Factors Contributing to Financially Distressed Companies in Malaysia

By using a total of 52 distressed and non-distressed listed companies during the period 1990 to 2000, debt to total assets was found to be significant in predicting distressed companies for the multiple discriminant analysis (MDA), logit and hazard models. It appears that the higher the debt, the higher is the probability of defaulting among the financially distressed companies. MDA identified net income growth as another predictor whereas the logit and hazard model found that return on asset (ROA) to be an important predictor. Nevertheless, the sign of the ROA coefficient differred between the two models. Furthermore, company size was also identified as a contributing factor to financially distressed companies for the hazard model.

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Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis

Equilibrium ◽

10.24136/eq.2018.028 ◽

2018 ◽

Vol 13 (3) ◽

pp. 569-593 ◽

Cited By ~ 7

Author(s):

Tomas Kliestik ◽

Jaromir Vrbka ◽

Zuzana Rowland

Keyword(s):

Discriminant Analysis ◽

Prediction Models ◽

Value Added ◽

Bankruptcy Prediction ◽

Net Income ◽

Multiple Discriminant Analysis ◽

Financial Situation ◽

The Future ◽

A Company ◽

Visegrad Group

Research background: The problem of bankruptcy prediction models has been a current issue for decades, especially in the era of strong competition in markets and a constantly growing number of crises. If a company wants to prosper and compete successfully in a market environment, it should carry out a regular financial analysis of its activities, evaluate successes and failures, and use the results to make strategic decisions about the future development of the business. Purpose of the article: The main aim of the paper is to develop a model to reveal the un-healthy development of the enterprises in V4 countries, which is done by the multiple discriminant analysis. Methods: To conduct the research, we use the Amadeus database providing necessary financial and statistical data of almost 450,000 enterprises, covering the year 2015 and 2016, operating in the countries of the Visegrad group. Realizing the multiple discriminant analysis, the most significant predictor and the best discriminants of the corporate prosperity are identified, as well as the prediction models for both individual V4 countries and complex Visegrad model. Findings & Value added: The results of the research reveal that the prediction models use the combination of same financial ratios to predict the future financial development of a company. However, the most significant predictors are current assets to current liabilities ratio, net income to total assets ratio, ratio of non-current liabilities and current liabilities to total assets, cash and cash equivalents to total assets ratio and return of equity. All developed models have more than 80 % classification ability, which indicates that models are formed in accordance with the economic and financial situation of the V4 countries. The research results are important for companies themselves, but also for their business partners, suppliers and creditors to eliminate financial and other corporate risks related to the un-healthy or unfavorable financial situation of the company.

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Bankruptcy risk assessment models as a tool for diagnosing the financial condition of capital market companies

Zeszyty Teoretyczne Rachunkowości ◽

10.5604/01.3001.0014.5550 ◽

2020 ◽

Vol 110 (166) ◽

pp. 31-76

Author(s):

Paweł Kopczyński

Keyword(s):

Discriminant Analysis ◽

Economic Crisis ◽

Listed Companies ◽

The State ◽

Multiple Discriminant Analysis ◽

Financial Condition ◽

Large Populations ◽

State Of The Economy ◽

Conducting Research ◽

Analysis Models

Purpose: The main purpose of this article is to examine the usefulness of multiple discriminant analysis models in assessing the financial condition of individual enterprises, the state of the economy, and its sectors. The study assessed the financial situation of Polish listed companies at the end of the global economic crisis that started in 2007. Methodology/approach: Seven discriminant functions were used to assess the actual changes in the finan-cial situation of listed companies during the period of 2009–2014. In order to diagnose the end of the crisis, the period in which countries emerged from the global economic crisis was taken into account. The study covered 175 Polish companies listed on the regulated market operated by the Warsaw Stock Exchange, whose standalone financial statements were used. These companies belong to 22 sectors of the economy. It was assumed that the number of companies at risk of bankruptcy should have decreased during this period. Findings: The study showed that it is difficult to determine when the crisis ended and stopped affecting Polish listed companies. Their financial condition gradually improved during the period 2013–2014. Multiple discriminant analysis models are useful in assessing the risk of bankruptcy, but the study results show that it is safer to use several models simultaneously and to eliminate outliers. Research limitations/implications: The discriminant models used in the study are suitable for conducting research on large populations within enterprises and can be used by state and financial institutions (including banks) and authorities in Poland to facilitate the conduct of economic statistics, forecasting economic situation, etc. Originality/value: In Poland, many studies have been carried out on the usefulness of multiple discrimi-nant analysis models for the purposes of forecasting the bankruptcy of individual enterprises. However, there are few studies devoted to assessing the usefulness of the models in conducting research on large populations within enterprises (i.e., assessing the state of the economy and its sectors). This research helps to explore and fill this research gap.

