scholarly journals Analysis Financial Distress Prediction With Model Altman Z-Score, Zmijewski, And Grover In The Sub Sector Retail Listed On The Indonesian Stock Exchange (Idx) 2014-2018 Period

2020 ◽  
Author(s):  
Megalasmi Helastica ◽  
Santi Paramita
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Z Score ◽  
Financial Distress Prediction ◽  
Distress Prediction

FINANCIAL DISTRESS PREDICTION AND SEEING THE INFLUENCE OF THE ALTMAN RATIO ON COMPANY FINANCIAL DISTRESS

2018 ◽  
Vol 23 (3) ◽  
pp. 236-243
Author(s):  
Hadhi Dharmaputra Juliyan ◽  
Bertilia Lina Kusrina
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Bankruptcy Prediction ◽  
Z Score ◽  
Large Role ◽  
Corporate Bankruptcy ◽  
Mining Companies ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Score Model

This research aims to determine the level of the bankruptcy of the company and to see if the Altman ratio can predict the condition of corporate bankruptcy in mining companies on the Indonesia Stock Exchange because mining companies have a large role in the Indonesian economy. This study uses the Altman Z-Score model analysis to see how much the company's bankruptcy prediction and uses logistic regression to see how much the influence of the Altman ratio in predicting corporate bankruptcy. Keywords: financial distress, the Altman z–score, bankruptcy prediction

scholarly journals Post COVID-19 Financial Distress: Prediction of Corporate Defaults at Pakistan Stock Exchange (PSX)

2021 ◽  
Vol 5 (1) ◽  
pp. 286-400
Author(s):  
Khalid Mumtaz Khan ◽  
Naeem Ullah
Keyword(s):  
Financial Markets ◽  
Financial Distress ◽  
Stock Exchange ◽  
Economic Recession ◽  
Financial Statements ◽  
Future Research ◽  
Z Score ◽  
Corporate Default ◽  
Financial Distress Prediction ◽  
Distress Prediction

COVID-19 has slowed down the global economic activity which is expected to turn into an economic recession, where firms are expected to experience financial distress leading to corporate defaults. Predicting such defaults is important to safeguard stakeholders’ interest in financial markets. This study has estimated extent of financial distress among firms listed at PSX and constituting KSE 30 index, by using Altman’s Z-Score. The score has been computed using financial statements of 2019-20, and on proforma financial statements for 2020-21 2019-20, considering these financial years as pre and post COVID-19 closing dates respectively for financial statements. The proforma financial statements have been drawn for financial 2020-21 2019-20 using established accounting conventions of prudence, conservatism, substance over form, ad foreseeable future. The results of Z-score in pre and post COVID-19 have been compared to assess the change in degree of financial distress among the selected firms. A significant increase in the degree of financial distress has been observed, which may lead to an increased number corporate default for the firms listed at PSX. Suggestion have been made to the firms and corporate regulators to curtail the rate of corporate defaults, along with limitation of this study and areas of future research.

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.

scholarly journals PREDIKSI FINANCIAL DISTRESS BERDASARKAN VARIABEL-VARIABEL KEUANGAN PADA INDUSTRI TEKSTIL

2018 ◽  
Vol 4 (1) ◽  
pp. 54-60
Author(s):  
REFNI SUKMADEWI
Keyword(s):  
Prediction Model ◽  
Financial Distress ◽  
Z Score ◽  
Financial Distress Prediction ◽  
Distress Prediction

Penelitian ini bertujuan untuk memberikan bukti empiris mengenai faktor-faktor yang mempengaruhi financial distress perusahaan. Penelitian ini menguji peran rasio keuangan dalam memprediksi terjadinya financial distress pada perusahaan industri tekstil yang tercatat di Bursa Efek Jakarta. Analisis diskriminan digunakan untuk menguji kemampuan rasio keuangan untuk memprediksi financial distress dan membangun model prediksi distress financial dengan menggunakan prosedur stepwise. Variabel indikator adalah rasio keuangan. Hasil penelitian menunjukkan bahwa ada empat rasio yang berbeda dan secara signifikan mempengaruhi model prediksi distress keuangan. Rasio tersebut adalah Rasio Aktiva Lancar / Kewajiban Lancar, Modal Kerja / Jumlah Aktiva, Pendapatan Bersih / Total Aktiva, Kewajiban / Total Aset. Hasil klasifikasi berdasarkan nilai cut-off-Z Score mampu memprediksi kesulitan finansial perusahaan pada industri tekstil dengan tingkat akurasi 0f 100%. Tingkat akurasi model menunjukkan bahwa model diskriminan akurat dalam mengukur tekanan keuangan pada perusahaan industri tekstil. Kata kunci: Financial distress, Prediction Model, Rasio Keuangan

scholarly journals Does audit report information improve financial distress prediction over Altman's traditional Z ‐Score model?

