An Analysis of Financial Distress Prediction of Selected Listed Companies in Colombo Stock Exchange

Kennedy Degaulle Gunawardana
Additional contact information
Kennedy Degaulle Gunawardana: University of Sri Jayewardenepura, Sri Lanka

International Journal of Sociotechnology and Knowledge Development (IJSKD), 2021, vol. 13, issue 2, 48-70

Abstract: The main objective of the study is to predict financial distress and developing a prediction model using accounting related variables in selected listed firms in Sri Lanka. Decision criteria for financial distress has been selected based on the existing literature on financial distress prediction applicable to the Sri Lankan firms. A sample of 22 financially distressed firms along with 33 financially non-distressed firms have been used to conduct this study. Artificial neural network was used as the basic approach to the study in predicting financial distress. A neural network to predict financial distress was developed with an accuracy of 85.7% one year prior to its occurrence. The second analysis conducted was the panel regression considering five years of cross-sectional data for the sample of companies selected. This analysis was able to identify a significant relationship of leverage, price-to-book ratio and Tobin's Q ratio to the prediction of financial distress of a firm.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJSKD.2021040104 (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:jskd00:v:13:y:2021:i:2:p:48-70

Access Statistics for this article

International Journal of Sociotechnology and Knowledge Development (IJSKD) is currently edited by Lincoln Christopher Wood

More articles in International Journal of Sociotechnology and Knowledge Development (IJSKD) from IGI Global
Bibliographic data for series maintained by Journal Editor ().