Predicting financial distress in Malaysian market: a logit analysis

<p>This study was carried out to apply logit analysis in the prediction of financial distress in Malaysia. Its focus is to appraise whether the factors of firm's performance, risks, age, size and industrial grouping sector can be a good forecast for financial distress in Malaysia....

Full description

Saved in:
Bibliographic Details
Main Author: Rusliza Yahaya
Format: thesis
Language:eng
Published: 2003
Subjects:
Online Access:https://ir.upsi.edu.my/detailsg.php?det=9977
Tags: Add Tag
No Tags, Be the first to tag this record!
id oai:ir.upsi.edu.my:9977
record_format uketd_dc
spelling oai:ir.upsi.edu.my:99772024-04-02 Predicting financial distress in Malaysian market: a logit analysis 2003 Rusliza Yahaya <p>This study was carried out to apply logit analysis in the prediction of financial distress in Malaysia. Its focus is to appraise whether the factors of firm's performance, risks, age, size and industrial grouping sector can be a good forecast for financial distress in Malaysia. In this study, financial distress is defined as companies placed under PN 4 sector on the Kuala Lumpur Stock Exchange (KLSE). There are 86 companies from the main board of KLSE designated as samples in this study. Amongst these, 43 companies are classified as PN4 and the other 43 companies are considered as 'healthy' ones. When the gathered data are analysed, only total debt ratio (TOR), earning per share (EPS) and construction sector proved to be significant in classifying the firms into the correct group (distressed vs 'healthy'), based on logit analysis. Among these three variables, TDR is the most influential one as it can predict financial distress cent up to three years prior to it. The model is accurate in classifying 75.73 per accurate of the total sample. The logit analysis also shows that, higher level of prediction is achieved when the data are analyzed separately according to years. We observe excellent and accurate classification based on data from the first two years and it can also correctly classify 92.59 per cent and 90.48 per cent for the first and second yearpreceding to financial distress respectively.</p> 2003 thesis https://ir.upsi.edu.my/detailsg.php?det=9977 https://ir.upsi.edu.my/detailsg.php?det=9977 text eng closedAccess Masters Universiti Pendidikan Sultan Idris Fakulti Pengurusan dan Ekonomi N/A
institution Universiti Pendidikan Sultan Idris
collection UPSI Digital Repository
language eng
topic
spellingShingle
Rusliza Yahaya
Predicting financial distress in Malaysian market: a logit analysis
description <p>This study was carried out to apply logit analysis in the prediction of financial distress in Malaysia. Its focus is to appraise whether the factors of firm's performance, risks, age, size and industrial grouping sector can be a good forecast for financial distress in Malaysia. In this study, financial distress is defined as companies placed under PN 4 sector on the Kuala Lumpur Stock Exchange (KLSE). There are 86 companies from the main board of KLSE designated as samples in this study. Amongst these, 43 companies are classified as PN4 and the other 43 companies are considered as 'healthy' ones. When the gathered data are analysed, only total debt ratio (TOR), earning per share (EPS) and construction sector proved to be significant in classifying the firms into the correct group (distressed vs 'healthy'), based on logit analysis. Among these three variables, TDR is the most influential one as it can predict financial distress cent up to three years prior to it. The model is accurate in classifying 75.73 per accurate of the total sample. The logit analysis also shows that, higher level of prediction is achieved when the data are analyzed separately according to years. We observe excellent and accurate classification based on data from the first two years and it can also correctly classify 92.59 per cent and 90.48 per cent for the first and second yearpreceding to financial distress respectively.</p>
format thesis
qualification_name
qualification_level Master's degree
author Rusliza Yahaya
author_facet Rusliza Yahaya
author_sort Rusliza Yahaya
title Predicting financial distress in Malaysian market: a logit analysis
title_short Predicting financial distress in Malaysian market: a logit analysis
title_full Predicting financial distress in Malaysian market: a logit analysis
title_fullStr Predicting financial distress in Malaysian market: a logit analysis
title_full_unstemmed Predicting financial distress in Malaysian market: a logit analysis
title_sort predicting financial distress in malaysian market: a logit analysis
granting_institution Universiti Pendidikan Sultan Idris
granting_department Fakulti Pengurusan dan Ekonomi
publishDate 2003
url https://ir.upsi.edu.my/detailsg.php?det=9977
_version_ 1804890531489120256