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Purpose. This research paper aims to underscore the significance of Z-score models in assessing the financial status of loan applicants “financially distressed” in their relationship with commercial banks “financially solvent” entities. For this purpose, it uses the results of financial analysis, including various financial ratios, allowing for a comparison between the findings of financial equilibrium analysis methods and the results obtained from applying Z-score models. Additionally, it seeks to identify the most suitable Z-score models for the Algerian business environment in terms of detecting cases of financial insolvency and debt default. Results. The results of a comparative empirical study are presented by juxtaposing the use of financial analysis against scoring models in order to evaluate the decision to grant a bank loan, because the firm’s set of specific investments and contracts is related with the part of the financial surplus and financial support entities. The empirical study found that the reliability of Z-score model outcomes is associated with the nature of financial analysis outputs, particularly with the specificity of the estimation model built according to data specific to a particular business environment, e.g. “SAIDAL Group” during 2021–2023 as one of pharmaceutical firm listed company in Algerian stock exchange. This is because the relative weights embodied in the financial ratio parameters relied upon in each estimation model are linked to the study sample within the context of sectorial benchmark ratios of a specific economic environment. A notable congruence emerges between the findings of financial analysis, particularly in the domain of financial equilibrium scrutiny and the improvement of the financial standing of the focal institution, and the results obtained from the assessment of the efficiency of bank lending processes using Z-score models. Scientific novelty. For the evaluation of loans applicant via Z-score based on financial ratios and financial statement, it is necessary to reformulate scoring models to align with the Algerian business environment, given their critical importance in evaluating loan applications from several clients applying for such financial requests, particularly from the perspective of commercial banks. Initially, the author undertook an analysis of Saidal’s financial situation to identify potential insolvency risks. Subsequently, however, through this study, we reassessed its situation after entering into international partnership contracts with international pharmaceutical laboratories. This is particularly significant given the inherent risks associated with pharmaceutical products, including: quick expiration, scientific and technological obsolescence, and intense scientific competition among pharmaceutical factories in the field of innovation, after the Covid-19. Practical value. The study aims to apply Z-scoring models in one of the leading Algerian company in pharmaceutical production “SAIDAL Group”, following an analysis of its financial stability and tracking its financial status before and after the COVID-19 period. Through a comparative approach, the author compared the digital field of institutional failure between the outputs of financial equilibrium analysis and scoring models. In this paper, the author tried to apply Z-scoring models to the accounting information of Saidal Complex after its listing on the Algerian stock exchange, against the backdrop of the Algerian government’s new policy aiming to involve commercial banks as intermediaries in stock market operations, in particular with the inclusion of Algerian Popular Loan Bank in the official market as one of the listed companies, given its leading role in providing investment loans to industrial institutions.

This study aims to highlight the importance and effectiveness of using the Altman and Sherrod models in predicting the financial failure of Saidal institution during the period 2017-2020 to give early warning in disclosing the likelihood of bankruptcy. In order to achieve the objectives of the study, the two models were applied based on the institution's financial statements and the most important financial indicators. Thus, the study found that the two models are effective in predicting the future financial failure of Saidal institution during the period studied, 100% is needed.

This study aims to highlight the importance and effectiveness of using the Altman and Sherrod models in predicting the financial failure of Saidal institution during the period 2017-2020 to give early warning in disclosing the likelihood of bankruptcy. In order to achieve the objectives of the study, the two models were applied based on the institution's financial statements and the most important financial indicators. Thus, the study found that the two models are effective in predicting the future financial failure of Saidal institution during the period studied, 100% is needed. [ABSTRACT FROM AUTHOR]

