Altman Model Research Articles

PENERAPAN MODEL ALTMAN (Z-SCORE) UNTUK MENGUKUR POTENSI FINANCIAL DISTRESS PERUSAHAAN MASKAPAI PENERBANGAN SEBAGAI DAMPAK PANDEMI COVID-19

The purpose of the study is to measuring potential financial distress in airlines as the impact of the covid-19 pandemic by applying the altman model ( Z Score ). This study is a descriptive quantitative will analyze the potential for financial distress of airlines companies listed on the IDX, the method used in this study is the altman Z-Scroe as a bankcruptcy prediction tool. The data used in this study is secondary data in the form of financial reports and annual reports of PT Garuda Indonesia Tbk (GIAA), PT AirAsia Indonesia Tbk (CMPP), PT Indonesia Transport & Infrastructure Tbk (IATA), and PT Jaya Trishindo Tbk (HELI) period 2020 and 2021 Quarter III. The result of the study can be concluded that based of the results of the calculation of banckruptcy prediction with altman Z score models that has been carried out on each of the airlines sector companies that are the sample, the classification of the bankruptcy predictions shows the results are indications of banckruptcy.

Early Warning Sistem Perusahaan Era Pandemi Covid-19

To break the chain of spread of the Covid-19 virus, the Government implemented a policy of restricting people's movements. The fact that occurred due to restrictions on community activities, the wheels of the economy were disrupted, even some business activities were closed. The formulation of the research problem, how are the results of the analysis of the Springate, Altman, Grover and Zmijewski Bankruptcy prediction models in the Covid-19 pandemic era. The purpose of the study was to empirically test the springate, altman, grover and zmijewski bankruptcy prediction models in companies during the covid-19 pandemic era. The research subjects are companies listed on the Indonesia stock exchange for the hotel, restaurant and tourism sub-sector in 2020. The results of the springate model test (s-score) show that 25 companies are predicted to have the potential to go bankrupt, 3 companies are predicted to be healthy. Altman model (Z-Score) shows that 16 companies are predicted to have the potential to go bankrupt, 4 companies experience a slight potential for bankruptcy and 4 companies are healthy. The grover model (G-Score) shows that 6 companies are predicted to have the potential to go bankrupt, 22 companies are healthy. The zmijewski model (X-Score) shows that 1 company is predicted to have the potential to go bankrupt, 27 companies are predicted to be healthy. The four models that show the same results for companies that are predicted to be healthy are PT. Asia Sejahtera Mina, Tbk with the issuer code AGAR.

Pengaruh Kegagalan Keuangan terhadap Nilai Pasar Perusahaan-Perusahaan Retail Indonesia Yang Terdaftar di Bursa Efek Indonesia

The development of e-commerce in Indonesia became a factor that threatened retail businesses, even some of them went bankrupt due to liquidation that led to financial failure. Financial failure is not detected within a certain time so it is likely that the company will go bankrupt. This study aims to determine the effect of financial distress on the market value of Indonesian retail companies listed on the Indonesia Stock Exchange. The study was conducted quantitatively. The study was conducted on 17 retail companies in Indonesia on the Indonesia Stock Exchange which published their audited financial statements for 3 consecutive years, namely 2016-2018. The sampling technique used was purposive sampling. The independent variable is financial failure measured by using the Altman Z-score and the dependent variable is market value. Data were analyzed by linear regression panel data with fixed effect method. The results show that the Altman model can be used to measure signs of corporate financial distress. Most of the 2016-2018 IDX retail companies are not vulnerable to financial distress and are in a healthy financial situation. Financial distress is stated to have a significant and positive effect on the market value of BEI retail industry companies in 2016-2018.

