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... sampling technique adopted for the study is convenience sampling. Table 1 shows the sample count of Non-Banking Financial Institutions (NBFIs) based on their classification. For this research purpose Non-Banking Financial Institutions (NBFIs) were divided into three segments such as Asset or Leasing Finance Company, Investment Finance Company, Housing Finance Company. ...Context 2
... sampling technique adopted for the study is convenience sampling. Table 1 shows the sample count of Non-Banking Financial Institutions (NBFIs) based on their classification. For this research purpose Non-Banking Financial Institutions (NBFIs) were divided into three segments such as Asset or Leasing Finance Company, Investment Finance Company, Housing Finance Company. ...Similar publications
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Citations
... However, for non-public companies the situation is different: only Fulmer's model has 100% accuracy. This is in line with studies conducted by Putri and Werastuti (2021), Mustofa and Fahad Noor (2020), Oz and Yelkenci (2015), Masdiantini and Warasniasih (2020), which show that Fulmer's model is the most accurate. ...
This study examines if there are variations among financial crisis models. It is intended to investigate whether it has the most significant level of accuracy in predicting potential corporate bankruptcies. This is a quantitative study; Secondary information from financial reports serves as the data source. The study population is public and non-public companies in the construction sector listed on the Indonesia Stock Exchange (IDX) for 2014–2020. In order to obtain a sample of eight businesses, targeted selection was used for sampling. The results of this study show that the conditions differ from those of financial distress models for public and non-public companies. For public companies, the most accurate models are Grover and Lavin’s (2001), Karas and Srbová’s (2019), Fulmer’s (1984), and Ohlson’s (1980) models proven to be 100 percent. In contrast, only Fulmer’s model is entirely applicable to non-public companies. Forecast results and best-fit models can provide positive information or warnings for external and internal parties.
... The Altman's Z score score distress prediction is not only the prediction model of bankruptcy in the world. There is also some other model like Springate, Fulmer, and Logit model can be used to predict companies' failure (Jaloudi, 2019;Mustofa & Fahad Noor, 2020). However, the Altman's Z score score model can be used widely by the investors and stakeholders to make the optimum decision regarding the company's financial performance and prediction of company failure. ...
Nowadays, the forecast of the financial crisis is a significant concern for all companies and stakeholders. As a fast-growing economy, the insurance industry in Bangladesh is also a significant concern. Nevertheless, the performance and contribution of Bangladeshi insurance companies are highly criticized by scholars. Therefore, with the motivation to provide a comprehensive overview of the financial health of the general insurance industry in Bangladesh, we have done secondary research on a total of 18 general insurance companies in Bangladesh from 2014-2018. Throughout the study, we tested the widely accepted Altman Z Score model to predict a major financial concern called bankruptcy. We found that 95% of the selected companies secured the safe but not in the highly satisfactory calculated value of the Altman Z score model. Therefore, as expected, this finding highlights the success of the growing nature insurance business in Bangladesh. Also, as the Z score model has high predictive power in the case of predicting financial distress, our findings could be valuable path for stakeholders in making the right decision for investment.
This study is conducted to evaluate the financial health of Non-Bank Financial Institutions (NBFIs) in Bangladesh and to point out financially distressed companies through the use of different models. The study also determined whether is there any distinction in the result of different models for anticipating the insolvency of 21 NBFIs listed in Dhaka Stock Exchange (DSE) before 2012. Panel data (a mixture of time series and cross-section data) have been utilized. According to Altman’s Z score 48% (10 companies) of the total operating NBFIs of Bangladesh are facing the threat of insolvency, Zmijewski X score shows that 62% or 13 firms are in threat, Springate S score identifies 52% or 11 firms and Grover G score pinpoints 48% as financially distressed organizations. There are seven companies to face probable bankruptcy. Models which have been utilized here have correctly identified the 2 organizations such as ILFSL and PLFSL as prone to bankruptcy. Zmijewski x score model indicates financial distress better than other models for the NBFIs listed on DSE. Scholars can depend on the outcome and further research through the addition of other insolvency prediction models. Authorities, shareholders and decision-makers can utilize this study for taking preventive measures along with making future investment decisions.
İşletmeler faaliyetleri süresince farklı zorluklarla karşı karşıya kalmaktadırlar. Bu zorluklardan biri de finansal yükümlülüklerini zamanında yerine getirememe durumudur. Söz konusu durum işletmenin iç koşullarına veya dış çevre faktörlerine bağlı olarak ortaya çıkabilmektedir. Dolayısıyla işletmelerin sağlıklı bir mali yapıya sahip olabilmelerinde finansal risklerinin belirlenmesi önem arz etmektedir. Bu çalışmada çimento sektöründe faaliyet gösteren ve Borsa İstanbul'da işlem gören işletmelerin Altman, Springate ve Fulmer modelleri aracılığıyla finansal başarısızlık tahmininin yapılması amaçlanmıştır. 2017-2021 dönemini kapsayan çalışma sonucunda işletmelerin finansal başarı/başarısızlık durumlarının modele göre farklılaştığı gözlemlenmiştir. Bununla birlikte gerçekleştirilen analiz sonuçlarına göre tüm modellerde finansal açıdan başarılı olan işletme sayısı 2 olurken, finansal başarısızlık riski taşıyan işletme sayısı da 1 olarak tespit edilmiştir. (During their operations, businesses face various challenges, and one of these challenges is the inability to fulfill their financial obligations on time. This situation can be caused by internal conditions of the business or external environmental factors. Therefore, it is important to identify financial risks for businesses to have a healthy financial structure. In this study, it is aimed to predict financial failure of cement companies operating in Borsa Istanbul through the Altman, Springate and Fulmer models. The study, which covers the period of 2017-2021, reveals that the financial success/failure of companies differs according to the model. However, according to the analysis results, in all models, the number of companies that are financially successful is 2, while the number of companies that carry the risk of financial failure is 1.)
Non-bank financial institutions (NBFIs) are recognized as the fundamental of a financial market as they complement the banking institutions. Since 1981, NBFIs have been playing a vital role in the economic growth of Bangladesh. Unfortunately, in the recent years most of the NBFIs have been found financially distressed. However, few NBFIs that were included in our sample claimed themselves as potential companies with sound financial performance though it was highly criticized. Therefore, the motivation for conducting this study is to examine the financial soundness of selected NBFIs using Altman’s Z score (1995). This study involved 20 NBFIs out of 23 Dhaka Stock Exchange (DSE) listed institutions, which were selected based on information availability by considering A, B and Z categories from 2014 to 2018 period. The secondary data were collected from the annual reports of the selected companies over the period. The findings are as follows: 95% of the 20 NBFIs were in distress zone during the study period and only 5% NBFIs were in safe zone during 2017-2018 period. Therefore, the analysis predicted that within the upcoming years a few of the NBFIs will be approaching bankruptcy. Finally, it is suggested that the government, respective regulatory authority, and policy makers to pay an immediate attention on mitigating the factors affecting the financial distress.