2019 ◽  
Vol 31 (1) ◽  
pp. 65-97
Author(s):  
Nora Muñoz‐Izquierdo ◽  
Erkki K. Laitinen ◽  
María‐del‐Mar Camacho‐Miñano ◽  
David Pascual‐Ezama
Keyword(s):  
Financial Distress ◽  
Z Score ◽  
Audit Report ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Score Model

PREDICTION OF FINANCIAL DISTRESS BY MULTIVARIATE STATISTICAL ANALYSIS: THE CASE OF FIRMS TAKEN INTO THE SURVEILLANCE MARKET IN THE ISTANBUL STOCK EXCHANGE

2006 ◽  
Vol 09 (01) ◽  
pp. 133-150
Author(s):  
SERPIL CANBAŞ ◽  
YILDIRIM B. ÖNAL ◽  
HATICE G. DÜZAKIN ◽  
SÜLEYMAN B. KILIÇ
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Istanbul Stock Exchange ◽  
Study Support ◽  
One Year ◽  
Warning Model

The purpose of this paper is to investigate whether or not firms that are taken into the surveillance market in Istanbul Stock Exchange are experiencing financial distress. The surveillance firms present irregular behaviors and have difficulty complying with current regulation. It can be expected that the basic reason behind these irregular behaviors is financial distress. Results of the study support this expectation and show that it is possible to predict financial distress one year in advance. Principal component analysis and discriminant analysis are combined in order to estimate an integrated early warning model for financial distress prediction.

scholarly journals Prediction modelling the financial distress using corporate governance indicators in Indonesia

2021 ◽  
Vol 10 (1) ◽  
pp. 18
Author(s):  
Irdha Yusra ◽  
Novyandri Taufik Bahtera
Keyword(s):  
Corporate Governance ◽  
Financial Distress ◽  
Audit Committee ◽  
Prediction Models ◽  
Stock Exchange ◽  
Governance Indicators ◽  
Financial Distress Prediction ◽  
Corporate Governance Mechanisms ◽  
Distress Prediction ◽  
The Board Of Directors

   We examine whether the indicators of company governance procedures are associated with the risk of bankruptcy or financial distress in Indonesia. An empirical study we conducted using a causal model of corporate governance indicators in forecasting financial distress. The data used in this study is panel data. Using samples from assembling companies registered on the Indonesia Stock Exchange during the 2017-2019 period, we obtained as many as 105 observations selected by the purposive sampling method. Our results indicate that financial distress can be predicted by corporate governance mechanisms, although statistically it is only proven by a few indicators in our study. Specifically, our results demonstrate that institutional ownership, managerial ownership, and independent commissioners do not affect financial distress. Furthermore, our study shows evidence of a significant influence between the size of the board of directors and audit committee on financial distress. Our interpretation is that research on financial distress prediction models using corporate governance indicators has provided empirical evidence. 

PENGARUH RASIO KEUANGAN DALAM MEMPREDIKSI POTENSI KEBANGKRUTAN PADA PERUSAHAAN CUSTOMER GOODS YANG TERDAFTAR DI BURSA EFEK INDONESIA

2015 ◽  
pp. 70-82
Author(s):  
Lamria Sagala
Keyword(s):  
Capital Market ◽  
Financial Distress ◽  
Sampling Method ◽  
Stock Exchange ◽  
Secondary Data ◽  
Return On Assets ◽  
Current Ratio ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
A Company

This study aims to identify and analyze the influence of Current Ratio, Debt To Assets Ratio, Return on Assets, and Earning Per Share partially or simultaneously to the prediction of financial distress on customer goods companies listed in Indonesia Stock Exchange.The population in this study is a company customer goods listed in Indonesia Stock Exchange in 2010-2012. Of the 36 listed companies, 32 companies selected samples using purposive sampling method. The data used in this research is secondary data, to gather the information needed from www.idx.co.id and Indonesian Capital Market Directory (ICMD). This study analyzed using logistic regression analysis. The conclusion that can be drawn from this study is that the Current Ratio, Debt To Assets Ratio, Return on Assets, and earning per share has an influence on the prediction of financial distress. While only partially Debt To Assets Ratio which has a significant influence on the prediction of financial distress while the three other independentvariables have no effect on financial distress prediction.

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