scholarly journals An Update on Airline Financial Condition and Insolvency Prospects Using the Altman Z" Score Model

2011 ◽  
Vol 47 (2) ◽  
Author(s):  
Richard D. Gritta ◽  
Bahram Adrangi ◽  
Brian Adams ◽  
Nina Tatyanina
Keyword(s):  
Financial Stress ◽  
Stress Model ◽  
Z Score ◽  
Fuel Prices ◽  
Financial Condition ◽  
Score Model ◽  
Altman Model

Stricken by the events of Sept. 11, 2001, and by increases in fuel prices, a key cost which has spiked upward as the price of oil has risen to more than $130 per barrel, the airlines find themselves, once again, in fragile financial shape. The purpose of this article is to measure the financial condition of the industry, given this situation, using a popular financial stress model, the Altman Model.

scholarly journals Is it Worth Comparing Different Bankruptcy Models?

2015 ◽  
Vol 63 (2) ◽  
pp. 525-531 ◽  
Author(s):  
Miroslava Dolejšová
Keyword(s):  
Financial Performance ◽  
Financial Statements ◽  
T Test ◽  
Small Enterprises ◽  
Z Score ◽  
Statistical Data Analysis ◽  
Financial Condition ◽  
Score Model ◽  
The One ◽  
Paired T Test

The aim of this paper is to compare the performance of small enterprises in the Zlín and Olomouc Regions. These enterprises were assessed using the Altman Z-Score model, the IN05 model, the Zmijewski model and the Springate model. The batch selected for this analysis included 16 enterprises from the Zlín Region and 16 enterprises from the Olomouc Region. Financial statements subjected to the analysis are from 2006 and 2010. The statistical data analysis was performed using the one-sample z-test for proportions and the paired t-test. The outcomes of the evaluation run using the Altman Z-Score model, the IN05 model and the Springate model revealed the enterprises to be financially sound, but the Zmijewski model identified them as being insolvent. The one-sample z-test for proportions confirmed that at least 80% of these enterprises show a sound financial condition. A comparison of all models has emphasized the substantial difference produced by the Zmijewski model. The paired t-test showed that the financial performance of small enterprises had remained the same during the years involved. It is recommended that small enterprises assess their financial performance using two different bankruptcy models. They may wish to combine the Zmijewski model with any bankruptcy model (the Altman Z-Score model, the IN05 model or the Springate model) to ensure a proper method of analysis.

scholarly journals The Prediction of Bankruptcy using Altman Z-Score Model (Case Study in BRI Bank, BNI Bank, Mandiri Bank, BTN Bank)

2018 ◽  
Vol 2 (1) ◽  
pp. 12
Author(s):  
Mrs Herlin
Keyword(s):  
Z Score ◽  
Model Case ◽  
Gray Area ◽  
Score Model ◽  
Altman Model

Based on the calculation of the Altman model in predicting bankrupt at PT. Bank Rakyat Indonesia (Persero) Tbk in 2014, 2015, 2016, PT. Bank Mandiri (Persero) Tbk in 2014 and 2015 and is PT.Bank Tabungan Negara (Persero) Tbk in 2014 with a score of Z-score above 2.99 indicates that included in the company healthy or not potential to go bankrupt. Companies included in the category of unhealthy or potential companies to go bankrupt with a Z-score of less than 1.81 ie PT. Bank Tabungan Negara (Persero) Tbk in 2014 with a Z-score of 1.405 (<1.81). Companies included in the Gray Area (unpredictable) are PT. Bank Negara Indonesia (Persero) Tbk 2015 with Z-score of 2, 753 and year 2016 with Z-score 2,858. PT Bank Tabungan Negara in 2015 and 2016 with Z-score of 2,138 and 1,906 and PT. Bank Mandiri (Persero) Tbk in 2016 which shows the value of Z-score of 2,168.

scholarly journals ANALISIS PREDIKSI KEBANGKRUTAN MENGGUNAKAN MODEL ALTMAN Z-SCORE PADA PERUSAHAAN RITEL YANG TERDAFTAR DI BURSA EFEK INDONESIA TAHUN 2014-2018

2021 ◽  
Vol 9 (1) ◽  
pp. 84
Author(s):  
Rosmayana Rusman
Keyword(s):  
Sampling Technique ◽  
Financial Statements ◽  
Critical Issue ◽  
Bankruptcy Prediction ◽  
Z Score ◽  
Financial Condition ◽  
Dangerous Zone ◽  
Score Model ◽  
Retail Organizations ◽  
High Degree

