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 STABILITY OF ISLAMIC AND CONVENTIONAL BANKS IN BANGLADESH: REVISITING STABILITY MEASURES AND ANALYZING STABILITY BEHAVIOR

2018 ◽  
Vol 3 (2) ◽  
pp. 293-314
Author(s):  
Md Enayet Hossain ◽  
Mahmood Osman Imam
Keyword(s):  
Financial Stability ◽  
Panel Data Analysis ◽  
Capital Adequacy ◽  
Islamic Banks ◽  
Z Score ◽  
Panel Regressions ◽  
Capital Adequacy Ratio ◽  
Z Scores ◽  
Conventional Banks ◽  
The Stability

This study intends to assess the relative financial stability of Islamic banks in Bangladesh using three different Z-Scores as financial stability measures, based on a sample of 29 listed commercial banks (23 conventional and 6 Islamic) in Bangladesh over the period 2005-2016. Apart from the existing measure of financial stability, Z-Score, the paper contributes to the literature by developing an alternative Z-Score based on bank’s loan portfolio infection ratio. We first use pair-wise comparison and find that Islamic banks are financially more stable in two stability measures i.e. Z-Score (based on Capital Adequacy Ratio) and Z-Score (based on Infection Ratio). We then perform static (random effects) and dynamic (GMM) panel data analysis. By controlling for bank-specific, industry-specific and macroeconomic variables in the regressions, we find that Islamic banks are financially more stable in 2 panel regressions of Z-Score (based on Infection Ratio). We also find that the presence of Islamic banks increases the stability of all banks in the system including their conventional peers.

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.

Financial Risk and Financial Imbalances

2020 ◽  
pp. 1-31
Author(s):  
Carlos Piñeiro Sanchez ◽  
Pablo de Llano-Monelos
Keyword(s):  
Random Sample ◽  
Financial Stability ◽  
Financial Risk ◽  
Financial Data ◽  
Financial Ratios ◽  
Diagnostic Tools ◽  
Z Score ◽  
Resource Based View ◽  
It Resources ◽  
The 1960S

The study of the financial imbalances of companies is a common topic for academics and practitioners because bankruptcy affects financial stability and modifies the investors' behavior. Since the 1960s, financial ratios have been used as diagnostic tools and also as independent variables within models aimed at quantifying firms' financial risk (e.g., Altman's Z-Score). In parallel, the strategic theory has developed theoretical constructs to explain why competitiveness is empirically heterogeneous. The resource-based view argues that companies can outperform rivals if they manage scarce, expensive, and hard-to-imitate resources. Ultimately, outperformers should be able to avoid (or overcome) financial imbalances. This chapter intends to analyze whether IT resources modify firm performance and financial risk. To do that, the authors collected data from a random sample of Galician SMEs, combining questionnaires, focused interviews, and public financial data. Hypotheses are explored by applying parametric statistical methods.

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 Operational control over the financial stability of banking

2020 ◽  
Vol 15 (1) ◽  
pp. 51-58
Author(s):  
Angela Kuznetsova ◽  
Borys Samorodov ◽  
Galyna Azarenkova ◽  
Kateryna Oryekhova ◽  
Maksym Babenko
Keyword(s):  
Credit Risk ◽  
Financial Stability ◽  
Control Charts ◽  
Banking System ◽  
Banking Regulation ◽  
Capital Adequacy ◽  
Operational Control ◽  
Regulatory Capital ◽  
Control Value ◽  
Banking Institutions

Maintaining proper financial stability of each banking institution is one of the main tasks facing the banking system of Ukraine. This enables operational control over the financial strength of banking activities.The purpose of the article is to develop recommendations on the operational control of financial stability of banking and to test them using banking institutions in Ukraine as an example.To execute operational control over the financial stability of banking, economic standards of banking regulation are grouped under the “at least” or “not exceeding” principle. To determine their change over time, Shewhart control charts are proposed.The recommendations were tested through the example of the Ukrainian banking institutions (with state, foreign and private capital). It was found out that in 2017–2019, the following three economic standards of banking regulations were not met: regulatory capital adequacy, high credit risk, and average investments; besides, there were two standards at the limit of control value: the ratio of regulatory capital to total assets and the maximum amount of credit risk per counterparty.To improve the financial status of banking institutions, it is recommended to take organizational and financial measures to change the average value of the relevant economic standards for banking regulation to a level that ensures financial stability.

scholarly journals LEVERAGE INDUCED FINANCIAL DISTRESS OF MANUFACTURING FIRMS IN BANGLADESH: A COMPARISON BETWEEN LISTED MNCS AND DOMESTIC FIRMS BY APPLYING ALTMAN’S Z SCORE MODEL

2021 ◽  
Vol 5 (2) ◽  
pp. 28-43
Author(s):  
Syed Mohammad Khaled Rahman ◽  
Md. Khairul Islam ◽  
Md. Mofazzal Hossain
Keyword(s):  
Financial Distress ◽  
Business Performance ◽  
Secondary Data ◽  
Jel Classification ◽  
Grey Zone ◽  
Z Score ◽  
Industrial Sectors ◽  
Total Debt ◽  
Z Scores ◽  
Domestic Companies

