Islamic assets, assets compliant with ethical and religious norms as codified in Sharia law, broaden the investor base. Do such investments contribute to mean-variance efficiency, and if so, how? Using daily data on stock, bond, and money market indices from nine Islamic countries and 37 non-Islamic ones from May 2007 to June 2010, we show that adding Islamic assets to an existing portfolio of conventional (non-Islamic) assets can expand the mean-variance frontier and thereby create additional value through diversification. The “specialness” of Islamic assets comes from a smaller set of common information and a lower degree of cross-market hedging between Islamic and conventional markets. This reduces volatility linkages between Islamic and conventional assets relative to volatility linkages between two conventional assets. Including one Islamic asset lowers volatility linkages by up to 3.16 percentage points after controlling for country-level fixed effects and time-varying characteristics. Low volatility linkages are key to increasing diversification benefits that arise from improvements in the global mean-variance portfolio. Our research contributes to the international business literature by highlighting the potential benefits of bridging religious, ethical, and cultural differences to add new markets to an incomplete international market structure and in so doing increase diversification benefits.

We investigate how environmental, social, and governance (ESG) risk can spread between peers and its impact on long-term firm performance. Using data across six geographically diverse countries over a fourteen-year period, we find a significant spillover of ESG risks among multinational firms, which fails to yield a meaningful impact on the performance of affected firms. These findings place a spotlight on a critical gap in ESG risk management and echo an urgent signal for policy intervention, aligning with the United Nations’ faltering Sustainable Development Goals for 2030. This work is a clarion call for immediate academic and practical action in a world teetering on the brink of unsustainable practices. Our findings suggest that market-based mechanisms alone may be insufficient to discipline ESG risk, highlighting a potential role for regulatory oversight and policy attention.

This study empirically examines how artificial intelligence (AI) adoption and innovation shape product market dynamics and financial performance in multinational enterprises (MNEs) using a global firm sample over 1980–2023. We construct an unbalanced panel dataset by integrating textual analysis, manual verification, and data merged from nine major databases, identifying 411 AI-classified MNEs and a matched 411 non-AI MNEs. Using panel regression models with industry and year fixed effects, we test how AI intensity (the proportion of AI-related products/assets) and R&D—individually and jointly—affect product portfolio breadth and change, market share, industry concentration (HHI), and profitability. The results show that greater AI integration is associated with higher product diversification and a stronger competitive positioning, and that the interaction of AI and R&D is particularly important for explaining market share, concentration, and profitability differences across AI and non-AI MNEs. Overall, the findings highlight the strategic value of aligning AI adoption with innovation investments to strengthen product market outcomes and financial performance in global markets.