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Despite the sophisticated regulatory regime established in Solvency II, analysts should be able to consider other less complex indicators of the soundness of insurers. The Z-score measure, which has traditionally been used as a proxy of individual risk in the banking sector, may be a useful tool when applied in the insurance sector. However, different methods for calculating this indicator have been proposed in the literature. This paper compares six different Z-score approaches to examine which one best fits insurance companies. The authors use a final dataset of 183 firms (1,382 observations) operating in the Spanish insurance sector during the period 2010–2017.

In the first stage, the authors opt for a root mean squared error (RMSE) criterion to evaluate which of the various mean and SD estimates that are used to compute the Z-score best fits the data. In the second stage, the authors estimate and compare the explanatory power of the six Z-score measures that are considered by using an ordinary least squares (OLS) regression model. Finally, the authors report the results of the baseline equation using the system-GMM estimator developed by Arellano and Bover (1995) and Blundell and Bond (1998) for dynamic panel data models.

The authors find that the best formula for calculating the Z-score of insurance firms is the one that combines the current value of the return on assets (ROA) and capitalization with the SD of the returns calculated over the full sample period.

The main limitation of the research is that it addresses only the Spanish insurance sector, and consequently, the implications of the findings must be framed in this institutional context. However, the authors think that the results could be extrapolated to other countries. Future research should consider including different countries and analyzing the usefulness of aggregated insurer-level Z-scores for macroprudential monitoring.

The Z-score may be a useful early warning indicator for microprudential supervision. In addition to being an indicator of the soundness of insurers simpler than those established in the current regulation, the information provided by this accounting-based measure may help analysts and investors obtain a better understanding of insurance firms' risk factors.

To the best of the authors’ knowledge, this study is the first to examine and compare different approaches to calculating Z-scores in the insurance sector. The few available results on the predictive power of the Z-score are mixed and focus on the banking sector.

雖然在償付能力標準II 內已建立了精密的監管制度，但分析人員應可以考慮以不太複雑的指標，來分析保險公司的穩健程度。Z-分數的估量在銀行業一向作為是個體風險的代理而使用，而Z-分數如應用於保險業，或許會成為有用的工具。唯在文獻裏，學者和研究人員提出了不同的方法來計算這個指標。本文比較六個不同的Z-分數估量方法，以研究出最適合保險公司的方法。我們使用一個最終數據集，包括在2010年至2017年期間在西班牙保險業界營運的183間公司（1382 個觀察）。

在首個階段，我們選擇使用一個方均根誤差(RMSE) 標準來衡量用來計算Z-分數的各個平均值和標準差估量中哪個最適合使用於有關的數據。在第二個階段， 我們以普通最小平方 (OLS) 迴歸模型，去估計並比較被考慮的六個Z-分數估量的解釋力。最後，我們以Arellano與Bover (1995), 以及Blundell與Bond (1998) 為動態追蹤資料模型而發展出來的系統-廣義動差估計推定量，來發表我們基線方程式的結果。

我們發現，計算保險公司Z-分數的最佳公式是把資產收益率及資本總額的現值，和在整個樣本期間計算出來的囘報的標準差結合起來的公式。

我們研究主要的局限為：研究只涉及西班牙的保險業；因此，研究結果的含意，必須在這個體制的背景框架下來闡釋。唯我們相信研究結果或許可外推至其它國家。未來的研究，應考慮納入不同國家作為研究對象，並分析保險公司層面的集成Z-分數的功用，以求達到宏觀審慎監控的目的。

Z-分數或許就微觀審慎監管而言是一個有用的早期警告器。這些以會計為基礎的估量而提供的資訊，除了較現時規例内已建立顯示保險公司穩健程度的各個指標更簡單外，還會幫助分析人員和投資者更了解保險公司的風險因素。

據我們所知，本研究為首個研究，去探討並比較保險業內的Z-分數的計算方法。以前關於Z-分數預測能力的，為數不多並可供取閱的研究結果均不統一；而且，這些研究都聚焦探討銀行業。

This study aims to develop a model based on the financial variables for better accuracy of financial distress prediction on the sample of private French, Spanish and Italian firms. Thus, firms in financial difficulties could timely request for troubled debt restructuring (TDR) to continue business.

This study used a sample of 312 distressed and 312 non-distressed firms. It includes 60 French, 21 Spanish and 231 Italian firms in both distressed and non-distressed groups. The data are extracted from the ORBIS database. First, the authors develop a new model by replacing a ratio in the original Z”-Score model specifically for financial distress prediction and estimate its coefficients based on linear discriminant analysis (LDA). Second, using the modified Z”-Score model, the authors develop a firm TDR probability index for distressed and non-distressed firms based on the logistic regression model.

The new model (modified Z”-Score), specifically for financial distress prediction, represents higher prediction accuracy. Moreover, the firm TDR probability index accurately depicts the probabilities trend for both groups of distressed and non-distressed firms.

The findings of this study are conclusive. However, the sample size is small. Therefore, further studies could extend the application of the prediction model developed in this study to all the EU countries.

