scholarly journals Classic Insolvency Prediction Models Tested On Romanian Insurance Companies

2016 ◽  
Vol 12 (13) ◽  
pp. 18
Author(s):  
Alexandra Oniga
Keyword(s):  
Emerging Markets ◽  
Prediction Models ◽  
Factor Model ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Insurance Companies ◽  
Insurance Sector ◽  
Altman Model

This paper aims to analyse the applicability of classical bankruptcy prediction models for the Romanian insurance companies. Using four models, the Altman model, the Z-factor model, the Springate model and the model used to determine insolvency probability for the emerging markets we have conducted a study to see if they apply to Romanian insurance companies’ financial statements for the years between 2011 and 2013. We will present each model separately, analysing the indicators that led to the obtained results. In the end, we will combine the results to establish the applicability of these models to the Romanian insurance sector.

scholarly journals Laporan Keuangan dan Prediksi Kebangkrutan Perusahaan

2020 ◽  
Vol 5 (1) ◽  
pp. 196
Author(s):  
Putu Riesty Masdiantini ◽  
Ni Made Sindy Warasniasih
Keyword(s):  
Data Analysis ◽  
Prediction Model ◽  
Prediction Models ◽  
Stock Exchange ◽  
Secondary Data ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Analysis Techniques ◽  
Accuracy Level ◽  
Altman Model

This study aims to determine differences in bankruptcy predictions at company’s sub-sector of cosmetics and household listed on the Indonesia Stock Exchange (IDX) using the Altman model, Springate model, Zmijewski model, Taffler model, and Fulmer model, and to determine the bankruptcy prediction model that is the most accurate of the five bankruptcy prediction models. This study uses secondary data in the form of company financial statements for the period 2014-2018. Data analysis techniques in this study used the Kruskal-Wallis test. The results showed there were differences in bankruptcy predictions using the Altman model, Springate model, Zmijewski model, Taffler model, and Fulmer model. The Zmijewski, Taffler, and Fulmer models have the same accuracy level of 100% so that the three prediction models are the most accurate prediction models for predicting the potential bankruptcy at companies sub-sector of cosmetics and household listed on the IDX.

scholarly journals Asset Quality as a Predictor of Rural Bank Bankruptcy in Indonesia

2021 ◽  
Vol 4 (1) ◽  
pp. 44-45
Author(s):  
Hesti Budiwati ◽  
Ainun Jariah
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Classification Accuracy ◽  
Prediction Models ◽  
Predictor Variable ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Asset Quality ◽  
Productive Assets

The study aims to form a bankruptcy prediction model of rural bank in Indonesia at a time variation of 1 quarter (MP1), 2 quarters (MP2), 4 quarters (MP4), and 8 quarters (MP8) before bankruptcy. The quality of productive assets as a predictor variable consist of CEA, CEAEA, and NPL. The condition of rural bank bankrupt and non bankrupt as a dependent variable. The analytical method used is logistic regression followed by testing the model accuration. The population of this study is rural bank in Indonesia. The sample used was 241 rural banks that consist of 41 bankrupt rural banks and 200 non bankrupt rural banks. The data used are the quarterly financial statements of 2006 to 2019. The study result showed that of the four prediction models that successfully built, the 1 quarter (MP1) is the most feasible and accurate used as bankruptcy prediction model of rural banks in Indonesia that formed by CEAEA and NPL ratio. The MP1 has a classification accuracy of 93,8% at the level of modelling with cut off point of 0,29 and it has a classification accuracy of 83,93% at the level of validation with cut off point of 0,12. Based on those advantage, the MP1 was chosen as a model that able to predict the bankruptcy of rural bank in Indonesia.

scholarly journals АНАЛИЗА РЕВИЗОРСКИХ ИЗВЕШТАЈА ОСИГУРАВАЈУЋИХ КОМПАНИЈА У РЕПУБЛИЦИ СРБИЈИ

TEME ◽  
2019 ◽  
pp. 1277
Author(s):  
Biljana Jovkovic
Keyword(s):  
Financial Reporting ◽  
Financial Statements ◽  
Insurance Companies ◽  
Social Significance ◽  
Financial Reporting Standards ◽  
Insurance Sector ◽  
Audit Firms ◽  
The Social ◽  
Big Four

