An alternative Z-score measure for downside bank insolvency risk

Lepetit, Lætitia; Strobel, Frank; Tran, Thu Ha

doi:10.1080/13504851.2020.1739222

Cited by 11 publications

(13 citation statements)

References 18 publications

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“…The dependent variable of FS consists of bank z -score (FST1), which is considered an unbiased indicator measuring the risk faced by banks, their probability of default and the capacity of the banking system to absorb shock (Ahamed and Mallick, 2019; Lepetit et al. , 2021; Vo et al.…”

Section: Methodsmentioning

confidence: 99%

Role of social sustainability for financial inclusion and stability among Asian countries

Malik

Jais

Isa

et al. 2022

IJSE

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PurposeAsia is the largest and most densely inhabited region in the world. Despite exhibiting an extremely expeditious economic growth, the majority of the world population categorized as poor resides in Asia, with more than a billion people financially excluded. This study aims to assess how social sustainability (SS) may increase financial inclusion (FI) and maintain financial stability (FS) in Asia.Design/methodology/approachEstablished on the stakeholder theory, the study analyzed the association among SS, FI and FS in Asia, employing a generalized method of moment’s estimation. The mediation of FI was also investigated in the relationship between SS and FS. Moreover, this study has analyzed the alternative proxies for the variables of interest to ensure dynamic results.FindingsThe findings point toward a positive association among SS, FI and FS. Furthermore, FI is observed to be undertaking a partial mediating role between SS and FS.Practical implicationsThis study emphasizes that both SS and FI have individual parts in the amelioration of FS in Asia, whereas previous studies implied that FI is a mere tool for stimulating SS. Hence, Asian policymakers must keep these outcomes in mind due to their simultaneous contribution to FS.Originality/valueThe relationship between SS, FI and FS has received little attention in the literature. No previous study has deduced that increasing SS may instigate an increase in FI and FS. Additionally, quite contrary to previous studies that relied on narrow indicators, this study develops a broad measurement of SS by considering a wide range of crucial indicators for a sustainable society.

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Section: Methodsmentioning

confidence: 99%

Role of social sustainability for financial inclusion and stability among Asian countries

Malik

Jais

Isa

et al. 2022

IJSE

View full text Add to dashboard Cite

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“…The bank is categorized as insolvable if the Z-Score < 0 and/or the CAR ratio less than its threshold based on the standard of each bank. The higher the CAR and Z-Score, the more solvable the bank (Lepetit et al 2020). The Z-Score formula used was: Z-Score: ((ROA + (equity/total assets))/ROA standard deviation…”

Section: Methodsmentioning

confidence: 99%

“…The increase in NPL reduced cash flow, profit, and CAR (Mayes and Stremmel 2012;Donnellan and Rutledge 2016). Consequently, a decrease in CAR increased the bank solvency risk, as measured by Z-Score (Lepetit et al 2020). This research used the NPL, CAR, and Z-Score ratios to show the solvency risk.…”

Section: Literature Reviewmentioning

confidence: 99%

Early Warning Early Action for the Banking Solvency Risk in the COVID-19 Pandemic Era: A Case Study of Indonesia

et al. 2021

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The COVID-19 pandemic has affected people’s lives and increased the banking solvency risk. This research aimed to build an early warning and early action simulation model to mitigate the solvency risk using the system dynamics methodology and the Powersim Studio 10© software. The addition of an early action simulation updates the existing early warning model. Through this model, the effect of policy design and options on potential solvency risks is known before implementation. The trials conducted at Bank BRI (BBRI) and Bank Mandiri (BMRI) showed that the model had the ability to provide an early warning of the potential increase in bank solvency risk when the loan restructuring policy is revoked. It also simulates the effectiveness of management’s policy options to mitigate these risks. This research used publicly accessible banking data and analysis. Bank management could also take advantage of this model through a self-stimulation facility developed in this study to accommodate their needs using the internal data.

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Section: Z-score Bankrupt Modelmentioning

confidence: 99%

“…Applying the well-known Altman Z-score model [43][44][45][46][47][48] by financial economist Altman [49] allows observing US bankrupt and non-bankrupt companies using 22 financial ratios and mathematical statistics method to establish the Z-score model with five financial variables. In particular, the approach of the Z-score model is well designed, and this model has been widely applied in various fields, such as for studying obesity risk [43], learning disabilities [44], BMI [45], insolvency risk [46], and coronary artery disease [47]. Based on the five main variables required by the Z-score model [48,49], its definition mainly includes 10 financial ratios: working capital, total assets, working capital, statutory surplus reserve, undistributed earnings, earnings before interest and taxes (EBIT), common stock market value at the end of the quarter, total liabilities, and net operating income, and different calculations are given to construct similar regression equations to distinguish functions, so as to confirm whether the company is facing financial bankruptcy.…”

Section: Z-score Bankrupt Modelmentioning

confidence: 99%

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

Chen

Lin

et al. 2021

Mathematics

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In recent years in Taiwan, scholars who study financial bankruptcy have mostly focused on individual listed and over-the-counter (OTC) industries or the entire industry, while few have studied the independent electronics industry. Thus, this study investigated the application of an advanced hybrid Z-score bankruptcy prediction model in selecting financial ratios of listed companies in eight related electronics industries (semiconductor, computer, and peripherals, photoelectric, communication network, electronic components, electronic channel, information service, and other electronics industries) using data from 2000 to 2019. Based on 22 financial ratios of condition attributes and one decision attribute recommended and selected by experts and in the literature, this study used five classifiers for binary logistic regression analysis and in the decision tree. The experimental results show that for the Z-score model, samples analyzed using the five classifiers in five groups (1:1–5:1) of different ratios of companies, the bagging classifier scores are worse (40.82%) than when no feature selection method is used, while the logistic regression classifier and decision tree classifier (J48) result in better scores. However, it is significant that the bagging classifier score improved to over 90% after using the feature selection technique. In conclusion, it was found that the feature selection method can be effectively applied to improve the prediction accuracy, and three financial ratios (the liquidity ratio, debt ratio, and fixed assets turnover ratio) are identified as being the most important determinants affecting the prediction of financial bankruptcy in providing a useful reference for interested parties to evaluate capital allocation to avoid high investment risks.

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An alternative Z-score measure for downside bank insolvency risk

Cited by 11 publications

References 18 publications

Role of social sustainability for financial inclusion and stability among Asian countries

Role of social sustainability for financial inclusion and stability among Asian countries

Early Warning Early Action for the Banking Solvency Risk in the COVID-19 Pandemic Era: A Case Study of Indonesia

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

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