scholarly journals Measuring Financial Distress and Predicting Corporate Bankruptcy: An Index Approach

2016 ◽  
Vol 9 (1) ◽  
pp. 33-51 ◽  
Author(s):  
Qunfeng Liao ◽  
Seyed Mehdian
Keyword(s):  
Financial Distress ◽  
Predictive Power ◽  
Predictive Accuracy ◽  
Parametric Models ◽  
Financial Ratios ◽  
Z Score ◽  
Corporate Bankruptcy ◽  
Financial Failure ◽  
Index Approach ◽  
Multivariate Discriminant

AbstractIn this paper, we follow Anderson et al. (2009) and suggest a simple approach to employ a set of financial ratios as inputs to estimate an aggregate bankruptcy index (ABI). This index is a within sample measure, ranges between 0 and 1, and ranks the firms on the basis of their relative financial distress. ABI can be used to predict the propensity of financial failure and corporate bankruptcy. For the purpose of comparison and assessment of the robustness of this index, we estimate Z-score by multivariate discriminant analysis, using the same set of financial ratios to compare the predictive accuracy of two approaches.We find that, to some extent, ABI can predict the bankruptcy of the firms more accurately than Z-score. The empirical results of the paper suggest that ABI has relatively robust predictive power and, therefore, can be applied together with other, based on parametric and non-parametric models to predict corporate bankruptcy.

scholarly journals Financial Distress Analysis Using Altman Z-Score Model In Sharia Banking In Indonesia

2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Firman Setiawan
Keyword(s):  
Financial Distress ◽  
Quantitative Data ◽  
Good Condition ◽  
Good Health ◽  
Financial Ratios ◽  
Islamic Banks ◽  
Z Score ◽  
Gray Area ◽  
Financial Failure ◽  
Score Model

This study is conducted to explain the risk of financial failure that may occur in Sharia banking which is known asfinancial distress. Quantitative data in the formof financial ratios fromseveral Islamic banks which are the object of the study were analyzed using Altman Z- score model. After analyzing it, it is known that at Bank BRI Syariah Inc, the company experienced financial distress in 2015, in the following year the company experienced improvementthus in 2016-2019, the company was in good health. Bank BTPN Syariah Inc in 2015-2019 was in a good condition, which means that there was no financial distress or was in a gray area position. Bank Syariah Mandiri Inc experienced financial distress in 2015-2019, which means that during that time the company was in a gray area position

scholarly journals ANALISIS FINANCIAL DISTRESS PADA PT TUNAS BARU LAMPUNG TBK DI BURSA EFEK INDONESIA

2017 ◽  
Vol 13 (2) ◽  
pp. 129-141
Author(s):  
Umi Ambarwati ◽  
Sudarwati Sudarwati ◽  
Rochmi Widayanti
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Financial Ratios ◽  
Z Score ◽  
Effective Strategies ◽  
Operational Costs ◽  
Score Method

This article aims to analyze the health of the company in PT Tunas Baru Lampung TBK in Indonesia Stock Exchange. The data comes from PT Tunas Baru Lampung TBK in 2013-2015. The methods used are Altman Z-Score, Springate, Zmijewski and Fulmer methods. The results of the study show that there are differences in predicted bankruptcy results between the Altman Z-score method, Springate, Zmijewski and Fulmer. This is due to differences in the use of financial ratios and criteria bankruptcy between Altman Z-score, Springate, Zmijewski and Fulmer. For that company is expected to increase sales, perform effective strategies, reduce operational costs to be more efesian so that companies can meet the company's health criteria.   Artikel ini bertujuan untuk menganalisis kesehatan perusahaan pada PT Tunas Baru Lampung TBK di Bursa Efek Indonesia. Data berasal dari PT Tunas Baru Lampung TBK pada tahun 2013-2015. Metode yang digunakan adalah metode Altman Z-Score, Springate, Zmijewski dan Fulmer. Hasil penelitian menunjukkan adanya perbedaan hasil prediksi kebangkrutan antara metode Altman Z-score, Springate, Zmijewski dan Fulmer. Hal ini karena adanya perbedaan penggunaan rasio keuangan dan kriteria kebangkrutan antara Altman Z-score, Springate, Zmijewski dan Fulmer. Untuk itu perusahaan diharapkan meningkatkan penjualan, melakukan strategi yang efektif, menekan biaya operasional agar lebih efesian sehingga perusahaan dapat memenuhi kriteria kesehatan perusahaan.

scholarly journals Analysis on the Performance of Technology Companies with Z-score Model

