CORPORATE FINANCIAL DISTRESS PREDICTION USING STATISTICAL EXTREME VALUE-BASED MODELING AND MACHINE LEARNING

Autor:	Dedy Dwi Prastyo, Rizki Nanda Savera, Danny Hermawan Adiwibowo
Jazyk:	English<br />Indonesian
Rok vydání:	2023
Předmět:	classification feature selection financial distress gevr imbalanced data machine learning. Probabilities. Mathematical statistics QA273-280
Zdroj:	Media Statistika, Vol 16, Iss 1, Pp 1-12 (2023)
Druh dokumentu:	article
ISSN:	1979-3693 2477-0647
DOI:	10.14710/medstat.16.1.1-12
Popis:	The industrial sector plays a leading role in an economy such that the financial stability of companies from this sector be a big concern. Two financial ratios, i.e., the Interest Coverage Ratio (ICR) and the Return on Assets (ROA), are used to determine the corporate financial distress conditions. This work considers two schemes for determining financial distress. First, a company is categorized as distressed if either ICR
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/19b819b5e77d413382c69377fb9dac97 Zobrazit plný text záznamu View record in DOAJ