Notice of Retraction: An empirical analysis of the financial distress prediction based on data mining

Data mining method for listed companies’ financial distress prediction

Knowledge-Based Systems ◽

10.1016/j.knosys.2006.11.003 ◽

2008 ◽

Vol 21 (1) ◽

pp. 1-5 ◽

Cited By ~ 97

Author(s):

Jie Sun ◽

Hui Li

Keyword(s):

Data Mining ◽

Financial Distress ◽

Listed Companies ◽

Mining Method ◽

Data Mining Method ◽

Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman's Z- Score Model

Journal of International Financial Management and Accounting ◽

10.1111/jifm.12053 ◽

2016 ◽

Vol 28 (2) ◽

pp. 131-171 ◽

Cited By ~ 116

Author(s):

Edward I. Altman ◽

Małgorzata Iwanicz-Drozdowska ◽

Erkki K. Laitinen ◽

Arto Suvas

Keyword(s):

Empirical Analysis ◽

Financial Distress ◽

Z Score ◽

International Context ◽

Distress Prediction ◽

Score Model

Corrigendum “Using neural networks and data mining techniques for the financial distress prediction model” [Experts Systems with Applications 36 (2P2) (2009) (4075–4086)]

Expert Systems with Applications ◽

10.1016/j.eswa.2008.12.061 ◽

2009 ◽

Vol 36 (6) ◽

pp. 10204

Author(s):

Wei-Sen Chen ◽

Yin-Kuan Du

Keyword(s):

Data Mining ◽

Neural Networks ◽

Prediction Model ◽

Financial Distress ◽

Data Mining Techniques ◽

European country heterogeneity in financial distress prediction: An empirical analysis with macroeconomic and regulatory factors

Economic Modelling ◽

10.1016/j.econmod.2019.09.050 ◽

2020 ◽

Vol 88 ◽

pp. 398-407 ◽

Cited By ~ 2

Author(s):

Manuel Ángel Fernández-Gámez ◽

Juan Antonio Campos Soria ◽

José António C. Santos ◽

David Alaminos

Keyword(s):

European Country ◽

Empirical Analysis ◽

Financial Distress ◽

Regulatory Factors ◽

Advances in Data Mining. Applications and Theoretical Aspects - Lecture Notes in Computer Science ◽

Data Mining for Municipal Financial Distress Prediction

10.1007/978-3-319-95786-9_23 ◽

2018 ◽

pp. 296-308 ◽

Cited By ~ 2

Author(s):

David Alaminos ◽

Sergio M. Fernández ◽

Francisca García ◽

Manuel A. Fernández

Keyword(s):

Data Mining ◽

Financial Distress ◽

The performance of corporate financial distress prediction models with features selection guided by domain knowledge and data mining approaches

Knowledge-Based Systems ◽

10.1016/j.knosys.2015.04.017 ◽

2015 ◽

Vol 85 ◽

pp. 52-61 ◽

Cited By ~ 32

Author(s):

Ligang Zhou ◽

Dong Lu ◽

Hamido Fujita

Keyword(s):

Data Mining ◽

Financial Distress ◽

Domain Knowledge ◽

Prediction Models ◽

Features Selection ◽

Using neural networks and data mining techniques for the financial distress prediction model

Expert Systems with Applications ◽

10.1016/j.eswa.2008.03.020 ◽

2009 ◽

Vol 36 (2) ◽

pp. 4075-4086 ◽

Cited By ~ 118

Author(s):

Wei-Sen Chen ◽

Yin-Kuan Du

Keyword(s):

Data Mining ◽

Neural Networks ◽

Prediction Model ◽

Financial Distress ◽

Data Mining Techniques ◽

Improved Data Mining Method for Class-Imbalanced Financial Distress Prediction

10.1145/3467707.3467754 ◽

2021 ◽

Author(s):

Tingting Ren ◽

Tongyu Lu ◽

Yuanyuan Yang

Keyword(s):

Data Mining ◽

Financial Distress ◽

Mining Method ◽

Data Mining Method ◽

Financial Distress Prediction of Iranian Companies Using Data Mining Techniques

Organizacija ◽

10.2478/orga-2013-0003 ◽

2013 ◽

Vol 46 (1) ◽

Cited By ~ 1

Author(s):

Mahdi Moradi ◽

Mahdi Salehi ◽

Mohammad Ebrahim Ghorgani ◽

Hadi Sadoghi Yazdi

Keyword(s):

Data Mining ◽

Financial Distress ◽

Data Mining Techniques ◽

Distress Prediction ◽

Using Data

Financial Distress Prediction Model with the Incorporation of Soft Information

SSRN Electronic Journal ◽

10.2139/ssrn.1663784 ◽

2010 ◽

Author(s):

Yang-Cheng Lu ◽

Yu-Chen Wei

Keyword(s):

Prediction Model ◽

Financial Distress ◽

Soft Information ◽