Modern methods for selecting a supplier as a necessary component of effective enterprise management

Methods. The results were obtained through the use of the following methods: general scientific and specific methods of system-structural analysis to clarify the stages of procurement management using a functional approach; grouping different supply conditions and setting scores depending on a specific condition; diagnostic methods (Altman's five-factor model) for assessing the level of financial stability of suppliers; integral assessment for comparison of suppliers. Results. The interest of scientists to the introduction of effective methods for managing both the enterprise and its inventory has been revealed. It has been shown that the majority of Ukrainian scientists focus their attention on the supply and on distinguishing factors of external and internal influence as well as classification of procurement methods. Foreign researchers recommend controlling raw materials and materials using ABC-analysis, VED-analysis & EOQ and optimizing supply chain management. It has been found that the composite model of supplier selection, which is recommended for using, involves a point-based assessment of the weighted average price of suppliers of raw materials and resources; assessment of their quality; supply quality; the level of the supply organization (taking into account the conditions of the supply organization); assessment of the suppliers’ loyalty; determining the financial stability of suppliers and carrying out a comprehensive integral assessment. Novelty. A comprehensive integral assessment of suppliers includes the price factor, quality, supply organization, loyalty and financial status, which makes it possible to compare suppliers and choose those that will ensure the most effective management of the enterprise. Practical value. The proposed methodology is based on the use of an available mathematical apparatus, operates with open and publicly available data, the results obtained are visual and easy to use.

Methodological and theoretical fundamentals of bankruptcy probability assessment of "UkSATSE" enterprise

In the economic sciences today there is a large number of developments in the field of analysis and forecasting of insolvent companies, which in turn helps to find and assess the criteria for the probability of bankruptcy of insolvent entities on the example of state enterprise “Ukrainian State Air Traffic Services Enterprise (UkSATSE)”. This paper is devoted to the reflection of the main methods and models for estimating the probability of bankruptcy of the enterprise, which were proposed by foreign and domestic scientists. In particular, it is noted that the assessment of the threat of bankruptcy on the model of Altman in our economic conditions can be considered as conditional, preliminary, especially if the business entity cannot be classified as medium or large. This model does not allow to obtain a fairly objective result. This is due to differences in the accounting of individual indicators, the impact on their formation of a number of external factors (primarily inflation), the mismatch between the book and market value of individual assets and other objective reasons. Such features determine the need to adjust the coefficients of significance of the indicators given in the Altman model, which in a developing economy is hardly possible. It is noted that in general, only financial ratios are used to predict the probability of bankruptcy, and the company's bankruptcy depends not only on the financial area. The calculated indicators do not include such coefficients as: return on assets, the degree of suitability of fixed assets, material consumption, instability of personnel - these are some of the main coefficients of the company. Methods of domestic scientist O. Tereshchenko will be suitable for our country. Thus, all models of the probability of bankruptcy have a serious drawback: they do not take into account the state of development of the industry and the country. The main directions and ideas in business are considered, the forecast of Eurocontrol concerning changes of traffic of flights for 4 years is resulted. The use of a discriminant model for estimating the probability of bankruptcy on the example of the enterprise state enterprise “UkSATSE” is analyzed. Based on all the data, an analysis and description of the shortcomings of the use of discriminant analysis is created. Recommendations for improving the state of the enterprise and timely forecast of the probability of bankruptcy are given.

Implementation of Springate, Altman, Grover and Zmijewski Models in Measuring Financial Distress

Companies that have improved company performance will have good prospects in the future. In addition, the company also needs some good strategy and planning to stay afloat in running its business. One of the strategies carried out by the company is to avoid the occurrence of a level of financial difficulties (financial distress). The goal of the study was to determine bankruptcy predictions and find out the most accurate methods for measuring bankruptcy among Springate, Altman, Grover and Zmijewski models. The data collection techniques used are documentation techniques while the data analysis techniques used are qualitative descriptive analysis techniques and comparative analysis techniques. The results showed that there was a predictive difference between Springate, Altman, Grover and Zmijewski models in predicting bankruptcy (financial distress). The Altman model is the most accurate prediction model in predicting bankruptcy with the highest degree of accuracy which then continued the Springate model, Grover model and finally the Zmijewski model. The renewal of previous research is to use four methods of predicting bankruptcy at once, different research objects and times from previous researchers

