Search Results (59)

The transition to a low-carbon economy is the cornerstone of global sustainability, requiring high-emission enterprises to shift from carbon-intensive production to genuine green innovation. However, this study uncovers a significant structural impediment to this transition: the “defensive greenwashing” response to climate stress. Focusing on listed companies in China’s high-emission industries (2009–2024), we employ a Debiased Machine Learning (DML) framework and Causal Forest analysis to capture the non-linear impacts of multi-dimensional climate risks. Our findings reveal a robust “threshold-trigger” mechanism: once climate pressures—whether physical shocks or policy-induced transition risks—exceed corporate endurance levels, firms aggressively pivot toward strategic “information arbitrage” rather than substantive decarbonization. We identify a profound “capability paradox” in sustainability governance, where firms with higher digital maturity and resource slack leverage their technical prowess to “calibrate” sophisticated narratives, thereby widening the monitoring gap and distorting green asset pricing. Furthermore, CEO risk preference acts as a psychological accelerator, amplifying strategic decoupling, particularly under transition-risk-induced uncertainty. By demonstrating how climate stress inadvertently incentivizes symbolic compliance over sustainable transformation, this research offers critical micro-level insights for policymakers. These findings are vital for refining sustainability oversight and ensuring that capital allocation fosters a resilient, equitable transition toward true ecological and economic decoupling. Full article

Financial markets are characterized by non-stationarity, regime shifts, and complex cross-asset interactions, which challenge traditional portfolio optimization and motivate reinforcement learning (RL) for adaptive decision-making. However, many RL-based approaches remain predominantly return-centric, with risk, uncertainty, and human oversight only weakly integrated, limiting robustness and practical applicability. This review provides a critical synthesis of risk-aware and uncertainty-sensitive reinforcement learning for portfolio optimization from a human–AI collaboration perspective. We analyze major architectural paradigms—including single-agent, hierarchical, multi-agent, and modular systems—together with risk modeling strategies (e.g., reward shaping, constraint-based optimization, and downside risk measures such as CVaR) and probabilistic approaches to uncertainty estimation (e.g., Bayesian neural networks, Monte Carlo dropout, and ensembles). A structured analysis of 57 fully assessed studies reveals that only 5 (9%) explicitly couple uncertainty estimation with risk constraint mechanisms, while 38 (69%) treat risk and uncertainty as structurally independent components. We identify a central structural limitation: risk objectives are rarely conditioned on epistemic uncertainty, while uncertainty estimates seldom influence constraint mechanisms or capital allocation. This decoupling leads to fragmented frameworks that remain difficult to deploy in real financial environments. By integrating architectural design, risk modeling, uncertainty estimation, and evaluation practices, this review proposes a unified, deployment-oriented perspective for developing governance-aligned portfolio decision-support systems. Full article

This paper introduces the Copula Asymmetry Index (CAI), a rolling, rank-based measure of asymmetric tail dependence between equity returns and implied-volatility proxies. CAI is defined as the difference between the empirical frequency of joint “equity-down & volatility-up” tail events and that of the mirror state (“equity-up & volatility-down”) within a rolling window. Building on this core asymmetry measure, we develop CAI++, an implementation framework that transforms CAI into an operational defensive allocation signal through smoothing, standardization, delayed execution, hysteresis, and cost-aware portfolio mapping. Using daily data from 2000 onward across a broad cross-section of 50 equity-volatility pairs, we evaluate the CAI++ strategy against buy-and-hold equity, a 60/40 benchmark, an inverse-volatility risk-parity portfolio, and a moving-average timing rule. Cross-sectional results indicate that CAI improves terminal outcomes relative to equity-only exposure for most pairs and shows particularly strong performance versus 60/40 in both final wealth and Sharpe. However, CAI does not dominate structurally diversified low-volatility allocations: risk parity retains a pronounced advantage in downside risk and risk-adjusted metrics. Overall, the findings support CAI as a tail-aware overlay for equity-centric and balanced portfolios rather than a substitute for institutional low-volatility baselines. Full article

