Using Accounting Ratios in Predicting Financial Distress: An Empirical Investigation in the Vietnam Stock Market

2015 ◽  
pp. 41-49
Author(s):  
Vinh Vo Xuan
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Financial Ratios ◽  
Ho Chi Minh City ◽  
Data Set ◽  
The Past ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Practical Research ◽  
Accounting Ratios

Financial distress prediction is an important and practical research topic for many stakeholders and has attracted extensive studies over the past decades. This paper investigates the challenging issue of financial distress in Vietnam by distinguishing “healthy” companies from “financially distressed” companies using a data sample of firms listed on the Ho Chi Minh City Stock Exchange. Employing the logistic regression model to predict financial distress with a unique data set, we characterize the determinants of financial distress in terms of firm accounting and financial ratios over the period from 2007 to 2012. The results indicate that financial ratios can be employed as an early warning of financial distress as financial ratios are significantly correlated with the probability of firm financial distress.

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

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.

Predicting Financial Distress for Listed MENA Firms

2019 ◽  
Vol 9 (2) ◽  
pp. 51
Author(s):  
Osama El-ansari ◽  
Lina Bassam
Keyword(s):  
Prediction Model ◽  
Financial Distress ◽  
Historical Data ◽  
Bankruptcy Prediction ◽  
Mena Region ◽  
Macroeconomic Variables ◽  
Listed Firms ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Accounting Ratios

Financial distress prediction gives an early warning about defaulting risk for firms; thus, it is a real concern of the entire economy.Purpose: To examine the determinants of financial distress across MENA region countries, by using definitions of distress and historical data from active listed firms in the region.Methodology: logistic regression is run on firm-specific variables and a set of macroeconomic variables to develop a prediction model to examine the effect of these predictors on the probability of financial distress.Findings: it has been found that after controlling for country effects, accounting ratios, firm size, and macroeconomic variables provided an acceptable prediction model for listed MENA firms.Originality: a gap exists in the literature of developing countries’ prediction for financial distress. Many studies addressed bankruptcy prediction for a certain country in the region, however, a limited number of researches approached predicting distressed models for listed firms in the region.

scholarly journals A Novel Soft Ensemble Model for Financial Distress Prediction with Different Sample Sizes

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Xu ◽  
Hongyong Fu ◽  
Yuchen Pan
Keyword(s):  
Financial Distress ◽  
Soft Set ◽  
Optimal Decision ◽  
Ensemble Model ◽  
Sample Sizes ◽  
Data Set ◽  
Financial Distress Prediction ◽  
Predicting Performance ◽  
Distress Prediction ◽  
Internet Searches

This work presents a novel soft ensemble model (ANSEM) for financial distress prediction with different sample sizes. It integrates qualitative classifiers (expert system method, ES) and quantitative classifiers (convolutional neural network, CNN) based on the uni-int decision making method of soft set theory (UI). We introduce internet searches indices as new variables for financial distress prediction. By constructing a soft set representation of each classifier and then using the optimal decision on soft sets to identify the financial status of firms, ANSEM inherits advantages of ES, CNN, and UI. Empirical experiments with the real data set of Chinese listed firms demonstrate that the proposed ANSEM has superior predicting performance for financial distress on accuracy and stability with different sample sizes. Further discussions also show that internet searches indices can offer additional information to improve predicting performance.

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