Analisis Perbandingan Model Altman, Springate, Zmijewski, Dan Grover Dalam Memprediksi Kebangkrutan Perusahaan Di Bursa Efek Indonesia (Studi Empiris Pada Perusahaan Manufaktur Yang Terdaftar Di Bursa Efek Indonesia Periode 2012-2017)

This study compares the feasibility of the Bankruptcy prediction model if used to predict manufacturing companies. The test in this study is logistic regression. The sample in this study consisted of 79 manufacturing companies listed on the Indonesia Stock Exchange from 2012-2017 with a purposive sampling method. This research is quantitative research where the analytical tool used is logistic regression test with SPSS version 18.
 The study results indicate that of the four models studied. The Springate model cannot predict the bankruptcy of manufacturing companies listed on the Indonesia Stock Exchange because it has a significant value of 0.175 > 0.05. This study also indicates that the Altman model is the most appropriate for predicting the bankruptcy of manufacturing companies compared to Springate, Zmijewski, and Grover.

Perbandingan Tingkat Akurasi Model Prediksi Financial Distress pada Perusahaan Sektor Property dan Real Estate

This study aims to find the most accurate financial distress prediction model for use in the property and real estate sectors listed on the Indonesia Stock Exchange. The financial distress prediction models used in this study are the Altman model, the Springate model, the Zmijewski model, and the Grover model. The population in this study amounted to 79 of property and real estate sector companies listed on the IDX. There are 28 companies in this study that were used as research samples with a total of 112 observations for 4 periods. The sampling process used a purposive sampling technique. The data analysis method used is a different test using McNemar Test on SPSS version 26, the data from the model predictions are compares with sample category 1 (financial distress) and category 0 (non-financial distress). This study also uses robustness check to test the robustness of the first prediction results. The results showed that Grover model was the most accurate predictive model with an accuracy rate of 88 percent, then the Altman model at 76,8 percent, the Springate model at 55,3 percent, and the Zmijewski model at 68 percent.

Assessing the financial health of agricultural enterprises incorporating the spatial dimension

In practice, different methods are used to assess the financial health of a company, and the fact that models use the various indicators complicates the comparison of their results. The aim of this paper was to identify the financial health of agricultural enterprises operating in the Slovak Republic using three selected creditworthy models (the Altman model, Taffler model and Bonity Index). It was concluded that the differences in the evaluations of agricultural enterprises using individual creditworthy models were significant. The analysis was supplemented by the spatial distribution of the enterprises at the level of individual regions of the Slovak Republic. It was confirmed that the spatial differences in the evaluations of agricultural enterprises using individual creditworthy models were not statistically significant and were therefore random. The proposal of own assessment of regions is based on the combination of the obtained results and their spatial distribution. We assume that such comprehensive assessment over a longer period of time may be an appropriate tool for assessing the financial health of agricultural enterprises, thus contribute to the early identification of potential risks and the long-term economic sustainability. This study can be the contribution in helping to design policies or effective solve the important challenges of agricultural enterprises to the path of sustainable development of the regions and to environmental management.

Validity Analysis of Financial Failure Prediction Models During the Covid-19 Pandemic

Unexpected bankruptcies of companies cause significant damage to company partners, investors, employees, countries, commercial and financial creditors. In this context, the financial failure risks should be constantly measured and controlled. However, it is important to constantly check the validity of financial models that measure the risk of the financial failure of companies. Because the higher the validity rates of these models, the higher the probability of investors and researchers to make the right decision according to the results obtained from these models. In this context, validity tests widely used in the literature, namely Altman (1968), Springate (1978), Ohlson (1980), Fulmer (1984), Zmijewski (1984), Canada (1987) and Grove (2001) models, were applied in this study. The study sample comprises of 8 energy companies in the US markets, which were officially declared bankrupt between 2018 and 2020. The universe of the study includes all companies and excludes financial institutions, financial companies and insurance companies. The research results have shown that Springate and Altman models best predict the financial failure risk of companies. However, Canadian and Fulmer models are the second group of models with the highest performance. The overall accuracy rate of the Grover model is also slightly above the overall average, while the overall accuracy rate of the Zmijewski and Ohlson models is below the general average.

