scholarly journals Do Accounting, Market, and Macroeconomic Factors Affect Financial Distress? Evidence in Indonesia

2021 ◽  
Vol 5 (2) ◽  
pp. 166
Author(s):  
Muhammad Taufik ◽  
Clarita Valeria Sugianto
Keyword(s):  
Financial Distress ◽  
Macroeconomic Factors ◽  
Retained Earnings ◽  
Market Factor

This paper aims to investigate the effect of accounting, market, and macroeconomic factors on financial distress. The investigations were expanded by constructing seven research models to simulate all factors. The research sample includes companies listed on the IDX from 2016 to 2020 which produce 1.710 data. This paper finds that retained earnings (RETA) and earnings (EBITTA) as part of accounting factors have a role in weakening financial distress and can be consistently tested in several research models. Equity (MVE) as part of the market factor weakens financial distress and is consistently tested. Although solvency (SOLV) was described as the company's ability to maximize debt, it is not consistently tested in several research models. Finally, it was found that deflationary conditions caused financial distress which represented macroeconomic factors. This paper makes a practical contribution to companies and governments to evade financial distress

scholarly journals MODEL PREDIKSI FINANCIAL DISTRESS : PENGARUHNYA TERHADAP KINERJA SAHAM INDUSTRI TEKSTIL DAN GARMEN DI INDONESIA

BISMA ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
Suskim Riantani ◽  
Sherly Delvia ◽  
Gugun Sodik
Keyword(s):  
Stock Returns ◽  
Financial Distress ◽  
Descriptive Analysis ◽  
Market Value ◽  
Garment Industry ◽  
Z Score ◽  
Financial Problems ◽  
Verification Methods ◽  
The Impact ◽  
Retained Earnings

This study aims to predict the possibility of Financial distress in the textile and garment industry using the Altman Z-score prediction model and analyze the impact of Financial distress on the company's stock performance as measured by stock returns. This research used descriptive analysis and verification methods. Applying a purposive sampling method, the research sample consisted of 15 issuers from the textile and garment industry during the observation period of 2012-2016. This research used panel data regression to analyze research data. The results showed that one company did not experience financial problems, one company had potential financial problems, and the 13 other companies experienced Financial distress. The results of model testing showed that Altman Z-score can be used to predict Financial distress conditions in the textile and garment industry for the 2012-2016 period. The results of hypothesis testing showed that only one ratio of the Altman Z-score model has a significant effect on stock returns, i.e., market value of equity to book value of debt, while other ratios of working capital to total assets, retained earnings to total assets, earnings before interest and taxes to total assets, and sales to total assets have no significant effect on stock returns. These findings imply that the investors in the textile and garment industry attentively observe the company's market value in making any investment decisions. Keywords: Altman Z-score, Financial distress, stock returns, textile and garment industry

scholarly journals Corporate Failure

2013 ◽  
Vol 2 (3) ◽  
pp. 103-110
Author(s):  
Ben Jabeour Sami
Keyword(s):  
Systematic Study ◽  
Predictive Models ◽  
Financial Distress ◽  
Macroeconomic Factors ◽  
Financial Ratio ◽  
Macroeconomic Variables ◽  
Corporate Failure ◽  
Ratio Variables ◽  
The Impact ◽  
Number Of Authors

A number of authors suggested that the impact of the macroeconomic factors on the incidence of the financial distress, and afterward in case of failure of companies. However, macroeconomic factors rarely, if ever, appear as variables in predictive models that seek to identify distress and failure; modellers generally suggest that the impact of macroeconomic factors has already been taken into account by financial ratio variables. This article presents a systematic study of this domain, by examining the link between the failure of companies and macroeconomic factors for the French companies to identify the most important variables and to estimate their utility in a predictive context. The results of the study suggest that several macroeconomic variables are strictly associated to the failure, and have a predictive value by specifying the relation between the financial distress and the failure.

scholarly journals Integrating Capital Structure, Financial and Non-Financial Performance: Distress Prediction of SMEs

