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This paper aims to examine the dynamic conditional correlation (DCC) and hedging ratios between Dow Jones markets and oil, gold and bitcoin. Using daily data, including the COVID-19 pandemic and the Russia–Ukraine war. We employ the DCC-generalized autoregressive conditional heteroskedasticity (GARCH) and asymmetric DCC (ADCC)-GARCH models.

DCC-GARCH and ADCC-GARCH models.

The most of DCCs among market pairs are positive during COVID-19 period, implying the existence of volatility spillovers (Contagion-effects). This implies the lack of additional economic gains of diversification. So, COVID-19 represents a systematic risk that resists diversification. However, during the Russia–Ukraine war the DCCs are negative for most pairs that include Oil and Gold, implying investors may benefit from portfolio-diversification. Our hedging analysis carries significant implications for investors seeking higher returns while hedging their Dow Jones portfolios: keeping their portfolios unhedged is better than hedging them. This is because Islamic stocks have the ability to mitigate risks.

Our paper may make a valuable contribution to the existing literature by examining the hedging of financial assets, including both conventional and Islamic assets, during periods of stability and crisis, such as the COVID-19 pandemic and the Russia–Ukraine war.

The existing literature offers various perspectives on integrating cryptocurrencies into investment portfolios; yet, there is a gap in understanding the behaviours, attitudes and cross-investment links of individual investors. This study, grounded in the modern portfolio theory and the random walk theory, aims to add empirical insights that are specific to the UK context. It explores four hypotheses related to the influence of socio-demographics, digital adoption, cross-investment behaviours and financial attitudes on cryptocurrency owners.

This study uses a logistic regression model with secondary data from the Financial Lives Survey 2020 to assess the factors impacting cryptocurrency ownership. A total of 29 variables are used, categorized into four groups aligned with the hypotheses. Additionally, hierarchical clustering analysis was conducted to further explore the cross-investment links.

The study reveals a significant lack of diversification among UK cryptocurrency investors, a pronounced inclination towards high-risk investments such as peer-to-peer lending and crowdfunding, and parallels with gambling behaviours, including financial dissatisfaction and a propensity for risk-taking. It highlights the influence of demographic traits, risk tolerance, technological literacy and emotional attitudes on cryptocurrency investment decisions.

This study provides valuable insights into cryptocurrency regulation and retail investor protection, underscoring the necessity for tailored financial education and a holistic regulatory approach for investment products with comparable risk levels, with the aim of minimizing regulatory arbitrage. It significantly enhances our understanding of the unique dynamics of cryptocurrency investments within the evolving financial landscape.

This study aims to examine the herding behavior of socially responsible exchange traded funds (SR ETFs) in comparison to conventional ETFs during the COVID-19 pandemic.

To test for herding behavior, the authors use the cross-sectional absolute deviation and a quadratic market model.

During the pandemic, investments in socially responsible financial products grew rapidly. And investors in the popular SR ETFs herd during this special period, while holders of conventional ETFs did not.

Investors in socially responsible investments must do their own research and make their own financial decisions, rather than follow the crowd, especially during extreme events like the COVID-19 pandemic.

The evidence shows that, during the pandemic, socially responsible ETFs behaved in line with theoretical predictions of herding, that is, herding is more significant during extreme market conditions.

This study analyses the role of individuals' objective financial knowledge in shaping preferences for ethical intermediaries and sustainable investments in Italy. Another goal of this study is to assess the impact of individuals' misperceptions about their own financial knowledge and to test for gender-related differences in attitudes towards socially responsible investing (SRI).

Using nationally representative microdata from the Bank of Italy’s “Italian Literacy and Financial Competence Survey” (IACOFI), the authors use probit models, extended to account for potential endogeneity issues, to assess the causal effects of financial knowledge and confidence on stated preferences for SRI. Empirical models also allow to explicitly assess the moderating role of gender on the effects of financial knowledge and confidence on attitudes towards sustainable investing.

Results indicate that individuals' preferences for sustainable finance significantly increase with financial knowledge, suggesting that inadequate financial competencies represent a barrier to participation in SRI. At the same time, lack of confidence in one’s own financial knowledge significantly hampers attitudes towards sustainable investments. Furthermore, the authors show that women have a greater preference for sustainable finance than men and point out that financial knowledge and confidence exert heterogenous effects on attitudes towards SRI.

This study provides several contributions to the literature on SRI. First, the authors give evidence of the causal effect of financial knowledge on preferences for both ethical financial intermediaries and sustainable investments. Moreover, this is the first study to investigate the role of financial underconfidence bias in shaping individuals' SRI attitudes. Finally, extending previous research, the authors assess differences in SRI preferences between women and men and provide novel evidence on gender-related heterogeneity in the effects of financial knowledge and underconfidence.