Copyright of Muhasebe ve Vergi Uygulamalari Dergisi (MUVU) / Journal of Accounting & Taxation Studies (JATS) is the property of Ankara Serbest Muhasebeci Mali Musavirler Odasi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Insurance firms are known to have unique financial failure characteristics that affect the financial environment of the countries. Therefore, the purpose of this study is to assess the validity of the model used in predicting the financial failures of insurance companies. The model is believed to help in stabilizing the financial environment of the countries by predicting any collapses in the insurance sector. A discriminate regression technique was used to test 28 indicators chosen from 11 financial failure model parameters. 11 parameters of the model are the following: solvency, profitability, operational capabilities, structural soundness, capital expansion capacity, capital adequacy, reinsurance and actuarial issues, management soundness, capital expansion capacity, earnings and profitability, and liquidity. The results of the study proved that 22 variables from 11 parameters were significant; the study also validated the use of the financial failure model as a stable predictor of the financial failure of ASE insurance firms. The stability of the insurance industry is interpreted through the minimum deviation between the real and measured performances. The deviation was present in 3 out of 95 observations, and it affected only 3 firms out of 19, 1 firm out of 3 turned out to be affected by the risker deviation which is the type II error distorted observation. To conclude, the study by mentioning that insurance firms are not threatened by failure or distress and the financial failure model is a valid prediction model.

This study aimed to predict the 1 to 2 year future time of the financial failure of 86 manufacturing companies that are operating in Borsa İstanbul. The data comprised of 2010-2012 period, and it depends on 8 quantitative financial variables. Beside 6 variables come from non financial statements. In the study, Artificial Neural Network (NN), Classification and Regression Trees (CART), Support Vector Machine (SVM) and k-Nearest Neighbors (KNN) were used to compare classification performances of related methods. ROC Curve was used to compare the classification performance of the methods. As a result of the analyseis, the overall classification accuracy from the highest to the lowest was SVM (92,31%), CART (88,46%), ANN (84,62%) and KNN (80,77%) 2 years before the financial failure. The overall classification accuracy from the highest to the lowest was CART (96,15%), ANN (92,31%), SVM (80,77%) and KNN (84,62%) 1 year before the financial failure. Return on Equity (ROE) and Return on Assets Ratio (ROA) were found as important variables in the creation of the CART decision tree. The fact that the four models obtained in thise study predicted financial success/failure at a higher rate, and it shows that the models obtained in this study can be included in the models used by relevant people.

This study aimed to predict the 1 to 2 year future time of the financial failure of 86 manufacturing companies that are operating in Borsa İstanbul. The data comprised of 2010-2012 period, and it depends on 8 quantitative financial variables. Beside 6 variables come from non financial statements. In the study, Artificial Neural Network (NN), Classification and Regression Trees (CART), Support Vector Machine (SVM) and k-Nearest Neighbors (KNN) were used to compare classification performances of related methods. ROC Curve was used to compare the classification performance of the methods. As a result of the analyseis, the overall classification accuracy from the highest to the lowest was SVM (92,31%), CART (88,46%), ANN (84,62%) and KNN (80,77%) 2 years before the financial failure. The overall classification accuracy from the highest to the lowest was CART (96,15%), ANN (92,31%), SVM (80,77%) and KNN (84,62%) 1 year before the financial failure. Return on Equity (ROE) and Return on Assets Ratio (ROA) were found as important variables in the creation of the CART decision tree. The fact that the four models obtained in thise study predicted financial success/failure at a higher rate, and it shows that the models obtained in this study can be included in the models used by relevant people. [ABSTRACT FROM AUTHOR]

The study aimed to use financial analysis to manage financial risk and predict financial failure by focusing on some financial indicators, and forecasting models (sherrod, kida, altman) that give a financial analyst a vision of determining the financial position of The Great Belgeth Mills Based on Financial Statements 2016-2018. The study found that the results of the organization's performance through the negative indicators that appeared on it confirm its prediction of failure, so it had to take the necessary measures to manage the financial risks it faces by addressing weaknesses and taking advantage of the strengths. [ABSTRACT FROM AUTHOR]

In this study, Artificial Neural Networks (NN), C5.0 Classification Algorithm, Classification and Regression Trees (CART) analyses were used to predict the financial success/failure of 126 businesses that are operating in the BIST (Borsa İstanbul) Manufacturing Industry Sector. The data contains the years 2006 to 2009. In the study, 25 quantitative variable and 4 qualitative variable were used. The overall classification accuracy from the highest to the lowest of 3 years prior to successful-failure year (for 2006) is 84.21% for CART, 81.58% for ANN and 76.32% for C5.0, respectively. The overall classification accuracy from the highest to the lowest of 2 years prior to successful-failure year (for 2007) is 86.84% for CART, 84.21% for ANN, 78.95% for C5.0, respectively. The overall classification accuracy from the highest to the lowest of 1 year prior to successful-failure year (for 2008) is 92.11% for CART, 92.11 for ANN and 86.84% for C5.0, respectively. ANN and CART models are notable in terms of their ability to predict upcoming financial failure of unsuccessful businesses with 100% classification accuracy from a year ago. The prediction of the financial success/failure by the three models obtained in the study more than one, two and three years ago shows that the models used in this study can be included in the model used by those concerned. [ABSTRACT FROM AUTHOR]

