Altman Z-Score Model Research Articles

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

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

The Comparative Analysis of Altman Z-Score, Springate, Zmijewski, And Internal Growth Rate Model in Predicting the Financial Distress (Empirical Study on Mining Companies Listed on Indonesia Stock Exchange 2014-2017)

This study aims to: (1) determine whether there are differences in scores between the Altman model, Springate, Zmijewski and Internal Growth Rate in predicting financial distress, (2) find out the most accurate prediction model in predicting financial distress of mining companies in Indonesia. The data used in this study is the company's financial statements published on Indonesia Stock Exchange. The population in this study is the mining companies listed on the Indonesia Stock Exchange during 2014-2017 which are 41 issuers. The sampling technique used purposive sampling so that 36 issuers were obtained as the research samples. This study compares the scores of four financial distress prediction models using statistical techniques and the accuracy of the prediction model by considering the level of accuracy and type I error. The conclusions from this study indicate the differences between the four prediction models. The Springate model is the best with an accuracy rate of 88.89% and an 8% type I error, the second is the Zmijewski model with an accuracy rate of 88.89% and a type I error rate of 42.86%, the third is the Altman model with 75% accuracy and error type I 46.67%, and the last is an internal growth rate model with an accuracy rate of 66.69% and type I error rate of 11.11%.Keywords: financial distress, financial statements, mining, prediction models

Pengaruh Prediksi Kebangkrutan Model Altman Z-Score, Reputasi Auditor dan Opinion Shopping terhadap Opini Audit Going Concern

This study aims to determine the effect of the bankruptcy prediction of the Altman z-score model, auditor reputation and opinion shopping on going concern audit opinion in manufacturing companies listed on the Indonesia Stock Exchange in 2015-2019. This research is a causal associative research with a quantitative approach. The sample in this study were 25 manufacturing companies listed on the Indonesia Stock Exchange which were determined using purposive sampling technique. Observations in this study were carried out throughout the period 2015 to 2019 so that the number of observations was 125 data. The type of data used in this study is secondary data. While the data analysis method used in this research is panel data regression analysis with statistical data processing software, namely STATA. Based on the results obtained in this study, it can be seen that the prediction of bankruptcy based on the Altman z-score model has no significant effect on going concern audit opinion on manufacturing companies listed on the Indonesia Stock Exchange. Auditor reputation is proven to have a negative and significant effect on going concern audit opinion on manufacturing companies listed on the Indonesia Stock Exchange. And opinion shopping is also proven to have a negative and significant effect on going concern audit opinion on manufacturing companies listed on the Indonesia Stock Exchange.

Financial Distress and Bankruptcy Prediction: an Empirical Analysis of the Manufacturing Industry in Albania

Bankruptcy is the conclusive affirmation of the inability of a company to support and endure current operations given its current financial position and debt obligations. If bankruptcy could be expected with affordable precision ahead of time, managers and investors of companies may have the possibility to take action to secure their companies, reduce risk and loss of business and perhaps even avoid bankruptcy itself. The aim of this paper is to test the suitability and predictive accuracy of the Altman Z-Score model in the Albanian manufacturing industry. After performing the empirical analysis, the conclusion is that this model clearly fails to effectively predict financial distress and bankruptcy and it isn’t reliable in our case. Lastly, a logistic regression model is proposed, which is more adequate for the Albanian context.

Implementasi Model Finansial Distres Pada Perusahaan Manufaktur Yang Terdaftar di IDX Tahun 2015-2017

Manufacturing Company is a sample of this study, the dependent variable used in this study is a binary variable, namely whether the company is in financial distress or non-financial distress. Hypothesis testing uses binary logistic regression (Binary Logistic Regression) because the dependent variable is a combination of metric and non-metric (nominal). The model used is the Altman Z-Score model, Springate S-Score, Grover G-Score, Zmijewski X-Score, and univariate models. Of the five models, the best model is the Springate S-Score with a Nagelkerke R2 value of 0.582. the second is, Zmijewski X-Score with a value of 0.227 and the third best is the Univariate Model with a value of 0.042. Of the three best models, namely the Springate S-Score, Zmijewski X-Score and the Univariate Model. The implementation is that the ratios in these models are very important to be considered by companies as a sensitivity tool so that companies do not experience financial distress. ratios that are often used are ratios related to the company's ability to manage and produce net working capital, sales, debt and ability to generate profits from sales and profits from assets.

