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

scholarly journals THE DETERMINANTS OF HEDGING AND FIRM VALUE: An Empirical Study in Mining Companies Listed on Indonesia Stock Exchange

2018 ◽  
pp. 104
Author(s):  
Garnis Irawanti
Keyword(s):  
Logistic Regression ◽  
Empirical Study ◽  
Financial Distress ◽  
Firm Value ◽  
Stock Exchange ◽  
T Test ◽  
Corporate Hedging ◽  
Determinant Factors ◽  
Mining Companies ◽  
Derivative Instruments

<p class="Keywords">This study aims to determine the determinant factors in the company's hedging decisions and to determine whether the activities of corporate hedging decisions through derivative instruments provide increased value for the company. The sample consisted of 33 mining companies listed on Indonesia Stock Exchange during 2011-2015 period. The method used in this study is logistic regression and independent sample t-test. The result of logistic regression by using variable of financial distress, underinvestment cost, and size showed a positive correlation to corporate hedging decision. Meanwhile, by using an independent sample t-test found that the company's hedging decisions significantly affect the value of firms and the companies with hedging decision activity through derivative instruments have more superior value than companies by using natural hedging decisions.</p><p> </p>

An Empirical Study on Identification of False Accounting Information of Listed Companies -Based on the Comparison between Logistic Regression Model and Multiple Eigenvalues Detection Model

2011 ◽  
Vol 268-270 ◽  
pp. 753-758
Author(s):  
Ai Hao Guan
Keyword(s):  
Logistic Regression ◽  
Empirical Study ◽  
Regression Model ◽  
Empirical Analysis ◽  
Logistic Regression Model ◽  
Accounting Information ◽  
Listed Companies ◽  
Detection Model ◽  
Multiple Eigenvalues

In this study, multiple eigenvalues detection model has been put forward and empirical analysis and comparison between logistic regression model and multiple eigenvalues detection model has been conducted in the same sample on the basis of overall data. The results have showed the characteristics of accuracy, stability or extensive adaptability of multiple eigenvalues detection model in detecting false accounting information.

scholarly journals Pengaruh Rasio Keuangan Terhadap Financial Distress Pada Perusahaan Manufaktur Yang Terdaftar Di Bursa Efek Indonesia Periode 2009-2013

2018 ◽  
Vol 21 (1) ◽  
pp. 43
Author(s):  
Steven Sean, Viriany
Keyword(s):  
Logistic Regression ◽  
Data Analysis ◽  
Financial Distress ◽  
Sampling Method ◽  
Financial Ratios ◽  
Manufacturing Companies ◽  
Analysis Techniques ◽  
Independent Variables ◽  
One Year ◽  
Manufacture Company

The purpose of this study is to determine the financial ratios partial effect on financial distress in manufacturing companies prior to the period of financial distress (t-n). Financial distress is defined as a late stage of corporate decline that precedes more cataclysmic events such as bankruptcy or liquidation.  Analysis of  financial ratios  is performed to  determine  the ratio that affect the probability of  financial distress. The method used is the  purposive  sampling  method.  Data analysis techniques logistic regression.  Hypothesis  testing  is  done  in  three  periods,  that  is  the period of  one  year  before the  financial distress  (t-1),  a  two-year period  before  the  financial  distress  (t-2) and a  three-year period  before the financial distress (t-3). Results indicate that  the independent variables  have a partial effect on manufacture company. The period  t-1, ratio TL/TA and  NI/TA  affect  financial  distress.  The  period  t-2,  ratio  NI/EQ affect financial  distress.  The period  t-3, ratio TL/TA and NI/TA affect financial distress.

scholarly journals MODEL PREDIKSI KESULITAN KEUANGAN DENGAN RASIO KEUANGAN (Studi kasus Pada Perusahaan Manufaktur di Indonesia yang telah terdaftar selama 5 Tahun di BEI 2008-2012)

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Diaz Lunardi Santoso
Keyword(s):  
Logistic Regression ◽  
Financial Distress ◽  
Manufacturing Industry ◽  
Sampling Method ◽  
Stock Exchange ◽  
Market Value ◽  
Financial Ratios ◽  
Predicting Model ◽  
Book Value ◽  
Purposive Sampling

This research aimed to figure financial distress model and to determined wihich financial ratios can predict financial distress for 1 year; 2 years; and 3 years before. This research was using samples of manufacturing industry thst listed on The Indonesian Stock Exchange in 2008-2012. Based on purposive sampling method, the research samples total are 160 manufactured companies. To figure the model, this research used logistic regression. This research indicated that financial ratios likes leverage, profitability, activity, RE to Total Assets, Market value of Equity to Book Value of Debt can predict financial distress 1 year; 2 years; and 3 years before. These financial ratios can predict above 64% of financial distress for 1 year; 2 years, and 3 years before, while around 36% were influeced by others factors. The predicting model for 1 year have 96,3% clasification accuracy ,while 2 years model have 96,3% clasification accuracy and 3 years model  have 92,5% clasification accuracy

scholarly journals Analisis Rasio-Rasio Dan Ukuran Keuangan, Prediksi Financial Distress, Dan Reaksi Investor

2014 ◽  
Vol 5 (1) ◽  
pp. 26
Author(s):  
Mekani Vestari ◽  
Dessy Noor Farida
Keyword(s):  
Logistic Regression ◽  
Firm Size ◽  
Financial Distress ◽  
Financial Ratios ◽  
Business Risk ◽  
Independent Variables ◽  
Long Period ◽  
Investor Reaction

AbstractThe purpose of this paper is to investigate financial ratios and financial measurements that can predict financial distress. This study also examined investor reaction. To proved the effect for the long period this study not only examined the effect of independent variables per year to the prediction of financial distress, but also examined the average for five years.Using logistic regression the results showed that there are four financial ratios that can predict financial distress. Business risk and firm size is not proven to predict financial distress. Using Kruskall-Wallis test this study also proved that investors can predict financial distress.

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