Predicting Bank Fragility by Applying Logistic Regression Model using R-Programming; A Supervised Learning Approach
Year of publication: |
2020
|
---|---|
Authors: | Untwal, Nitin |
Publisher: |
[2020]: [S.l.] : SSRN |
Subject: | Bankenaufsicht | Banking supervision | Theorie | Theory | Regressionsanalyse | Regression analysis | Prognoseverfahren | Forecasting model |
Description of contents: | Abstract [papers.ssrn.com] |
Extent: | 1 Online-Ressource |
---|---|
Type of publication: | Book / Working Paper |
Language: | English |
Notes: | In: IJESC, April 2020 Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments April 12, 2020 erstellt Volltext nicht verfügbar |
Source: | ECONIS - Online Catalogue of the ZBW |
-
Stress-testing US bank holding companies : a dynamic panel quantile regression approach
Covas, Francisco B., (2014)
-
Farnè, Matteo, (2018)
-
Non-linear dynamics in discretionary accruals : an analysis of bank loan-loss provisions
Balboa, Marina, (2012)
- More ...
-
Untwal, Nitin, (2020)
-
Application of Machine Learning Using R-Programming for Financial Forecasting
Untwal, Nitin, (2020)
-
Untwal, Nitin, (2020)
- More ...