The study aims to investigate the presence of herding behavior in the stock market of UK with a special emphasis on news sentiment regarding the economy. The authors focus on the news sentiment because in the current digital era, investors take their decision making on the basis of current trends projected by news and media platforms.

For empirical modeling, the authors use machine learning models to investigate the presence of herding behavior in UK stock market for the period starting from 2006 to 2021. The authors use support vector regression, single layer neural network and multilayer neural network models to predict the herding behavior in the stock market of the UK. The authors estimate the herding coefficients using all the models and compare the findings with the linear regression model.

The results show a strong evidence of herding behavior in the stock market of the UK during different time regimes. Furthermore, when the authors incorporate the economic uncertainty news sentiment in the model, the results show a significant improvement. The results of support vector regression, single layer perceptron and multilayer perceptron model show the evidence of herding behavior in UK stock market during global financial crises of 2007–08 and COVID’19 period. In addition, the authors compare the findings with the linear regression which provides no evidence of herding behavior in all the regimes except COVID’19. The results also provide deep insights for both individual investors and policy makers to construct efficient portfolios and avoid market crashes, respectively.

In the existing literature of herding behavior, news sentiment regarding economic uncertainty has not been used before. However, in the present era this parameter is quite critical in context of market anomalies hence and needs to be investigated. In addition, the literature exhibits varying results about the existence of herding behavior when different methodologies are used. In this context, the use of machine learning models is quite rare in the herding literature. The machine learning models are quite robust and provide accurate results. Therefore, this research study uses three different models, i.e. single layer perceptron model, multilayer perceptron model and support vector regression model to investigate the herding behavior in the stock market of the UK. A comparative analysis is also presented among the results of all the models. The study sheds light on the importance of economic uncertainty news sentiment to predict the herding behavior.

The purpose of this study is to examine the impact of technological diversification (TD) on enterprise innovation performance, meanwhile focusing on the moderating effects of various organizational slack (i.e. absorbed and unabsorbed slack) and ownership types (i.e. state-owned or privately-owned) in the context of Chinese listed firms.

This study formulates five hypotheses based on organization and agency theories. Our empirical analysis employs a fixed-effect regression estimator with a unique panel dataset of Chinese-listed manufacturing firms and 13,566 firm-year observations over 9 years from 2012 to 2020.

Our findings show that an inverted U-shaped relationship exists between TD and innovation performance, varying with different types of organizational slack and ownership. In state-owned enterprises (SOEs), unabsorbed slack negatively moderates the inverted U-shaped relationship; however, in privately-owned enterprises (POEs), this relationship is positively moderated. Although absorbed slack has negative moderating effects in both SOEs and POEs, its impact is only significant for POEs.

Our results imply that organizational slack has a contrasting impact on the relationship between TD and innovation performance when the type of ownership varies. Therefore, the managers that intend to achieve optimal innovation performance through TD should understand how organizational slack can be leveraged.

This study contributes to the existing literature by applying the relationship between TD and innovative performance to the transition economy, as well as examining the double-edged sword impact of state ownership on firm innovation performance.

The purpose of this study is to investigate the adoption of waqf technology (Waqftech) using blockchain smart contracts for corporate waqf crowdfunding. Despite the growing interest in Waqftech, Malaysian enterprises have not fully embraced this emerging technology because of uncertainty regarding the benefits it offers to contributors. The research incorporates two theoretical frameworks: the electronic data interchange (EDI) model for firms’ technology adoption, and the triple bottom line theory (TBL) for corporate social responsibility.

A quantitative method using a cross-sectional survey design with a five-point Likert scale questionnaire was used. Data was collected from 210 decision-makers representing small and medium-sized enterprises and analyzed using partial least squares-structural equation modeling.

The findings from this research suggest that Malaysian enterprises are influenced by both corporate and social predictive benefits when using blockchain crowdfunding, but not by environmental benefits. The adoption of blockchain smart contracts does not correlate with predictive environmental benefits because of misconceptions about the disruptive technology’s impact on biological and digital environmental preservation.

This research focuses on organizational behavior rather than individual users of waqf crowdfunding, and it is limited, primarily focusing within Malaysia and regions with similar waqf structures.

The Waqftech framework allows innovative mechanisms for executing corporate waqf investment returns to the intended beneficiaries through the smart contracts’ platform. In addition, this study supports relevant corporate social responsibility and creating shared value technology adoption theories, including EDI and TBL. Aside from this, the study provides empirical implications for waqf management using fintech platforms.

This groundbreaking study focuses on creating a Waqftech model for corporate waqf crowdfunding. The results of this study are important for the development of government policies that support the use of Waqftech in charitable fundraising. More research on biological and digital environmental perspectives is proposed to foster investors’ confidence in the visibility of digital tracking and lead to swift investments in future metaverse fundraising platforms.