Predicting corporate defaults using machine learning with geometric-lag variables

IF 1.2 4区经济学 Q3 BUSINESS, FINANCE

Investment Analysts Journal Pub Date : 2021-07-03 DOI:10.1080/10293523.2021.1941554

Hyeongjun Kim, Hoon Cho, Doojin Ryu

引用次数: 3

Abstract

ABSTRACT This study examines whether corporate default prediction techniques based on machine learning can achieve better performance by using geometrically declining weighted average values of the time series variables, that is, geometric-lag variables. We test four machine learning algorithms: logistic regression, random forest, support vector machine, and feedforward neural network. The geometric-lag financial variables capture each company’s historical financial information. Using such variables reduces the computation time and improves the prediction performance. The actual default rates increase with the predicted default probabilities, suggesting that our model predictions can help investors make better investment decisions.

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使用带有几何滞后变量的机器学习预测企业违约

本研究考察了基于机器学习的企业违约预测技术是否可以通过使用时间序列变量(即几何滞后变量)的几何下降加权平均值来获得更好的性能。我们测试了四种机器学习算法:逻辑回归、随机森林、支持向量机和前馈神经网络。几何滞后财务变量捕获每个公司的历史财务信息。使用这些变量减少了计算时间，提高了预测性能。实际违约率随着预测违约概率的增加而增加，表明我们的模型预测可以帮助投资者做出更好的投资决策。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Investment Analysts Journal BUSINESS, FINANCE-

CiteScore

1.90

自引率

11.10%

发文量

期刊介绍： The Investment Analysts Journal is an international, peer-reviewed journal, publishing high-quality, original research three times a year. The journal publishes significant new research in finance and investments and seeks to establish a balance between theoretical and empirical studies. Papers written in any areas of finance, investment, accounting and economics will be considered for publication. All contributions are welcome but are subject to an objective selection procedure to ensure that published articles answer the criteria of scientific objectivity, importance and replicability. Readability and good writing style are important. No articles which have been published or are under review elsewhere will be considered. All submitted manuscripts are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous expert referees. All peer review is double blind and submission is via email. Accepted papers will then pass through originality checking software. The editors reserve the right to make the final decision with respect to publication.