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Digital transformation is reshaping industries, business models, and investment opportunities, creating new challenges for international portfolio management. The European communication services sector has become a strategic component of the digital economy, driven by advances in artificial intelligence (AI), digital platforms, 5G infrastructure, cloud computing, cybersecurity, and data-driven business models. Despite its importance, limited evidence exists regarding the effectiveness of portfolio optimisation strategies within digitally transforming sectors. This study investigates international portfolio optimisation using constituent firms of the MSCI Europe Communication Services 35/20 Capped Index. Drawing upon Modern Portfolio Theory and the Treynor–Black framework, an actively managed portfolio is constructed and evaluated against the SPDR^® MSCI Europe Communication Services UCITS ETF and an equal-weight portfolio. Using daily market data, the analysis estimates asset returns, alpha and beta coefficients, portfolio weights, and risk-adjusted performance measures, including the Sharpe and Treynor ratios. Paired-samples t-tests are employed to assess the statistical significance of performance differences among investment strategies. The findings show that the Treynor–Black portfolio generated the highest annual return (27.32%), outperforming both the benchmark and equal-weight portfolios, and the highest percentage of Sharpe ratios (1.2159), suggesting that diversification benefits outweighed the advantages of active security selection. Hypothesis testing indicates no statistically significant difference between the Treynor–Black and equal-weight portfolios, and no statistically significant difference exists between the proposed and benchmark portfolios. The study extends the international portfolio management literature by applying the Treynor–Black model to a digitally transforming sector. The findings suggest that portfolio performance is influenced not only by firm-level financial characteristics but also by broader digital and institutional environments. Firms operating within digitally advanced and well-governed economies appear better positioned to exploit technological innovation and generate sustainable long-term value. Overall, the results demonstrate that successful international portfolio optimization requires balancing active security selection with diversification while recognizing the role of digital transformation, governance quality, and innovation ecosystems in shaping investment performance within the digital economy. Full article

Amid shifting urban renewal approaches and industrial restructuring in China, small and medium-sized cities face the challenge of conserving industrial heritage through adaptive reuse. Drawing on gazetteers, planning documents, and interviews, this paper examines the transformation of the Anqing Baiqitun Cement Plant into a cultural and creative park across environmental, social, economic, and governance dimensions. The case is significant as a typical heavy industrial heritage in a smaller city, while the enterprise’s restructuring and relocation have produced a rupture in collective memory and identity. The study finds that minimal intervention in the heavy industrial remains achieved carbon sequestration and emission reduction, succeeded in ecological restoration, and attained financial viability by leveraging local markets and intangible assets. However, the industrial past has been selectively reconstructed, creating a divide between the former industrial community and the present one, partly due to an over-reliance on Authorised Heritage Discourse logic and insufficient collaboration with the existing enterprise. Participatory governance, driven mainly by government coordination and market capital, proved efficient but lacked public engagement. The paper argues that regenerating industrial heritage in small and medium-sized cities requires not only physical upgrading but also social mechanisms that enable public participation in conservation. Full article

Carbon price instability within the EU Emissions Trading System (EU ETS) is associated with financial stress that propagates across green finance markets, yet the system-level dynamics linking carbon allowance instruments, clean energy equities, green bonds, and oil volatility remain insufficiently characterized over the turbulent 2021–2026 period. This paper applies the DCC-GARCH $R^2$ decomposed connectedness framework to five exchange-traded funds and one volatility index spanning the principal channels through which EU ETS regulatory shocks propagate to financial markets, and derives a novel Connectedness-Based Hierarchy Index (CBHI) that translates the transmitter–receiver hierarchy into a time-varying portfolio desirability index. Five key findings emerge. First, the carbon allowance futures ETF (KRBN) and the Paris-aligned equity ETF (CARB) form a near-closed systemic bloc within this asset universe: bilateral connectedness reaches $0.867$, with to and from directional connectedness values both approaching 80%. Second, this co-transmitter structure is highly contingent on the joint inclusion of both instruments; excluding CARB raises KRBN’s CBHI from $0.251$ to $22.204$, reclassifying it as a structural diversifier and reducing the mean Total Connectedness Index (TCI) from 52.18% to 30.81%. Third, system-wide connectedness averages 52.18% but surges to nearly 73% during EU ETS regulatory crises. Fourth, connectedness-aware portfolios outperform the minimum-variance benchmark across all risk-adjusted metrics, yielding an annualized Sharpe ratio of $0.148$ vs. $-1.066$ ($T=1035$ observations, daily rebalancing, zero transaction costs); this improvement is driven by reallocation toward crude oil volatility (OVX), the sole non-ETF and most peripheral instrument (to = 5.55%, $\mathrm{CBHI}=14.310$), which the CBHI identifies as the system’s dominant structural diversifier and whose weight rises from 0.4% in the benchmark to 31.6%. Fifth, the CBHI uncovers a structural tension in Paris-aligned mandates: CARB records the second-lowest CBHI ($0.254$), indicating that climate alignment and systemic risk minimization are partially conflicting objectives. Full article