This paper estimates how corporate innovation investment responds to climate policy uncertainty using panel data with 3197 listed firms from 2010 to 2022 in China. The findings show that climate policy uncertainty positively contributes to corporate innovation investment, and this result continues to hold after controlling for endogeneity and conducting a series of robustness tests. Furthermore, we find that stringent government environmental regulation serves as a potential mechanism, compelling firms to adopt cleaner production and increase their investment in innovation. Additionally, this positive relationship is stronger for firms with higher government subsidies and disappears for firms with a higher allocation of fixed assets. We also find that firms with fewer connections to the government are more sensitive to climate policy uncertainty and they tend to increase their investment in innovation to mitigate the uncertainty. Furthermore, when firms invest more in innovation during periods of high policy uncertainty, their long-term performance and firm value are likely to improve. This study sheds light on the importance and influence of climate policy uncertainty on corporate innovation investment in China. Full article

This paper examines how assets from emerging and developed stock markets can be efficiently allocated during periods of financial crisis by integrating traditional portfolio theory with Extreme Value Theory (EVT), using the Generalized Pareto Distribution (GPD) and Generalized Extreme Value (GEV) approaches to model tail risks. This study evaluates mean-variance portfolios constructed under each EVT framework and finds that portfolios based on GPD estimates consistently favour emerging market assets, which outperform both developed market and internationally diversified portfolios during extreme market conditions. In contrast, GEV-based portfolios indicate superior performance for developed market assets, highlighting the distinct behaviour of returns in the upper and lower tails of the distribution. These contrasting results reveal the unique nature of safe-haven characteristics associated with developed economies, the assets of which demonstrate greater stability and resilience during episodes of financial stress. By showing how tail-risk modelling alters optimal portfolio weights across market types, this paper contributes new evidence to the literature on crisis-informed asset allocation and offers practical insights for investors seeking robust diversification strategies under extreme market fluctuations. Full article

Transfer pricing has become a major channel through which multinational enterprises shift profits across countries. This study examines the macroeconomic and institutional determinants of transfer pricing in seven European economies (France, Spain, Germany, the United Kingdom, Italy, the Netherlands, and Portugal) over the period 1985–2025. The main objective is to identify the key factors influencing profit shifting and to analyze the mechanisms through which multinational firms allocate profits across jurisdictions. The study employs panel data techniques and uses two different proxies to capture transfer pricing practices (trade-based and intangible-based channels). To analyze both long-run and short-run relationships between transfer pricing, exchange rate dynamics, foreign direct investment, inflation and institutional quality, the analysis relies on heterogeneous panel estimators and cointegration tests, supported by several robustness checks. The empirical results reveal the existence of a long-run relationship between transfer pricing and its macroeconomic and institutional determinants. Exchange rate fluctuations and inflation exert a negative effect on transfer pricing, whereas Foreign Direct Investment has a positive impact by expanding multinational investment networks and intra-group transactions. The effect of institutional quality, proxied by control of corruption, appears more heterogeneous and may vary across jurisdictions as well as across the type of transfer pricing channel, whether related to tangible trade or intangible assets. These results emphasize the importance of institutional quality and international tax coordination in limiting aggressive profit-shifting practices. Full article

Accurately forecasting the risk matrix and constructing a well-controlled portfolio based on these forecasts is the core objective of effective asset allocation. This paper takes the Chinese stock market as the research object, employing multiple machine learning algorithms to systematically compare the predictive performance of the Financial Stress (FS) indicator and the Economic Policy Uncertainty (EPU) index in sectoral risk management. The forecast results are subsequently applied to portfolio construction and optimization. The findings indicate that, in terms of predictive dimensions, EPU demonstrates strong performance in short-term forecasts, but its explanatory power decays rapidly as the forecasting horizon extends. In contrast, the FS factor achieves forecasting accuracy that is significantly superior to both the EPU factor and traditional price series across all time horizons, exhibiting robust long-memory characteristics and cross-period stability. At the portfolio application level, the minimum variance strategy constructed based on FS forecasts effectively reduces out-of-sample portfolio variance, achieving superior risk control performance compared to strategies based on EPU factor forecasts. This result reveals the differentiated mechanisms of the two factor types: EPU acts as a driving force for short-term risk structure reshaping, while financial stress serves as the core variable driving the evolution of long-term risk structures. Machine learning methods provide an effective technical pathway for capturing these complex nonlinear relationships. The research conclusions offer new empirical evidence for investors to optimize asset allocation decisions and for regulatory authorities to improve risk monitoring systems. Full article

