scholarly journals Predicting financial distress in the Indian textile sector

2021 ◽  
Vol 72 (05) ◽  
pp. 503-508
Author(s):  
RASHMI RUPESH SONI ◽  
IQBAL THONSE HAWALDAR ◽  
ANJU SUNNY VASWANI ◽  
CRISTI SPULBAR ◽  
RAMONA BIRAU ◽  
...  
Keyword(s):  
Financial Distress ◽  
Large Scale ◽  
Research Study ◽  
Listed Companies ◽  
Z Score ◽  
Split Method ◽  
Median Split ◽  
The Mean ◽  
Textile Sector

The purpose of this paper is to predict the financial distress of companies of the Indian textile sector using Altman Z score. The analysis conducted on 161 listed textile companies in India for a period of 10 years from 2009 to 2018. All the listed companies are categorized into large, medium, and small using the median split method based on the size of total assets. Kruskal Wallis test is applied to test whether the mean z-score is different for each category of companies. This research study shows that majority of the companies in the Indian textile sector are facing financial distress. Further, it shows that the z score of small, medium, and large-scale textile companies in India is significantly different.

scholarly journals Financial distress prediction of listed companies – empirical evidence on the Vietnamese stock market

2020 ◽  
Vol 17 (2) ◽  
pp. 377-388
Author(s):  
Tran Quoc Thinh ◽  
Dang Anh Tuan ◽  
Nguyen Thanh Huy ◽  
Tran Ngoc Anh Thu
Keyword(s):  
Stock Market ◽  
Financial Distress ◽  
Financial Statements ◽  
Listed Companies ◽  
Research Model ◽  
Z Score ◽  
Economic Decisions ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
High Level

Financial distress is a matter of concern in the recent period as Vietnam gradually enters global markets. This paper aims to examine the factors of Altman Z-score to detect the financial distress of Vietnamese listed companies. The authors use a sample of 30 delisted companies due to financial problems and 30 listed companies on the Vietnamese stock market from 2015 to 2018. They employ Independence Samples T-test to test the research model. It is found that there are significant differences in the factors of Altman Z-score between the group of listed companies and the group of delisted companies. Further analyses using subsamples of delisted companies show that the factors of Altman Z-score are also statistically different between companies with a low level of financial distress and those with a high level of financial distress. Based on the results, there are some suggestions to assist practitioners and the State Securities Commission in detecting, preventing, and strictly controlling financially distressed businesses. These results also enable users of financial statements to make more rational economic decisions accordingly.

Financial Analysis During the Pandemic and New Consumption

2021 ◽  
pp. 112-125
Author(s):  
Liliane Cristina Segura ◽  
Murillo Jose Torelli Pinto
Keyword(s):  
Financial Distress ◽  
Oil And Gas ◽  
Financial Analysis ◽  
Economic Consequences ◽  
Z Score ◽  
The Social ◽  
The World ◽  
Distressed Firms ◽  
The Mean ◽  
Oil And Gas Sector

The economic consequences of the COVID-19 pandemic are not yet known. It is, however, observed that the consumptions in the world have changed dramatically in 2020, and it will keep changing as the pandemic evolves. It is already observed that in consumer confidence, there is a change in the use of energy and petroleum. People are not moving a lot during this pandemic, and they also discovered that they might stay this way in some occasions. It is affecting the petrol sector, maybe one of the most affected in the pandemic, because of the social isolation. This chapter analyzed 44 companies from the oil and gas sector around the world in relation to their financial distress. The Altman´s Z-score was the methodology used, and the mean of the sector was compared with the five most distressed firms and the five least distressed. It is possible to observe that the sector suffered with this pandemic, and most of the companies are already in financial distress.

scholarly journals Indicators of financial distress - An empirical study of Indian Textile sector

2016 ◽  
Vol 12 (2) ◽  
pp. 101-110
Author(s):  
Jyoti Jaydeep Nair ◽  
J K Sachdeva
Keyword(s):  
Logistic Regression ◽  
Empirical Study ◽  
Key Words ◽  
Financial Distress ◽  
Listed Companies ◽  
Financial Ratios ◽  
Remedial Measures ◽  
Technological Environment ◽  
Contractual Obligations ◽  
Textile Sector