Successful and long-term compliance of insurance company’s operations with assumed risks is of fundamental importance for the economic system. Starting from the social significance of insurance institution and the need for stability in this activity, the systematization and analysis of the independent auditors’ reports of insurance companies’ financial statements between 2009 and 2016 was conducted. The structure and frequency of qualifications in auditors' reports, which are the result of non-compliance with financial reporting standards, were analyzed. All insurance companies are stratified to companies that were always issued auditor’s unmodified opinion in the observed period and companies that were at least once issued some form of qualification in the observed period, including the reasons for issuing unmodified opinion with on emphasis of matter. The results of the analysis showed that companies with unmodified opinion were mostly profitable, of foreign capital origin, and engaged auditors of the “Big Four”. Companies that were at least once issued some form of qualification in auditor’s opinion are predominant in the insurance sector, mostly operated at a loss and were of domestic sources of financing. Companies that were continuously issued modified opinion disappeared from the insurance market, entered into a voluntary liquidation procedure, or their operating license was revoked. Moreover, the frequency of certain audit firms in the verification of companies’ financial statements was analyzed. The results show that PWC audit firm had the largest participation in the audit of insurance companies, Deloitte of companies which were issued modified in opinion, while KPMG auditing firm reported the highest number of unmodified opinions in the insurance sector.

scholarly journals Forecasting of Bankruptcy: Evidence from Insurance Companies in Russia

2021 ◽  
Vol 13 (4) ◽  
pp. 75-90
Author(s):  
Julia A. Tarasova ◽  
◽  
Ekaterina S. Fevraleva ◽  
Keyword(s):  
Financial Stability ◽  
Predictive Power ◽  
Prediction Models ◽  
Binary Classification ◽  
Classification Tree ◽  
Bankruptcy Prediction ◽  
Insurance Companies ◽  
Random Forest Algorithm ◽  
The Past ◽  
Net Profit

This work is devoted to creating a model which could predict bankruptcy of Russian insurance companies. The aim of the study is to build a model based on panel data; its final version should have a good predictive power. Said topic is relevant because the number of revoked licenses has changed a lot over the past few years — this situation may influence both insurance organizations and the population in a negative way. The paper reflects the main characteristics of bankruptcy as well as analyzes the bankruptcy prediction models which have been made by various authors since the 20th century. In the practical part of the study, an econometric analysis of the collected data was carried out and a logit model was built. The model’s predictive power was tested on a sample of insurers. In addition, a random forest algorithm and a binary classification tree algorithm were used. As a result, it was discovered that the volume of insurance premiums to net profit ratio, which could be calculated only for insurers, and financial stability coefficients influence insurance companies’ bankruptcy the most. Further research can be expanded by including new, more sophisticated methods, such as neural networks or boosting.

scholarly journals The Accuracy of the Altman, Ohlson, Springate and Zmejewski Models in Bankruptcy Predicting Trade Sector Companies in Indonesia

2020 ◽  
Vol 3 (1) ◽  
pp. 334-347 ◽  
Author(s):  
Sri Elviani ◽  
Ramadona Simbolon ◽  
Zenni Riana ◽  
Farida Khairani ◽  
Sri Puspa Dewi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Prediction Models ◽  
Binary Logistic Regression ◽  
Bankruptcy Prediction ◽  
Accurate Model ◽  
Analysis Technique ◽  
Trade Sector ◽  
Altman Model

Bankruptcy prediction models continue to develop both in terms of forms, models, formulas, and analysis systems. Various bankruptcy prediction studies currently conducted aim to find the most appropriate and accurate bankruptcy prediction model to be used in predicting bankruptcy. This study aims to determine the most appropriate and accurate model in predicting the bankruptcy of 53 trade sector companies in Indonesia. The analysis technique used in this study is binary logistic regression. The results of this study prove that the most appropriate and accurate model in predicting bankruptcy of trade sector companies in Indonesia is the Springate model and the Altman model

scholarly journals COMPARISON OF BANKRUPTCY PREDICTION MODELS ANALYSIS (ALTMAN, SPRINGATE, GROVER, OHLSON AND ZMIJEWSKI) ON PT. ASURANSI HARTA AMAN PRATAMA, TBK.

2018 ◽  
Vol 1 (2) ◽  
pp. 156
Author(s):  
Dyah Puspitasari Sunaryo Putri
Keyword(s):  
Analysis Of Variance ◽  
Financial Services ◽  
Quantitative Research ◽  
Prediction Models ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Annual Reports ◽  
Research Approach ◽  
General Insurance

PT. Asuransi Harta Aman Pratama, Tbk. is a financial services company which is specialized in general insurance. This research is conducted based on five annual reports spanned from 2012 to 2016 which are independently audited. This study, therefore, aims to compare with five models bankruptcy predictions; the Altman, Springate, Grover, Ohlson and Zmijewski. This study uses a quantitative research approach, using the financial statements data of PT. Asuransi Harta Aman Pratama, Tbk. which also has been published in BEI. This paper analyze the prediction of every method using analysis of variance. The result of this analysis show the differences between the applied models.

scholarly journals PREDICTIVE POTENTIAL AND RISKS OF SELECTED BANKRUPTCY PREDICTION MODELS IN THE SLOVAK BUSINESS ENVIRONMENT

