Search Results (813)

This paper investigates the long-run relationship and short-run price dynamics between the German electricity and natural gas markets to assess market efficiency, with a focus on the impact of the Russia–Ukraine conflict. Employing Johansen cointegration tests and a Vector Error Correction Model (VECM) on weekly data from 2018 to 2025, we find a stable long-run equilibrium between the two prices. The results show that while the electricity market exhibits a self-correcting mechanism, indicating a certain degree of efficiency, this efficiency significantly deteriorated following the conflict’s outbreak. The natural gas market lost its error-correction capability post-conflict, and momentum effects became pronounced, suggesting impaired price discovery and weakened market efficiency under severe geopolitical stress. The findings provide empirical evidence supporting the reform of marginal pricing models in Europe to enhance resilience against geopolitical shocks. Full article

Recent advances in large language models, tool-using agents, and financial machine learning are shifting financial automation from isolated prediction tasks to integrated decision systems that can perceive information, reason over objectives, and generate or execute actions. The paper develops an integrative framework for analysing agentic finance: financial market environments in which autonomous or semi-autonomous AI systems participate in information processing, decision support, monitoring, and execution workflows. The analysis proceeds in three steps. First, the paper proposes a four-layer architecture of financial AI agents covering data perception, reasoning engines, strategy generation, and execution with control. Second, it introduces the Agentic Financial Market Model (AFMM), a stylised agent-based representation linking agent design parameters such as autonomy depth, heterogeneity, execution coupling, infrastructure concentration, and supervisory observability to market-level outcomes including efficiency, liquidity resilience, volatility, and systemic risk. Third, it presents an illustrative empirical application based on event studies of AI-agent capability disclosures and heterogeneous market repricing. It argues that the systemic implications of AI in finance depend less on model intelligence alone than on how agent architectures are distributed, coupled, and governed across institutions. The empirical application is intentionally exploratory: it does not validate the full AFMM but shows how one observable expectations channel can be studied using public data. In the near term, the most plausible equilibrium is bounded autonomy, in which AI agents operate as supervised co-pilots, monitoring systems, and constrained execution modules embedded within human decision processes. Full article

As the integrated energy market evolves toward a multi-stakeholder coexistence model, balancing economic efficiency, user well-being, and system-level sustainability among interacting stakeholders has become a key challenge, particularly in the rapidly developing regional integrated energy markets in China. Thus, to satisfy user comfort and energy substitution requirements while achieving cost-effective electricity and heating supply, this study proposes a Stackelberg game-based market trading framework involving an integrated energy producer (IEP), an integrated energy operator (IEO), and a load aggregator (LA). First, the integrated energy market framework and transaction modes are established, and the profit models of IEP and IEO are formulated. Considering users’ energy substitution behavior, user comfort is quantified to explicitly reflect user welfare in market decision making, and a consumer surplus model is developed for LA participating in market transactions. Second, a Stackelberg game framework is constructed to coordinate the strategies of all participants by incorporating source–load energy flows, and the equilibrium solution is proven to be unique and solvable using quadratic programming. Finally, a case study based on historical data from Hebei Province, China, is conducted to validate the proposed strategy. The results demonstrate that the proposed method effectively coordinates the interests of all stakeholders, enhances demand response capability without reducing user comfort, and improves economic benefits for both supply and demand sides in regional integrated energy markets. Full article

In this paper, a supply–demand–price network model incorporating a marginal feedback mechanism is proposed to characterize the evolution of market prices. Unlike classical supply–demand models, the marginal effect of excess demand, defined as the rate of change in excess demand, is explicitly introduced into the price adjustment process. As the coefficient of the marginal feedback term varies, the system exhibits rich and complex nonlinear dynamics. In particular, the model gives rise to a centrally symmetric double-wing chaotic attractor, as well as a pair of coexisting single-wing chaotic attractors. The transition routes among different dynamical regimes are systematically analyzed using phase portraits, bifurcation diagrams, and Lyapunov exponents. Furthermore, multistability phenomena are observed, including the coexistence of equilibrium points, limit cycles, and chaotic attractors. The corresponding basins of attraction are illustrated to reveal their intricate and interwoven structures. In addition, the emergence of endogenous chaos is investigated through both theoretical analysis and numerical simulations. Finally, the consistency between the model dynamics and real market data provides empirical evidence supporting the validity and applicability of the proposed framework. Full article