2018 ◽  
Vol 7 (4) ◽  
pp. 633-639
Author(s):  
Lam Weng Hoe ◽  
Yeoh Hong Beng ◽  
Lam Weng Siew ◽  
Chen Jia Wai
Keyword(s):  
Organizational Performance ◽  
Financial Distress ◽  
Dominant Factor ◽  
Z Score ◽  
Financial Ratio ◽  
Technology Sector ◽  
Financial Failure ◽  
Distress Level ◽  
Information And Communication ◽  
Score Model

Local technology sector plays a significant role in information and communication technology (ICT) based innovations and applications which enhance organizational performance as well as national economic growth and labor productivity. In this paper, financial performance of the listed Malaysia companies in technology sector is analyzed and evaluated. Altman’s Z-score model is proposed due to its robustness in determining companies’ financial distress level using five financial ratios as variables. The computed Z-score values classify the financial status of the companies into distress, grey and safe zones. This study investigates the financial data of 23 listed technology-based companies in the Main Market of Bursa Malaysia over the period of 2013 to 2017. The findings reveal that the percentage of safe zone companies increase throughout the five years whereas distress zone companies decline. It is concluded that financial ratio for market value of equity to total liabilities is the dominant factor that directly influences the level of financial distress among these technology-based companies in Malaysia. These research outcomes provide an insight to investors or policy makers to develop future planning in order to avoid financial failure in local technology sector.

FINANCIAL DISTRESS PREDICTION AND SEEING THE INFLUENCE OF THE ALTMAN RATIO ON COMPANY FINANCIAL DISTRESS

2018 ◽  
Vol 23 (3) ◽  
pp. 236-243
Author(s):  
Hadhi Dharmaputra Juliyan ◽  
Bertilia Lina Kusrina
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Bankruptcy Prediction ◽  
Z Score ◽  
Large Role ◽  
Corporate Bankruptcy ◽  
Mining Companies ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Score Model

This research aims to determine the level of the bankruptcy of the company and to see if the Altman ratio can predict the condition of corporate bankruptcy in mining companies on the Indonesia Stock Exchange because mining companies have a large role in the Indonesian economy. This study uses the Altman Z-Score model analysis to see how much the company's bankruptcy prediction and uses logistic regression to see how much the influence of the Altman ratio in predicting corporate bankruptcy. Keywords: financial distress, the Altman z–score, bankruptcy prediction

scholarly journals Predicting Financial Stability of Select BSE Companies Revisiting Altman Z Score

2014 ◽  
Vol 26 ◽  
pp. 92-105 ◽  
Author(s):  
Vineet Chouhan ◽  
Bibhas Chandra ◽  
Shubham Goswami
Keyword(s):  
Financial Distress ◽  
Financial Stability ◽  
Financial Analysis ◽  
Optimal Solution ◽  
Capital Adequacy ◽  
Financial Ratios ◽  
Z Score ◽  
Regulatory Capital ◽  
Explanatory Variables ◽  
Z Scores

In the era of globalization, prediction of financial distress is of interest not only to managers but also to external stakeholders of a company. The stakeholders are continuously seeking the optimal solution for performance forecasting, as a way to rationalize the decision-making process. The recent past shows that financial stability of companies is at the stake. Stockholders, Managers, Creditor and employees of the business are always concerned about financial stability of the companies. The most frequently tool for financial analysis is financial ratios. However, financial ratios are no-longer proved appropriate for „Stockholders‟ equity position and creditors‟ claims. Stakeholder‟s have concerns about the consequences of financial distress for companies, and controls of capital adequacy through the regulatory capital requirement (Mingo, 2000). This shared interest creates persistent investigations and continuing attempts to answer an incessant question that how financial distress can be predicted, or what reveals the credit risk of firms. For this purpose most commonly used tool is Altman Z score, but due to nature of the explanatory variables, financial distress prediction research has not reached an unequivocal conclusion. The primary goal of this paper is to analyze and reexamine the Altman Z score. In order to facilitate the current research, various ratios were taken from Altman‟s Z score. To fulfill our objective Z score ratios were used to divide sample firms into healthy and unstable among BSE-30 companies. First the Z score is calculated for 10 companies selected for this purpose for a period of 5 years each. And then it is divided as per z scores, later the significant in the changes in the ratio is calculated with the help of One sample Komogrov-Smirnow test, which resulted that the change in the z scores is not significant in case of all the companies.

