Search Results (35)

A replicable methodology for testing the size and placement of Battery Energy Storage Systems connected to high-voltage transmission networks is presented in this study. The proposed approach involves the power flow analysis inside a Renewable Energy Zone, namely a high-renewable area prone to grid congestion during peak generation periods, based on time-series hourly analysis over a critical month. The model includes detailed operational descriptions such as lines ampacity, battery state of charge limits, round-trip efficiency, self-discharge behavior, and ramp rate restrictions. The methodology distinguishes itself by its simplicity, flexibility, and use of open-source tools, making it a valuable asset for supporting future transmission planning in high-renewable-energy scenarios. The model was developed in Python (version 3.12) using the open-source Pandapower library, introducing an innovative constraint management criterion, and validated against real data provided by the national Transmission System Operator. The approach was then applied to a portion of the Sicilian grid with massive wind and solar penetration. Results show that strategic allocation of batteries leads to a significant reduction in line overloads (up to 13 GWh mitigated in one month), improves the dispatch of renewable energy generated within the Renewable Energy Zone and allows a more sustainable exercise of the power system. Full article

This study investigates the probability of consumer default across both secured and unsecured assets, with a particular focus on borrower behavior and the role of moral hazard in shaping individual credit risk. It examines how different borrower decisions, such as investing in secured and unsecured projects after loan disbursement, affect default outcomes, especially under limited lender supervision. The Ornstein–Uhlenbeck process is used to capture the dynamics of risky asset returns and identifies the conditions under which borrowers are likely to switch from safer to riskier investments. We assume that borrowers may allocate loan funds to both secured and unsecured projects, thereby recognizing that credit risk assessment inherently involves behavioral factors that are difficult to quantify. Monte Carlo simulations are used to assess how return volatility influences borrower decision-making, showing that higher uncertainty increases the probability of returns exceeding the repayment obligation, thereby incentivizing risk-shifting behavior. The results indicate that unsecured lending is more exposed to strategic risk shifting and experiences more frequent and severe default outcomes than secured lending. As a result, this study recommends that microfinance institutions prioritize collateral-backed lending as a more effective strategy for mitigating credit risk and reducing exposure to borrower opportunism. 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

The Auditing of Natural Resource Assets (ANRA) is an institutional arrangement in China that evaluates leading cadres’ performance in the management and protection of natural resource assets at the time of their departure from office. Although existing studies have examined the institutional design and implementation mechanisms of ANRA, empirical evidence on its direct impact on farmland protection remains limited. Moreover, previous research has largely overlooked spatial heterogeneity in ANRA’s effects across diverse local contexts such as economic regions and different grain functional areas. To narrow these gaps, this study generated a panel data set, covering 275 prefecture-level cities from 2011 to 2017. The study employed a staggered difference-in-differences (DID) model to empirically evaluate the effects of ANRA on farmland protection. The results show that the implementation of the ANRA policy has significantly increased farmland area in pilot regions, with an average annual increase of approximately 5800 hectares relative to non-pilot regions during the post-policy period. The policy effects varied across regions and the positive impact is more pronounced in the eastern regions and major grain-producing regions. Mechanism evidence suggests that the ANRA contributes to farmland protection by reshaping local land-use behavior. Based on these findings, the paper recommends promoting the normalization and standardization of ANRA, strengthening land use regulation to enhance resource allocation efficiency, and improving the design of policy classifications based on regional heterogeneity. Full article

