scholarly journals Analisis Akurasi Model-Model Prediksi Financial Distress

2021 ◽  
Vol 9 (3) ◽  
pp. 1196-1204
Author(s):  
Inggar Nur Arini
Keyword(s):  
Early Warning ◽  
Financial Distress ◽  
Annual Reports ◽  
Retail Industry ◽  
Accuracy Rate ◽  
Accurate Predictor ◽  
Predictor Models ◽  
Total Data ◽  
Predictor Model ◽  
Altman Model

This study aims to find the most accurate predictor model of financial distress. The company has the potential to go bankrupt. Bankruptcy can be predicted using an accurate predictor model as an early warning to anticipate financial distress. This research was conducted on the global retail industry which is included in Kantar's 2019 Top 30 Global Retails (EUR). The data in this study were taken from 60 annual reports for the 2018-2019 period and a sample of 30 on global retail companies. The accuracy rate is calculated by the number of correct predictions divided by the total data and multiplied by one hundred percent. This study compares four predictor models of financial distress, namely the Altman model, the Springate model, the Taffler model, and the Grover model. With the results of the study, the Grover model has the highest level of accuracy, which is 76.67%.

scholarly journals Analisis Model Altman, Grover, dan Zmijewski dalam Memprediksi Financial Distress Perusahaan pada Industri Manufaktur di Indonesia

Eksos ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. 13-21
Author(s):  
Luthfi Jauharotul Husna
Keyword(s):  
Financial Distress ◽  
Manufacturing Industry ◽  
Stock Exchange ◽  
Accuracy Rate ◽  
Purposive Sampling ◽  
Analysis Models ◽  
A Company ◽  
Altman Model

This research was conducted to analyze the model altman score, grover score, zmijewski score, in predicting financial distress in the manufacturing industry. This type of research in this research is quantitative with descriptive methods. The object of this research is a company that has been delisted from the Indonesia Stock Exchange in the 2015-2018 period. The object-taking technique in this study was purposive sampling, amounting to 16. The results of this study indicate that the Altman model has an accuracy rate of 25%, Grover has an accuracy rate of 6.25%, Zmijewski has an accuracy rate of 50%. From the three bankruptcy analysis models used in this study it can be concluded that the Zmijewski model is best used as a bankruptcy detector with an accuracy rate of 50%. This is because a company that goes bankrupt has a tendency to generate a small net capital of its total assets, the company's ability to generate profit before interest and taxes from its assets is getting smaller, the lower the level of company sales using all of its assets, and the less likely the profit before tax can be cover current debts owned by the company.

MOTIVES FOR CORPORATE HEDGING: EVIDENCE FROM THE UK

2005 ◽  
Vol 01 (01) ◽  
pp. 0550003 ◽  
Author(s):  
EPHRAIM CLARK ◽  
AMRIT JUDGE
Keyword(s):  
Risk Management ◽  
Financial Distress ◽  
Economies Of Scale ◽  
Foreign Currency ◽  
Annual Reports ◽  
Data Sets ◽  
Growth Options ◽  
Corporate Hedging ◽  
Currency Exposure ◽  
The Uk

In this paper, we use survey data and data from annual reports to identify the determinants of hedging activity of United Kingdom (UK) firms in the context of an overall program of risk management. Comparing the two sets of data makes it possible to identify misclassified firms, that is, firms whose hedging claims are not consistent across the two data sets. Our results on the consistent data show that the likelihood of hedging is related to growth options, foreign currency exposure, liquidity and economies of scale in hedging costs. Contrary to many previous US studies, we also find strong evidence linking the decision to hedge and the expected costs of financial distress. Results for the misclassified firms suggest that they are actually hedgers that hedge less extensively than the correctly classified (CC) hedgers.

Financial Early Warning System for Risk Detection and Prevention from Financial Crisis

Data Mining ◽  
2013 ◽  
pp. 1559-1590
Author(s):  
Nermin Ozgulbas ◽  
Ali Serhan Koyuncugil
Keyword(s):  
Financial Crisis ◽  
Early Warning ◽  
Financial Distress ◽  
Business Performance ◽  
Global Financial Crisis ◽  
Stock Exchange ◽  
Warning System ◽  
Chi Square ◽  
Risk Detection ◽  
The Global Financial Crisis

Risk management has become a vital topic for all enterprises especially in financial crisis periods. All enterprises need systems to warn against risks, detect signs and prevent from financial distress. Before the global financial crisis that began 2008, small and medium-sized enterprises (SMEs) have already fought with important financial issues. The global financial crisis and the ensuring flight away from risk have affected SMEs more than larger enterprises When we consider these effects, besides the issues of poor business performance, insufficient information and insufficiencies of managers in finance education, it is clear that early warning systems (EWS) are vital for SMEs for detection risk and prevention from financial crisis. The aim of this study is to develop and present a financial EWS for risk detection via data mining. For this purpose, data of SMEs listed in Istanbul Stock Exchange (ISE) and Chi-Square Automatic Interaction Detector (CHAID) Decision Tree Algorithm were used. By using EWS, we determined the risk profiles and risk signals for risk detection and road maps for risk prevention from financial crisis.