Financial performance analysis of select Indian Public Sector Banks using Altman's Z-Score model

This study aims to assess the financial performance of select public sector banks, having the highest level of gross non-performing assets, using the Altman's Z-Score model. It was found that all the select banks were in the safe zone, with the average Altman's Z-Score value being higher than the prescribed safe zone cut-off limit of 2.9. The Altman's Z-Score values differed significantly between the banks, possibly due to their varying asset sizes. However, when considered individually for each bank, the Altman's Z-Score did not exhibit statistically significant variation between the years, in the ten year study period. The Altman's Z-Score value, for the first five year period, was found to be statistically different from the last five year period when all the banks were pooled together. This may be on account of the increase in the non-performing assets in the last five year period. The results of linear regression analysis indicated that for every 1% increase in the gross non-performing assets, the Altman's Z-Score decreased by about 3.1%. However, for every 1% increase in the net profits, the Altman's Z-Score increased by about 15.31%. Hence the public sector banks not only have to keep their non-performing assets under control but also devise innovative ways to increase their profits.

Analiza finansijskog položaja mlinskih preduzeća u Vojvodini

The subject of this research is the financial position of mill companies in Vojvodina. The specific aim of the research is to analyze the is to compare the applied models for the analysis of the financial position. The basis of the analysis are the financial reports of the observed companies in the period from 2015 to 2019. The analysis of the financial position was performed using two models: Altman's Z-score model and Kralicek's Quick test model. These models use standard financial ratio indicators to assess the risk of bankruptcy of companies, i.e. their financial stability and profitability. The obtained results suggest that the company MLINTEST stood out as the most successful company and that the company KIKINDSKI MLIN stood out as the company with the worst financial position. Both models gave similar results, and their further use is recommended when analyzing the financial position of mill companies in Vojvodina financial position, i.e. to assess the risk of bankruptcy of medium-sized mill companies in Vojvodina. The general aim of the research.

LIQUIDITY-PROFITABILITY ANALYSIS & PREDICTION OF BANKRUPTCY- A STUDY OF SELECT TELECOM COMPANIES

Indian Telecommunication sector is going through intense competition and this has increased after the Reliance Jio entered into the Indian Telecom sector. Companies have started to enter into mergers in order to survive in this competitive environment. Due to this intense competition, one of the Telecom companies has already filed for bankruptcy and other players in the sector have shown a low profitability performance due to the price war between the players. Despite many initiatives by the government, the sector is still not showing evident signs of improvement and if this condition persists, there can be some more bankruptcy filings in this sector. The present study aims at analyzing the financial health of Indian Telecom Industry and predicting the bankruptcy of selected companies. There are around ten telecom companies in Indian Telecom Industry. The top six companies were selected for the study on the basis of market capitalization. The study analyzedimpact of liquidity and profitability on Altman Z-score model developed exclusively for service companies. The study is empirical and descriptive in nature and the period under study was from 2006-07 to 2016-17. The data was extracted from the financial reports of selected telecom companies. The study found that most of the telecom companies in Indian Telecom Industry fall in “Grey” zone of Altman Z-score model, which is not a good sign for the Industry and study also found out that most of the companies, did not perform well in terms of liquidity and profitability. There was a significant impact of liquidityZ-score, but impact of profitability on Z-score not significant. The study concluded that Indian Telecom companies should improve their liquidity and profitability performance so that the sector shows signs of stability.

Comparative analysis of Z-score, Springate, and Zmijewski models in predicting financial distress conditions

This study aims at finding the accuracy level of financial distress prediction on companies in the property, real estate, and building construction sectors. The financial distress prediction models employed here are Z-score, Springate, and Zmijewski. The research population involved all property companies registered at the Indonesian Stock Exchange in the period of 2014-2018. Purposive sampling was used to collect 45 companies. The number of observations taken was 225. The data used were the secondary data taken from the Indonesian Stock Exchange website. The findings showed that the models with high accuracy to predict financial distress in the property, real estate, and building construction sector were the Altman Z-Score model, followed by Zmijewski and Springate models.

