Academic research and the financial industry have recently paid great attention to Machine Learning algorithms due to their power to solve complex learning tasks. In the field of firms' default prediction, however, the lack of interpretability has prevented the extensive adoption of the black-box type of models. To overcome this drawback and maintain the high performances of black-boxes, this paper relies on a model-agnostic approach. Accumulated Local Effects and Shapley values are used to shape the predictors' impact on the likelihood of default and rank them according to their contribution to the model outcome. Prediction is achieved by two Machine Learning algorithms (eXtreme Gradient Boosting and FeedForward Neural Network) compared with three standard discriminant models. Results show that our analysis of the Italian Small and Medium Enterprises manufacturing industry benefits from the overall highest classification power by the eXtreme Gradient Boosting algorithm without giving up a rich interpretation framework.
翻译:学术和金融业最近非常关注机器学习算法,因为它们有能力解决复杂的学习任务。然而,在公司违约预测领域,由于缺乏解释性,无法广泛采用黑箱型模型。为了克服这一缺陷并保持黑箱的高性能,本文依靠的是模型-不可知性方法。累积的局部效应和虚幻值被用来决定预测者对违约可能性的影响,并根据其对模型结果的贡献对其进行排序。预测是通过两种机器学习算法(eXtreme Gradient Bushing和Feedward Neal Network)实现的,而三种标准模型则不同。结果显示,我们对意大利中小企业制造业的分析得益于EXtreme Gread Abusting算法的总体最高分类能力,而没有给出丰富的解释框架。