Use of Data Reduction Process to Bankruptcy Prediction: Evidence from an Emerging Market

Morteza Shafiee Sardasht and Saeed Saheb
Additional contact information
Morteza Shafiee Sardasht: Faculty of Management and Accounting, IAUM, Mashhad, Iran
Saeed Saheb: Faculty of Management and Accounting, IAUM, Mashhad, Iran

International Journal of Information Systems and Social Change (IJISSC), 2016, vol. 7, issue 2, 27-46

Abstract: Predicting corporate bankruptcy has been an important challenging problem in research topic in accounting and finance. In bankruptcy prediction, researchers often confront a range of observations and variables which are often vast amount of financial ratios. By reducing variables and select relevant data from a given dataset, data reduction process can optimize bankruptcy prediction. This study addresses four well-known data reduction methods including t-test, correlation analysis, principal component analysis (PCA) and factor analysis (FA) and evaluated them in bankruptcy prediction in the Tehran Stock Exchange (TSE). To this end, considering 35 financial ratios, the results of data reduction methods were separately used to train Support Vector Machine (SVM) as the powerful prediction model. Regarding the empirical results, among the aforementioned methods, the t-test lead to the most prediction rate with 97.1% of predictability and PCA by 95.1% provides the next position.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/IJISSC.2016040102 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jissc0:v:7:y:2016:i:2:p:27-46

Access Statistics for this article

International Journal of Information Systems and Social Change (IJISSC) is currently edited by John Wang

More articles in International Journal of Information Systems and Social Change (IJISSC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().