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Based on a matched sample of 34 Islamic banks and 89 conventional ones, the purpose of this paper is to analyze and compare the risk-capital-efficiency interconnection.

Based on the triple square model (3SLS), two major risk measures have been accounted for, namely, the ratio of non-performing loans to total loans (credit risk) and the z-score indicator (risk insolvency). In addition, certain bank-specific factors as well as macroeconomic ones have also been considered in the model.

The reached results appear to reveal that the best capitalized Western banks turn out to be more engaged in an excessive risk-taking behavior, resulting in increased toxic-loan ratios and, simultaneously, a rather shaken stability. Concerning Islamic banks, cost efficiency has proven to have a negative and significant effect on NPLs. However, the capital, technical efficiency, competitiveness and macroeconomic factors turn out to have a significant and positive effect on Islamic banks’ insolvency risk, thus helping promote these banks’ stability.

In addition to the enrichment of literature regarding dual-banking systems, the authors hope the present work would provide a modest contribution to the regulators belonging to the MENA region and Asia with useful results. In particular, the authors recommend developing some management and monitoring tools whereby the risk-taking behavior of highly capitalized conventional banks could be moderated. As a matter of fact, special attention should be paid to the agency problems prevalent within Islamic financial institutions, particularly the best capitalized ones.

Analysis of the trade-off between competition and financial stability has been at the center of academic and policy debate for over two decades and especially since the 2007-2008 global financial crises. This study aims to provide particular attention to the Islamic banking system which principally involves with the riba-free instruments as compared to the conventional interest-based system. The results show that an increase in the concentration in the conventional banking sector can lead to the deterioration of stability through the increased prices. For Islamic banks, an increase of the market power can positively affect the banking stability.

Two complementary approaches, namely, one-step generalized method of moment (GMM) system analysis and panel vector autoregressive (PVAR) framework, were applied.

The results show the same effect of Islamic and conventional banks’ market power on banking soundness; yet, a different effect is displayed with non-performing loans (NPLs). In particular, the “competition–fragility” assumption for both banking industries is supported when considering z-score as the dependent variable. Including NPLs, this postulation is still approved for conventional banks; however, the “competition–stability” postulation is supported for Islamic banks.

The existent literature was scarcely interested in exploring the concept of competitivity in the context of Islamic banking sector as compared to the conventional one by applying two complementary approaches, namely, GMM and PVAR. This later allows to test the effect and the feedback effect of the competition and stability concepts.

Data mining for predicting credit risk is a beneficial tool for financial institutions to evaluate the financial health of companies. However, the ubiquity of selecting parameters and the presence of unbalanced data sets is a very typical problem of this technique. This study aims to provide a new method for evaluating credit risk, taking into account not only financial and non-financial variables, but also the class imbalance.

The most significant financial and non-financial variables were determined to build a credit scoring model and identify the creditworthiness of companies. Moreover, the Synthetic Minority Oversampling Technique was used to solve the problem of class imbalance and improve the performance of the classifier. The artificial neural networks and decision trees were designed to predict default risk.

Results showed that profitability ratios, repayment capacity, solvency, duration of a credit report, guarantees, size of the company, loan number, ownership structure and the corporate banking relationship duration turned out to be the key factors in predicting default. Also, both algorithms were found to be highly sensitive to class imbalance. However, with balanced data, the decision trees displayed higher predictive accuracy for the assessment of credit risk than artificial neural networks.

Classification results depend on the appropriateness of data characteristics and the appropriate analysis algorithm for data sets. The selection of financial and non-financial variables, as well as the resolution of class imbalance allows companies to assess their credit risk successfully.

Behavioral finance and market microstructure studies suggest that the investor sentiment and liquidity are related. This paper aims to examine the aggregate sentiment–liquidity relationship in emerging markets (EMs) for both the sample period and crisis period. Then, it verifies this relationship, using the asymmetric sentiment.

