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The present study investigates the impact of financial soundness variables on bank performance in emerging countries.

This study uses macro-level panel data from 17 countries from 2011 to 2020. The analysis adopts six models. While four models include bank profitability, the dependent variable of the other models is Bank Z Scores. Regulatory Capital to Risk-Weighted Assets, Liquid Assets to Total Assets, Non-Performing Loans to Total Gross Loans and Non-Interest Expenses to Gross Income are proxies of financial soundness variables.

The authors estimate fixed and random effects models with the Arellano, Froot and Rogers methods. Empirical results show that Non-Performing Loans to Total Gross Loans harm ROA and ROE. Regulatory Capital to Risk-Weighted Assets negatively affects ROE. Non-Interest Expenses to Gross Income on Bank Z Scores have a significant and negative effect. Moreover, Inflation, Foreign Direct Investment and GDP are macroeconomic variables that increase bank profitability.

This study contributes to the literature in different aspects. The first is the model of the study. The authors contribute to the literature regarding the variables used to measure financial soundness. Secondly, emerging countries are samples in the study. A significant part of the studies on financial soundness has focused on developed countries. Finally, the authors analyze the macro-level data. Bank soundness studies mainly investigate country-level variables. Macro-level analysis may provide an advantage in combating global financial crises.

The contribution of the banking industry to the financial crisis of 2007/8 has raised public concerns about the financial soundness of banks around the world with many countries still suffering the backlogs of this crisis. The continuous emergence of such crises at both national and international levels increases governments', bank regulators' and financial market participants' need for reliable tools to assess the financial soundness of banks. In this context, this study investigates the financial soundness of the Kazakh banking sector, which is ranked by the World Bank as the first in the world in terms of the percentage of nonperforming loans (NPL) to total gross loans in 2012.

Using data about all Kazakh banks over the period January 01, 2008 to January 01, 2014, the study identifies a number of accounting indicators that influence the financial soundness of banks using principal component analysis (PCA). Then, it uses the outcomes of the PCA in a cluster analysis and groups the Kazakh banks into sound, risky and unsound banks at two points in time: January 01, 2008 and January 01, 2014. This methodology was further tested against a ranking system of banks and proved to be more reliable in detecting risky banks.

Fifteen financial ratios were initially selected as accounting indicators for the assessment of bank financial soundness. Using PCA, twelve indicators were isolated, which explain five principal components of capital adequacy, return on assets, profitability, asset quality, liquidity and leverage. Then using the “k-means” method, the results suggest a structure of the Kazakh banking sector on January 01, 2008 that includes two groups of banks: sound and risky banks. On January 01, 2014, this structure of the banking system has changed to include three groups of banks: sound, risky and unsound banks. Thus, in 2014 a new group of banks has emerged, i.e. financially unsound banks.

The proposed cluster-based methodology has proven to be a reliable tool to detect the financial soundness of Kazakh banks, which makes us advocate its employability for bank monitoring and supervision purposes.

This study is the first to employ a cluster-based methodology to assess the financial soundness of a banking sector. This methodology can be used at a micro-level to determine the structure of a banking sector. Also, it can be used to monitor any changes in the structure of a banking sector and provide early warning signals about the financial health of banks.

This study analyzed the moderating effect of bank size on the relationship between financial soundness and financial performance of commercial banks in Kenya.

The study employed data from 39 commercial banks for ten years from 2009 to 2018. Panel data regression model was used to analyze data.

The study results established a negative moderating effect of bank size on the relationship between commercial banks' financial soundness and net interest margin (NIM) and return on assets (ROA) with the results indicating a correlation coefficient of −0.1699 and −0.218, respectively. However, an absence of moderating effect was established when return on equity (ROE) was used as a measure of financial performance.

The paper finding recommends that banks' management and other policy makers should consider the effect of bank size while devising financial soundness policies to ensure optimal level of banks' financial soundness aimed at improving banks' financial performance. In addition, bankers associations should come up with policies to standardize asset quality management practices to ensure continuous positive performance of the banking sector.

The study shows the contribution and applicability of the theory of production in the banking sector.

The study aims to analyze the effect of financial soundness on financial performance of commercial banks in Kenya.

The study used dynamic panel model to analyze data from commercial banks for the period 2009 to 2020. The study was modeled on the concept of CAMEL approach using five CAMEL variables as financial soundness indicators. Four indicators that is, net interest margin (NIM), earnings per share (EPS), return on assets (ROA) and return on equity (ROE) were used as measures of financial performance.