Could the Altman Z-score model detect the financial distress in Ghana? Multivariate discriminant analysis

The purpose of this paper is to assess the effectiveness of the Altman Z-score model to discriminate between financially distressed and non financially distressed manufacturing firms listed on the Ghana Stock Exchange. Eleven firms consisting of two financially distressed and nine non-financially distressed manufacturing firms were analysed. Independent descriptive statistics, independent sample t-test, and multivariate discriminant analysis were the analytical tools used to analyse the hypotheses of this study. The study revealed that working capital/total assets and sales/total assets were the major discriminators of financially distressed firms on the Ghana Stock Exchange. Multivariate discriminant analysis revealed an accuracy rate of 79.9% to detect financially distressed firms in Ghana.

Predicting Nursing Home Financial Distress Using the Altman Z-Score.

This article uses a modified Altman Z-score to predict financial distress within the nursing home industry. The modified Altman Z-score model uses multiple discriminant analysis (MDA) to examine multiple financial ratios simultaneously to assess a firm’s financial distress. This study utilized data from Medicare Cost Reports, LTCFocus, and the Area Resource File. Our sample consisted of 167 268 nursing home-year observations, or an average of 10 454 facilities per year, in the United States from 2000 through 2015. The independent financial variables, liquidity, profitability, efficiency, and net worth were entered stepwise into the MDA model. All of the financial variables, with the exception of net worth, significantly contributed to the discriminating power of the model. K-means clustering was used to classify the latent variable into 3 categorical groups: distressed, risk-of-financial distress, and healthy. These findings will provide policy makers and practitioners another tool to identify nursing homes that are at risk of financial distress.

Merenje rizika pozajmljivanja malim i srednjim preduzećima u Republici Srbiji u svetlu savremenih bankarskih regulativa

The subject matter of this paper is measuring the risk of lending to Small and Medium-sized Enterprises (SMEs) from the point of view of the existing banking regulations. The paper starts from the hypothesis that an increase in the transparency of the credit risk measurement process would enable the timely detection of problems and leave room for the actions necessary for the management of small and mediumsized enterprises, as well as all creditors, and generate an opportunity for SMEs to provide more favorable sources of financing. In the research study, the well-known Altman Z-Score model was used to assess the probability of default and rank a company. The results of the application of the Z-Score model indicate that, to a certain extent, they can detect the companies in which bankruptcy may occur in the two years following the assessment, on the one hand, but they cannot be considered as reliable for the assessment of the probability of the bankruptcy of SMEs in the Republic of Serbia, on the other.

Insolvency Risk. Application of Altman Z-Score to the Auto Parts Sector in Romania

This paper intends to apply the Altman Z-score model to all the companies active in the wholesale of motor vehicle parts and accessories (NACE 4531), with extended financial statements. Using the panel data model over the time series for 2008-2016 on the companies of this sector, we conclude that 99% of the Z-score is explained by the independent variables (working capital, capital structure, turnover, earnings before interest and tax), with estimated parameters very close to the model`s classical values. The sample description of the paper and the corresponding results highlight the Z-score evolution by turnover clusters and principal components, with the largest companies performing the best (the only cluster with Z-score median above 3). We notice a tendency for decreasing high-risk companies and increase in the medium risk companies, whereas the low-risk companies are relatively stable. This improvement is mostly due to the increasing capitalization rate and less external debt, despite the deteriorating working capital and operating margin. We believe that future research to evaluate Z-score sensitivity under stress test scenarios would be very useful to provide an insight into companies’ insolvency risk amid increasing interest rates and different fiscal tax on dividends.

An application of Altman Z-score model to analyze the bankruptcy risk: cases of multidisciplinary enterprises in Vietnam

This study applied Altman Z-score model to assess the bankruptcy risk of a set of multidisciplinary enterprises of various types, mainly small and medium enterprises, with data taken from official financial reports of 180 enterprises in Soc Trang province. The binary logistic regression was employed to assess the impact of non-financial and financial factors on the bankruptcy risk of enterprises. The research findings showed that both the non-financial factors such as business area, types and size of the business, the educational level of managers and executors and other characteristics, and the financial factors (indicators) such as earnings before tax, net profit/equity ratio, earnings before interest and tax/total assets ratio, equity/total debt ratio, affect the bankruptcy risk of enterprises. Predicting the bankruptcy risk and measuring its determinants play an important role not only as an effective managing tool of the business, but also as evidence for policymakers to support the sustainable development of business.