 AbstractBusinesses across the globe faces challenges to ensure stability, growth and sustainability. Companies have to deal with changes in economic, social, cultural, political and technological environment. Companies failing to do may face financial distress causing default in payment of contractual obligations and erosion of shareholders wealth. In a business scenario where the stakeholders are many viz. shareholders, lenders, employees, government and society at large, protection of the interests of the stakeholders assume prime importance. Company’s management are expected to identify signals that indicate distress and take remedial measures. This paper attempts to identify distress signals in textile sector in India. Textile sector is one of the largest sector in India. However one third of companies in this sector have reported losses for the previous year. This study aims to examine the factors that can differentiate a distressed company from a non- distressed company so that the factors signifying distress can be studied. Listed companies in textile sector incurring continuous losses for three years were selected for the study. Financial ratios were used as variables. Logistic regression was applied to identify the most important factors indicating distress. It was observed that ratios measuring profitability and efficiency were significant in predicting distress. Key words:  Financial distress, distress signals, textile sector, continuous losses, financial ratios

Corporate governance and financial distress: Asian emerging market perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Noman Younas ◽  
Shahab UdDin ◽  
Tahira Awan ◽  
Muhammad Yar Khan
Keyword(s):  
Corporate Governance ◽  
Financial Distress ◽  
Positive Impact ◽  
Emerging Market ◽  
Random Effect Model ◽  
Listed Companies ◽  
Z Score ◽  
Content Type ◽  
Governance Practices ◽  
The Impact

Purpose The purpose of this paper is to examine the impact of corporate governance index (PAKCGI) on firm financial distress for a sample of 152 non-financial firms listed at Pakistan Stock Exchange (PSX) over the period from 2003 to 2017. Design/methodology/approach To examine the impact of PAKCGI on financial distress (Altman Z-Score), random effect model is applied. The PAKCGI is a self-constructed index based on the five important factors of corporate governance practices, i.e. board of directors, audit committees, right of shareholders, disclosures and risk management. The binary coding approach is adopted for the construction of PAKCGI. Altman Z-Score model is used as a proxy for financial distress indicator. The absolute value of Altman Z-score has been taken as financial distress indicator. Findings The outcomes of the study indicate a positive impact of PAKCGI on risk of firms’ financial distress. The positive coefficient of PAKCGI implies that the good corporate practices work as catalyst to reduce risk of financial distress in Pakistan. A significant negative impact of block holders on financial distress suggests that the concentrated block ownership take monopolistic decision to protect their interests. It has also been observed that significant positive impact of institutional ownership on financial distress exists in the Pakistani listed firms. Furthermore, this study also reveals that significant negative association between board size, CEO duality and financial distress indicator. Research limitations/implications The findings may encourage the Pakistani listed companies to follow and implement good corporate governance practices, which would lead to increase the confidence of investors, regulators and stakeholders. Originality/value The current study extends the corporate governance literature by examining the relationship between the corporate governance attributes and the financial distress status of Pakistani listed companies. From the academic perspective, this paper adds to the knowledge concerning the association between corporate governance practices and risk of financial distress in emerging markets.

scholarly journals Financial Distress and Forewarning Bankruptcy: An Empirical Analysis of Textile Sector in Pakistan

2020 ◽  
Vol 3 (3) ◽  
pp. 493-506
Author(s):  
Nosheen Rasool ◽  
Muhammad Sohail ◽  
Muhammad Usman ◽  
Muhammad Mubashir Hussain
Keyword(s):  
Financial Distress ◽  
Z Score ◽  
Firm Level ◽  
Statistical Measures ◽  
Group I ◽  
Regulatory Bodies ◽  
Group Ii ◽  
Near Future ◽  
Textile Sector ◽  
Bankruptcy Rate

This study aims to measure the financial distress and forewarn bankruptcy in Textile Sector of Pakistan using popular statistical measures i.e., Z-Score, O-Score, Probit and D-Score. First, applicable financial ratios (profitability, liquidity, leverage, market ratios) and scores (Z-Score, O-Score, Probit and D-Score) of all 77 textile companies were calculated then estimated scores were compared with cut-off point of respective model. Based on findings, models are categorized in two groups: (a) Group-I (Z-Score and O-Score), (b) Group-II (Probit  and D-Score). Results indicate that some of the textile firms are about to face financial distress in near future, which could ultimately lead those firms to bankruptcy. The findings of Group-I indicate that about 43% - 44% companies in the textile sector are in the phase of financial distress; whereas the results of Group-II reveal that about 8% - 16% companies are in financial distress phase. Thus, we could draw two conclusions: (1) the two models (Z-Score and O-Score) in Group-I were found to be robust for assessing financial distress and (2) the two models  (Probit  and D-Score) in Group-II were found to be less rigorous in forecasting financial distress. The previous studies attempted to compare the prediction accuracy of various models by examining the data of both financially distress firms and financially stable firms. But this study is aimed to foretell bankruptcy using comprehensive models (Z-Score, O-Score, Probit and D-Score), to compare the consistency of results across all four models of the study and to categorize financially stable and financially distress companies under each model. The findings of the study are expected to be beneficial at coutry level, firm level and indiviual level such as government and regulatory bodies of Pakistan can intervene to avert bankruptcy rate, management can devise appropriate strategies  to reduce financial distress. Moreover. investors can safeguard their investment by making right decissions based on the findings.