Against the backdrop of rapid technological innovation and the growing use of alternative investment instruments, this study examines the dynamic connectedness among decentralized finance assets, AI-based stocks, Islamic stocks and commodities. Covering the period from December 2019 to June 2022, we use the time-varying parameter vector autoregression (TVP-VAR) model to measure the magnitude, direction and evolution of return spillovers across Chainlink, Maker, Basic Attention Token, NVIDIA, Amazon, Google, Microsoft, DJIM World, DJIM EM, gold, crude oil and Global X Lithium and Battery Tech. The connectedness literature has examined spillovers across different asset classes during crisis periods. However, much of this literature focuses mainly on pairwise relationships among traditional asset classes, with limited attention to how emerging, alternative and technology-driven assets interact within a single network. We further assess the role of investor sentiment and network topology in identifying systemic transmitters and receivers. The results show strong interconnectedness, with an average total connectedness index (TCI) of 68.81%. Notably, AI-based stocks, especially Microsoft and NVIDIA, consistently emerge as net transmitters of return shocks, while commodities like gold and crude oil serve as absorbers of shocks. The portfolio results show that network centrality improves risk-adjusted performance by reducing volatility and downside risk. These insights have practical implications for policymakers and market participants, offering guidance for developing effective regulatory frameworks, investment strategies and risk management approaches in an increasingly interconnected financial landscape. Full article

While green bonds are increasingly used to channel capital towards environmentally responsible projects, their effectiveness depends not only on market growth, but also on the credibility of the institutional frameworks that support the green label. Through focused bibliometric positioning, scoping synthesis and comparative document analysis, the paper examines how seven major green-bond frameworks organise credibility: the EU European Green Bond Standard, the ICMA Green Bond Principles, the Japan Green Bond Guidelines, the ASEAN Green Bond Standards, China’s catalogue-plus-principles framework, India’s Sovereign Green Bond Framework and China’s Sovereign Green Bond Framework 2025. The findings show that frameworks converge in product grammar, including project selection, management of proceeds, reporting and external review, but diverge substantially in credibility architecture, especially regarding external review, supervision, refinancing governance and environmental additionality. Current frameworks generally make green-bond labels more transparent, comparable and verifiable, but not necessarily more additional in environmental terms. Important challenges therefore remain: fragmented standards, greenwashing risks, information asymmetries, weak impact-reporting comparability and limited safeguards against refinancing existing assets. The paper argues that the contribution of green bonds to the green transition depends on how credibility is institutionally organised. Full article

Global angel investment and venture capital are key financial drivers supporting the long-term growth of technology companies, and they play a vital role in improving the global science and technology innovation financial system and advancing green and sustainable transformation. This paper uses data on technology-sector companies listed on the A-share market from 2017 to 2025 to construct a multi-period DID model. It empirically examines the impact of angel investment and venture capital on the sustainable development of technology companies and investigates the moderating effect of ESG performance. The study finds that angel investment can significantly enhance the level of sustainable development in technology firms. Mechanism tests indicate that angel investment indirectly empowers sustainable development by attracting and introducing venture capital. The moderating effect shows that strong ESG performance positively reinforces the promotional role of angel investment and venture capital in the sustainable development of technology firms. Heterogeneity analysis reveals that these enhancement and moderating effects are more pronounced in high-tech industries, private enterprises, and asset-light technology firms. These findings provide empirical evidence and policy guidance for governments worldwide to direct venture capital toward supporting science and technology enterprises, help technology firms improve their ESG governance systems, and achieve long-term sustainable operations. Full article

