Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Machine Learning Approaches on the Bankruptcy Modeling: Altman z-score, Neural Net, and CatBoost Classifier models

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

Yazici, Murat

Paperback

Business General

ISBN10: 9999319335
ISBN13: 9789999319331
Publisher: Eliva Press
Published: Apr 18 2024
Pages: 42
Weight: 0.15
Height: 0.09 Width: 6.00 Depth: 9.00
Language: English
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.

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