This study investigates the role of artificial intelligence (AI) tokens in dynamic interactions, diversification, and hedging capabilities, in relation to non-fungible tokens (NFTs), decentralised finance (DeFi) tokens, and renewable energy assets. Using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model, we examine return, volatility, and higher-order spillovers across both time and frequency domains. The results show that NFTs serve as persistent channels for the transmission of return and volatility shocks, driven by their speculative nature. AI and renewable tokens primarily absorb systemic risk due to their lower liquidity and niche adoption. DeFi tokens play flexible roles, shifting between transmitters and receivers across market regimes. The results demonstrate asset-specific idiosyncrasies and that volatility spillovers are generally stronger than return spillovers. Frequency-domain analysis highlights that digital tokens dominate short-term spillovers, while renewable assets absorb shocks across horizons. However, higher-order moment results reveal that extreme risk linkages shift transmission channels. Our results also confirm that oil market (OVX) shocks drive short-term return connectedness, CBOE volatility (VIX) volatility, and policy uncertainty (EPU) significantly impact return linkages. The results of our portfolio analysis show that AI tokens form the core of diversification, NFTs provide short-term speculative hedging, and renewable assets, particularly solar-linked tokens, act as low-cost stabilisers, underscoring the need for active rebalancing under different market regimes. These findings provide meaningful implications for policymakers, regulators, and portfolio managers for strengthening systemic risk oversight and considering asset-specific idiosyncrasies in investment strategies.

The advancement in digital and information technology (DIT) has profound effects on individuals, corporations, and society. The benefits of DIT, such as enhanced efficiency, easily access to information, and increased connectivity, are clear. However, scholars have raised various concerns regarding the plethora of information. For example, how DIT affects investors’ information acquisition behavior is unclear. Competing theories offer conflicting predictions in investor information acquisition. These predictions have different implications regarding whether investors acquire more information about local firms (local attention bias), versus about non-local firms, when information becomes easily accessible. Our empirical results show that as DIT develops, investors pay more attention to local firms, amplifying local attention bias. Economic development and a better developed institutional environment amplify rather than attenuate local attention bias. Mediation analysis further shows that DIT development increases attention co-movement and stock return correlation not only directly but also indirectly through local attention bias as a mediator. Our novel evidence suggests that when information is more easily accessible associated with DIT development, information asymmetry can be amplified when agents can choose what to learn, increasing polarization of information acquisition and selective exposure to information.

We document a robustly positive association between corporate leasing and geopolitical risk (GPR). This relation is stronger in cases where firms have greater earnings and returns volatility, face more severe product market competition, are more financially constrained, and are subject to more investment irreversibility. The impact of geopolitical risk is particularly relevant for short-term leases. Leasing during times of heightened GPR is associated with larger sales growth and higher firm value. Overall, our results are consistent with predictions of real options theory, where firms strategically use leasing as a mechanism to create flexibility in ongoing operations when facing uncertainty.

We investigate the impact of escalating temperatures and heavy rainfall on the default probability of small and micro enterprises (SMiEs) in six European countries between 2005 and 2014. Our findings reveal that a one standard deviation increase ( 2.56 ∘ C ) in the yearly mean temperature raises a firm's default probability by 86.5 basis points. Additionally, a one standard deviation increase (2.46 mm) in the Simple Precipitation Intensity Index increases the default probability by 32.4 basis points. We argue that one channel explaining the adverse impact of climate risk on default probability is labour productivity loss. In addition, micro and financially constrained firms exhibit increased vulnerability to these risks. However, when the ultimate owners also serve as the firms' managers, they can mitigate the adverse effects of rising temperatures and heavy rainfall.

This Special Issue brings together papers that offer a diverse view of fintech’s evolving role in financial markets, institutions, and the real economy. Collectively, the studies show that fintech innovations generate significant benefits while also introducing new sources of risk, including volatility spillovers and systemic interconnectedness. Several contributions emphasise the importance of behavioural factors, financial literacy, routines, and organizational culture in shaping fintech adoption, investor behaviour, and banks’ responses to competition. Other papers highlight the real economic impact of fintech, including its predictive power for equity returns, its role in reducing the cost of capital, facilitating M&A activity and its stabilising effects during crises. The SI also advances our understanding of fintech entrepreneurship, documenting how human capital, founder characteristics, and business models influence innovation and firm performance. We conclude by offering possible future research directions.

Shareholder activists vary in investment style, expertise, time horizon, incentives, and engagement mode. This study examines four activist types, namely, Exclusive, Substantial, Limited, and Non-specialists, classified by degree of specialism. Using a U.S. sample of 3,903 activist campaigns (2008–2021), we analyze how specialism shapes campaign demands, tactics like Wolf Packs, and outcomes. We find that higher activist specialism significantly influences campaign themes and increases the likelihood of campaign success. Market reactions to campaign announcements are positively associated with specialism, with Exclusive specialists generating the highest abnormal returns. However, analysis of long-term shareholder returns and operating performance reveals a reversal of these initial gains over the three-year post-campaign period. Exclusive specialists underperform Non-specialists in long-term shareholder value and operating performance, though they outperform other specialist categories in limiting value deterioration. The reversal from short-term gains to long-term losses appears driven by specialists' preference for campaign themes that yield immediate payoffs but undermine long-term value. In contrast, fewer specialised activists often secure partial success through messy compromises that also erode long-term shareholder value. Robustness tests almost fully confirm the validity of these findings.

We examine the factors influencing equity market liquidity through explainable machine learning techniques. Unlike previous studies, our approach is entirely nonparametric. By studying daily placement orders for equity securities managed by a European asset management institution, we uncover multiple nonlinear relationships between market liquidity and placement characteristics. As expected, the results show that liquidity tends to increase in highly active markets. However, we also note that liquidity remains relatively stable within certain trading volume ranges. Price volatility, broker efficiency, and the market impact of the trade are important predictors of liquidity. Price volatility shows a linear relationship with bid-ask spreads, whereas broker efficiency and market impact have nonsymmetric convex effects. Large bid-ask spreads are linked to increased uncertainty and weak economic activity.

It is well recognized that relationship banking helps to relieve the credit constraints faced by SMEs to access bank finance. Trust is an important part of relationship banking. However, the term trust is nebulous, and relationship banking means different things to different banks and different borrowers. How trust enables the credit market for SMEs through relationship banking is largely unexplored. Using a unique primary dataset of SMEs in the UK, we construct a measure of trust-based relationship banking from the perspective of the borrower that places mutual trust centre stage. We show that trust-based relationship banking is enhanced by the organizational trust in the Relationship Manager, defined by the delegation of operational autonomy. Along with bank, firm, and market factors, trust-based relationship banking helped to reduce the credit constraints faced by SMEs in the decade following the global financial crisis.

This paper investigates extreme risk spillovers in global energy markets using the enhanced high-dimensional time-varying parameter vector autoregressive spillover (HD-TVP-VAR-SP) model. We employ the Long Short Term Memory (LSTM) model to develop an energy risk warning system, identifying key factors in risk contagion. Our findings reveal robust connectivity in global energy market risks, characterized by high-dimensional complex networks with marked temporal variations. The Americas region emerges as the leading contributor to systemic risk shocks, primarily through positive spillovers in its energy markets. The LSTM model demonstrates superior extreme risk prediction compared to other machine learning models like Gradient Boosting Machines, Random Forest, and Decision Trees. The oil market is identified as a critical driver of risk contagion in the energy sector. These insights provide valuable guidance for effectively identifying and managing global energy market risks and enhancing risk warning systems.