Bankruptcy is a critical issue that companies must be aware of. Bankruptcy and the level of the company's performance can be seen from the company's financial condition by analyzing the company's financial statements. The most widely used bankruptcy prediction model is the Altman Z-Score model..The Altman Z-Score model analysis was chosen as the model used in bankruptcy prediction because, this model is easy to use with a high degree of accuracy. The purpose of this research is to determine bankruptcy predictions using the Altman Z-Score model in retail companies listed on the IDX in 2014-2018. This kind of exploration is expressive quantitative utilizing monetary reports as an examination instrument. The examining method was,carried out by utilizing purposive sampling,technique which was then controlled by nine retail organizations as the sample. The results show that on average six companies are in a safe zone, including issuers ECII, HERO, MPPA, RANC, SKYB, SONA and two companies in the gray zone or prone to bankruptcy, namely CENT and KOIN, one company in the dangerous zone, namely RIMO

scholarly journals Analisis Penilaian Financial Distress Dengan Menggunakan Model Altman Z-Score Dan Model Springate Pada Perusahaan Manufaktur Yang Terdaftar Di Bursa Efek Indonesia Periode Tahun 2015-2019

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Febriana Anindyka ◽  
Makhmud Zulkifli
Keyword(s):  
Data Analysis ◽  
Financial Distress ◽  
Descriptive Statistics ◽  
Z Score ◽  
Financial Condition ◽  
Similarities And Differences ◽  
The Difference ◽  
Score Model ◽  
A Company ◽  
Data Analysis Methods

The aim of this research to analyze financial distress of Manufature Company by using Altman Z-Score and Springate Models. Moreover, this research aimed to know aims to determine the similarities and differences in the results of the analysis of financial distress assessment using the Altman Z-Score model and the Springate Model. This research used Descriptive statistics and data analysis methods used  in this research were Altman Z-Score and Springate Model. To the finding on the research, it showed that (1) ) After evaluating the Altman Z-Score and Springate models, there are fifty companies that fall into different conditions. (2) The similarities and differences in the results of the Atman Z-Score model and the Springate model are the results of the two models that can be seen from having almost the same variable components and the difference is that the results of the financial distress assessment using the Altman Z-score model and the Springate model show that both These models have different criteria in determining the financial condition of a company.

scholarly journals Analisis Potensi Kebangkrutan PT. Berlian Laju Tanker, Tbk. dengan Menggunakan Altman’s Z Score

2012 ◽  
Vol 3 (2) ◽  
pp. 654
Author(s):  
Iswandi Iswandi
Keyword(s):  
Financial Markets ◽  
Financial Institutions ◽  
Stock Exchange ◽  
Financial Statements ◽  
Financial Statement ◽  
Z Score ◽  
Transportation Services ◽  
Financial Condition ◽  
Score Model ◽  
A Company

PT. Berlian Laju Tanker, Tbk. (BLTA) is a company engaged in the ocean transportation services listed on the Indonesia Stock Exchange and the Singapore Stock Exchange. In 2009 and 2010 BLTA experienced a net loss. At the end of 2011 the company rocked the financial markets in Indonesia and Singapore being unable to meet financial obligations to financial institutions and corporate bondholders. Given such conditions until the end of August 2012 BLTA can not submit audited financial statement of year 2011 to the authorities of stock exchange and public. By using the 2007 to 2010 audited financial statements and June 2011 inhouse financial statement were analyzed using Altman's Z score model can be known that since 2007 BLTA produce a Z score were classified bankruptcy. Investors should analyze the financial condition by using Z Score in order to minimized shareholders and bondholders potential losses.

scholarly journals PREDICTING BANKRUPTCY ON MINING COMPANIES USING ALTMAN Z-SCORE MODEL (EMPIRICAL STUDY ON MINING COMPANIES LISTED IN INDONESIAN STOCK EXCHANGE YEAR 2014-2015)

2019 ◽  
Vol 4 (3) ◽  
pp. 394-404
Author(s):  
Husnul Akhir ◽  
Islahuddin Islahuddin
Keyword(s):  
Stock Exchange ◽  
Good Condition ◽  
Secondary Data ◽  
Management Policy ◽  
Z Score ◽  
Mining Companies ◽  
Financial Condition ◽  
Grey Area ◽  
Analysis Technique ◽  
Score Model

