Predicting corporate financial distress using data mining techniques : an application in Tehran Stock Exchange
Mahdi Salehi (Department of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran), Mahmoud Mousavi Shiri (Department of Economics and Administrative Sciences, Payamnoor University, Tehran, Iran), and Mohammad Bolandraftar Pasikhani (Department of Planning and Managerial Systems, Bandar Abbas Oil Refining Company, Bandar Abbas, Iran)
| Year of publication: |
2016
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|---|---|
| Authors: | Salehi, Mahdi ; Shiri, Mahmoud Mousavi ; Pasikhani, Mohammad Bolandraftar |
| Published in: |
International journal of law and management : IJLMA. - Bingley : Emerald, ISSN 1754-243X, ZDB-ID 2428717-9. - Vol. 58.2016, 2, p. 216-230
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| Subject: | Support vector machines | Financial distress | k-Nearest neighbor | Insolvenz | Insolvency | Data Mining | Data mining | Prognoseverfahren | Forecasting model | Mustererkennung | Pattern recognition | Börsenhandel | Stock exchange trading | Börsenkurs | Share price |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1108/IJLMA-06-2015-0028 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10011485759