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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 : open access journal 8 (2020) 2/52, pp. 1-22
Banks play a vital role in strengthening the financial system of a country; hence, their survival is decisive for the stability of national economies. Therefore, analyzing the survival probability of the banks is an essential and continuing research activity. However, the current literature...
Persistent link: https://www.econbiz.de/10012292870
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Anticipating financial distress of high-tech startups in the European Union : a machine learning approach for imbalanced samples
Liu, Yang; Zeng, Qingguo; Li, Bobo; Ma, Lili; … - In: Journal of forecasting 41 (2022) 6, pp. 1131-1155
Persistent link: https://www.econbiz.de/10013465685
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Machine learning-based failure prediction in industrial maintenance : improving performance by sliding window selection
Leukel, Jörg; González, Julian; Riekert, Martin - In: International journal of quality & reliability management 40 (2023) 6, pp. 1449-1462
Persistent link: https://www.econbiz.de/10014313829
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Predicting business bankruptcy in Colombian SMES : a machine learning approach
Correa, Alexander - In: Journal of international commerce, economics and policy 14 (2023) 3, pp. 1-21
Persistent link: https://www.econbiz.de/10014438200
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Towards an early warning system for sovereign defaults leveraging on machine learning methodologies
Petropoulos, Anastasios; Siakoulis, Vasilis; … - In: Intelligent systems in accounting, finance & management 29 (2022) 2, pp. 118-129
Persistent link: https://www.econbiz.de/10013274258
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CatBoost model and artificial intelligence techniques for corporate failure prediction
Jabeur, Sami Ben; Gharib, Cheima; Mefteh-Wali, Salma; … - In: Technological forecasting & social change : an … 166 (2021), pp. 1-19
Persistent link: https://www.econbiz.de/10012694147
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No more black boxes! : explaining the predictions of a machine learning XGBoost classifier algorithm in business failure
Carmona, Pedro; Dwekat, Aladdin; Mardawi, Zeena - In: Research in international business and finance 61 (2022), pp. 1-18
Persistent link: https://www.econbiz.de/10014246624
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Bankruptcy prediction using the XGBoost algorithm and variable importance feature engineering
Jabeur, Sami Ben; Stef, Nicolae; Carmona, Pedro - In: Computational economics 61 (2023) 2, pp. 715-741
Persistent link: https://www.econbiz.de/10014228460
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An Analysis of Features Predicting Bankruptcy of Newly Formed Japanese Small and Medium-Sized Firms Using Machine Learning Techniques
Xu, Hong - 2018
The study analyses the bankruptcy of newly formed Japanese small and medium-sized entities (SMEs) by comparing specific features with newly formed but survived SMEs. It applies Resource-based Firm Theory and other theories to identify the bankruptcy features and uses logit regression to validate...
Persistent link: https://www.econbiz.de/10012917907
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Bankruptcy prediction using machine learning techniques
Shetty, Shekar; Musa, Mohamed; Brédart, Xavier - In: Journal of risk and financial management : JRFM 15 (2022) 1, pp. 1-10
In this study, we apply several advanced machine learning techniques including extreme gradient boosting (XGBoost … Belgian Small and Medium Enterprises (SME’s) during the period 2002-2012. Using the above-mentioned machine learning …
Persistent link: https://www.econbiz.de/10012814176