The aim of this study is to calculate Altman Z-Score to predict the possibility of bankruptcy of mining companies listed in Indonesia stock exchange based on the information from the annual financial statementwith observation ranging from 2014 until 2015. The type of data used is secondary data. The study employed Altmant Z-Score calculation on a targeted population of 22 mining companies listed on the Indonesia Stock Exchange.The analysis technique used is the prediction model of bankruptcy Altman Z-score. Using the formula Z-Score = 1,2X1 + 1,4X2 + 3,3X3 + 0,6X4 + 1,0X5, where X1 represents the ratio of liquidity, X2 and X3 as profitability ratios, and X4 and X5 are activity ratios. Then with the use of Z-Score assessment criteria 2.99 categorized as a very healthy company.  1.81 Z-Score 2.99 is in the grey area so the possibility of rescue and possibly bankrupt is as great as it depends on the decision of the company management policy as the decision maker. Zscore 1.81 is categorized as a company that has very large financial difficulties and high risk so that the possibility of bankruptcy is very large. The results show that in 2014, 55% of mining companies predicted bankruptcy, 18% are in grey area and the remaining 27% of mining companies have a healthy financial condition. Then in 2015, 64% of mining companies are predicted to go bankrupt, 23% are in grey area and 13% of companies are in good condition or not bankrupt.

B. Analisis Komparasi Model Altman, Zmijewski Dan Springate Untuk Memprediksi Financial Distress Pada Perusahaan Yang Terdaftar Di Bursa Efek Indonesia

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Dyah Pelitawati ◽  
Ragil Alex Kusumawardana
Keyword(s):  
Financial Distress ◽  
Z Score ◽  
Error Margin ◽  
Score Model ◽  
Altman Model

ABSTRAKSI Financial distress adalah suatu keadaan dimana perusahaan mengalami kesulitan keuangan yang jika tidak segera diatasi atau diambil langkah-langkah penyelamatan, akan berujung pada bangkrutnya atau pailitnya suatu perusahaan. sangat penting kiranya bagi setiap perusahaan untuk bisa memprediksi kejadian financial distress di perusahaannya. Tujuan dari penelitian ini adalah untuk mengetahui bagaimana memprediksi financial distress di masa mendatang dengan menambil data-data dari laporan keuangan yang ada. Penelitian ini menggunakan metode kuantitatif denngan menggunakan model-model yang dikemukakan oleh tokoh-tokoh akademisi terdahulu untuk memprediksi financial distress. Teknik analisis data menggunakan tiga model yang telah dijabarkan oleh akademisi terdahulu yaitu Model Altman (Z-Score), Model Zmijewski (X-Score), dan Model Springate (S-Score). Hasil Penelitian ini menunjukkan bahwa analisis rasio keuangan pada laporan keuangan yang di-publish oleh perusahaan yang tercatat di Bursa Efek Indonesia tetapi kemudian mengalami delisting di tahun 2018 bisa memprediksi terjadinya financial distress. Perusahaan yang mengalami financial distress dengan dibuktikan di-delistingnya perusahaan tersebut dari Bursa Efek Indonesia dan menjadi objek penelitian ini berjumlah empat perusahaan. Dari keempat perusahaan yang mengalami delisting tersebut kemudian dianalisis rasio keuangan pada laporan keuangannya hingga dua tahun sebelum terjadinya financial distress. Dari analisis rasio keuangan ini kemudian dimasukkan ke dalam model yang telah diteorikan oleh tokoh-tokoh tadi.  Dari analisis rasio keuangan dan kemudian dianalisis menggunakan model yang dibuat oleh tokoh-tokoh terdahulu mempunyai hasil bahwa model yang dibuat oleh tokoh-tokoh tersebut akurat untuk memprediksi kejadian financial distress di masa mendatang. Urutan akurasi dari prediksi financial distress ini adalah Model Altman (Z-Score) yang mampu memprediksi sejak dua tahun sebelum terjadinya financial distress kemudian berikutnya adalah Model Zmijewski yang akurat memprediksi setahun sebelum terjadinya financial distress. Terakhir adalah Model Springate (S-Score) yang bisa memprediksi sejak dua tahun sebelum terjadi financial distress akan tetapi model ini mempuyani error margin yang cukup tinggi yaitu 25%.   Kata Kunci:  Financial Distress, Analisis Rasio Keuangan, Model Altman, Model Zmijewski, Model Springate

scholarly journals EVALUATING FINANCIAL HEALTH OF GUJARAT STATE FERTILIZERS THROUGH ‘Z’ SCORE MODEL