COMPARATIVE ANALYSIS OF Z-SCORE AND SPRINGATE ALTMAN MODELS ON REGISTERED COAL COMPANIES BEI IN

Subsector coal mining sector experienced a decrease from the years 2011-2015, if the company fails to carry out operations to generate a profit, then this may lead to bankruptcy. The purpose of this study was to determine whether there are differences in the potential bankruptcy of the Altman Z-score models and models Springate, as well as to determine the model of bankruptcy is most appropriate for the coal company listed on the Indonesia Stock Exchange in 2011 to 2015. This research method is quantitative secondary data is the data from this study. The results of this study, based on the results of hypothesis testing with different test paired samples t-test mennjukkan that there are differences in the potential bankruptcy of the Altman Z-score models and models Springate. Then, based on the calculation and oengamatan writer, a model that can assess more fair and appropriate for the coal company listed on the Indonesia Stock Exchange period 2011 - 2015 is the model of the Altman Z-score.

Influence of bankruptcy prediction and residual income on company share prices in various industry sectors

Research use the panel data regression analysis model aims to determine the predictions of bankruptcy with the Altman Z-Score model and residual income influence the stock prices of companies listed in the Indonesian Syariah Stock Index (ISSI) in the period 2014 to 2018. Research This is a quantitative study using secondary data in the form of annual financial statements published by companies, with sampling using purposive sampling to obtain a sample of 23 manufacturing companies engaged in various industrial sectors. The research method uses the fixed effect model approach. The analysis said that the bankruptcy prediction using the Altman Z-Score model showed a significant negative effect on stock prices, while residual income had no effect on stock prices. In addition, the Altman Z-Score shows that many companies enter the gray area cut-off point and even go bankrupt, thus making shareholders reduce their share prices for the safety of their shares in the capital market.

Altman Z-Score Model Effects of Non-Performing Assets (NPAs) in Public and Private Bank In India

The study aims to measure the effectiveness of Altman's Z-score model using Non-Performing Assets (NPA) as a level fixture index. Paper examines if Altman's Z-score model captures the decline in the financial health of the banks caused by the NPAs. The sample size is taken in selected two public (CBI & PNB) & two private sector banks (ICICI & HDFC). The study is based on secondary data & data will be collected for the last five years 2014 to 2018. The findings of this paper suggest that, due to the uniqueness of the Indian banking sector during the NPA crisis, the 'Emerging Market Model' does not produce any significantly better results. Therefore, there is a future scope to develop a tailor-made model suitable to the Indian Banking sector.

Analisis Kinerja Keuangan Sektor Manufacture dengan Multiple Discriminant Analysis Bankcruptcy Model di Bursa Efek Indonesia Periode 2014 – 2017