2019 ◽  
Vol 4 (2) ◽  
pp. 56-63
Author(s):  
Farida Titik Kristanti ◽  
Sri Rahayu ◽  
Deannes Isynuwardhana
Keyword(s):  
Capital Structure ◽  
Financial Performance ◽  
Financial Distress ◽  
Positive Impact ◽  
Jel Classification ◽  
Operating Income ◽  
Distress Prediction ◽  
Retained Earnings ◽  
Research Period ◽  
Made In

Objective – The growth of SMEs in Indonesia is rising from year to year. As an anticipation of bankruptcy, predictions can be made in an integrated means from the perspective of capital structure, financial, and non-financial performance. Methodology/Technique – A sample of 39 companies were selected using purposive sampling during the research period of 2013-2017. The results of the statistical logistic regression show that profitability is an important factor in predicting financial distress of the SMEs in Indonesia. Findings – The operating income to total assets has a negative and significant effect on SMEs financial distress. Meanwhile, retained earnings to total assets have a positive impact. Indonesian SMEs must be efficient in their operational costs to avoid financial distress. Novelty – In addition, sales are also important. If the company's sales are high, and the operational cost efficiency is maintained, the retained earnings will increase. This means that the company will be safe and able to avoid financial distress. Type of Paper: Empirical. Keywords: Capital Structure; Financial; Distress; Non-Financial; Performance. Reference to this paper should be made as follows: Kristanti F T; Rahayu S; Isynuwardhana D; 2019. Integrating Capital Structure, Financial and Non-Financial Performance: Distress Prediction of SMEs, Acc. Fin. Review 4 (2): 56 – 62 https://doi.org/10.35609/afr.2019.4.2(4) JEL Classification: G32, G33, G34.

scholarly journals Corporate Financial Distress of Industry Level Listings in Vietnam

2019 ◽  
Vol 12 (4) ◽  
pp. 155 ◽  
Author(s):  
Duc Hong Vo ◽  
Binh Ninh Vo Pham ◽  
Chi Minh Ho ◽  
Michael McAleer
Keyword(s):  
Financial Distress ◽  
Global Financial Crisis ◽  
International Exchange ◽  
Macroeconomic Factors ◽  
Alternative Models ◽  
Listed Firms ◽  
Industry Level ◽  
Macroeconomic Indicators ◽  
Market Factors ◽  
The Global Financial Crisis

Any critical analysis of the corporate financial distress of listed firms in international exchange would be incomplete without a serious dissection at the industry level, because of the different levels of risks concerned. This paper considers the financial distress of listed firms at the industry level in Vietnam over the last decade. Two periods are considered, namely during the Global Financial Crisis (GFC) (2007–2009) and post-GFC (2010–2017). The logit regression technique is used to estimate alternative models based on accounting and market factors. The paper also extends the analysis to include selected macroeconomic factors that are expected to affect the corporate financial distress of listed firms at the industry level in Vietnam. The empirical findings confirm that the corporate financial distress prediction model, which includes accounting factors with macroeconomic indicators, performs much better than alternative models. In addition, the evidence confirms that the GFC had a damaging impact on each sector, with the Health & Education sector demonstrating the most impressive recovery post-GFC, and the Utilities sector recording a dramatic increase in bankruptcies post-GFC.

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

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.

scholarly journals Predicting Auditor Changes With Financial Distress Variables: Discriminant Analysis And Problems With Data Mining Approaches

2012 ◽  
Vol 28 (6) ◽  
pp. 1357 ◽  
Author(s):  
Susan Eldridge ◽  
Wikil Kwak ◽  
Roopa Venkatesh ◽  
Yong Shi ◽  
Gang Kou
Keyword(s):  
Data Mining ◽  
Discriminant Analysis ◽  
Financial Distress ◽  
Kappa Statistic ◽  
Predictor Variables ◽  
Analysis Model ◽  
Data Set ◽  
Auditor Change ◽  
Retained Earnings ◽  
Accuracy Rates