Portfolio selection is a fundamental task in quantitative finance that aims to allocate capital across assets to balance risk and return. While deep learning has shown great promise in this field, extracting reliable feature representations from non-stationary and noisy financial data remains a significant challenge. The existing models often fail to simultaneously capture the temporal dynamics of price series and complex inter-asset correlations, which limits their trading performance. To address these issues, we propose Denoising-Sequence-Correlation Reinforcement Learning (DSCRL), a novel portfolio selection framework based on deep reinforcement learning. DSCRL employs a dual-stream feature extraction network, where one stream aims to learn temporal market dynamics and the other aims to capture asset correlations, enabling more informative representations. A denoising module is further integrated to mitigate the impact of noise, ensuring stability and robustness in the learning process. Furthermore, a deterministic policy gradient (DPG)-based decision network is designed to directly optimize continuous portfolio weights and normalize them to satisfy budget constraints while preserving the importance. Extensive experiments conducted on multiple benchmark datasets demonstrate that DSCRL consistently outperforms both traditional financial heuristics and advanced deep reinforcement approaches. The results highlight its superior ability to achieve higher cumulative returns with lower volatility. Overall, DSCRL provides an effective and robust solution that strikes a better trade-off between pursuing profits and managing risks in dynamic financial markets. Full article

Crowd mass disasters occur over a relatively short time, and rescue operations in disasters, such as earthquakes, are challenging because of people’s behavior, type, or location. Therefore, it is essential to devise means and methods to manage such problems to minimize the consequences as much as possible. During disasters, rescue operations should be conducted in a timely conducted to save people’s lives. Otherwise, losses and consequences are severe, and if there are no proper rescuing operation models, the situation worsens, and the consequences are devastating. In particular, the allocation and coordination of limited rescue resources have a critical impact on response times and the number of lives saved. This paper aims to develop an Agent-Based Simulation (ABS) model for rescuing operations in crowd-mass disasters with six main intelligent agents. The proposed model explicitly represents the interactions among victims, rescuers, command-and-control entities, transportation assets, road networks, and affected infrastructure within a GIS-based urban environment. The developed model is based on an enhanced approach to improve rescue agents’ tasks allocation operations that enable modeling and simulation to make critical decisions for people to be rescued in a crowded mass disaster. Our task-allocation mechanism incorporates dynamic accessibility of roads, time-dependent rescue capacity, and context-aware prioritization of victims. Three related task-allocation strategies from the literature are used as baselines under identical scenarios, and performance is compared in terms of average rescue time and number of rescued victims. Results show that the proposed model achieves more efficient and robust rescue operations in most simulated experiments. Full article

Traditional mean–variance portfolio optimization proves inadequate for cryptocurrency markets, where extreme volatility, fat-tailed return distributions, and unstable correlation structures undermine the validity of variance as a comprehensive risk measure. To address these limitations, this paper proposes a unified entropy-based portfolio optimization framework grounded in the Maximum Entropy Principle (MaxEnt). Within this setting, Shannon entropy, Tsallis entropy, and Weighted Shannon Entropy (WSE) are formally derived as particular specifications of a common constrained optimization problem solved via the method of Lagrange multipliers, ensuring analytical coherence and mathematical transparency. Moreover, the proposed MaxEnt formulation provides an information-theoretic interpretation of portfolio diversification as an inference problem under uncertainty, where optimal allocations correspond to the least informative distributions consistent with prescribed moment constraints. In this perspective, entropy acts as a structural regularizer that governs the geometry of diversification rather than as a direct proxy for risk. This interpretation strengthens the conceptual link between entropy, uncertainty quantification, and decision-making in complex financial systems, offering a robust and distribution-free alternative to classical variance-based portfolio optimization. The proposed framework is empirically illustrated using a portfolio composed of major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)—based on weekly return data. The results reveal systematic differences in the diversification behavior induced by each entropy measure: Shannon entropy favors near-uniform allocations, Tsallis entropy imposes stronger penalties on concentration and enhances robustness to tail risk, while WSE enables the incorporation of asset-specific informational weights reflecting heterogeneous market characteristics. From a theoretical perspective, the paper contributes a coherent MaxEnt formulation that unifies several entropy measures within a single information-theoretic optimization framework, clarifying the role of entropy as a structural regularizer of diversification. From an applied standpoint, the results indicate that entropy-based criteria yield stable and interpretable allocations across turbulent market regimes, offering a flexible alternative to classical risk-based portfolio construction. The framework naturally extends to dynamic multi-period settings and alternative entropy formulations, providing a foundation for future research on robust portfolio optimization under uncertainty. Full article