The Use Of CAMELS In Detecting Financial Distress Of Islamic Banks In Malaysia

2014 ◽  
Vol 30 (2) ◽  
pp. 445 ◽  
Author(s):  
Rashidah Abdul Rahman ◽  
Mazni Yanti Masngut
Keyword(s):  
Neural Network ◽  
Quality Management ◽  
Financial Distress ◽  
Capital Adequacy ◽  
Advisory Council ◽  
Annual Reports ◽  
Islamic Banks ◽  
Return On Assets ◽  
Management Quality ◽  
Asset Quality

The current study uses CAMEL (Capital Adequacy, Asset Quality, Management Quality, Earnings Efficiency, and Liquidity) ratings system, with the addition of Shariah Compliance Ratio (CAMELS) in order to detect the financial distress of Islamic banks in Malaysia. Using neural network, the study analyses data collected from the 17 Islamic banks annual reports for the period 2006 to 2010. It was found that all Islamic banks have higher ETA ratios which portray a good performance of capital adequacy and are less likely to face financial distress. As for asset quality, all Islamic banks did not have the possibility to face financial distress as they are able to handle their non-performing loans throughout the years. Meanwhile for management quality, all Islamic banks show lower ratios in paying salaries to their employee. Earning efficiency for all Islamic banks show better performance and will be less likely to face financial distress in terms of return on assets but not for return of equity. Liquidity indicates that the Islamic banks have a large number of loans but they have sufficient liquid assets in order to cover their liabilities and commitments. Lastly for Shariah Compliance, Islamic banks have complied with all rules and regulations that have been regulated by Bank Negara Malaysias Shariah Advisory Council.

scholarly journals Pengaruh Likuiditas, Leverage, Profitabilitas, dan Ukuran Perusahaan terhadap Financial Distress di Sektor Trade, Service, and Invesment

2021 ◽  
Vol 9 (3) ◽  
pp. 1227-1240
Author(s):  
Hasivatus Sariroh
Keyword(s):  
Firm Size ◽  
Financial Distress ◽  
Ordinal Data ◽  
Stock Exchange ◽  
Secondary Data ◽  
Sampling Technique ◽  
Annual Reports ◽  
Return On Assets ◽  
Current Ratio ◽  
Independent Variables

This study is a quantitative study that aims to determine the effect of the current ratio, debt to asset ratio, return on assets, and firm size on financial distress. Logistic regression method was used to test all relationships between independent variables and dependent variables with nominal/ordinal data scales. The dependent variable in this study is financial distress. The independent variables in this study are liquidity, leverage, profitability and firm size. This study uses secondary data from annual reports of trading, service, and investment companies listed on the Indonesia Stock Exchange from 2016 to 2018. The population used is companies in the trade, services, and investment sectors listed on the Indonesia Stock Exchange (IDX). from 2016 to 2018 with a total of 162 companies selected using purposive sampling technique. The results of hypothesis testing indicate that the current ratio, debt to asset ratio, return on assets, and firm size have no effect on the company's financial distress. From research conducted by researchers, for management to be used as a basis to take corrective actions if there are indications that the company experiencing financial distress. For investors, to be used as a basis in making the right decision to invest in a company.

scholarly journals ASOSIASI KARAKTERISTIK CEO TERHADAP POTENSI KESULITAN KEUANGAN PERUSAHAAN

2020 ◽  
Vol 1 (2) ◽  
pp. 153-168
Author(s):  
Felix Leonardo Tanjaya ◽  
Eko Budi Santoso
Keyword(s):  
Financial Distress ◽  
Work Experience ◽  
Stock Exchange ◽  
Linear Regression Analysis ◽  
Secondary Data ◽  
Total Sample ◽  
Annual Reports ◽  
Company Performance ◽  
Data Types ◽  
Facial Masculinity

This study aims to determine the effect of CEO characteristics interms of facial masculinity, education, and experience to potential of financialdistress. Facial masculinity was measured using dummy variables consistingof masculinity and feminism. Education was measured using dummyvariables of educational level; meanwhile, experience was measured usingdummy variables from CEO work experience. This research used quantitativewith secondary data types taken from annual reports of non-financialcompanies that are listed on Indonesia Stock Exchange. The sampling methodused purposive sampling with the observation period of 2016–2018 andobtained a total sample of 259 samples. The method data analysis usedmultiple linear regression analysis. The result showed that: (1) CEO facemasculinity did not affect financial distress, (2) CEO education did notaffect financial distress, (3) CEO experience positively influenced financialdistress. The results showed the fact that CEO experience is an importantfactor that could improved company performance for avoiding financialdistress potential.

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