scholarly journals Financial Distress Prediction of Airlines Companies

2021 ◽  
Vol 5 (2) ◽  
pp. 3-15
Author(s):  
Afiruddin Tapa ◽  
Nurfarah Lyana Ahmad Razif
Keyword(s):  
Middle East ◽  
Financial Distress ◽  
Descriptive Analysis ◽  
Z Score ◽  
Financial Distress Prediction ◽  
Financial Failure ◽  
Significant Difference ◽  
Distress Prediction ◽  
Score Model ◽  
Significant Model

The purpose of this study is to compare three financial failure models: the Altman Z-Score Model, the Springate Model, and the Zmijewski Model, in terms of predicting financial difficulty among airlines in Asia and the Middle East. Based on the results of this study, it is proven by the result of the analysis done for Airlines in Asia and the Middle East that all the three models have predicted that these companies are in financial distress. But, the Altman Z-Score model is the most significant model to forecast financial distress. Although the models employ different ratios in their analyses, this study demonstrates that there is a substantial difference in the analysis of these three models. Another independent T-test demonstrates that the Altman Z-Score Model and the Zmijewski Model, as well as the Springate Model and the Zmijewski Model, have substantial differences. The study employed a descriptive and comparative analysis method, and this model was created to compare the independent variables. The Altman Z-Score model is the most significant model for predicting the financial failure of enterprises, according to the descriptive analysis in this study. While the comparison findings show a large difference between the Altman Z-Score Model and the Zmijewski Model, there is also a significant difference between the Springate Model and the Zmijewski Model. The Altman Z-Score Model and the Springate Model, on the other hand, imply that there is no significant model.

scholarly journals PREDICTION OF FINANCIAL DISTRESS IN THE AUTOMOTIVE COMPONENT INDUSTRY: AN APPLICATION OF ALTMAN, SPRINGATE, OHLSON, AND ZMIJEWSKI MODELS

2020 ◽  
Vol 1 (4) ◽  
pp. 606-618
Author(s):  
Hendra Pratama ◽  
Bambang Mulyana
Keyword(s):  
Panel Data ◽  
Financial Distress ◽  
Sampling Technique ◽  
Safe Zone ◽  
Financial Ratios ◽  
Grey Zone ◽  
Z Score ◽  
Automotive Component ◽  
Automotive Component Industry ◽  
Early Warnings

This study aims to identify and examine the condition of financial distress in the automotive component industry issuers in the period 2014 ~ 2018, using the Altman Z-score, Springate S-score, Ohlson O-score, and Zmijewski X-score against financial ratios as an analysis form of company management to predict the early warnings of company bankruptcy. This study uses quantitative, secondary, and panel data; while the sample uses a non-probability boring sampling technique of 11 companies. The results showed that these four models can predict financial distress by identifying each model. Altman’s model found 8 distress zone points, 16 grey zone points, and 31 safe zone points. Springate’s model found 37 points in the distress zone, and 18 points in the safe zone. Ohlson's model found 3 points in the distress zone, and 52 points in the safe zone. Zmijewski's model found only 1 point in the distress zone

scholarly journals The Effect Of Financial Ratios To Financial Distress Using Altman Z-Score Method In Real Estate Companies Listed In Indonesia Stock Exchange Period 2014 - 2018

2019 ◽  
Vol 19 (2) ◽  
pp. 119
Author(s):  
Ouw Desiyanti ◽  
Wahyoe Soedarmo ◽  
Kristian Chandra ◽  
Kusnadi Kusnadi
Keyword(s):  
Real Estate ◽  
Financial Distress ◽  
Stock Exchange ◽  
Financial Ratios ◽  
Z Score ◽  
Return On Equity ◽  
Financial Ratio ◽  
The Real Estate ◽  
Financial Condition ◽  
Gray Areas

<p><strong><em>Purpose</em></strong><em> - The purpose of this paper is to find out the effect of Financial Ratio on Financial Distress using Z-Score Altman method.</em></p><p><strong><em>Design/Methodology/approach</em></strong><em> - This paper uses data from 21 property and real estate companies listed in BEI period 2014-2018 with 105 data observations. The variables used are ROE (Return On Equity), DER (Debt to Equity Ratio), CR (Current Ratio), WCR (Working Capital Ratio) and Z-Score.</em></p><p><strong><em>Findings </em></strong><em>- The results show that ROE and WCR have a positive significant effect on Z-Score Altman's financial distress, DER and CR have negative significant effect on Z-Score Altman's financial distress. While simultaneously shows that at least one variable have a significant effect on Z-Score Altman financial distress.</em></p><p><em>The financial condition of companies in the real estate sector has worsened over the years, marked by the increasing number of companies that were in financial distress from 5 companies in 2014 to 9 companies in 2018. Likewise with companies in the financial condition of gray areas from 8 companies in 2014 became 9 companies in 2018. While companies with a healthy financial condition decreased from 8 companies in 2014 to 3 companies in 2018.</em></p><p><strong><em>Research limitation/implications</em></strong><em> - The sample is small, and consequently, findings may not be generalisable to the population.</em></p><p><strong><em>Originality/value</em></strong><em> - This paper aims to obtain empirical evidence of how financial ratios affect financial distress and also the exposure of financial distress probabilities to real estate companies that are used as research samples.</em></p>