To explore the evolutionary dynamics of green product markets under bounded rationality, this study develops a tripartite evolutionary game model involving the government, passenger vehicle enterprises, and consumers, using China’s new energy vehicle (NEV) market as a case study. By integrating system dynamics with real-world data and policies, the paper simulates strategy evolution paths and identifies equilibrium conditions. The results show a unique evolutionarily stable strategy: the government refrains from regulation, enterprises actively produce NEVs, and consumers actively purchase green products. The government’s strategy is primarily influenced by enterprises, while enterprises’ strategy is mainly driven by consumers. Numerical analysis reveals that when the premium payment ratio of green products (price difference relative to conventional vehicles) is controlled between 27.27% and 31.82%, the market evolves most rapidly toward the ideal equilibrium. Furthermore, when the additional positive benefit ratio of green consumption falls below 36.36%, market formation and development are severely hindered; raising this ratio to 40.91% yields significant promotion effects, beyond which marginal benefits diminish. These findings provide quantitative benchmarks for policy design and strategic decision-making to foster self-sustaining green product markets. Full article

This paper investigates duopoly competition under both constant and decreasing returns to scale in a market characterized by an isoelastic demand function, where firms adjust their strategies using a gradient adjustment mechanism. To establish the stability conditions of the model, we adopt different analytical approaches depending on the type of returns to scale. Under constant returns to scale, we employ a traditional approach by deriving the closed-form solution of the Nash equilibrium and analyzing the Jacobian matrix to verify whether the moduli of all eigenvalues are less than one. In contrast, under decreasing returns to scale, we analyze the local stability of the Nash equilibrium using symbolic computation methods without deriving a closed-form solution. The results show that when firms have heterogeneous costs, the model can exhibit both period-doubling and Neimark–Sacker bifurcations under both types of returns to scale. However, when costs are homogeneous, only period-doubling bifurcations occur. Numerical simulations support these analytical results and further demonstrate the emergence of complex dynamics, including chaotic behavior. Full article

Against the backdrop of a technological revolution centered on green and low-carbon development, the deep integration of digitalization and greening has become a core engine for high-quality progress. Moving beyond linear perspectives of environmental governance, this study constructs tripartite evolutionary game models to dissect the strategic interactions among government, enterprises, and consumers. Focusing on the institutional context of e-commerce, we examine how platform-enabled transparency mechanisms (e.g., blockchain traceability and carbon labeling) shape these interactions through key parameters: greenwashing detection (θ), premium loss coefficient (η), and information screening cost (C_D). The analysis reveals that the long-term trajectory is fundamentally determined by the intrinsic economic viability of corporate transformation. Government intervention acts as an equilibrium selector, influencing the speed of convergence, while product value (consumer utility and premium) and platform transparency determine the sustainability of the equilibrium. Critically, the tripartite model shows that the optimal outcome—full enterprise transformation and consumer adoption—can be achieved without sustained government intervention when product fundamentals are sufficiently attractive. This demonstrates the potential for market self-regulation to sustain digital–green synergy. The study makes three contributions: it captures the full tripartite feedback loop, reveals the saturation effect of policy intensity, and embeds platform transparency mechanisms into an evolutionary framework. The findings reframe the government’s role as a temporary enabler and position e-commerce platforms as key governance intermediaries, offering a theoretical basis for adaptive governance strategies in digital commerce. Full article

