Combining corporate governance indicators with stacking ensembles for financial distress prediction
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DOI: 10.1016/j.jbusres.2020.07.052
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- Fedorova, Elena & Ledyaeva, Svetlana & Drogovoz, Pavel & Nevredinov, Alexandr, 2022. "Economic policy uncertainty and bankruptcy filings," International Review of Financial Analysis, Elsevier, vol. 82(C).
- Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
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- Darko B. Vukovic & Lubov Spitsina & Ekaterina Gribanova & Vladislav Spitsin & Ivan Lyzin, 2023. "Predicting the Performance of Retail Market Firms: Regression and Machine Learning Methods," Mathematics, MDPI, vol. 11(8), pages 1-23, April.
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- Jiang, Cuiqing & Zhou, Yiru & Chen, Bo, 2023. "Mining semantic features in patent text for financial distress prediction," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
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- Michal Pavlicko & Marek Durica & Jaroslav Mazanec, 2021. "Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries," Mathematics, MDPI, vol. 9(16), pages 1-26, August.
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- Mst. Maksuda Begum & Niluthpaul Sarker & Shamsun Nahar, 2023. "The Impact of Corporate Governance Attributes on Financial Distress among the Listed Firms in Pharmaceuticals Industry of Bangladesh," International Journal of Economics and Financial Issues, Econjournals, vol. 13(6), pages 155-167, November.
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Keywords
Data mining; Financial distress prediction; Bankruptcy prediction; Corporate governance indicators; Stacking ensembles;All these keywords.
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