Emerging markets offer some of the world’s most impactful investment possibilities for investors concerned with addressing global climate and socioeconomic issues. Sophisticated investors conjectured that socially responsible investment (SRI) or environmental social and governance (ESG) might give greater returns than investing in conventional stocks in an emerging market. At the same time, the investors favour conserving the environment while generating long-term economic growth. Being able to earn greater returns is an indication of an inefficient market. This paper investigates the random walk (weak-form of the efficient market) of SRI/ESG indices in the emerging market (based on IMF emerging market criteria). We use the daily data as a sample. Random walk is tested using an Augmented Dickey–Fuller (ADF) Unit Root test, Variance ratio test and Hurst exponent test. The findings report that all the indices are not following a random walk. Lack of ESG disclosure, inadequate corporate governance regulation and behavioural bias might be reasons for market inefficiency. Its implications for investors to reap abnormal market returns by identifying the undervalued stock in the emerging economies. The regulator’s approval of operational guidelines and the licensing of exchanges and clearing houses help maintain the markets’ fairness. Then, the regulator should intensify corporate governance enhancement, implementation and enforcement continuously; enhance the market and institutional infrastructure, and focus policy on encouraging a more significant and more diverse investor base.

As the demand for investment products tied to environmental, social and governance (ESG) concerns rises, ESG stock indices have been established. These indices aim to aid investors in navigating and assessing the risks associated with firms based on ESG factors and potential investment returns. The objective of the article is to predict ESG stock indices using a machine learning approach. We use daily data of Dow Jones Sustainability Index (DJSI) World, DJSI Asia Pacific and DJSI Emerging Market from 2018 to 2022 as samples. Two-layer ensemble model – combination of support vector machine (SVM), random forest (RF), long short-term memory (LSTM) and gated recurrent unit (GRU) algorithms – is employed to forecast the indices. The results show that the ensemble model accurately forecasts the indices, with the prediction line closely matching the actual values. It gives the implication that investors are able to improve investment decisions, assist in managing investment risk, and optimize their portfolio diversification. Meanwhile, policymakers are able to anticipate economic trends, inflation and interest rates, assisting in the development of successful economic policies. This research article presents a machine learning approach for predicting ESG stock indices. The proposed model combines SVM, RF, LSTM and GRU algorithms to create a powerful two-layer ensemble model that outperforms individual models. The results show that the ensemble model accurately forecasts ESG stock indices, with the prediction line closely matching the actual values. The model offers insights into the behaviour of different algorithms, highlighting their strengths and limitations. The proposed model can guide decision-making processes, support investment strategies, and ultimately contribute to advancing sustainable investment practices.

Green Waqf is a potential method for achieving prosperity, expanding financial inclusion, advancing Islamic justice, and eliminating disparities in poverty levels. This research aims to find a model of good waqf governance to generate sustainable income increases. It will be accomplished by developing an integrated waqf model incorporating an environmental perspective as a balanced scorecard. This study is a literature review that also includes descriptive analysis, and it employs balanced scorecard analysis as its framework for conducting research. The results of the research are used to compile the data. During this phase of the process, data collection methods are utilized in order to locate and collect information pertinent to green Waqf, waqf productivity, the balanced scorecard, and an Islamic perspective in a structured manner. The conversation illuminated that the Waqf regulatory system was not functioning at its full potential and required enhancement. The conversation inspired that the Waqf regulatory system needed to be operating at its full potential; this needs to be emphasized, especially as a response to the low levels of waqf literacy, nazhir capacity, and technology utilization. The waqf management system does not exist in a vacuum free of defects. It is hoped that by using a balanced scorecard, gaps will be closed by implementing strategies, particularly the productivity and growth strategies, to maximize waqf management and provide an understanding of waqf ecosystems, thereby increasing the productivity of waqf management, generating the profitability of waqf assets, and maximizing the distribution of Waqf to beneficiaries (mauquf 'alaih). It will result in closing the gaps.

Undoubtedly, financial markets are not only affected by common economic, financial and behavioral factors but also by other global risk shocks. In this paper, we investigate the relevance of global geopolitical risk in stock-bond relationships. Specifically, we assess the possibility and manner in which time-varying correlations of Islamic stocks and bonds (sukuk) returns are affected by geopolitical risk and whether they behave differently from their conventional counterparts. We do so by implementing a multivariate GARCH model under dynamic conditional correlations (DCCs) to daily data of the Gulf Cooperation Council (GCC) markets (2013-2019). Our outcomes unveil that geopolitical risks positively affect the dependence between Islamic stocks and sukuk, as well as their conventional counterparts. Regional rather than global geopolitical risk factors positively affect the volatility of conventional stock-bond correlation, while the volatility of Islamic stock-sukuk correlation decreases during times of high geopolitical risks. Our findings offer several prominent implications in terms of financial stability, asset allocation of portfolios and policy design.