2017 ◽  
Vol 18 (6) ◽  
pp. 1156-1173 ◽  
Author(s):  
Beata GAVUROVA ◽  
Miroslava PACKOVA ◽  
Maria MISANKOVA ◽  
Lubos SMRCKA
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Business Environment ◽  
Bankruptcy Prediction ◽  
The Other ◽  
Prediction Ability ◽  
Other Hand ◽  
Analytical Structure ◽  
Ohlson Model ◽  
Altman Model

In our study, we focused on the assessment of four bankruptcy prediction models, to figure out which model is most appropriate in the conditions of the Slovak business environment. Based on the previous research within the Slovak conditions, we set a portfolio of 4 models to be assessed: Altman model (1984), Ohlson model (1980), indexes IN01 and IN05 that were validated on the sample of 700 Slovak companies. Based on previous studies we expected that IN indexes are superior to Ohlson and Altman model. The excellency of our research lies in validation and assessing the accuracy of bankruptcy prediction models at three levels: the overall accuracy, accuracy of the bankruptcy prediction, and the non-bankruptcy prediction accuracy. This analytical structure enables to look at the topic more complexly and to increase the objectification of accuracy of analysed models. Based on the results, we showed that Ohlson model is not applicable to predict bankruptcy in the Slovak conditions as reached the lowest bankruptcy prediction ability even if has high non bankruptcy prediction ability. On the other hand, we have confirmed our expectation about the bankruptcy prediction ability of index IN05, that is proven to be superior to Ohlson and Altman model and so is the most appropriate model for Slovak business environment.

Prediksi Kebangkrutan Pada Perusahaan Yang Terdaftar Di Bursa Efek Indonesia

2021 ◽  
Vol 5 (2) ◽  
pp. 126-141
Author(s):  
Melati Eka Putri ◽  
Auliffi Ermian Challen
Keyword(s):  
Prediction Models ◽  
Stock Exchange ◽  
Secondary Data ◽  
Sampling Technique ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Analytical Models ◽  
Ratio Analysis ◽  
Debt Capital ◽  
Score Model

This study aims to examine the potential for bankruptcy of companies with three analytical models, namely Altman Z-Score, Springate S-Score, and Zmijewski X-Score, and assess the level of accuracy of the three models. Each model uses ratio analysis with the elements of assets, debt, capital, and company profits. This study uses a sample of coal mining companies listed on the Indonesia Stock Exchange (IDX) during the 2014-2018 period. The sampling technique in this study used purposive sampling and obtained 24 sample companies. This study uses secondary data, namely the company's financial statements obtained from IDX's official website. This study calculates financial ratios, compares the scores of the three bankruptcy prediction models, and tests the model's accuracy. The results of this study show that of the three models, the Springate S-Score model is the most accurate in predicting bankruptcy, with an accuracy rate of 83.33%, as evidenced by two companies that were delisted from the IDX. This study can be used as a reference and as material for consideration in making investment decisions for companies and investors.

scholarly journals REASSESSMENT OF CORPORATE BANKRUPTCY PREDICTION MODELS EFFICIENCY: EVIDENCE FROM SERBIA/ПОНОВНА ПРОЦЕНА ПРОГНОСТИЧКЕ МОЋИ МОДЕЛА ЗА ПРЕДВИЂАЊЕ СТЕЧАЈА У РЕПУБЛИЦИ СРБИЈИ

TEME ◽  
2017 ◽  
pp. 1367
Author(s):  
Vule Mizdraković ◽  
Milena Bokić
Keyword(s):  
Prediction Models ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
The Other ◽  
Z Score ◽  
Corporate Bankruptcy ◽  
Statistical Relevance ◽  
The Republic ◽  
Business Entities ◽  
Corporate Bankruptcy Prediction

Having in mind various negative influences that corporate bankruptcy has on the economy of the Republic of Serbia, corporate bankruptcy prediction is of extreme importance. Therefore, the basic motive for writing this paper was an attempt to assess the possibility of forecasting bankruptcy of business entities which operate on the Republic of Serbia's market. We have calculated the already formed M-score, formed based on the data from the financial statements of Serbian business entities. As a comparison models, we have calculated the two most acknowledged Z-score models. The randomly chosen sample consisted of 35 entities in bankruptcy and the same number of non-bankrupt entities. The goal of the research was to reassess the relevance of the tested models for a longer period, as well as their precision in the corporate bankruptcy prediction in an unstable economic environment of the Republic of Serbia. According to the results, the conclusion is that the tested M-score proved its precision in bankruptcy prediction in Serbia, and its use is, therefore, recommended. On the other hand, the Altman’s Z-score models do not have statistical relevance and hence we recommend that their use for bankruptcy prediction in the Republic of Serbia should be with caution.

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