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EN
This paper contains an analysis of the level of security of banks operating in Poland, with forms of the ownership taken into account. The results of the analysis have shown specificity of our country when compared to research already published. During volatile conditions in global markets nearly constant level of security of the cooperative banks and high dynamicity of indicators of commercial banks should be pointed out. Both BFG safety and the Z-score indexes lead to the conclusion that the financial crisis has highlighted the weakness of basing the security of banks on apparent, above-average financial results and used leverage, with the simultaneous abandonment of strengthening the capital base and of maintaining quality of loan portfolios.
EN
Research background: Since the turn of the 21st century financial statement manipulations became the center of attention for accountants, auditors and financial analysts. Since being classified by the regulators as fraudulent, earnings management has required a separate detection methodology. The majority of detection research is performed through the comparison of a large number of statements for the same company in order to find irregularities in earnings behavior. Shortening of the detection time and the amount of data becomes important. Purpose of the article: The goal was to compare the characteristics of M-Score and ∆P-∆R and to find their advantages and limitations. Applying both indicators to the different samples, the research attempted to determine the statistical connection between them and to set up the limits of their applicability. Since M-Score indicator is liquidity-based, this research attempted to determine to which extent M-Score and Z-Score are statistically related. Methods: The research paper compares the behavior of both indicators using various samples of financial data: the sample of companies, charged with fraud, the sample with exceptional liquidity, the large random sample and the sample from the emerging market economy. Based on the original observations, two other subsamples (one based on poor Z-Score and one based on exceptional Z-Score) were extracted from the main sample. For all samples ∆P-∆R, M-Score and Z-Score were statistically compared among and between themselves. Findings/value added: The research found the limitations of ∆P-∆R and M-Score in the stable markets and was able to connect them in the emerging market by using linear regression model (also including Z-Score). The research confirmed that M-Score can mistake exceptional performance for manipulations, resulted in Type I errors. ∆P-∆R appeared somewhat coarse and prone to Type II errors. The combined use of both in the emerging markets will provide the best approach.
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Discriminatory Power of the Altman Z-Score Model

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EN
This article aims to assess the discriminatory power of one of the most famous and most discussed corporate predictive models, the Altman ZScore. This model ranks among the bankruptcy models, whose main purpose is to detect the impending bankruptcy in good time. The research focuses on three main areas of assessing the discriminatory power of the model. The first part deals with the overall discriminatory power of the model; the second part is aimed at quantifying the impact of individual variables on misclassification of enterprises in bankruptcy. The last part quantifies the discriminatory power of individual variables of the model. The results are compared with the findings of the author of the model. The empirical research is based on the accounting data of Czech companies from the manufacturing industry. Both thriving and bankrupt companies are included in the research.
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