FINANCIAL DISTRESS PERUSAHAAN TRANSPORTASI DI INDONESIA SEBELUM DAN SAAT PANDEMI COVID-19

Bankruptcy is a frightening specter where companies are powerless to handle financial distress situations due to various causes. In 2020 the health crisis due to the Covid-19 pandemic hit all countries without exception. One of the sectors affected is transportation, on the Indonesian stock exchange listed transportation companies in the sub-sector are 74 transportation. This study examines the financial distress of 16 major transportation companies with the Altman and Grover bankruptcy indicators before and during the Covid-19 pandemic. Based on the results of the calculation of the Altman model, it indicates that the transportation companies in the period before Covid-19 and during Covid-19 were more in the red zone of financial distress compared to the Grover model. However, on average, both models are in financial distress both during the Covid-19 pandemi and before the Covid-19 pandemic. This result is confirmed by the paired sample t-test, where the financial distress of transportation companies in Indonesia is not significantly different both before and during the Covid-19 pandemic in the two financial distress measurement models. In other words, both before Covid-19 and during Covid-19, transportation companies in Indonesia had experienced financial distress.

ANALISIS FINANCIAL DISTRESS PADA PT. WIJAYA KARYA DAN PT. ADHI KARYA YANG TERDAFTAR SEBAGAI PERUSAHAAN BUMN INDONESIA

This study aims to provide empirical evidence related to the potential for financial distress experienced by State-Owned Enterprises. Through this research, a description of the potential financial distress experienced by PT Wijaya Karya (Persero) Tbk and PT Adhi Karya (Persero) Tbk can be obtained during the period 2010 - 2019. The Financial distress prediction model uses the Altman Model (Z-Score) and Springate Model (S-Score). The results show that from 2010 – 2019, PT Wijaya Karya (Persero) Tbk is still in a healthy condition so that the possibility of going bankrupt is very small. However, year by year, the condition of PT Wijaya Karya (Persero) Tbk is still in a gray condition where in this condition the company is prone to bankruptcy and experiencing financial problems that must be handled by the company's management appropriately. Meanwhile, PT Adhi Karya (Persero) from 2010 – 2019 showed the potential for financial distress which was strengthened by an unhealthy balance sheet because most of the funding sources came from debt. This study shows that the potential for financial distress arises because of a shift in government policy in development

ОЦІНКА ФАКТОРІВ ОПТИМІЗАЦІЇ ДІЯЛЬНОСТІ СУБ’ЄКТА МІЖНАРОДНОЇ ДІЯЛЬНОСТІ

The research is focused on the analysis of the activities of the subject of international business. The practical aspects of the analysis of multifaceted influencing factors on the export organization of an international company have been studied. The problem of complex analysis by groups of indicators, which allows retrospectively to assess the work of the company (enterprise) based on statistical reporting data, is gaining theoretical understanding. The need to use various approaches to evaluate the effectiveness of the international economic activity of the enterprise is substantiated. An analysis of the indicators was carried out in order to assess the level of creditworthiness of the enterprise. The compliance of the indicators with the normative values was determined. An assessment of the enterprise's creditworthiness is provided as one of the possible factors for optimizing the enterprise's foreign economic activity. The crisis state of the enterprise was also assessed according to Altman's model. The obtained results characterize the financial stability of the enterprise at a high level, testify to the ability to finance its current activities, to conduct settlement operations in a timely manner. In order to improve the organization of international activities, a factor analysis of activity results was carried out. It was established that the financial result was influenced by a number of factors, both positively and negatively. It was emphasized that when organizing work for export, an important issue is the choice of partners on the world market. The principles of foreign economic activity are considered. For effective management of international economic activity, it is proposed to: analyze the indicators that characterize the crisis state of the enterprise according to Altman's five-factor model; determine creditworthiness indicators; give an assessment of factors influencing the financial result. The development of specific plans for the implementation of measures will allow the enterprise to decide on the main types of actions for entering foreign markets and consolidate its competitive advantages.