This study has important practical implications. This study responds to the EU directive call by developing the financial distress prediction model to allow debtors to do timely debt restructuring and thus continue their businesses. Therefore, this study could be useful for practitioners and firm stakeholders, such as banks and other creditors, and investors.

This study significantly contributes to the literature in several ways. First, this study develops a model for predicting financial distress based on the argument that corporate bankruptcy and financial distress are distinct events. However, the original Z”-Score model is intended for failure prediction. Moreover, the recent literature suggests modifying and extending the prediction models. Second, the new model is tested using a sample of firms from three countries that share similarities in their TDR laws.

The pandemic COVID-19 has affected every sector of an economy in every possible way. Banking sector of Bangladesh has been affected by it badly. The purpose of this paper is to find out the impact of COVID-19 on the liquidity and financial health of the listed banks in Bangladesh.

Liquidity ratios are calculated to measure the liquidity condition of the banks and revised Altman's Z-Score Model for non-manufacturing companies is used to measure the financial health. The ratios are compared before and during the COVID-19 periods to assess the impact.

The findings of this study indicate a deterioration of liquidity position and financial health of the listed banks after the emergence of this pandemic. Though the banks have poor liquidity ratios and financial health prior to the emergence of this pandemic, they have decreased more in the second quarter of 2020. Most of the banks have poor liquidity ratios and cash position. The listed Islamic Banks have poor financial health than the listed Commercial Banks and all the banks belong to the red zone in all the quarters.

The results of this study will have policy implications for companies and regulators of money market.

This paper is a pioneer initiative in assessing the impact of COVID-19 pandemic on liquidity and financial health based on empirical data.

This study aims to examine whether there are differences between financial statements prepared in accordance with International Financial Reporting Standards (IFRS) and financial statements prepared in accordance with local accounting standards in terms of its ability to present the financial conditions of companies listed on the Saudi Stock Exchange as one of the emerging markets.

Data on study variables were obtained from the published financial statements of 67 of listed companies in the Saudi Stock Exchange during the period 2014–2019. The study addressed the research hypotheses by using Altman Z-score model. Both the T-test and Wilcoxon rank test were used to investigate the significance of differences between the values of Z-score and the individual variables included in the model in the pre- and post-IFRS mandatory adoption periods.

The results revealed a decrease in the values of Z-score as well as the values of the individual variables included in the model in the period following the adoption of IFRS than it was before the adoption of IFRS, which indicates the ability of IFRS to show the financial conditions of companies more transparently than local accounting standards. However, the results of the T-test and Wilcoxon test showed that these decreases were not statistically significant.

This study has some limitations, including the small sample size as a result of the small size of the Saudi Stock Exchange, As well as the reliance of this study only on the Altman model with its five variables in assessing financial conditions without examining the impact of other factors that may affect the financial conditions of companies.

Financial conditions of the companies have important implications for multiple parties such as management, government, investors and others as an early warning sign that enables them to take the necessary measures early before the actual bankruptcy occurs and what results in costs.

Although assessing financial conditions of the companies is one of the basic uses of accounting information, this topic has not received sufficient attention as a means to test the benefits of adopting IFRS, especially in emerging markets such as Saudi Stock Exchange. This is the first study to examine the impact of adopting IFRS on the transparency of financial reporting in assessing financial conditions in Saudi Arabia.

This research investigates the top five pharmaceutical companies in India to determine whether their financial structures are sound and if they face the risk of bankruptcy, highlighting the potential contribution of intellectual capital (IC) to financial stability.

The analysis outlines operating ratios, profitability ratios, possibility of bankruptcy (through Z-scores) and attractiveness of the financial structure (through the F-score), with consequent focus on (IC).

The financial structure of the selected companies seems stable. Changes in the Indian pharmaceutical scenario, above all, regarding the patent system, will force the companies to consider the impact of IC carefully.

Indian pharmaceutical companies need sustainability and development, with increasing focus on patent issues. To enhance innovation capabilities and overcome international competition, they should redesign their business orientation towards IC, mainly when impacting patents.

Using established approaches for predicting potential bankruptcy, this study focuses on the financial performance of top Indian pharmaceutical companies. IC can support financial stability, and this study provides further perspectives for managing their financial structure, both statically and dynamically.

This study aims to examine the effects of board structures (BS) on the financial performance and stability of banks in Africa.

Using annual data of 366 banks from 26 African countries from 2007 to 2015, the study estimates growths in financial performance using net interest margin and risk-adjusted return on assets; bank stability using z-scores; and BS using board size, board independence and board gender diversity. The system generalized method of moments and ordinary least squares panel-corrected standard error estimation strategies are used to estimate panel regressions.

The study concludes that board independence has a negative and significant relationship with financial stability but has diverse relationships with financial performance. Board size and board gender diversity have insignificant relationships with financial performance and stability.

The study has relevant implications for practitioners, policymakers and the academic community. The findings provide evidence of the extent to which BS have been instituted to influence the financial profitability and stability of banks in Africa.

This study offers robust evidence on the role of BS in the performance and stability of banks; using a multidimensional conceptualization of the performance and stability of banks in 26 countries in Africa.

