A study of financial distress prediction on nonfinancial sector in Pakistan

Financial distress has become an eye-catching issue among the researchers. As the number of companies filling for bankruptcies in Pakistan are increasing due to the uncertainty of the economy. A specific model is needed in helping companies to identify significant predictors of financial distress. T...

Full description

Saved in:

Bibliographic Details
Main Author:	Hassan, Ehsan Ul
Format:	Thesis
Language:	eng eng eng
Published:	2021
Subjects:	HG Finance HJ Public Finance
Online Access:	https://etd.uum.edu.my/10299/1/Permission%20to%20deposit_s900671.pdf https://etd.uum.edu.my/10299/2/s900671_01.pdf https://etd.uum.edu.my/10299/3/s900671_02.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uum-etd.10299
record_format	uketd_dc
spelling	my-uum-etd.102992023-02-09T02:16:28Z A study of financial distress prediction on nonfinancial sector in Pakistan 2021 Hassan, Ehsan Ul Zainuddin, Zaemah School of Economics, Finance & Banking School of Economics, Finance & Banking HG Finance HJ Public Finance Financial distress has become an eye-catching issue among the researchers. As the number of companies filling for bankruptcies in Pakistan are increasing due to the uncertainty of the economy. A specific model is needed in helping companies to identify significant predictors of financial distress. This study intends to analyze the financial distress scenario of non-financial companies listed in the Pakistan Stock Exchange for the period of 2005 to 2016. Various financial ratios were identified which can predict financial distress in the Pakistani companies. In this study, the Principal Component Analysis (PCA) and Logit were utilized in predicting financial distress using individual financial ratio and financial ratios indices. A total of 23 financial ratios was divided into six main categories which are profitability, liquidity, leverage, asset efficiency, size and growth. Results indicate that the financial ratio indices are better in predicting financial distress as compared to the individual ratios with precision models. Also, the accuracy rates prior to bankruptcy are 93.90 percent for the first year followed by 81.93 percent, 78.67 percent, 75.71 percent and 73.71 percent for the second, third, fourth and fifth year, respectively. The accuracy rates of individual financial ratios are generally less than the financial ratio indices in all five years period. The results also indicate that all financial ratio categories are significantly predict the financial distress of a company. The model developed from this study is useful for policymakers at securities and exchange commission, State Bank of Pakistan, commercial banks, and investors. Being an indigenous model, it can capture more accounting information and has practical application in predicting financial distress in Pakistan. 2021 Thesis https://etd.uum.edu.my/10299/ https://etd.uum.edu.my/10299/1/Permission%20to%20deposit_s900671.pdf text eng staffonly https://etd.uum.edu.my/10299/2/s900671_01.pdf text eng staffonly https://etd.uum.edu.my/10299/3/s900671_02.pdf text eng staffonly other doctoral Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng eng
advisor	Zainuddin, Zaemah
topic	HG Finance HJ Public Finance
spellingShingle	HG Finance HJ Public Finance Hassan, Ehsan Ul A study of financial distress prediction on nonfinancial sector in Pakistan
description	Financial distress has become an eye-catching issue among the researchers. As the number of companies filling for bankruptcies in Pakistan are increasing due to the uncertainty of the economy. A specific model is needed in helping companies to identify significant predictors of financial distress. This study intends to analyze the financial distress scenario of non-financial companies listed in the Pakistan Stock Exchange for the period of 2005 to 2016. Various financial ratios were identified which can predict financial distress in the Pakistani companies. In this study, the Principal Component Analysis (PCA) and Logit were utilized in predicting financial distress using individual financial ratio and financial ratios indices. A total of 23 financial ratios was divided into six main categories which are profitability, liquidity, leverage, asset efficiency, size and growth. Results indicate that the financial ratio indices are better in predicting financial distress as compared to the individual ratios with precision models. Also, the accuracy rates prior to bankruptcy are 93.90 percent for the first year followed by 81.93 percent, 78.67 percent, 75.71 percent and 73.71 percent for the second, third, fourth and fifth year, respectively. The accuracy rates of individual financial ratios are generally less than the financial ratio indices in all five years period. The results also indicate that all financial ratio categories are significantly predict the financial distress of a company. The model developed from this study is useful for policymakers at securities and exchange commission, State Bank of Pakistan, commercial banks, and investors. Being an indigenous model, it can capture more accounting information and has practical application in predicting financial distress in Pakistan.
format	Thesis
qualification_name	other
qualification_level	Doctorate
author	Hassan, Ehsan Ul
author_facet	Hassan, Ehsan Ul
author_sort	Hassan, Ehsan Ul
title	A study of financial distress prediction on nonfinancial sector in Pakistan
title_short	A study of financial distress prediction on nonfinancial sector in Pakistan
title_full	A study of financial distress prediction on nonfinancial sector in Pakistan
title_fullStr	A study of financial distress prediction on nonfinancial sector in Pakistan
title_full_unstemmed	A study of financial distress prediction on nonfinancial sector in Pakistan
title_sort	study of financial distress prediction on nonfinancial sector in pakistan
granting_institution	Universiti Utara Malaysia
granting_department	School of Economics, Finance & Banking
publishDate	2021
url	https://etd.uum.edu.my/10299/1/Permission%20to%20deposit_s900671.pdf https://etd.uum.edu.my/10299/2/s900671_01.pdf https://etd.uum.edu.my/10299/3/s900671_02.pdf
_version_	1776103785240723456

A study of financial distress prediction on nonfinancial sector in Pakistan

Similar Items