Background: Circular economy (CE) practices, such as glass bottle refilling, are critical to the beverage industry’s sustainability. However, coordinating manufacturer marketing efforts with collector reverse logistics investment remains a strategic challenge. Methods: This study develops a Stackelberg game-theoretic model featuring a manufacturer and a collector. The model incorporates communication effort as a demand driver and analyzes the role of bottle quality (damage rates) and the reusable bottle unit cost on the optimal decisions of the players and the collection rate. Results: Equilibrium analysis shows that the quality of the reusable bottle and the rate of bottle damage are crucial in reducing the operational costs of the refilling program. Additionally, these factors significantly influence the decisions made by manufacturers and collectors regarding their investments in communication and collection systems. Conclusions: The study demonstrates that successful refilling requires strategic coordination between manufacturers and collectors, particularly in terms of communication and investment in reverse logistics. Managerial insights indicate that investing in the quality of bottles is the key factor for achieving joint profitability. Full article

The rise in aggregation platforms has reshaped the competitive ride-hailing market. Display slots (i.e., platform-determined ranking priority) have become a key tool for influencing order allocation. Their interaction with drivers’ multi-homing behavior poses new challenges for platform sustainability. This paper constructs a two-stage Stackelberg game model with one aggregator and two underlying ride-hailing platforms. Display slots enhance supply-side lock-in, while a waiting time function links passenger utility to demand allocation. Building on theoretical analysis of two-sided market competition and multi-homing effects, we propose two hypotheses: (H1) under specific conditions, competition for display slots may lead to a Prisoner’s Dilemma equilibrium, and (H2) the proportion of multi-homing drivers positively moderates this dilemma, thereby expanding its occurrence range. Numerical simulation results under baseline parameter settings reveal that display slots generate a supply-side amplification effect by locking in multi-homing drivers. In symmetric markets, a prisoner’s dilemma range exists where mutual purchase erodes collective profits; this range expands with the share of multi-homing drivers. Higher driver profit sensitivity raises the threshold required for display slots to be profitable. In asymmetric markets, dominant platforms (strong brands, low costs) gain more from display slots, potentially leading to unilateral purchasing. Social welfare effects of display slot competition depend on a critical threshold of waiting-time sensitivity: social welfare improves above the threshold and declines below it. This study clarifies the boundaries of display slots as supply-side non-price competitive tools, offering quantitative insights for aggregator platform design and regulatory policy. The findings carry managerial implications for platform strategy and policy aimed at sustainable development. Full article

This paper analyzes a monopolistic market with a continuum of consumers in the linear case. Consumers are vertically differentiated by a one-dimensional preference for quality, and the monopolist is allowed to offer a menu of quality-price pairs. The analysis shows that, in the linear case, the monopolist’s optimal offer endogenously collapses to a single quality-price pair, where the quality equals the highest feasible level. In addition, welfare maximization is achieved if and only if the market is fully served in equilibrium. Full article

Optimal dividend policy remains a key topic of debate in corporate finance, particularly in emerging markets where investor preferences and macroeconomic volatility affect decision making. This study therefore examines the relationship between dividend policy and the Johannesburg Stock Exchange (JSE) market index over the period 2000 to 2020. The study uses firm-level dividend data to construct a market-capitalization-weighted aggregate dividend index. The paper further employs an Autoregressive Distributed Lag (ARDL) and error correction model to assess the long-run equilibrium relationship and short-run adjustments. The results show evidence of a long-run relationship between dividends and the JSE index price. In the short run, dividend payments exhibit negative effect on index prices while lagged dividends have a significant positive effect on index implying delayed market response. These findings suggest that South African investors place more confidence and emphasis on capital gains rather than dividend distributions. This study contributes evidence on the aggregate dividend dynamics within the context of an emerging market and offers practical insights for managers, investors and policy makers. Full article