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Analysis of Altman Model and Ohlson Model in Predicting Financial Distress of Manufacturing Companies in the Indonesia Stock Exchange

Indian-Pacific Journal of Accounting and Finance ◽

10.52962/ipjaf.2017.1.1.4 ◽

2017 ◽

Vol 1 (1) ◽

pp. 51-63

Author(s):

Elsa Imelda ◽

Ignacia Alodia

Keyword(s):

Discriminant Analysis ◽

Financial Distress ◽

Stock Exchange ◽

Logit Analysis ◽

Net Income ◽

Manufacturing Companies ◽

Multiple Discriminant Analysis ◽

Retained Earnings ◽

Ohlson Model ◽

Altman Model

The purpose of this research is to examine the accuracy of the Altman Model and the Ohlson Model in Bankruptcy Prediction.The research population is all companies who are listed on the Indonesian Stock Exchange. The sample of the research is 40 manufacturing companies listed on the Indonesian Stock Exchange in the period of 2010-2014 that are divided into companies with financial distress and those without financial distress.The data analysis technique is the Multiple Discriminant Analysis and Logit Analysis. The Multiple Discriminant Analysis is derived from the Altman Model while the Logit Analysis is derived from theOhlson Model. The results show that the Ohlson Model and the Logit Analysis are more accurate than the Altman Model and the Multiple Discriminant Analysis in predicting bankruptcy of manufacturing firms in the Indonesian Stock Exchange (BEI) in 2010-2014. Also, the results of the study reveal that the ratio of retained earnings to total assets; earning before interest and taxes to total assets; market value of equity to total liabilities; sales to total assets; and debt ratio, return on assets, working capital to total assets and net income were negative in the last two years. Hence constitutes the benchmark for consideration in determining the financial distress of a company.

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Comparison of Examinees’ and Doctors’ Judgement of Health Status Using Data in an AMHTS

Methods of Information in Medicine ◽

10.1055/s-0038-1635319 ◽

1984 ◽

Vol 23 (01) ◽

pp. 15-22

Author(s):

Y. Sekita ◽

T. Ohta ◽

M. Inoue ◽

H. Takeda

Keyword(s):

Health Status ◽

Discriminant Analysis ◽

Explanatory Power ◽

Laboratory Data ◽

Subjective Symptom ◽

Health Conditions ◽

Testing System ◽

Multiple Discriminant Analysis ◽

Canonical Axis ◽

Using Data

SummaryJudgements of examinees’ health status by doctors and by the examinees themselves are compared applying multiple discriminant analysis. The doctors’ judgements of the examinees’ health status are studied comparatively using laboratory data and the examinees’ subjective symptom data.This data was obtained in an Automated Multiphasic Health Testing System. We discuss the health conditions which are significant for the judgement of doctors about the examinees. The results show that the explanatory power, when using subjective symptom data, is fair in the case of the doctors’ judgement. We found common variables, such as nervousness, lack of perseverance etc., which form the first canonical axis.

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The validity of diagnostic systems for common mental disorders: a comparison between the ID-CATEGO and the DSM-III systems

Psychological Medicine ◽

10.1017/s0033291700013386 ◽

1990 ◽

Vol 20 (1) ◽

pp. 209-218 ◽

Cited By ~ 11

Author(s):

David Grayson ◽

Keith Bridges ◽

Diane Cook ◽

David Goldberg

Keyword(s):

Discriminant Analysis ◽

Depressive Disorders ◽

Common Mental Disorders ◽

Latent Trait ◽

Mental Illnesses ◽

Anxiety And Depression ◽

Multiple Discriminant Analysis ◽

Diagnostic Systems ◽

Scale Scores ◽

Initial Scale

SYNOPSISIt is argued that latent trait analysis provides a way of examining the construct validity of diagnostic concepts which are used to categorize common mental illnesses. The present study adds two additional aspects of validity using multiple discriminant analysis applied to two widely used taxonomic systems. Scales of anxiety and depression derived from previous latent trait analyses are applied to individuals reaching criteria for ‘caseness’ on the ID-CATEGO system and the DSM-III system, both at initial diagnosis and six months later. The first multiple discriminant analysis is carried out on the initial scale scores, and the results are interpreted in terms of concurrent validity. The second analysis uses improvement scores on the two scales and relates to predictive validity. It is argued that the ID-CATEGO system provides a better classification for common mental illnesses than the DSM-III system, since it allows a better discrimination to be made between anxiety and depressive disorders.

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