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Bankruptcy prediction and stress quantification using support vector machine: Evidence from Indian banks

Shrivastava, Santosh Kumar; Ramudu, P. Janaki - In: Risks 8 (2020) 2, pp. 1-22

quantification in countries like India where there have been fewer failed banks. The literature also indicates a lack of scientific … banks in India for the period January 2000 through December 2017. The explanatory features of bank failure are chosen by … using a two-step feature selection technique. First, a relief algorithm is used for primary screening of useful features …

Persistent link: https://www.econbiz.de/10013200586

Predicting bank failure : an improvement by implementing a machine-learning approach to classical financial ratios

Le, Hong Hanh; Viviani, Jean-Laurent - In: Research in international business and finance 44 (2018), pp. 16-25

Persistent link: https://www.econbiz.de/10011982957

A coherent framework for predicting emerging market credit spreads with support vector regression

Anderson, Gary S.; Audzeyeva, Alena - 2019

models for predicting emerging market sovereign credit spreads. Our framework adapts a global optimization algorithm …

Persistent link: https://www.econbiz.de/10012182398

Forecasting the insolvency of US banks using Support Vector Machines (SVMs) based on local learning feature selection

Papadimitriou, Theophilos; Gkonkas, Periklēs; … - In: International journal of computational economics and … 3 (2013) 1/2, pp. 83-90

Persistent link: https://www.econbiz.de/10010250320

Predicting Bankruptcy with Support Vector Machines

Härdle, Wolfgang - 2017

The purpose of this work is to introduce one of the most promising among recently developed statistical techniques – the support vector machine (SVM) – to corporate bankruptcy analysis. An SVM is implemented for analysing such predictors as financial ratios. A method of adapting it to...

Persistent link: https://www.econbiz.de/10012966212

Predicting bank insolvencies using machine learning techniques

Petropoulos, Anastasios; Siakoulis, Vasilis; … - In: International journal of forecasting 36 (2020) 3, pp. 1092-1113

Persistent link: https://www.econbiz.de/10012497724

Machine learning-based classifiers ensemble for credit risk assessment

Pandey, Trilok Nath; Jagadev, Alok Kumar; Choudhury, D.; … - In: International journal of electronic finance : IJEF 7 (2013) 3/4, pp. 227-249

Persistent link: https://www.econbiz.de/10010400393

Bankruptcy prediction using machine learning

Wang, Nanxi - In: Journal of mathematical finance 7 (2017) 4, pp. 908-918

Persistent link: https://www.econbiz.de/10011859939

Estimation procedures of using five alternative machine learning methods for predicting credit card default

Teng, Huei-Wen; Lee, Michael - In: Review of Pacific Basin financial markets and policies 22 (2019) 3, pp. 1950021-1-1950021-27

Persistent link: https://www.econbiz.de/10012156160

A deep learning approach to estimate forward default intensities

Divernois, Marc-Aurèle - 2020 - Preliminary draft

This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...

Persistent link: https://www.econbiz.de/10012419329