This study examines the risk and the return characteristics of the Islamic market indices versus their conventional counterpart indices. For this purpose, a large international data of 35 indices combining developed, emerging and GCC markets over the period of Jun 2002 to April 2012 is used. The t test has been employed to investigate the mean returns difference between both types of indices. The results show that there is no significant difference in mean between Islamic and conventional indices except for Italy and Australia. The EGARCH estimation results reveal the presence of a leverage effect risk in all studied indices. The study of the risk adjusted performances of Islamic stock market indices versus their conventional counterpart indices using differences-in-Sharpe ratio test and the CAPM model show that in the entire period as well as in the crisis period there is no difference between performance the types of indices in risk adjusted return basis. Consequently, Muslim investors can pursue passive stock investments in conformity to their religious beliefs without sacrificing financial performance.

Purpose: This study aims to investigate the transmission of shock between the oil market and the Islamic and conventional stock markets of the Gulf Cooperation Council (GCC) countries during the oil shocks of 2008 and 2014.Design/methodology/approach: This study uses two models. First, the dynamic conditional correlation–generalized autoregressive conditionally heteroskedastic model has been used to capture the fundamental contagion effects between the oil market and the Islamic and conventional stock markets during the tranquil and turmoil-crisis periods of 2008-2014. Second, the filter of Kalman has been used to capture the effects of pure contagion between the oil market and the GCC Islamic and conventional stock markets. The authors analyze the dynamic correlation between forecasting errors of oil returns and stock returns of GCC Islamic and GCC conventional indices.Findings: The main findings of this investigation are: first, the estimation of the dynamic conditional correlation– generalized autoregressive conditionally heteroskedastic model for oil market and the Islamic and conventional stock markets proves that the Islamic and conventional stock markets and oil market displayed a significant increase in the dynamic correlation during the turmoil period, from mid-2008 and mid-2014. This proves the existence of contagion between the markets studied. Second, the authors analyze the dynamic correlation between forecasting errors of oil returns and stock returns of GCC Islamic and GCC conventional indices. They show a strong increase in the correlation coefficients between the oil market and the conventional GCC stock markets, and between the conventional and Islamic GCC stock markets during the oil crisis of 2014. However, there is no change in regime in the figure of the correlation coefficient between the oil market and the GCC Islamic stock markets during the 2008 financial crisis. This pure contagion is mainly attributed to the herding bias in 2014 oil crisis.Originality/value: This study contributes to identifying the contribution of herding bias on the volatility transmission between the oil markets, and the GCC Islamic and conventional stock market, especially during two controversial shocks: the 2008 oil-price increase and the 2014 oil drop.

Purpose: This study investigates clean energy, commodities, green bonds and environmental, social and governance (ESG) index prices forecasting and assesses the predictive performance of various factors on these asset prices, used for the development of a robust forecasting support decision model using machine learning (ML) techniques. More specifically, we explore the impact of the financial stress on forecasting price. Design/methodology/approach: We utilize feature selection techniques to evaluate the predictive efficacy of various factors on asset prices. Moreover, we have developed a forecasting model for these asset prices by assessing the accuracy of two ML models: specifically, the deep learning long short-term memory (LSTM) neural networks and the extreme gradient boosting (XGBoost) model. To check the robustness of the study results, the authors referred to bootstrap linear regression as an alternative traditional method for forecasting green asset prices. Findings: The results highlight the significance of financial stress in enhancing price forecast accuracy, with the financial stress index (FSI) and panic index (PI) emerging as primary determinants. In terms of the forecasting model's accuracy, our analysis reveals that the LSTM outperformed the XGBoost model, establishing itself as the most efficient algorithm among the two tested. Practical implications: This research enhances comprehension, which is valuable for both investors and policymakers seeking improved price forecasting through the utilization of a predictive model. Originality/value: To the authors' best knowledge, this marks the inaugural attempt to construct a multivariate forecasting model. Indeed, the development of a robust forecasting model utilizing ML techniques provides practical value as a decision support tool for shaping investment strategies.