Financial Statement Ratio Analysis to Predict Bankruptcy nn Company Registered in BEI - Jakarta (Altman Z-Score Method and Zmijewski)

2020 ◽  
Vol 3 (3) ◽  
pp. 122
Author(s):  
Andi Silvan
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Good Condition ◽  
Financial Statement ◽  
Depth Information ◽  
Z Score ◽  
Industrial Manufacturing ◽  
Financial Difficulties ◽  
A Value ◽  
Score Method

AbstractThis study takes the topic of predicting corporate bankruptcies. This research dqlam use traditional methods Altman Z-Score and Zmijewski. The purpose of this study was to obtain in-depth information about predicting bankruptcy of companies that are not necessarily directly to bankruptcy, but there is financial distress.Based on the results of research conducted on the four (4) non industrial manufacturing company listed on the Indonesia Stock Exchange (BEI). Obtaining the value z-score represents the average company are in good condition, which means no financial distress. Acquisition value of x-score has a value of less than 0 (zero) which means that the company is in good condition and is predicted not experiencing financial difficulties. This study led to the conclusion that the Altman Z-Score and Zmijewski method can be used to predict corporate bankruptcy. Keywords: Financial Ratios, Bankruptcy, Company.

Planning and Conducting a Large-Scale Mixed Methods Research Study: Research Team Experiences and Recommendations

2018 ◽  
Vol 10 (1) ◽  
pp. 283-295
Author(s):  
Katrin Niglas ◽  
◽  
Meril Ümarik ◽  
Maarja Tinn ◽  
Ivor Goodson ◽  
...  
Keyword(s):  
Mixed Methods ◽  
Mixed Methods Research ◽  
Large Scale ◽  
Research Team ◽  
Research Study ◽  
Study Research

ANALISIS INDEX ALTMAN Z-SCORE DALAM MEMPREDIKSI KONDISI FINANCIAL DISTRESS (Studi Empiris Perusahaan Properti dan Real Estate Sub Sektor Perhotelan Tahun 2012-2017)

Kinerja ◽  
2018 ◽  
Vol 1 (01) ◽  
pp. 48-57
Author(s):  
Maryam Dunggio ◽  
Nur Aufa Mufidah
Keyword(s):  
Real Estate ◽  
Financial Distress ◽  
Z Score ◽  
Grey Area

Penelitian ini bertujuan untuk menganalisis Index Altman dalam memprediksi kondisi financialdistress pada perusahaan properti dan real estate tahun 2102-2017. Metode penelitian yangdigunakan adalah Altman Z-score. Teknik pengumpulan data yang dilakukan secara sekunderyaitu data yang diperoleh dari laporan keuangan tahunan yang dipublikasikan oleh setiapperusahaan. Hasil yang diperoleh dalam penelitian ini menunjukkan bahwa pada tahun 2012terdapat 2 perusahaan yang mengalami zona aman dan 8 perusahaan mengalami financial distress,tahun 2013 terdapat 2 perusahaan mengalami zona aman dan 1 perusahaan grey area dan 7perusahaan financial distress, tahun 2014 terdapat 2 perusahaan zona aman dan 8 perusahaanfinancial distress, tahun 2015 terdapat 2 perusahaan mengalami zona aman dan 8 perusahaanfinancial distress, tahun 2016 terdapat 2 perusahaan zona aman, 1 grey area dan 7 financialdistress, tahun 2017 terdapat 2 zona grey area dan 8 mengalami financial distress

Does Financial Distress Using Altman Z-Score Capital Affect Stock Price?

2020 ◽  
Vol 24 (02) ◽  
pp. 3127-3134
Author(s):  
Sakina Ichsani ◽  
Vincentia Wahju Widajatun ◽  
Dede Hertina
Keyword(s):  
Financial Distress ◽  
Stock Price ◽  
Z Score
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