Financial distress arises from excessive debt capital. The purpose of the study was to determine Altman's Z score and analyze as well as compare the effect of debt on Z scores of listed MNCs & domestic companies of Bangladesh over 24 years (1996-2019). The study was based on secondary data. Seven local companies and seven MNCs were selected as a sample from six manufacturing industrial sectors. It was found that on average one local firm was in the grey zone and the rest 13 firms were in the safe zone (Z scores>2.99). MNCs’ Z scores were significantly higher than that of domestic companies. The grand mean of the Z score of MNCs was 5.398 while that of domestic companies was 4.155. In the case of domestic companies, Z score changes by 0.01 or 0.24% for a 1% change of total debt in opposite direction. MNCs’ Z score decreases by 0.005 or 0.073% for a 1% increase of total debt. Domestic companies should increase Z score by redesigning the capital structure and improving basic earning power. The study has practical implications for corporate managers, policymakers, investors, and government because future strategy, policy, and business performance depend on the zone in which the firms are situated. JEL Classification Codes: G30, G32, G39.  

scholarly journals Financial distress in Brazilian banks: an early warning model,

2017 ◽  
Vol 29 (77) ◽  
pp. 312-331
Author(s):  
Paulo Sérgio Rosa ◽  
Ivan Ricardo Gartner
Keyword(s):  
Early Warning ◽  
Financial Distress ◽  
Financial Stability ◽  
Balance Sheet ◽  
Warning Signs ◽  
Capital Adequacy ◽  
Financial Indicators ◽  
Forecast Horizon ◽  
Early Warning Model ◽  
Warning Model

ABSTRACT This study aims to propose an early warning model for predicting financial distress events in Brazilian banking institutions. Initially, a set of economic-financial indicators is evaluated, suggested by the risk management literature for identifying situations of bank insolvency and exclusively taking public information into account. For this, multivariate logistic regressions are performed, using as independent variables financial indicators involving capital adequacy, asset quality, management quality, earnings, and liquidity. The empirical analysis was based on a sample of 142 financial institutions, including privately and publicly held and state-owned companies, using monthly data from 2006 to 2014, which resulted in panel data with 12,136 observations. In the sample window there were nine cases of Brazilian Central Bank intervention or mergers and acquisitions motivated by financial distress. The results were evaluated based on the estimation of the in-sample parameters, out-of-sample tests, and the early warning model signs for a 12-month forecast horizon. These obtained true positive rates of 81%, 94%, and 89%, respectively. We conclude that typical balance-sheet indicators are relevant for the early warning signs of financial distress in Brazilian banks, which contributes to the literature on financial intermediary credit risk, especially from the perspective of bank supervisory agencies acting towards financial stability.

Is Financial Distress Value Relevant? – Implications for Multiple-Based Valuation

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Sabrina Goetz
Keyword(s):  
Financial Distress ◽  
Firm Value ◽  
Peer Group ◽  
Z Score ◽  
Target Firm ◽  
Distress Risk ◽  
Percentage Difference ◽  
Standard And Poor's ◽  
Z Scores ◽  
Relative Valuation

AbstractIn relative valuation peer groups of comparable companies are essential to derive the value of the firm. Valuing a target firm that is in financial distress by using a set of healthy peer group firms probably leads to an overvaluation. We examine whether the financial distress risk has an influence on a company’s value and quantify the discount through financial distress. We identify financial distress by Standard and Poor’s long-term issuer ratings and Altman’s z″-score. We then match the identified firms in financial distress with healthy counterparts that are comparable in value relevant characteristics, i. e. profitability, risk, and growth, to estimate the percentage difference in valuation multiples. Using rating information, in every year almost half of the companies are in financial distress whereas by Altman’s z″-score about 20% of the companies in the sample are in financial distress. We find that the discount caused by financial distress makes up about 4–7% of firm value. The discount increases for lower rating classes and lower z″-scores. Besides the degree of financial distress, market downturns as the financial crisis affect the distress discount.

scholarly journals ESTIMATION OF Z-SCORE FOR UKRAINIAN BANKING SYSTEM

10.23856/3003 ◽  
2018 ◽  
Vol 30 (5) ◽  
pp. 43-51
Author(s):  
Bohdan Kyshakevych ◽  
Ivan Klymkovych
Keyword(s):  
Financial Stability ◽  
Banking Sector ◽  
Banking System ◽  
System Stability ◽  
Z Score ◽  
Macroeconomic Factors ◽  
Explanatory Variables ◽  
Banking Business ◽  
Score Model ◽  
Bank System

The article analyzes the problematic aspects of evaluating the financial stability of banking systems on the basis of the Z-score methodology. The econometric model estimation of Z-score for the Ukrainian banking system was constructed where the following indicators were chosen in role of explanatory variables:  the share of foreign capital in bank system, inflation, change in nominal GDP and share of overdue loans in credit portfolio. We have conducted the analysis of the banking sector in Ukraine on the base of the constructed Z-score model and determined macroeconomic factors that have the most significant impact on the Z-score assessment and banking system stability.  Drawbacks and limitations of the Z-score methodology usage in banking business are discussed.

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

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

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

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