Our study extends previous research that uses financial distress factors in predicting auditor changes by evaluating the effectiveness of the traditional discriminant analysis model, not used in previous auditor change studies, and by highlighting the importance of evaluating the likelihood that data mining approach classification results occurred by chance. Significance of individual predictor variables, as well as of the full set of 13 financial variables, can be tested using discriminant analysis. Kwak et al. (2011) document overall classification accuracy rates ranging from 61 to 63.5 percent for the four data mining models they compared but did not address whether these rates occurred by chance. Using Kwak et al.s (2011) data set of firms changing auditors in 2007 or 2008 and matching non-auditor change firms, our discriminant analysis test results show overall accuracy rates of less than 56 percent and true positive rates over 85 percent, but these rates are influenced by a disproportionate number of non-auditor change firms being classified as auditor change firms. Individual predictor variables that are important in the discriminant equation based on standardized canonical coefficients include losses (LOSS) and no payment of dividends (DIV) in the year prior to the auditor change, retained earnings as a percent of total assets (RE/TA), and earnings before interest and taxes as a percent of total assets (EBIT/TA). The Kappa statistic and AUC metrics for all 13 data mining algorithms we used indicate that classifications using these algorithms are no better than random classifications.

scholarly journals The Effect of Unemployment, Real Interest Rate, Debt Service, and Government Expenditure on the Z”-score of Lebanese Alpha Banks

2021 ◽  
Vol 1 (3) ◽  
Author(s):  
Jean Jean Elia ◽  
Elena Toros ◽  
Chadia Sawaya ◽  
Mohamad Balouza
Keyword(s):  
Interest Rate ◽  
World Bank ◽  
Financial Distress ◽  
Real Interest Rate ◽  
Z Score ◽  
Macroeconomic Factors ◽  
The World Bank ◽  
The World ◽  
Macroeconomic Indicators ◽  
Z Scores

Lebanon is currently witnessing the most severe banking sector crisis in its history. Thus, nowadays, the demand for financial analyses in banks has increased to examine the financial distress and the potential impact of the macroeconomic factors. Consequently, this research studies bank distress in Lebanese Alpha banks and addresses the question of how the Lebanese major macroeconomic factors affect it. The researchers calculated the mean Altman Z”-scores for 10 Lebanese Alpha banks for the period 2009 – 2018 as an indicator for financial distress. Furthermore, they collected data regarding the chosen macroeconomic indicators for the same period from the World Bank Data. Consequently, the researchers developed a Regression Model and analyzed the model and a multicollinearity test. The calculated Altman Z"-scores showed that Lebanese Alpha banks were very likely to be financially distressed. Moreover, the results showed that there is a positive relationship between debt service, government expenditures, unemployment, and the real interest rate on one side and alpha banks’ high probability to become distressed on the other side. First, gathering data regarding the macroeconomic indicators was a hurdle as there were differences among the sources (Lebanese Ministry of Finance, BDL, Bloomberg, IMF, and World Bank). This is why the authors depended on the values published by the World Bank Data as a reliable source. Second, there is a lack of studies analyzing the relationship between the banking sector’s current crisis and the individual macroeconomic variables. However, this limitation also gives value to the results of this study. This research sheds light on the significance of the Altman Z"-score as an indicator for financial distress in Lebanese Alpha banks. Thus, a model can be developed based on the basic Altman model that fits for Lebanese banks. Moreover, banking authorities (BdL, ABL, and BCC) should impose yearly calculations of this score to detect probable future distress. The value of this study stems from it being one of the first studies in the Lebanese market examining the impact of macroeconomic factors on the Z”-scores of the Lebanese Alpha banks using the Multiple Regression Model.

scholarly journals ANALISIS FINANCIAL DISTRESS PADA PERUSAHAAN BATUBARA YANG TERDAFTAR DI BURSA EFEK INDONESIA PERIODE 2011 - 2014

2016 ◽  
Vol 11 (2) ◽  
Author(s):  
Tiodorma Br. Sijabat ◽  
Cut Ermiati
Keyword(s):  
Stock Prices ◽  
Financial Distress ◽  
Stock Exchange ◽  
Descriptive Analysis ◽  
Secondary Data ◽  
Financial Ratios ◽  
Book Value ◽  
Corporate Earnings ◽  
Score Model ◽  
Retained Earnings