Quantitative asset allocation remains a critical challenge in modern finance, particularly due to the inherent uncertainty of expected returns (μ) and the sensitivity of portfolio outcomes to the stability of portfolio weights. This study conducts a comparative empirical analysis of three portfolio strategies—MVO, Static RP, and Dynamic RP—using a long-only portfolio of eleven highly liquid assets, consisting of U.S. large-cap equities and gold, over the period 2015–2025. Results from historical backtesting indicate maintaining a competitive Sharpe ratio (1.418) and the lowest Maximum Drawdown (−0.2770) relative to Markowitz MVO (−0.3120) and Static RP (−0.2788). Although Markowitz delivers the numerically highest Sharpe ratio (1.655), this advantage is largely driven by in-sample optimization, with limited robustness under realistic implementation settings. In contrast, Dynamic RP demonstrates superior downside risk management, weight stability, and adaptability to changing market conditions, suggesting a more practical and resilient framework for real-world investment applications. Overall, the findings indicate that Dynamic Risk Parity provides an effective and robust alternative to traditional mean-variance optimization, offering investors a strategy that balances return potential, risk mitigation, and portfolio stability, while addressing key limitations of classical MVO approaches. Full article

This paper develops a novel hybrid framework that integrates clustering-enhanced Particle Swarm Optimization (PSO) with stretching techniques to solve Markowitz’s quadratic portfolio optimization problem. The proposed approach avoids local optima traps that plague traditional optimization methods, while the stretching function modifications enhance the algorithm’s global search capabilities. The framework comprises four distinct algorithmic variants: a baseline SWARM PSO with stretching algorithm, and three clustering-enhanced extensions incorporating Hierarchical, K-means, and DBSCAN techniques. These clustering enhancements strategically group assets based on risk–return characteristics to improve portfolio diversification and risk management. Implementation in R enables comprehensive analysis of portfolio weight allocation patterns and diversification metrics across varying market structures. Empirical validation using daily price data from six major international stock market indices spanning January 2020 to December 2025 demonstrates the framework’s generalization capability in constructing buy-and-hold investment portfolios. The results reveal significant market-specific algorithmic effectiveness, with K-means variants achieving competitive efficacy in Eurostoxx and Belgian markets, DBSCAN demonstrating strong effectiveness in Chinese equity markets, Hierarchical clustering showing robust results in Indian market conditions, and the baseline SWARM algorithm exhibiting relative efficiency in French and Danish indices. Performance evaluation encompasses comprehensive risk-adjusted metrics, including Portfolio Return, Volatility, Sharpe Ratio, Calmar Ratio, and Value at Risk, providing portfolio managers with an adaptive, market-responsive optimization toolkit. Full article

Effective asset management is crucial for improving the reliability, resilience, and cost efficiency of distribution networks throughout the asset life cycle. Distribution transformers are among the most critical components, as their failures can cause extensive service interruptions and substantial economic impacts. Therefore, robust and transparent maintenance prioritization strategies are essential, particularly for utilities managing several transformers. Traditional time-based maintenance, while simple to implement, often results in inefficient resource allocation. Condition-based maintenance provides a more effective alternative; however, its performance depends strongly on the reliability of indicator selection and weighting. This study proposes a systematic weighting framework for distribution transformer maintenance prioritization using a multi-criteria decision-making (MCDM) approach. Each transformer is evaluated across two dimensions, including health condition and operational impact, based on indicators identified from the literature and expert judgment. To address uncertainty and judgmental inconsistency, particularly when the consistency ratio (CR) exceeds the conventional threshold of 0.10, the Fuzzy Analytic Hierarchy Process (FAHP) is employed. Seven condition parameters characterize transformer health, while impact is quantified using five indicators reflecting failure consequences. The proposed framework offers a transparent, repeatable, and defensible decision-support tool, enabling utilities to prioritize maintenance actions, optimize resource allocation, and mitigate operational risks in distribution networks. Full article