This paper develops a model-driven analytical framework for portfolio optimization under a multidimensional Black–Scholes model with time-varying parameters, where both the drift and volatility functions evolve linearly over time. Within this framework, explicit closed-form expressions are derived for the covariance matrix of normalized asset prices and subsequently incorporated into the classical Markowitz mean–variance framework to obtain analytical representations of the global minimum-variance portfolio, the mean–variance efficient portfolio, and the corresponding efficient frontier. The proposed methodology establishes a direct connection between continuous-time stochastic asset-price modeling and portfolio optimization through a model-implied covariance structure. Its practical implementation is investigated through both numerical experiments and an empirical study using daily stock price data from 20 constituents of the $S\&P$ 500 index over the period 2020–2024. Monte Carlo simulations demonstrate the finite-sample sensitivity of portfolio optimization to covariance estimation, while the empirical analysis illustrates how the estimated model parameters, obtained using the maximum likelihood framework of Aït-Sahalia for discretely sampled diffusion processes, can be incorporated into the analytical covariance matrix for constructing efficient frontiers under realistic market conditions. Overall, the proposed framework provides an analytically tractable methodology that integrates continuous-time asset pricing models with classical mean–variance portfolio optimization, offering a coherent model-based covariance representation for portfolio selection under time-varying market environments. Full article

The Markets in Crypto-Assets Regulation (MiCA) harmonizes market rules across the European Union, but it does not itself determine how entities should classify, measure, document, and tax crypto-asset transactions. This study examines whether Slovakia’s recent implementation measures have translated formal harmonization into operational accounting clarity. An anonymous online survey of 34 accounting, tax, finance, and business professionals recruited through purposive and convenience sampling was analyzed using counts, percentages, a descriptive cross-tabulation with Cramér’s V, and a documented but limited coding of two open-ended items. Because the sample is small and non-probability, all results are interpreted as indicative of the observed respondents rather than as population estimates. In the sample, 52.9% assessed the direction of legislative development positively, 72.7% of valid respondents considered current valuation rules inadequate, 60.6% reported that the reforms had not increased accounting clarity, and 60.6% perceived greater uncertainty. Tax obligations (50.0%) and record-keeping and documentation (35.3%) were selected more often than bookkeeping mechanics (14.7%). Practical experience co-varied with legislative monitoring in the realized sample (Cramér’s V = 0.62), although no population-inferential p-values or confidence intervals are reported. The pattern suggests a regulatory–operational clarity gap: legal taxonomy and market supervision have advanced faster than implementable valuation and documentation guidance. The proposed implementation framework combines survey indications with regulatory analysis, prior literature, and the authors’ professional judgment; it is a non-ranked proposal for consultation and further testing rather than a set of empirically validated policy priorities. Although derived from Slovakia, the framework is relevant to other EU jurisdictions translating MiCA’s common market rules into national accounting and tax practice. Full article

Fractional Brownian motion has been widely used in financial modeling to capture long-range dependence and persistent behavior in asset dynamics. In the fractional Black–Scholes framework, accurate estimation of the Hurst parameter is essential because estimation uncertainty can directly affect option pricing. In this paper, we propose a Bayesian framework for joint inference on the Hurst parameter and volatility in fractional stochastic differential equation models. Unlike approaches based solely on point estimation, the proposed framework propagates posterior uncertainty directly into option pricing distributions under the fractional Black–Scholes model. Simulation studies are conducted across multiple values of the Hurst parameter and sample sizes to evaluate estimation accuracy, posterior coverage, and pricing uncertainty. The results demonstrate stable posterior inference and coherent uncertainty quantification for both model parameters and option prices. The methodology is further illustrated using WTI crude oil and natural gas data under different market regimes. The empirical analysis indicates that differences in market behavior are driven primarily by changes in volatility rather than strong long-range dependence, while posterior option price distributions exhibit substantial variation in pricing uncertainty across regimes. These findings highlight the importance of incorporating joint parameter uncertainty into fractional financial models and demonstrate the practical value of Bayesian methods for option pricing. Full article