Comparison analysis of Altman's and Foster's Z-score model in predicting bankruptcy: evidence from Indonesian automotive and component industries

The purpose of this study is to determine differences in bankruptcy predictions of the Altman and Foster models. The sampling technique used is purposive sampling with an observation period of 2016-2018 with 12 company samples per year. The analysis technique uses the Altman and Foster method with the paired two-sample t-test as a hypothesis test tool. Based on the results of the Altman and Foster Z- Score model of automotive and component industries in 2016-2018 it can be concluded that the financial model of Altman and Foster can predict bankruptcy. Furthermore, the hypothesis testing found that there is no differences in the results of the Altman and Foster bankruptcy prediction.

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

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.

The Effect Of Financial Distress Of The Altman, Springate, And Zmijewski Models On Stock Returns Of Indonesian Transportation Issuers In 2016-2020

Financial distress is a condition where the company is unable to fulfill its obligations. This is usually accompanied by consecutive negative earnings. The transportation sector is one sector that is vulnerable to experiencing financial distress. This is due to the many new, more innovative competitors. As well as regulatory policies that are not in favor of this industry. Therefore, the burden borne by the company is not proportional to the income earned, resulting in financial difficulties. To measure this financial distress, the author uses several models such as: Altman model, Springate model, and Zmijewski model. This study examines the effect of financial distress from each of these models on stock returns. The data used in this study comes from financial reports and other financial data for the period 2016-2020. The results showed that there was no significant effect of the Altman, Springate, and Zmijewski models of financial distress on stock returns in this sector.

Analisis Prediksi Kebangkrutan pada Perusahaan Manufaktur Sub Sektor Food & Beverages Yang Terdaftar di Bursa Efek Indonesia: Menggunakan Model Altman, Springate, dan Grover

The objective of this research is to determine (1) the prediction of bankruptcy of manufacturing companies in the food and beverages sub-sector listed on the Indonesia Stock Exchange by using the Altman, Springate and Grover models in the 2016 to 2019 period, and (2) to find out the differences in the results of bankruptcy predictions, and also to determine the level of accuracy of the five models in predicting bankruptcy. Sampling method used Purposive Sampling with some criterias which produce 14 samples of food and beverages firms for 5 years of data observation. The results indicate that, first, by using the Altman models, it is predicted that two companies will experience bankruptcy. Altman model and Grover model predict three companies to go bankrupt, while the Springate model predicts four companies to go bankrupt. Second, there is no significant difference between the Altman, Springate and Grover models in predicting the bankruptcy rate of food and beverages sub-sector companies listed on the Indonesia Stock Exchange. Third, the Springate model is a model that has the highest level of accuracy in predicting the bankruptcy of the food and beverages sub-sector, which has an accuracy rate of 92.86%.

Testing Altman’s Z’’-Score to assess the level of accuracy of the model in Mexican companies