This study uses a sample consisting of stocks listed on the SSE Shanghai composite index (348 stocks), the JKSE (118 stocks), the IPC (14 stocks), the RTS (12 stocks), the WSE (106 stocks) and FTSE/JSE Africa (76 stocks). This is for the period ranging from February, 2002 until March, 2021 (230 monthly observations). We use the panel data and apply generalized method-of-moments (GMM) of dynamic panel estimators.

The empirical analysis shows the following results: first, it demonstrates a significant relationship between the aggregate investor sentiment and the stock market liquidity for the sample period and crisis one. Second, referring to the asymmetric sentiment, we have empirically given proof that the market is significantly more liquid in times of the optimistic sentiment than it is in times of the pessimistic sentiment. Third, using panel causality tests, we document a unidirectional causality between the investor sentiment and liquidity in a direct manner through the noise traders and the irrational market makers and also a bidirectional causality in an indirect channel.

The results reported in this paper have implications for regulators and investors in EMs. Firstly, the study informs the regulators that the increases and decreases in the stock market liquidity are related to the investor sentiment, not financial shocks. We empirically evince that the traded value is higher in the crisis. Secondly, we inform insider traders and rational market makers that the persistence of increases in the trading activity in both quiet and turbulent times is associated with investor participants such as noise traders and irrational market makers.

The originality of this work lies in employing the asymmetric sentiment (optimistic/pessimistic) in order to denote the sentiment–liquidity relationship in EMs for the sample period and the 2007–2008 subprime crisis.

Credit scoring datasets are generally unbalanced. The number of repaid loans is higher than that of defaulted ones. Therefore, the classification of these data is biased toward the majority class, which practically means that it tends to attribute a mistaken “good borrower” status even to “very risky borrowers”. In addition to the use of statistics and machine learning classifiers, this paper aims to explore the relevance and performance of sampling models combined with statistical prediction and artificial intelligence techniques to predict and quantify the default probability based on real-world credit data.

A real database from a Tunisian commercial bank was used and unbalanced data issues were addressed by the random over-sampling (ROS) and synthetic minority over-sampling technique (SMOTE). Performance was evaluated in terms of the confusion matrix and the receiver operating characteristic curve.

The results indicated that the combination of intelligent and statistical techniques and re-sampling approaches are promising for the default rate management and provide accurate credit risk estimates.

This paper empirically investigates the effectiveness of ROS and SMOTE in combination with logistic regression, artificial neural networks and support vector machines. The authors address the role of sampling strategies in the Tunisian credit market and its impact on credit risk. These sampling strategies may help financial institutions to reduce the erroneous classification costs in comparison with the unbalanced original data and may serve as a means for improving the bank’s performance and competitiveness.

This paper aims to investigate empirical evidence of behavioral contagion between oil market, US market and stock markets of oil-importing and oil-exporting countries, during the oil shock and US financial crisis period of 2008-2009, after controlling for fundamentals-driven co-movements.

To examine the volatility spillover among oil market and stock markets, the conditional variance of the trivariate BEKK-GARCH model includes three variables: oil returns, US index returns, and the respective individual market returns of 22 oil-importing and exporting countries. The authors estimate the time-varying correlation coefficients between the prediction error of oil market and each stock index. Also, the authors estimate the time-varying correlation coefficients between the prediction error of US market and each stock index.

The estimation of the trivariate BEKK-GARCH model for VIX, oil market and 23 stock markets of oil-importing and oil-exporting countries suggests the volatility spillover of American investor sentiment to stock market and oil market returns. To capture the pure contagion effects between oil market and stock markets, the authors estimate the forecasting errors of time-varying parameter using the Kalman independently of macroeconomic fundamentals factors. The authors analyze the dynamic correlation between forecasting errors of oil price returns and stock indices returns. The authors show a sharp increase in time-varying correlation coefficients during the oil crisis and US financial crisis period of 2008-2009, which provides strong evidence of herding contagion between oil market and stock markets during the turmoil period.

This paper makes an original contribution in identifying the behavioral contagion between oil market, US market and stock markets of oil-importing and exporting countries especially during the oil shock and US financial crisis period of 2008-2009. Specifically, the authors consider investor sentiment and herding bias to explain the volatility transmission between oil and stock market returns.