Purpose: This study aims to assess the predictive performance of various factors on Bitcoin returns, used for the development of a robust forecasting support decision model using machine learning techniques, before and during the COVID-19 pandemic. More specifically, the authors investigate the impact of the investor's sentiment on forecasting the Bitcoin returns.Design/methodology/approach: This method uses feature selection techniques to assess the predictive performance of the different factors on the Bitcoin returns. Subsequently, the authors developed a forecasting model for the Bitcoin returns by evaluating the accuracy of three machine learning models, namely the one-dimensional convolutional neural network (1D-CNN), the bidirectional deep learning long short-term memory (BLSTM) neural networks and the support vector machine model.Findings: The findings shed light on the importance of the investor's sentiment in enhancing the accuracy of the return forecasts. Furthermore, the investor's sentiment, the economic policy uncertainty (EPU), gold and the financial stress index (FSI) are the top best determinants before the COVID-19 outbreak. However, there was a significant decrease in the importance of financial uncertainty (FSI and EPU) during the COVID-19 pandemic, proving that investors attach much more importance to the sentimental side than to the traditional uncertainty factors. Regarding the forecasting model accuracy, the authors found that the 1D-CNN model showed the lowest prediction error before and during the COVID-19 and outperformed the other models. Therefore, it represents the best-performing algorithm among its tested counterparts, while the BLSTM is the least accurate model.Practical implications: Moreover, this study contributes to a better understanding relevant for investors and policymakers to better forecast the returns based on a forecasting model, which can be used as a decision-making support tool. Therefore, the obtained results can drive the investors to uncover potential determinants, which forecast the Bitcoin returns. It actually gives more weight to the sentiment rather than financial uncertainties factors during the pandemic crisis.Originality/value: To the authors’ knowledge, this is the first study to have attempted to construct a novel crypto sentiment measure and use it to develop a Bitcoin forecasting model. In fact, the development of a robust forecasting model, using machine learning techniques, offers a practical value as a decision-making support tool for investment strategies and policy formulation.

Purpose: This study aims to investigate the impact of the COVID-19 pandemic on the time-frequency connectedness between green bonds, stock markets and commodities (Brent and Gold), with a particular focus on China and its implication for portfolio diversification across different frequencies.Design/methodology/approach: To this end, the authors implement the frequency connectedness approach of Barunik and Krehlik (2018), followed by the network connectedness before and during the COVID-19 outbreak. In particular, the authors implement more involvement in portfolio allocation and risk management by estimating hedge ratios and hedging effectiveness for green bonds and other financial assets.Findings: The time-frequency domain spillover results show that gold is the net transmitter of shocks to green bonds in the long run, whereas green Bonds are the net recipients of shocks, irrespective of time horizons. The subsample analysis for the pandemic crisis period shows that green bonds dominate the network connectedness dynamic, mainly because it is strongly connected with the SP500 index and China (SSE). Thus, green bonds may serve as a potential diversifier asset at different time horizons. Likewise, the authors empirically confirm that green bonds have sizeable diversification benefits and hedges for investors towards stock markets and commodity stock pairs before and during the COVID-19 outbreak for both the short and long term. Gold only offers diversification gains in the long run, while Brent does not provide the desired diversification gains. Thus, the study highlights that green bonds are only an effective diversified.Originality/value: This study contributes to the existing literature by improving the understanding of the interconnectedness and hedging opportunities in short- and long-term horizons between green bonds, commodities and equity markets during the COVID-19 pandemic shock, with a particular focus on China. This study's findings provide more implications regarding portfolio allocation and risk management by estimating hedge ratios and hedging effectiveness.

Purpose: Corporate social responsibility (CSR) and fintech have emerged as critical megatrends in the banking industry. This study aims to examine the impact of financial technology on the relationship between CSR and banks' financial stability. Specifically, it investigates the moderating effect of fintech on the association between CSR and the financial stability of conventional banks operating in Qatar, UAE, Saudi Arabia, Kuwait, Bahrain, Jordan, Pakistan and Turkey from 2010 to 2021.Design/methodology/approach: To achieve the authors’ objective, the authors apply Baron and Kenny's three-link model, tested with fixed and random effects regression models.Findings: The results reveal that the development of fintech decreases banks' financial stability, whereas it promotes banks' involvement in CSR strategies. Furthermore, the findings indicate that fintech plays a moderating role in the relationship between CSR and financial stability. It positively moderates the impact of CSR on financial stability. The robustness analysis highlights the mutual reinforcement of fintech and CSR dimensions in improving the financial stability of banks. Thus, by fostering community and product responsibility, fintech could enhance the financial stability of banks.Practical implications: Finally, the authors recommend that banks focus more on developing technological and environmentally friendly financial products.Originality/value: This study contributes significantly by providing valuable insights for managers and policymakers seeking to improve banks' financial stability through the simultaneous adoption of new financial technology products and the strong commitment to CSR practices.