2018 ◽  
Vol 6 (7) ◽  
pp. 115-120
Author(s):  
Vikash Saini
Keyword(s):  
Financial Statements ◽  
Business Failure ◽  
Financial Health ◽  
Z Score ◽  
Gujarat State ◽  
Financial Position ◽  
Financial Soundness ◽  
Score Model ◽  
Altman Model

The evaluation of financial health is very useful for financial managers, investors and other users. In this study it is tried to know whether Z score model is able to evaluate financial health of Chambal Fertilizers and Chemicals ltd for past 10 years (2007-08 to 2016-17). Analysis of this paper shows that the model is useful to know the financial soundness of Chambal Fertilizers. In this paper overall results of Z score model indicating that the financial position of the corporation is on alert to exercise the caution. These result shows that Altman model can give good analysis for fertilizers sector in India. Hence it can be concluded that user of financial statements like financial managers, analysts, investors etc can predict business failure or financial soundness of companies through Altman Z score model.

scholarly journals Prediction of financial condition of Indonesian banks using Altman Z-Score model

2020 ◽  
Vol 2 (1) ◽  
pp. 67-78
Author(s):  
St Ibrah Mustafa Kamal ◽  
Luksi Visita
Keyword(s):  
Policy Decision ◽  
Z Score ◽  
Test Analysis ◽  
Financial Reports ◽  
Financial Condition ◽  
Gray Area ◽  
Financial Difficulties ◽  
Study Test ◽  
Score Model ◽  
A Company

Purpose – Banking is one of the hearts of the economy in Indonesia. This study aims to determine the financial condition of banking in Indonesia.Method – The data in this study uses financial reports of bank. The technique of data analysis uses the Altman Z-Score model. By using five variables that represent the liquidity ratio X1, X2 and X3 profitability, X4 and X5 activities. With the criteria of assessment Z-Score 2.99, it is categorized as a very healthy company. 1.81 Z-Score 2.99 is in the gray area so that the probability of being saved and the possibility of bankruptcy is the same depending on the policy decision of the company management as the decision maker. Z-Score 1.81 is categorized as a company which has an enormous financial problem at high risk, so that the possibility of bankruptcy is very largeResult – As the result, it can be seen that Indonesian banks from 2008-2010 were at risk of going through financial difficulties and then survived and in the following years became more stable, while some banks were in unstable but survived and fixed their financial issues.Implication – This research can help Indonesian banks to evaluate their financial performanceOriginality – The originality of this research lies in the object under study, test analysis, and research location.

FRAUD TRIANGLE ANALYSES IN DETECTING FRAUDULENT FINANCIAL STATEMENT USING FRAUD SCORE MODEL

2020 ◽  
Vol 25 (1) ◽  
pp. 29-44
Author(s):  
Mariati ◽  
Emmy Indrayani
Keyword(s):  
Financial Stability ◽  
Linear Regression Analysis ◽  
Financial Statements ◽  
Multiple Linear Regression Analysis ◽  
External Pressure ◽  
Financial Statement ◽  
Fraud Triangle ◽  
Financial Statement Fraud ◽  
Financial Condition ◽  
Score Model

Company’s financial condition reflected in the financial statements. However, there are many loopholes in the financial statements which can become a chance for the management and certain parties to commit fraud on the financial statements. This study aims to detect financial statement fraud as measured using fraud score model that occurred in issuers entered into the LQ-45 index in 2014-2016 with the use of six independent variables are financial stability, external pressure, financial target, nature of industry, ineffective monitoring and rationalization. This study using 27 emiten of LQ-45 index during 2014-2016. However, there are some data outlier that shall be removed, thus sample results obtained 66 data from 25 companies. Multiple linear regression analysis were used in this study. The results showed that the financial stability variables (SATA), nature of industry (RECEIVBLE), ineffective monitoring (IND) and rationalization (ITRENDLB) proved to be influential or have the capability to detect financial statement fraud. While the external pressure variables (DER) and financial target (ROA) are not able to detect the existence of financial statement fraud. Simultaneously all variables in this study were able to detect significantly financial statement fraud.

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