突发风险提供了理论方法指导。
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