Generalized method of moments results established that financial soundness had a statistically significant effect on NIM, ROA and ROE. It was also found that asset quality and earning quality had a statistically significant effect on net interest margin. In addition management efficiency had significant effect on ROE. However, the study established that capital adequacy, asset quality, earning quality and liquidity had a statistically insignificant effect on ROA and ROE respectively while capital adequacy, management efficiency and liquidity had statistically insignificant effect on NIM.

Bank managers should put into place effective financial policies to govern changes in CAMEL variables to ensure optimal banks' financial soundness to facilitate positive growth in banks' financial performance.

The current study is modeled on the concept of the CAMEL approach by employing the five CAMEL variables as financial soundness indicators. In addition, the study contributes to local literature by examining banks in a developing economy to provide reliable and relevant information on their differences to monitor their dynamics in financial soundness and financial performance which could not be provided by regional or global studies.

This paper aims to empirically assess the contribution of Islamic banks toward the financial stability of Pakistan. For this, the authors investigate the relative financial strength of Islamic banks and their contribution toward the financial stability. They also examine the relationship between the competitive conduct of banks and banking system stability.

The authors use quarterly data of ten conventional banks, four full-fledged Islamic banks and six standalone Islamic branches of conventional banks of Pakistan for the period 2006-2012. The z-score has been computed and used as the measure of stability of banks and the random effects estimator applied to quantify the impact of bank-specific variables and macroeconomic indicators on the financial stability. The empirical framework used in the paper enables the authors us to examine the differential effect of each underlying variable on the financial stability across Islamic and conventional banks. To check the robustness of the results, the authors have estimated several models with different specifications.

The regression results indicate that income diversity, profitability ratio, loan to asset ratio, asset size and the market concentration ratio of banks have significant effects on the stability of banks. Comparing Islamic and conventional banks, notable differential effects of the empirical determinants of financial stability for Islamic and conventional banks have been observed. The results suggest that Islamic banks have performed better as compared to conventional banks and contributed more effectively in the stability of financial sector. Overall, the results depict that the contribution of Islamic banks toward the financial stability has been reasonable and prospective.

The empirical results of the paper are very useful not only for banks’ managements but also for the investors, bank customers and policymakers. Specifically, the findings help in enhancing our understanding as to how the bank-specific variables and macroeconomic indicators are related to the financial stability of the banking system. The results also help understand the role of both Islamic and conventional banks in the financial stability. Further, the results suggest that the financial soundness can be enhanced by creating healthy competition in the banking industry. The results about macroeconomic indicators imply that protective measures are required to intensify (mitigate) the positive (negative) effect of gross domestic product (inflation) on banks’ financial stability.

This paper provides an overall comparative analysis of financial stability of both Islamic and conventional banks of Pakistan. First, the paper computes the z-score for each bank included in the sample, and then, it performs the regression analysis to study how bank-specific variables and macroeconomic factors are related to the financial stability of banks. Unlike the previous studies, our empirical framework enables the authors to examine the differential effect of each underlying variable on the financial stability across Islamic and conventional banks.

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。

The purpose of this paper is to investigate how much liquidity banks create and how liquidity creation changed over time in the MENA countries and to examine the soundness of banks in these countries based on the CAME rating system, in addition to investigating the relationship between CAME ratios and liquidity creation of these banks.

The study regresses the CAME ratios together with other control variables to model liquidity creation. The robustness of the results is evaluated by using a different measure of liquidity creation and by excluding the observations of the Islamic banks.

The results show that the CAME rating system, as an indicator of bank soundness, is negatively related to bank liquidity creation. Specifically, capital adequacy, management efficiency and earning ability ratios affect the on-balance sheet components of liquidity creation, while asset quality ratio affects its off-balance sheet component.

The paper offers insights to regulators and banks managers in terms of better understanding of the negative relationship between CAME rating system and bank liquidity creation.

This paper sheds more light on the relationship between bank soundness and liquidity creation by using the ratios of the CAMEL rating system as an indicator of bank strength and soundness.

Deposit insurance is a key element in modern banking, as it guarantees the financial safety of deposits at depository financial institutions. It is necessary to have at least a dual fair premium rate system based on creditworthiness of financial institutions, as considering singular premium system for all banks will have moral hazard. This paper aims to develop theoretical and empirical model for calculating dual fair premium rates.

The definition of a fair premium rate in this paper is a rate that covers the operational expenditures of the deposit insuring organization, provides it with sufficient funds to enable it to pay a certain percentage share of deposit amounts to depositors in case of bank default and provides it with sufficient funds as precautionary reserves. To identify and classify healthier and more stable banks, the authors use credit rating methods that use two major dimensional reduction techniques. For forecasting nonperforming loans (NPLs), the authors develop a model that can capture both macro shocks and idiosyncratic shocks to financial institutions in a vector error correction model.

