Machine-learning models for bankruptcy prediction: do industrial variables matter?

被引：10

作者：

Bragoli, Daniela ^{[1
]}

Ferretti, Camilla ^{[2
]}

Ganugi, Piero ^{[3
]}

Marseguerra, Giovanni ^{[1
]}

Mezzogori, Davide ^{[3
]}

Zammori, Francesco ^{[3
]}

机构：

[1] Univ Cattolica Sacro Cuore, Dept Math Econ Financial & Actuarial Sci, Milan, Italy

[2] Univ Cattolica Sacro Cuore, Dept Econ & Social Sci, Piacenza, Italy

[3] Univ Parma, Dept Engn & Architecture, Parma, Italy

来源：

SPATIAL ECONOMIC ANALYSIS | 2022年 / 17卷 / 02期

关键词：

firm distress analysis; machine learning; logistic regression; industrial variables; FINANCIAL RATIOS; DISCRIMINANT-ANALYSIS; CORPORATE BANKRUPTCY; RISK; AGGLOMERATION; PRODUCTIVITY; DIAGNOSIS; SELECTION; FORESTS; TREES;

D O I：

10.1080/17421772.2021.1977377

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

We provide a predictive model specifically designed for the Italian economy that classifies solvent and insolvent firms one year in advance using the AIDA Bureau van Dijk data set for the period 2007-15. We apply a full battery of bankruptcy forecasting models, including both traditional and more sophisticated machine-learning techniques, and add to the financial ratios used in the literature a set of industrial/regional variables. We find that XGBoost is the best performer, and that industrial/regional variables are important. Moreover, belonging to a district, having a high mark-up and a greater market share diminish bankruptcy probability.

引用

页码：156 / 177

页数：22

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