This paper aims to employ GARCH-class models (GARCH, IGARCH and CGARCH) to estimate the volatility persistence on crude oil, US, Gulf Corporation Council (GCC), Brazil, Russia, India and China (BRIC) stock markets. Also, the paper investigates the volatility spillover and the dynamic conditional correlation between crude oil, US stock index and stock indices of GCC and BRIC countries. The results prove a high degree of volatility persistence in the crude oil and stock markets. Based on the BEKK-GARCH and DCC-GARCH results, the paper finds strong evidence of the contagion effect of the oil shock and US financial crisis of 2008 on GCC and BRIC stock markets.

In the beginning, the paper uses univariate GARCH models to estimate the volatility persistence of the oil market, US stock market, and GCC and BRIC stock markets. Then, the paper uses a trivariate BEKK-GARCH model of Malik and Hammoudeh to examine the volatility spillover between oil market, US stock market and stock markets for GCC and BRIC countries. Finally, the paper analyses the dynamic conditional correlation between US market and each stock market of GCC and BRIC countries using the DCC-GARCH model. Also, the paper estimates the dynamic conditional correlation between oil market and all stock markets.

The results indicate the contagion effect of the oil shock and US financial crisis of 2008 on the GCC stock markets which are among the most important oil-exporting countries and also on BRIC stock markets which are among the emergent countries which are characterized by high economic growth level.

The contribution of this paper is to investigate the existence of contagion effect between oil market, US stock market and two panels of emerging stock markets which have different economic characteristics, the GCC and BRIC countries, during the oil shock and US financial crisis period of 2008-2009.

The purpose of this paper is to examine and compare the market power and the efficiency-stability of Islamic and conventional banks in the MENA zone and South East Asia during the 2005-2012 period.

The author applied an empirical approach in two steps. First, the author estimates the Lerner indicator, which is a measure of competition. Then, this measure is regressed and other explanatory variables on the banking “stability-efficiency” are derived simultaneously from the estimation of a stability stochastic frontier.

The author concludes that increased competition in the Islamic banking sector promotes the overall banking stability. Besides, whether there is a low or high competitiveness, the size of an Islamic bank is positively related to financial stability. However, large conventional banks operating in market with limited competitiveness become more involved in the risk behavior. The author concludes that capitalization has a positive effect on stability only in case of low competitiveness.

The originality of this research lies in the application of the stochastic frontier approach (SFA) on the Z-score indicator. This methodology enables to take into account the differences between the current and the optimum stability that each bank can achieve, thus creating a new measure of financial stability called “efficiency-stability”.

The purpose of this paper is to empirically investigate the volatility spillover between the Chinese stock market, investor’s sentiment and oil market, specifically during the 2014‒2016 turmoil period.

This study used the daily and monthly China market price index, oil-price index and composite index of Chinese investor’s sentiment. The authors first use the DCC GARCH model in order to study the correlation between variables. Second, the authors use a continuous wavelet decomposition technique so as to capture both time- and frequency-varying features of co-movement variables. Finally, the authors examine the spillover effects by estimating the BEKK GARCH model.

The wavelet coherency results indicate a substantial co-movement between oil and Chinese stock markets in the periods of high volatility. BEKK GARCH model outcomes confirm this relation and report the noteworthy bidirectional transmission of volatility between oil market shocks and the Chinese investor’s sentiment, chiefly in the crisis period. These results support the behavioral theory of contagion and highlight that the Chinese investor’s sentiment is a channel through which shocks are transmitted between the oil and Chinese equity markets. Thus, these results are important for Chinese authorities that should monitor the investor’s sentiment to better control the interaction between financial and real markets.

This study makes three major contributions to the existing literature. First, it pays attention to the recent 2015 Chinese stock market bumble. Second, it has gone some way toward enhancing our understanding of the volatility spillover between the investor’s sentiment, investor’s sentiment variation, oil prices and stock market returns (variables of interest) during oil and stock market crises. Third, it uses the continuous wavelet decomposition technique since it reveals the linkage between variables of interest at different time horizons.