Machine Learning Approaches on the Bankruptcy Modeling: Altman z-score, Neural Net, and CatBoost Classifier models

Yazici, Murat (Author)

ISBN 10: 9999319335 / ISBN 13: 9789999319331

Published by Eliva Press, 2024

About this Item

41 pages. 6.00x0.10x9.00 inches. In Stock.

Seller Inventory # x-9999319335

Bibliographic Details

• Title: Machine Learning Approaches on the Bankruptcy Modeling: Altman z-score, Neural Net, and CatBoost Classifier models
• Publisher: Eliva Press
• Publication Date: 2024
• Language: English
• ISBN 10: 9999319335
• ISBN 13: 9789999319331
• Binding: Paperback
• Condition: Brand New
• Weight: 0.07 kilograms

About this title

Synopsis

This study includes Machine Learning (ML) approaches on the bankruptcy modeling. The Altman z-score model was selected as the subject of the study due to its widespread usage and extensive scientific validation, as well as its reputation as one of the most reliable tools for predicting bankruptcy. Typically, z-score models are evaluated in concert with other models or metrics. The Altman z-score remains a popular tool amongst investors, accountants, and stakeholders due to its versatility in application. The z-score model is especially useful due to its ease of use, compared to other predictive models. The models chosen for this study provided clear results for predicting a company’s financial stability in two regions as bankrupt and non-bankrupt, making it easy to compare between companies.