Purpose: Several empirical studies have proven that emerging countries are attractive destinations for Foreign Institutional Investors (FIIs) because of high expected returns, weak market efficiency and high growth that make them attractive destination for diversification of funds. But higher expected returns come coupled with high risk arising from political and economic instability. This study aims to compare the linear (symmetric) and non-linear (asymmetric) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models in forecasting the volatility of top five major emerging countries among E7, that is, China, India, Indonesia, Brazil and Mexico.Design/methodology/approach: The volatility of financial markets of five major emerging countries has been empirically investigated for a period of two decades from January 2000 to December 2019 using univariate volatility models including GARCH 1, 1, Exponential Generalized Autoregressive Conditional Heteroscedasticity (E-GARCH 1, 1) and Threshold Generalized Autoregressive Conditional Heteroscedasticity (T-GARCH-1, 1) models. Further, to examine time-varying volatility, the distinctions of structural break have been captured in view of the global financial crisis of 2008. Thus, the period under the study has been segregated into pre- and post-crisis, that is, January 2001–December 2008 and January 2009–December 2019, respectively.Findings: The findings indicate that GARCH (1, 1) model is superior to non-linear GARCH models for forecasting volatility because the effect of leverage is insignificant. China has been considered as most volatile, whereas India is volatile but positively skewed and Indonesia is the least volatile country. The results can help investors in better international diversification of their portfolio and identifying best suitable hedging opportunities.Practical implications: This study can help investors to construct a more risk-adjusted returns international portfolio. Further, it adds to the scant literature available on the inconclusive debate on the choice of linear versus non-linear models to forecast market volatility.Originality/value: Earlier studies related to univariate volatility models are mostly applications of the models. Only few studies have considered the robustness while applying the models. However, none of the studies to the best of the authors’ searches have considered these models for identifying the diversification opportunity among the emerging countries. Hence, this study is able to derive diversification and hedging opportunities by applying wide ranges of the statistical applications and models, that is, descriptive, correlations and univariate volatility models. It makes the study more rigorous and unique compared to the previous literature.

Purpose: This study aims to conduct a comprehensive bibliometric analysis to determine the intellectual structure of cross-listing studies and suggests a road map for future research in this field.Design/methodology/approach: A step-by-step procedure was carried out. With the help of a defined search string, 580 articles from reputed journals have been retrieved from the Scopus database. Bibliographic coupling and keyword analysis were executed to understand the current research scenario and future research directions in this research field. In addition, R Studio combined with VOSviewer was employed to analyse and visualise the data.Findings: The results provide a deeper insight into publication trends, most prolific countries, institutions and journals in the area of cross-listing. The highest collaboration was observed between the authors in the USA and Canada. Moreover, the results contradict Bradford's and Lotka's laws. A thorough review of the literature identifies five clusters in this domain. Finally, keyword analysis offers a future road map in cross-listing research.Originality/value: Researchers have shown greater interest in cross-listing topics over the past decades. Even though the research volume on this subject is increasing, the current retrospective is still insufficient. To the best of the authors' knowledge, this study is the first to provide valuable insights to practitioners, academicians, and prospective researchers about the intellectual structure of cross-listing and also offers future avenues in this research field through bibliometric analysis.

Purpose: The COVID-19 pandemic resulted in a dramatic downturn in the global stock markets. Investors look for safe stocks that can provide better risk-adjusted returns. Stocks with higher Environmental, Social, and Governance (ESG) scores can be good choices for investors. This study focuses on this argument by examining the relationship between ESG indicators and stock returns while considering financial and macroeconomic variables.Design/methodology/approach: In this study, 39 non-financial firms listed in Nifty-50, for which data is available, have been included. Panel data from 2018 to 2021 is collected to examine this relationship in the presence of COVID-19. Additionally, the panel regression method is used.Findings: The empirical findings indicate a positive relationship between ESG scores and stock returns. This relationship holds even when the control variables like Return on Assets (ROA), Gross Domestic Product (GDP), Return on Equity (ROE), age, size, leverage of the firm, inflation, and crisis period are used in the model.Originality/value: This study contributes by examining the linkage between ESG indicators and stock return while controlling the impact of the financial and macroeconomic variables in Indian markets, which has not been undertaken so far. Moreover, this is the first study to use the ESG score data of S&P Global, which gives more weight to the material factors of a firm.Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2023-0819.

The study is about contributing to the ongoing discussion on the diversification opportunities for emerging markets with non-conventional asset class. The limited literature in the era of fourth industrial revolution motivates us to gauge diversification opportunities. This study is focusing on identifying diversification opportunities with a set of unique asset classes that are the proxies for Green Funds, FinTech and Artificial Intelligence-based index funds. The method and model applied in the study are time and frequency connectedness in a Wavelet Coherence, and for the robustness check—Network analysis has been applied. The originality of the study lies in identifying the impact of the outbreak of COVID-19. The results captured that FinTech-based asset was the most resilient asset class during the pre- and post-outbreak of COVID-19, followed by AI-based fund and finally by Green fund. Henceforth, FinTech provides superior diversification opportunities among all with MSCI Emerging Market. AI and Green funds are captured to be invested in the long term for diversification, whereas FinTech is suitable for both long- and short-term assets. The results are relevant for investors in emerging markets and for policymakers as well.

This study examines the relationship that both domestic and foreign institutional net equity flows have with the India stock markets. The motivation behind is the study to examine whether increased net equity investments from domestic institutional investors has reduced the influence of foreign equity flows on the Indian stock market volatility. Our results indicate that only during periods in which domestic equity inflows surpass foreign flows by a significant margin, as seen during 2015–2018, is the Indian stock market volatility not significantly influenced by foreign equity investments. However, during periods of re-emergence of strong foreign net inflows, the Indian market volatility is still being impacted significantly, as has been observed since 2019. Furthermore, we find that both large-scale net buying and net selling by domestic funds increased the stock market volatility as observed during 2015–2018 and COVID-impacted year 2020 respectively. The implications of this study are multi-fold. First, the regulators should discuss with industry bodies before enforcing major structural changes like reconstituting of mutual fund investment mandate in 2017 which forced domestic funds to quickly change portfolio allocation amongst large-cap, mid-cap and small-cap stocks resulting in higher stock market volatility. Second, adequate investor educational and awareness programmes need to be conducted regularly for retail investors to minimize herd behaviour of investing during market rise and heavy redemptions at times of fall. Third, the economic policies should be stable and forward-looking to ensure foreign investors remain attracted to the Indian stock markets at all times.

Purpose: The present study examines the foremost determinants of teachers' perception, i.e. teachers' satisfaction, attitude and continuance intention towards adopting e-learning in Higher Educational Institutions (HEIs) in India during the COVID-19 pandemic. Design/methodology/approach: Data were collected through online Google forms from 1,111 (1,060 considered useable) teachers of different HEIs in India using the purposive sampling technique and was analyzed by PLS-SEM (performing partial least squares-structural equation modeling). Findings: Results of this study show that perceived usefulness (PU) followed by institutional support, perceived ease of use (PEOU), and teacher-student interaction positively and significantly impact teachers' satisfaction. Results also revealed that perceived usefulness (PU), institutional support, and satisfaction significantly affect teachers' attitude. Finally and most notably, teachers' continuance intention towards using online teaching in HEIs is most significantly influenced by teachers' satisfaction than perceived usefulness (PU), perceived ease of use (PEOU), and attitude. Originality/value: The authors anticipate that this study brings a significant and valuable input to the existing literature by providing inclusive research in a more harmonizing understanding of the teachers' satisfaction, attitude, and continuance intention with online teaching-learning practices in diverse educational institutions.