This study examines the moderating role of liquidity in the relationship between extreme capital structure and firm performance among listed firms in emerging markets. It is motivated by the need to better understand how financing constraints and liquidity management influence firm performance in environments characterised by high financial frictions and limited access to external capital. Extreme capital structure is defined as firms maintaining very low levels of debt, measured using thresholds of 1% (ultra-low debt) and 5% for both long-term debt and total debt. The analysis is based on a panel dataset of non-financial listed firms over the period 2006–2024 and employs a dynamic panel System Generalised Method of Moments (System GMM) complemented by a Random Effects model for robustness. Empirical results indicate that liquidity has a meaningful and predominantly positive moderating effect. This is observed when firms maintain extremely low long-term debt (1% threshold) and low long-term debt (5% threshold). Liquidity enhances firm performance. This effect is strongest for return on assets (ROA) and return on equity (ROE). The effect on Tobin’s Q is weaker but remains generally positive. These findings highlight the strategic importance of liquidity in improving profitability and financial resilience under conservative financing structures. However, the findings are limited to listed non-financial firms in emerging markets and may not be generalizable to SMEs or unlisted firms. Future research could explore the threshold at which liquidity ceases to generate benefits or begins to produce diminishing returns in ultra-low leverage contexts. Full article

Machine learning technologies are increasingly embedded in financial decision-making processes, yet their influence on investor behavior and market efficiency remains insufficiently understood. This study investigates how machine learning-assisted investment decisions affect investor behavioral biases and asset pricing efficiency and whether these effects exhibit nonlinear characteristics. Using investor-level trading records, survey data, and market data from the Chinese A-share market (N = 12,846 investors; 3876 questionnaires; 3 million+ transactions), we construct measures of machine learning adoption intensity, investor behavioral biases, and asset pricing efficiency. Employing fixed-effects models, instrumental-variable estimation (2SLS), and mediation analysis, we examine the behavioral and market consequences of machine learning adoption. The results reveal a significant U-shaped relationship between machine learning adoption intensity and investor behavioral biases (inflection point: AIDI* = 0.731), and an inverted U-shaped relationship between AI market penetration and asset pricing efficiency (threshold: AIPM* = 0.733). Investor behavioral bias mediates 26.34% of the total effect of AI adoption on pricing efficiency. Moderate adoption reduces behavioral biases by improving information processing and decision quality, whereas excessive reliance on algorithmic recommendations generates automation bias and weakens investors’ independent judgment. At the market level, machine learning adoption exhibits an inverted U-shaped relationship with asset pricing efficiency. While moderate adoption enhances information incorporation into prices and reduces pricing deviations, excessive market penetration may induce algorithmic homogeneity and diminish efficiency gains. Furthermore, investor behavioral bias serves as an important transmission mechanism linking machine learning adoption to asset pricing outcomes. Heterogeneity analyses indicate that institutional investors benefit more from machine learning tools than individual investors, and the effects are stronger during periods of high market uncertainty. These findings provide new evidence on the optimal adoption of machine learning in financial markets and offer practical implications for intelligent investment platforms, investor education, and financial regulation. Full article

The rapid growth of electric vehicle (EV) adoption presents significant challenges for power system stability while creating new opportunities for decentralized energy management. Peer-to-peer (P2P) energy networks have emerged as a promising approach for transforming EVs from passive loads into coordinated grid assets. This paper presents a comprehensive review of EV-P2P integration through a three-layer architectural framework that systematically connects physical infrastructure, market mechanisms, and intelligent control strategies. The Physical Layer reviews how V2X technologies and bidirectional charging enable EVs to operate as flexible storage resources and ancillary service providers. The Transactional Layer reviews on blockchain-based platforms, auction mechanisms, and game-theoretic models for secure energy trading. The Intelligence Layer reviews advanced control strategies, including decentralized optimization methods such as the Alternating Direction Method of Multipliers (ADMM) and Deep Reinforcement Learning. Collectively, the reviewed studies demonstrate that these approaches enable EVs to operate as flexible loads, distributed storage resources, and ancillary service providers, while improving energy trading efficiency, reducing operating costs, and alleviating network congestion under simulated operating conditions. Despite these promising results, a substantial gap remains between simulation-based studies and practical implementation. Future research should prioritize integrated pilot projects to evaluate scalability, interoperability, cybersecurity, and regulatory compliance under realistic operating conditions. Full article