The response of NPLs/loans to macro shocks and idiosyncratic innovations shows that using a model with macro variables only is insufficient, as it is possible that under favorable economic conditions, some banks show negative performance due to bank level reasons such as mismanagement or vice versa. The final results show that deposit insurance premium rate needs to be vary based on banks’ creditworthiness.

The results provide interesting insight for financial authorities to set fair deposit insurance premium rate. A high premium rate reduces the capital adequacy of individual financial institutions, which endangers the stability of the financial system; a low premium rate will reduce the security of the financial system.

The safety and soundness of financial institutions has become a leading worldwide issue because of the recent global financial crisis. Historically, financial crises have occurred approximately every 20 years. The worst financial crisis in the last 75 years occurred in 2008–2009. US regulatory efforts with respect to capital reserve requirements are likely to have several unintended consequences for the agricultural lending sector—especially for smaller, less-diversified (and often, rural agricultural) lenders. The paper discusses these issues.

Simulation models and value-at-risk (VaR) criteria are used to evaluate the impact of capital reserve requirements on lending return on equity. In addition, simulations are used to calculate the effects of loan numbers and portfolio diversification on capital reserve requirements.

This paper illustrates that increasing capital reserve requirements reduces lending return on equity. Furthermore, increases in the number of loans and portfolio diversification reduce capital reserve requirements.

The simulation methods are a simplification of complex lending practices and VaR calculations. Lenders use these and other procedures for managing capital reserves than those modeled in this paper.

Smaller lending institutions will be pressured to increase loan sector diversification. In addition, traditional agricultural lenders will likely be under increased pressure to diversify portfolios. Because agricultural loan losses have relatively low correlations with other sectors, traditional agricultural lenders can expect increased competition for agricultural loans from non-traditional agricultural lenders.

This paper is novel in that the authors illustrate how lender capital requirements change in response to loan payment correlations both within and across lending sectors.

This study aimed at exploring the differential effects of different corporate governance (CG) indicators on risk management practices in Islamic financial institutions (IFIs) and conventional financial institutions (CFIs) of Pakistan. It also investigated the moderating role of institutional quality (IQ) in shaping the effects of CG practices on financial institutions of Pakistan.

A sample of 57 financial institutions including commercial banks, insurance companies and Modarba companies over the period 2006–2017 is used to carry out the empirical analysis. The authors applied the robust two-step system-generalized method of moments estimator, which is also called the dynamic panel data estimator. They also built the PCA-based composite index of CG and IQ by using different indicators to investigate the moderating role of IQ. They used three proxies for risk taking, five for CG and one for Shari’ah governance. To test the validity of the instruments, they applied the Arellano and Bond’s (1991) AR (1) and AR (2) tests and the J-statistic of Hansen (1982).

The results provided strong evidence that several individual characteristics of CG and the composite index are significantly related to the operational risk, the liquidity risk and the Z-score (a proxy for solvency risk). The results also revealed that IQ significantly and substantially contributes in reducing the level of risks. Finally, the estimation results indicated that the effects of CG on risk management are significantly different at IFIs and CFIs. This differential impact is mainly attributed to the fundamental differences in business models, operational strategies and contractual obligations of both types of institutions.

The findings of this study are important for enhancing our understanding of how CG relates to risk taking in Islamic and conventional financial services industries and how good quality institutions are important for formulating the governance effects on the risk-taking behavior of financial institutions. The findings suggest that a suitable size of board should be chosen to manage the risk effectively. As the findings show that the risk-taking behavior of IFIs differs from that of CFIs, the regulators and international standard setting bodies should tailor the regulatory frameworks accordingly.

This paper is different from the existing studies in four aspects. First, to the best of the authors’ knowledge, this is the first empirical investigation in Pakistan, which does the comparison of IFIs and CFIs while examining the impacts of CG on risk management. Second, the paper constructs the composite index of CG by considering several different indicators of governance and examines the combined effect of governance indicators on risk management process. Third, this paper adds to the growing literature on the role of IQ by investigating whether it acts as a moderator between CG structures and risk management and if yes, then whether this moderating role is different for IFIs and CFIs. Finally, the paper builds upon the existing research work on the CG effects for different types of financial institutions by proposing a single regression based analytical framework for comparing the effects across two different types of institutions, harvesting the benefits of higher degrees of freedom and avoiding/minimizing the measurement error.