scholarly journals Rancang Bangun Sistem Prediksi Financial Distress Menggunakan Metode Altman Z-Score Modifikasi Pada Koperasi XYZ

2021 ◽  
Vol 6 (2) ◽  
pp. 72
Author(s):  
Ni Made Lissa Primadani ◽  
Ni Kadek Ariasih
Keyword(s):  
Financial Distress ◽  
Financial Ratios ◽  
Prediction System ◽  
Z Score ◽  
Financial Reports ◽  
For Profit ◽  
Financial Condition ◽  
Calculation Results ◽  
Score Method ◽  
Z Value

Financial distress is a condition in which a company begins to show signs of bankruptcy due to a decline in the company's financial condition. XYZ Cooperative is a savings and loan cooperative that needs to be aware of experiencing financial distress. What's more, the XYZ cooperative has experienced fluctuating asset conditions, even for profit in a certain month, it has experienced a negative or loss condition. Another problem that occurred is the covid-19 pandemic which made it difficult for people to carry out their obligations such as paying credit. This made the XYZ Cooperative's income experience a drastic decline from the previous year. Meanwhile, currently XYZ Cooperative does not have a prediction system or has never predicted financial distress in its company Based on these problems, an idea emerged to design and build a financial distress prediction system at the XYZ Cooperative using the Modified Altman Z-Score method. This model was chosen because the financial ratios used in prediction calculations are in accordance with the financial ratios of the XYZ Cooperative. The final result of this research is the construction of a prediction system using the Altman Z-Score Modification method on the desktop-based XYZ Cooperative. From the 3 years of financial reports that were tested from 2017 to 2019, the prediction results were at a safe point, namely the Z value above 2.6. The calculation results for 2017 is 8.19, 2018 is 8.11 and 2019 is 7.82. However, the predicted Z value from 2017 to 2019 has decreased, although it is not significant. The accuracy calculation obtained compares the prediction results with the real situation of the cooperative using the average comparison formula and the typer error II formula. Where from the 3 sample data tested, the results of the sample received were 3 with a data error of 0.

ANALISIS PREDIKSI POTENSI KEBANGKRUTAN PADA PT BANK MUAMALAT INDONESIA Tbk PERIODE 2012-2016 DENGAN MENGGUNAKAN METODE ALTMAN MODIFIKASI

2017 ◽  
Vol 5 (2) ◽  
pp. 287-324
Author(s):  
Dewi Laela Hilyatin
Keyword(s):  
Financial Performance ◽  
Financial Structure ◽  
Financial Statements ◽  
Financial Ratios ◽  
Z Score ◽  
Altman Analysis ◽  
Gray Area ◽  
Modification Method ◽  
A Company

Abstract Bankruptcy is a very essential issue that every company should be aware of. Bankruptcy of a company can be minimized by advanced prediction; such as analyzing the financial statements. This study discusses the financial performance of PT Bank Muamalat Indonesia Tbk, which indicates that there is a degression in some number of financial ratios, the closing of offices and firing of employees in 2012-2016, causing he fact that BMI must pay attention and improve its financial performance and anticipate the existence of a bankruptcy in the company. Based on Altman analysis modification for financial performance of PT Bank Muamalat Indonesia Tbk in 2012-2016, it found Z-Score value of 0,825, 0,659, 1,243, 0,982 and 0,892. Based on Z-Score criteria, PT Bank Muamalat Indonesia Tbk is predicted to experience problems in management and financial structure and also in potentially bankruptcy due to Z-Score value <1,1 while the highest Z-Score value is in 2014, which shows the value of Z-Score>1,1 and <2,6, which means the company is in the gray area, meaning the company’s category is not said to be bankrupt and also not healthy. Keywords: Bankruptcy, Altman Modification Method

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