The purpose of this study was to determine whether the financial ratios in Altman can explain financial distress. There are five financial ratios in Altman, which is the ratio of Net Working Capital to Total Assets (X1), Retained Earnings to Total Assets (X2), Earnings Before Interest and Tax to Total Assets (X3), Market Value of Equity to Book Value of Total Debt (X4) and Sales to Total Assets (X5). The population in this study are listed coal company in Indonesia Stock Exchange period 2011 - 2014. This study used purposive sampling method of sampling so that there are 18 companies. The data used is secondary data that is accessed from the website www.idx.co.id. The analytical method used is descriptive analysis. The results of this study for five years of observation, the financial ratios in Altman can explain financial distress in the coal company listed on the Indonesia Stock Exchange. The coal company in this study more companies to financial distress or equivalent to 56.67 % of the company.Where it can be seen from the decline in corporate earnings and some have suffered losses in recent years, the decline in the sales of the company, the amount of the costs the company the amount of debt continues to rise and stock prices continue to fall in the capital market. Keywords: Financial Ratios, Altman Z-Score Model, Financial Distress

ANALISIS RASIO KEUANGAN UNTUK MEMPREDIKSI FINANCIAL DISTRESS PERUSAHAAN MANUFAKTUR DI BEI

2021 ◽  
Vol 9 (3) ◽  
pp. 31-40
Author(s):  
Akhmad Kurniadi
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Market Value ◽  
Z Score ◽  
Working Capital ◽  
Book Value ◽  
Discriminant Model ◽  
Validation Rate ◽  
Value Equity ◽  
Retained Earnings

ABSTRACT This study aims to examine the prediction of the company's financial difficulties using the Altman Z-score 1968 model and the effect of financial ratios including working capital to total assets, retained earnings to total assets, earnings before interest and tax to total assets, market value equity to book value. of total liabilities, and sales to total assets on financial distress. The sample used in this study is a manufacturing company listed on the Indonesia Stock Exchange (BEI) 2015-2019. Sampling in this study using purposive sampling method and obtained 64 companies. The results showed that the variables Working Capital to Total Assets (X1), Retained Earnings to Total Assets (X2), Earnings Before Interest and Tax to Total Assets (X3), Market Value Equity To Book Value of Total Liabilities (X4), and Sales to Total Assets (X5) has a positive effect on financial distress, and the most significant effect on financial distress is the variable Retained Earnings to Total Assets. From the results of SPSS 17.0 processing, the equation Z = -1,813 + 1,216 X1 + 1,837 X2 + 0.122 X3 + 0.070 X4 + 0.506 X5 is produced. Meanwhile, the discriminant model that was formed had a high enough validation rate, namely 97.6%. Keywords: Financial ratio analysis; Financial distress; Altman Z-score

The Impact of Auditors' Opinions, Macroeconomic and Industry Factors on Financial Distress Prediction: An Empirical Investigation

2009 ◽  
Vol 12 (03) ◽  
pp. 417-454 ◽  
Author(s):  
Bi-Huei Tsai ◽  
Cheng-Few Lee ◽  
Lili Sun
Keyword(s):  
Financial Distress ◽  
Financial Statements ◽  
Macroeconomic Factors ◽  
Financial Ratio ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Market Factors ◽  
The Impact ◽  
Better Than

This study investigates the usefulness of auditors' opinions, market factors, macroeconomic factors, and industry factors in predicting financial distress of Taiwanese firms. Specifically, two non-traditional auditors' opinions are evaluated: "long-term investment audited by other auditors" ("other auditor"), and "realized investment income based on non-audited financial statements" ("no auditor").The results of the 22 discrete-time hazard models show that "other auditor" opinions have incremental contribution in predicting financial distress, in addition to "going concern" opinions. This suggests that "other auditor" opinions possess higher risk of overstating earnings and firms with such income items are more likely to fail. Besides, we find that the macroeconomic factors studied significantly explain financial distress. Particularly, the survivals of electronic firms are more sensitive to earnings due to higher earnings fluctuations in such firms. Finally, models with auditors' opinions, market factors, macroeconomic factors, and industry factors perform better than the financial ratio-only model in financial distress prediction.

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