Predicting Corporate Bond Illiquidity via Machine Learning

IF 3.4 3区经济学 Q1 BUSINESS, FINANCE Financial Analysts Journal Pub Date : 2024-06-24 DOI:10.1080/0015198x.2024.2350952

Axel Cabrol, Wolfgang Drobetz, Tizian Otto, Tatjana Puhan

引用次数: 0

Abstract

This paper tests the predictive performance of machine learning methods in estimating the illiquidity of US corporate bonds. Machine learning techniques outperform the historical illiquidity-based ...

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来源期刊

Financial Analysts Journal BUSINESS, FINANCE-

CiteScore

5.40

自引率

7.10%

发文量

期刊介绍： The Financial Analysts Journal aims to be the leading practitioner journal in the investment management community by advancing the knowledge and understanding of the practice of investment management through the publication of rigorous, peer-reviewed, practitioner-relevant research from leading academics and practitioners.

期刊最新文献

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