Does the pricing of uncertainty in cryptocurrency markets depend on where in the return distribution one looks, and does investor attention carry information beyond uncertainty itself? We address these questions for ten cryptocurrencies spanning dominant and less-dominant coins over September 2018 to December 2021, using quantile regression, joint (multivariate) quantile regression, and Granger causality tests, together with a COVID-19 sub-sample. Grounding the analysis in the uncertainty- and attention-based asset-pricing literature, we test three hypotheses: that uncertainty is priced in the tails of the return distribution rather than at its centre; that cryptocurrency-specific uncertainty matters more than broad macroeconomic uncertainty; and that uncertainty and attention are complementary, so that pairing an uncertainty index with an attention index explains tail returns better than either index alone. The evidence supports all three. Single indices are largely irrelevant at the median but become influential in bear-market tails and over longer horizons; the cryptocurrency-specific UCRY indices dominate the broader macro proxies; and price-or-policy-plus-attention pairs show stronger and broader tail effects than either index alone, though part of this reflects the additional regressor in paired specifications. Causality runs mainly from indices to returns at longer horizons. The study shows that uncertainty is a tail phenomenon in cryptocurrency markets and that attention operates as a distinct, complementary channel. Full article

Understanding the pricing of climate risks in bond markets is relevant to financial stability. The real estate sector, characterized by geographically fixed and long-duration assets, exhibits high exposure to environmental shocks; yet, empirical matching between specific climate channels and real estate bond pricing remains sparse. This analysis examines the impact of climate risks on corporate bond credit spreads within the real estate sector by constructing three thematic indicators: transition risk (CTRI), chronic physical risk (ChroCPRI), and acute physical risk (AcuCPRI). Initial feature selection via machine learning suggests all three risk categories as predictive covariates for bond pricing. Subsequent regression estimations indicate that climate transition risk and acute physical risk expand credit spreads, whereas chronic physical risk compresses them—with these statistical patterns being more pronounced among state-owned enterprises (SOEs). Mechanism analyses yield threefold insights: first, transition risk elevates spreads by tightening financing constraints and restricting corporate asset growth, a channel concentrated in short-term tranches and low-liquidity firms; second, the counterintuitive spread-compressing effect of chronic risk is localized among firms with lower credit ratings and lower profitability, consistent with institutional climate support frameworks and strategic green adaptations; third, acute physical risk widens spreads by compressing operational cash flows and exacerbating financing friction, particularly for smaller enterprises. These channels align with the structural attributes of SOEs, which are characterized by larger asset scales, superior capital liquidity, and a higher propensity to secure state guarantees. Full article

Sustainable finance, digital financial innovation, and green innovation have become central strategic pressures in banking, yet their joint effects on bank performance and risk remain insufficiently understood. This study develops an integrated ESG-innovation framework and examines quarterly panel data for 24 Taiwanese financial holding and domestic commercial banks from 2016Q1 to 2025Q3, yielding 934 bank-quarter observations. Return on assets (ROA) and the Z-score are used to capture operating performance and financial stability, respectively, and bank fixed-effects panel models are estimated for high- and low-ESG as well as high- and low-innovation subsamples. The re-estimated results reveal a conditional sustainability effect: ESG and innovation do not generate homogeneous financial benefits, but depend on banks’ ESG foundations, innovation intensity, leverage, and scale. Environmental scores are positively associated with ROA in the high-ESG subsample and weakly positive in the low-ESG subsample, whereas credit card transaction expansion is costly for low-ESG banks. In high-innovation banks, credit card transaction volume and green patents are negatively associated with ROA, suggesting adjustment costs and diminishing marginal returns. For financial stability, ESG recognition and green patents are more beneficial in lower ESG or innovation contexts, while leverage is consistently negative across all specifications. These findings contribute to the sustainable finance literature by clarifying how ESG, FinTech-related innovation, and green innovation jointly shape bank performance and risk in a policy-driven emerging market. The results also suggest that banks and regulators should adopt differentiated ESG and innovation strategies rather than assuming that sustainability investment produces uniform outcomes. Full article