There are more models in the world to predict the financial stability of the enterprise. One of the models that proved successful in forecasting financial troubles is a Kralicek DF test, developed and used in developed countries of the West. Given the diversity of legal systems, the degree of development of the countries where this model was used has proved to be less or more successful. In this survey, a sample of 398 small and medium-sized enterprises in the Central Bosnia region tested the Kralicek DF test for financial stability assessment in order to determine the eligibility of the Kralicek DF test in Bosnia and Herzegovina. Data from the financial statements of the managed companies 2014. Model showed as successful as they were successful and some other models tested by other authors in this area, which proved to be partially usable.

Copyright of Finance & Business Economies Review is the property of Finance & Business Economies Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to examine the predictability of financial failure in Algerian Insurance Companies, the use of Kida and Sherrod models early predict financial failure and gives an indication concerning parties to intervene and take corrective action before the institution collapses. To achieve the objectives of the research we apply Kida and Sherrod models on Algerian Insurance Companies - CAAR and SAA-based on actual data derived the financial statements for period 2011 - 2012. The most important conclusions of the application of models Sherrod and Kida showed a clear contrast between a typical Kida and Sherrod. [ABSTRACT FROM AUTHOR]

Copyright of Financing is the property of Financing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Success of businesses is an important matter not only for the stakeholders and employees of businesses but also for the state and the society. Prediction of financial failure is also important due to its effects on the same interest groups. Precautions taken today for businesses predicted to face with financial failure in the future will have positive effects on their success. Similarly, they will also have positive effects on making the most accurate investment decisions. In this respect, in this study, financial failure predictions were made upon the data acquired from the 2012 annual financial statements of Borsa Istanbul's businesses in food, drink and tobacco sector. In the study, firstly, discriminant analysis was conducted on 43 financial ratios and five statistically significant financial ratios were acquired for financial failure prediction. Subsequently, using the ratios as criteria in ELECTRE TRI model of multi-criteria decision making (MCDM) approaches, the firms were classified as financially successful or unsuccessful. [ABSTRACT FROM AUTHOR]

• The new method of classifier selection for imbalanced data is proposed. • Selection rule uses the dominance in the FPR/FNR space and crowding distance. • Tests on two real datasets confirm that new method improves the prediction. The goal of the paper is to develop a new algorithm for predicting whether the company will go bankrupt on the base of unbalanced data. To do it, we propose to consider the classification as a multi-objective optimization problem and construct a prediction model as an ensemble while minimizing the parameters FPR (False Positive Rate) and FNR (False Negative Rate) at the same time. To create the ensemble, the proposed algorithm of a Multi-Objective Classifier Selection (MOCS) selects only classifiers that belong to the Pareto-optimal set in FPR/FNR space; that is, there is no dominance between them, and they satisfy some additional conditions. In the general case, MOCS is determined by three parameters: two threshold values that limit false rates (FNR and FPR), and the crowding distance, which defines the uniqueness of the classifier's results. We tested the proposed algorithm on data collected from 2457 Russian companies, 456 of which went bankrupt, and 5910 Polish companies, 410 of which received bankruptcy status. Datasets contain features such as financial ratios and business environment factors. In the testing, we used more than 70 combinations of under-sampling, over-sampling, and no sampling methods with static and dynamic classification models. Final ensembles include seven classifiers for the Russian dataset and four classifiers for the Polish dataset combined by soft voting rule. In both cases, the proposed algorithm produces a significant improvement of prediction results as in terms of standard metrics (geometric mean, the area under the ROC curve) and in the visual representation in the FNR/FPR space, namely in the shift from a Pareto-optimal set of classifiers. [ABSTRACT FROM AUTHOR]