Introduction: in 1968, Altman developed a multivariable predictive Z-score model to assess the probability of a public manufacturing company going to bankruptcy based on financial ratios. Later, Altman (1983) re-stated a more improved Z’’-Score model designed to apply in public or private, manufacturing, or non-manufacturing firms, but also in emerging countries. Prediction of the updated model proved to be highly efficient. This research was conducted to prove the level of accuracy of the Z’’-Score model applied to firms listed in the Mexican Stock Exchange (MSE) since there is little relevant research on this subject. Method: this research was conducted under a quantitative approach as a census and its scope was situational with a non-experimental and longitudinal research design. The period covered by this research was 2012-2019 since the data was available for those years under a somehow stable economic situation without significant economic ups and downs. This research considered the integration of a large financial database and the design of a typology to classify and analyze 155 firms based on a standard deviation and average results of 837 Z’’-scores. A second analysis was conducted to prove if the predicted situation (area) by the Z’’-Score corresponded to the real situation in the marketplace for every company. Results: the results showed that the accuracy level of the Altman model decreased when applied to Mexican firms. The error of the model applied to Mexican companies related to those classified in the bankruptcy prediction area was 75 % of misclassification cases. The total error of the model included all areas, or cases, was 18 % of misclassification cases. This model is supposed to be effective within a time frame of two years before a possible bankruptcy. Even considering a longer time frame, the companies located in the bankruptcy prediction area continued having misclassifications representing 57 % of error. The error for the model considering all cases and all areas, was 14 % of misclassification cases. This represented a high level of inefficiency of the model applied to an emerging country companies, such as Mexico. Discussion or conclusion: the model is certainly effective while predicting companies in the areas of non-bankrupt sector and grey, but it was inefficient when predicting the possibility of bankruptcy. It was also demonstrated that the time frame of two years is no longer effective when applying the model to Mexican companies. As a result, more research cases are needed to update the model to perform efficiently in emerging countries including country-specific conditions and considering a different time frame to predict bankruptcy.

IMPACTS OF EARNINGS MANAGEMENT ON CORPORATE FAILURE: A CASE STUDY OF WIRECARD

The purpose of this study is to find out whether earnings management has impacts on bankruptcy risk based on the data of Wirecard Company. The M-score of Beneish's (1999) model has been used to detect the probability of earnings management. On the other hand, the Z"-Score of Altman's (1968) model has been applied to detect Corporate Failure. Both the models are widely used models in their respective fields. The data from 2002 to 2019 were collected from the annual reports of the Wirecard Company. The result of M-Score indicates that earnings management has a significant impact on the corporate failure (Z-Score) of the company. This finding specifies that a financially distressed firm adopts earnings manipulations. The finding also implies that earnings manipulations harm the financial health of a firm. According to the findings, it can be suggested that to know the financial aspects of a company, both the (Beneish M-model and Altman Z-score model) models could be used concurrently. Beneish M-model is for detecting earnings management and the Altman Z-score model is for determining corporate failure. The novelty of the study is that no study was done on Wirecard Company focusing on the association between earnings management & bankruptcy risk.

Analysis of Financial Distress in Measuring Bankruptcy Before and After The Covid-19 Pandemic

The increasing spread of the Covid-19 virus at this time has forced several company sectors to experience setbacks in their operations. This epidemic has had a major impact, especially on the Transportation Sub-Sector Companies because they have to make some adjustments to government regulations such as implementing health protocols and physical restrictions on travel to break the chain of virus spread. The regulation has an impact on the company's revenue decline and the potency to suffer losses that can result in bankruptcy. This study aims to determine the bankruptcy prediction of the Transportation Sub-Sector Companies listed on the IDX before and after the covid-19 pandemic and to find out the most accurate method. The sampling technique used was non-probability sampling with the purposive sampling technique. The method used is descriptive with a quantitative approach. The results of the hypothesis test show that there are differences in predictions between the Altman and Springate models in predicting bankruptcy before and after the covid-19 pandemic. The Altman model is the most accurate prediction with an accuracy rate of 85.75%, while the Springate model has an accuracy rate of 73%. The study focused on companies listed on the IDX and used two bankruptcy measurement models, so researchers are next expected to use the entire company and other existing bankruptcy prediction, models. In addition, some factors beyond the control of researchers, such as economic conditions that cannot be measured. The renewal of previous research is to use two methods of prediction of bankruptcy, different objects, and research time (before and after the covid-19 pandemic).