The emergence of smart healthcare platforms has significantly enhanced the accessibility of medical services, yet it has also introduced critical challenges such as information overload and patient decision-making dilemmas. This study investigates the interaction and synergistic optimization of a dual-drive mechanism—comprising ‘patient proactive search’ and ‘artificial intelligence (AI)-driven recommendations’—within healthcare platform recommendation systems. By developing a game-theoretic model that incorporates heterogeneous users (including random single-search users and rational multi-stage decision-makers) and competitive medical institutions, we systematically analyze how different recommendation strategies influence market equilibrium, patient utility, and platform profit. The findings reveal that in the absence of AI-driven recommendations, a higher proportion of random users intensifies price competition among providers. In contrast, the integration of AI-driven recommendations with proactive search behavior effectively mitigates price wars and enhances matching efficiency. Furthermore, our analysis identifies an optimal recommendation strategy weight that enables the platform to simultaneously improve both equilibrium price and user demand. This research offers a theoretical foundation for the design of efficient and sustainable recommendation systems in smart healthcare platforms and provides practical managerial insights for improving medical service efficiency and optimizing resource allocation. Full article

China’s carbon neutrality plan centers on a carbon trading policy system integrating market economy, guidance, regulation, command-and-control, and fiscal measures into an inter-domain synergy. Based on the system science methodology, by leveraging the gray system evaluation method and the multiple regression model, a carbon neutrality policy analysis system has been formed. This paper constructs a policy synergy model to examine its role in achieving net-zero goals. This article measures the policy synergy and effectiveness of China’s carbon neutrality goals in different years through policy evaluation theory and coupling models. Results show China’s net carbon emissions have passed through three cycles—rapid rise, gradual growth, and slow decline—while policy synergy peaked in 2011, 2014, and 2016, aligning with changes in emission growth rates. The significance of this discovery indicates the pulse effect of China’s green policies. In key starting years such as the 12th and 13th Five-Year Plans, China has invested a significant amount of green policy resources. Synergy levels vary by measure: When applying market economy tools, the deterrent effect of command-and-control should be reduced; command-and-control should be paired only with regulation; fiscal measures should be balanced against guidance to avoid counteracting effects. Internal equilibrium between measures is crucial, with mandatory and flexible tools configured separately to maximize policy effectiveness for net-zero emissions. This study expands the quantitative research on policy coordination and effectiveness analysis. At the same time, it provides policy-level guidance and optimization for the realization of the carbon neutrality goal, avoiding the waste and redundancy of policy resources Full article

The rapid rise of live-streaming e-commerce has fostered a new “content clipping” model, in which secondary creators edit and republish anchors’ live-streaming content to promote product sales. While this model can expand market reach and enhance revenue, it also introduces copyright disputes, regulatory challenges, and profit-sharing conflicts among platforms, anchors, and secondary creators. This study develops a three-party evolutionary game model to examine strategic choices regarding platform regulation, anchor authorization, and secondary content creation. Results reveal that excessive regulation may undermine equilibrium and profitability, while appropriate authorization can balance risk and reward. Secondary creators’ participation is sensitive to commission rates and cost–benefit trade-offs. This research contributes to the literature by integrating copyright governance into live-streaming e-commerce game theory and offers actionable insights for designing regulatory mechanisms, optimizing authorization policies, and fostering sustainable multi-party collaboration. Full article

As wind power penetration increases, understanding its potential to exercise unilateral market power is critical. This dynamic is particularly relevant in systems like the Colombian wholesale electricity market, which is characterized by a strong dependence on reservoir-based hydropower and a concentrated oligopolistic structure. However, evaluating the threshold where a renewable generator transitions from a price-taker to a price-setter remains challenging. This article explores this strategic transition and its market implications. By isolating a wind agent’s actions against a competitive hydro-thermal fringe using a discretized bi-level approach, we analyze how physical capacity withholding strategies might evolve under varying wind availability and system stress. The findings suggest that wind market power operates across three dynamic regimes: (i) a defensive “Price-Support” strategy during low demand, where capacity may be withheld to prevent price collapses; (ii) a “Scarcity Creation” tipping point during peak demand (observed around a 20% wind availability factor), which appears to incentivize fractional withholding to force expensive thermal dispatch; and iii) a return to “Volume Maximization” when abundant wind renders manipulation economically suboptimal. Ultimately, these results indicate that renewable market power is highly transient and conditional on meteorological profiles, suggesting that regulators could benefit from shifting toward predictive, weather-driven market surveillance. Full article