This research aims to prove the financial performance of Manufacture sector with Multiple Discriminant Analysis Bankruptcy Model when measured using the Altman's Z-score first model, Altman's Z-score revision and Altman's Z-score modifications, Springate model and Zmiejewski model as well as to get the stocks that have the best performance based on the model. In this research the population used is the stock sector manufacturing group on the Indonesia Stock Exchange in the year 2014 – 2017 as many as 166 issuers manufacture. This research sample is the manufacture sector company, which provides complete data, and has a total asset of positive value and fluctuates as many as 22 companies. This form of research is an explolanatoris research (explanatory Research). The results showed, that based on the first Altman's Z-score model, in 2014 the company's performance in a healthy or not bankrupt category was 31.82%, while the company had a gray performance of 22.73%, as for The company's performance with the category broke by 45.45%. In 2015 the company's performance with a healthy category of 27, 27, 82%, a company that had a gray performance of 27.27%, the company's performance of the category went bankrupt by 45.45%. In 2016 the company's performance with a healthy category of 22, 73, 82%, the company had a gray performance of 36.36%, as for the performance of the company with the category bankrupt by 36.36%. In 2017 the company's performance with a category of 40.91%, the company has a gray performance of 22.73%, as for the performance of the company with the category bankrupt by 36.36%. Based on the model Altman Z-Score revision can be concluded that in 2014 the company's performance with a healthy category of 31.82%, a company that has a gray performance of 54.55%, as for the performance of the company with the category bankrupt 13.64%. Year 2015 the performance of the company with a healthy category of 27.27%, the company has a gray performance of 54.55%, the company with the category went bankrupt by 18.18%. Year 2016 the company's performance with a healthy category of 45.45%, while the company has a gray performance of 36.36%, the company with the category went bankrupt by 18.18%. Year 2017 the performance of the company with a healthy category of 50.00%, the company that has a gray performance of 31.82%, the performance of the company with the category bankrupt by 18.18%. Based on the model of Altman Z-score modification can be concluded, namely in 2014 the performance of the company with a healthy category of 72.73%, the company that has a gray performance of 22.73%, as for the performance of the company with the category bankrupt of 4.55%. In 2015 the company's performance with a healthy category of 72.73%, while the company had a gray performance of 18.18%, as for the company's performance in the category went bankrupt by 9.09%. In 2016 the company's performance with a healthy category of 77.27%, the company had a gray performance of 13.64%, the company's performance in the category went bankrupt by 18.18%. In 2017 the company's performance with a healthy category of 81.82%, while the company had a gray performance of 13.64%, as for the company's performance in the category went bankrupt by 4.55%. Based on Springate model can be concluded, namely in 2014 the performance of the company with a healthy category of 59.09%, while companies that have a gray performance is not there, as for the performance of the company with the category bankrupt by 40.91%. In 2015 the company's performance with a healthy category of 50.00%, while companies that had a gray performance did not exist, the performance of the company with the category went bankrupt by 50.00%. In 2016 the company's performance with a healthy category of 59.09%, while companies that have a gray performance does not exist, as for the performance of the company with the category bankrupt by 40.91%. In 2017 the company's performance with a healthy category of 63.64%, while companies that have a gray performance does not exist, as for the performance of the company with the category bankrupt by 36.36%. Based on Zmiejewski model can be concluded, in 2014 to 2017 the performance of companies with a healthy or not bankrupt category is 100%, while companies that have a gray performance does not exist, as for the performance of the company with Bankruptcy category does not exist. Stocks that have the best performance based on each multiple discriminant analysis bankruptcy models respectively, namely INTP, CEKA, MERK, GGRM, LMSH, CPIN, MLBI, IKBI, TRIS UNIC and INDF, who have never experienced bankruptcy or in financial distress.

ANALISIS MODEL ALTMAN Z-SCORE DALAM MENGUKUR POTENSI KEBANGKRUTAN PERUSAHAAN (STUDI EMPIRIS PADA PERUSAHAAN INDUSTRI BARANG KONSUMSI YANG TERDAFTAR DI BURSA EFEK INDONESIA TAHUN 2013 – 2017)

This research is one of the references or tools for investors by using the Altman Z-Score model in knowing the financial condition of companies, especially consumer goods industry companies listed on the Indonesia Stock Exchange in 2013 - 2017. Based on the results of research conducted from 36 companies, it can be concluded that out of 36 companies only 20 companies or around 55.56% of the total sample companies that have good financial conditions from 2013 - 2017 because they have a Z-Score value of >2,99. While 16 other companies or around 44.44% of the total sample companies that have unstable financial conditions will even experience bankruptcy.

Analysis of bankruptcy issues at manufacturing companies, sub-sectors of cosmetics and houseappliances registered in Indonesia Stock Exchange (IDX) year 2015-2017 using Altman Z-score

This research aims to figure out the level of companies bankruptcy by applying Altman Z-Score at the manufacturing companies registered in the Indonesia Stocks Exchange. The result of the research has indicated that ZScore model is applicable to detect the company’s potential bankruptcy issues, especially manufacturing company subsectors of cosmetics and houseappliances. Altman Z-Score model has classified the companies into three categories; safe, grey area and distress. Based on the result of the research, for the companies which are in the grey area category are suggested to improve their financial performance and to use the benefit of all the assets properly to get the revenue as much as possible. However, for the companies which are in the safe category are suggested to increase their performance, especially marketing performance so that they will receive bigger amount of the revenue, nevertheless, the potential of financial distress can be minimized accordingly. Keywords: manufacturing company, financial distress, Altman Z-Score.