The purpose of this study is to explore, develop, and evaluate a new sustainable development goals (SDG) index that quantifies corporate social responsibility (CSR). By providing a granular perspective with clear justification for methods, this index is more applicable to academic research in comparison with the CSR indices published by private companies.

Focusing on the Fortune 500 companies in 2017, this study uses data from Bloomberg, ASSET4, and the Carbon Disclosure Project. A z-score was calculated for each variable, which was then aggregated according to the SDG indicator list to calculate each SDG score. Various robust analyses were conducted.

The SDG index shows that companies tend to score worse on environment-related goals compared with social goals. Furthermore, for each SDG, there are differences across industrial sectors, a finding that is enabled by the more granular approach of this index. Additionally, the leaders and laggards are identified for each of the SDGs.

This study identifies the methodological weaknesses of the existing CSR indices and introduces and evaluates an alternative index based on the SDGs. This alterative index provides methodological clarity and granularity of data, which were lacking in previously established indices.

This study aims to analyse the following: first, the financial performance of General Insurance Re (GIC Re) using performance ratios (PRs); second, the uniformity of different financial performance indicators of GIC Re; third, the internal growth capacity of GIC Re; and finally, the likelihood of GIC Re going into financial distress.

As a sample, GIC Re, the lion shareholder in Indian Reinsurance Industry has been considered in the present study. All the necessary data have been extracted from the secondary sources over a time period of 16 years. The financial performance of GIC Re is assessed using five standard ratios, and the uniformity of different financial performance indicators of GIC Re has been examined using Kendall’s Coefficient of Concordance (W). To assess the internal growth capacity of GIC Re internal growth rate has been used, and the likelihood of GIC Re going into financial distress is analysed using multivariate discriminant approach, namely, modified Altman’s Z-score model and logit analysis technique, namely, Ohlson’s O-score model.

The results exhibit that financial performance of GIC Re is somewhat satisfactory over a few considerable areas. However, no notable degree of uniformity has been observed amongst the varied financial performance indicators, namely, performance ratio, expense ratio, return on assets, risk retention ratio and combined ratio of GIC Re. The results also reveal GIC Re is lacking ability of growing internally. Moreover, there remains a significant possibility of GIC Re going into financial distress in the near future and so.

This study is one of the first empirical research studies in India that examines the financial performance of GIC Re from different perspectives.

The banking sector in West Asia has always experienced positive growth except for Palestine. Apart from some negligible outlying outcomes in some countries that have faced political crises and war, most West Asian countries have gained bank profitability and efficiency. However, the stability in the banking sector has been rarely examined in the literature. Hence, this study sheds light on examining bank stability by considering 12 countries in West Asia.

A fixed effect panel data regression analysis is employed on strongly balanced panel data using data from 2004 to 2018.

Results reveal that the net interest margin has a positive relationship with bank stability. The bank’s stability rises as the net interest margin improves. Furthermore, the non-interest income reveals a positive significant impact on the stability of banks, depicting that the increase in non-interest income increases the stability of banks. Additionally, the non-interest expense also reveals positive significant results with the stability of banks. Nevertheless, leverage ratio and long-term debt portray a negative significant impact on banks’ stability. The finding reveals that higher long-term debt and leverage ratios may decrease the stability of the banks in West Asia.

Overall, the authors’ findings add to the literature on the stability of the banks by providing some new but significant information. Some of the recommendations may be beneficial to the long-term success of 12 Western Asian countries’ banks.

The study examines the stability of banks by incorporating both profitability and operating efficiency along with net-interest income, which extends to the current literature’s insight.

This paper aims to explore various factors associated with radio frequency identification (RFID) adoption with quantitative meta-analysis. More specifically, this paper attempts to measure key variables of RFID adoption derived from Rogers’ innovation theory and further examines how state intervention influences the process of RFID adoption. First, this paper compares, relying on a meta-analysis, various mean effect sizes among technological, organizational and environmental factors (i.e. government-driven policies) that Rogers suggested in his innovation model.

In mean effect size analysis, this paper finds that the technological factor is the most powerful factor that affects the RFID adoption. The technological factor is statistically significant across all regions, including North America, Europe and Asia. The organizational factor is significant only in developing countries like Southeast Asian countries and East Asian countries. Environmental factors like government intervention for facilitating RFID adoption are strong enough only in Southeast Asia and Europe.

This paper finds that government’s supportive policy is more effective in Europe but not in America, while external pressure is still more effective in Southeast Asia. These results implicate that developmentalism or government-driven policy can be effective not only in developing countries but also in the case of developed countries. In addition, this paper conducts a seemingly unrelated regression (SUR) analysis based on Fisher’s standardized score.

In SUR analysis, this paper finds that the correlations between RFID adoption intention and three innovation factors vary across industrial areas. More specifically, the manufacturing area shows negative moderating effect on all three equations where correlations between Rogers’ innovation factors and RFID adoption intention are meta-dependent variables. Also, RFID adoption is accelerated when the size of the firm is large or the location of the firm is in Southeast Asia. This result implicates that the aspect of technology adoption can be changed by region and type of industry.