The acceleration of financial innovation and pro-crypto regulations in the digital asset space have spurred interest in cryptocurrencies among funds, and institutional and retail investors. Like any risky assets, investment in digital assets offers opportunities in terms of returns and challenges in terms of risk. However, unlike traditional assets, digital assets like cryptocurrencies are highly volatile. Accordingly, applying conventional single-criterion financial metrics for portfolio construction may not be sufficient as the method falls short in capturing the complex, multidimensional risk-return dynamics of innovative financial assets like cryptocurrencies. To address this gap, this study introduces a novel, integrated hybrid Multi-Criteria Decision-Making (MCDM) framework that provides a structured, transparent, and robust approach to cryptocurrency fund selection. The framework seamlessly integrates three well-established operations research methodologies: the Decision-Making Trial and Evaluation Laboratory (DEMATEL), the Analytic Network Process (ANP), and the Vlse Kriterijumsk Optimizacija I Kompromisno Resenje (VIKOR) algorithm. DEMATEL is utilized to map and analyze the intricate causal interdependencies among a comprehensive set of evaluation criteria, categorizing them into foundational “cause” factors and resultant “effect” factors. This causal structure informs the ANP model, which computes precise criterion weights while accounting for complex feedback and dependency relationships. Subsequently, the VIKOR algorithm is invoked to use these weights to rank cryptocurrency fund alternatives, delivering a compromise between optimizing group utility and minimizing individual regret. To illustrate the application and efficacy of the proposed method, a diverse set of 20 cryptocurrency funds is analyzed. From the analysis, it is shown that foundational criteria, such as “Fee (%)” and “Annualized Standard Deviation,” are the primary causal drivers of financial performance outcomes of funds. This proposed framework supports strategic capital allocation in a rapidly evolving domains of digital finance. Full article

Forest ecological compensation policies (FECPs) are a key institutional arrangement for balancing ecological conservation and farmers’ development needs in national parks. Existing research has often treated such policies as a homogeneous whole, failing to clearly reveal the mechanisms through which different policy types affect farmers’ livelihoods, while also paying insufficient attention to complex property-rights settings. This study takes Wuyi Mountain National Park—a typical representative of collective forest regions in southern China—as a case study. Based on 239 micro-survey datasets from farming households and employing the mprobit model and moderating effect models, it investigates the influence, mechanisms, and heterogeneity of farmers’ livelihood capital in terms of their livelihood strategy choices under the moderating roles of “blood-transfusion” and “blood-making” FECPs. The results show the following: (1) Among the sample farmers, livelihood strategies are distributed as follows: pure agricultural type (31.8%), out-migration for work type (20.5%), and commercial operation type (47.7%). (2) Farmers’ livelihood capital has a significant impact on their livelihood strategy choice, with different dimensions of capital playing distinct roles. (3) FECPs follow differentiated moderating pathways. “Blood-transfusion” policies emphasize compensation and buffering functions, reducing farmers’ livelihood transition pressure through direct cash transfers; “blood-making” policies reflect empowerment and restructuring characteristics, activating physical assets and reshaping the role of social capital through productive investment. Together, they constitute a complementary system of protective security and transformative empowerment. Accordingly, this study proposes policy insights such as building a targeted ecological compensation system that is categorized, dynamically linked, and precise; innovating compensation fund allocation mechanisms that integrate collective coordination with household-level benefits; optimizing policy design oriented toward enhancing productive capital; and establishing robust monitoring, evaluation, and adaptive management mechanisms for dynamic FECPs. Full article