This paper develops a portfolio construction methodology integrating behavioral finance principles with machine learning to model how cognitive biases systematically alter asset allocation decisions. We introduce a Distorted Value Transformation framework wherein investors apply linear and non-linear value functions to individual asset returns before aggregating, exhibiting narrow framing from mental accounting bias. Using Random Forest regression, we quantify asset importance under three distinct investor personae, namely Cumulative Prospect Theory investors (loss aversion, diminishing sensitivity), Loss-Averse investors (asymmetric loss weighting), and Markowitz investors (risk-seeking preferences). Our empirical analysis of a multi-asset portfolio spanning traditional instruments and major cryptoassets (2015–2025, T = 2580 daily observations) reveals behavioral distortions produce systematic reweighting: CPT and LA investors substantially reduce exposure to high-volatility assets (Bitcoin allocation increases from 13.77% to 20.57% under CPT; XRP decreases from 17.82% to 13.51%), reflecting perceptions that volatile assets contribute disproportionately to negative experiences. Markowitz investors concentrate heavily on high-skewness cryptoassets (40.22% in XRP). Behaviorally constructed portfolios exhibit lower volatility (75.43% vs. 78.19% annualized) and reduced drawdowns versus undistorted benchmarks, albeit with foregone upside 29,732% vs. 51,005% cumulative return in the crypto-only scenario). These findings demonstrate that returns perceived through behavioral lenses via segregation rather than integration deviate systematically from rational benchmarks. Our framework provides a tractable method for modeling heterogeneous investor behavior and how psychological factors shape asset allocation. Full article

Using a time-frequency and quantile connectedness approach, our study examines the complex return spillovers dynamics between BRICS Plus stock markets, the volatility index (VIX), and the global geopolitical risk index (GPRD). By employing advanced models such as TVP-VAR, quantile connectedness, and spectral decomposition, we demonstrate how these markets interact across different market conditions and periods. Our results indicate that the VIX consistently acts as the dominant net transmitter of shocks, especially during periods of heightened uncertainty such as the COVID-19 pandemic, the Russian-Ukraine conflict, and the Trump-era U.S.-China trade tensions. In contrast, the GPRD functions predominantly as a net receiver of shocks, indicating its potential role as a hedge during geopolitical crises. BRICS Plus markets exhibit heterogeneous behavior: Brazil, South Africa, and Russia frequently emerge as net transmitters, while China, India, Egypt, Saudi Arabia, and the UAE primarily act as net receivers. Spillovers are strongest at the extremes of the return distribution and are mainly driven by short-term dynamics, underscoring the importance of high-frequency reactions over persistent long-term effects. These findings highlight the asymmetric, nonlinear, and state-dependent nature of global financial contagion, offering important insights for risk management, asset allocation, and macroprudential policy design in emerging market contexts. Full article

We study a finite-horizon optimal job-switching and portfolio allocation problem where an agent faces a mandatory retirement date. The agent can freely switch between two jobs with differing levels of income and leisure. The financial market consists of a risk-free asset and a risky asset, with the agent making dynamic consumption, investment, and job-switching decisions to maximize lifetime utility. The utility function follows a Cobb–Douglas form, incorporating both consumption and leisure preferences. Using a dual-martingale approach, we derive the optimal policies and establish a verification theorem confirming their optimality. Our results provide insights into the trade-offs between labor income and leisure over a finite career horizon and their implications for retirement planning and investment behavior. Full article

We develop a continuous-time model of optimal consumption, portfolio allocation, and early retirement that, to our knowledge, is the first to incorporate an implementation delay —a fixed lag $\delta$ between the retirement decision and the actual cessation of labor and income. Using a dual-martingale approach, we obtain closed-form solutions and quantify how $\delta$ affects optimal behavior. For example, when $\delta$ increases from $0.5$ to 2 years (baseline parameters: $\beta =0.04$, $r=0.02$, $\mu =0.08$, $\sigma =0.2$, $\gamma =3$, $k_B=0.3$, and $\epsilon =1$), optimal pre-retirement consumption rises by approximately 7%, the risky asset share falls by about 5 percentage points, the expected retirement time increases by over 1 year, and the retirement wealth threshold $x_R$ grows by roughly 10%. These results provide policy-relevant insights for retirement systems where procedural lags can distort incentives and reduce welfare. Full article

This study investigates whether green assets can serve as safe havens for dirty assets in the context of carbon and energy futures markets. Using daily data from April 2021 to June 2025, the analysis focuses on four key instruments: carbon emissions futures and crude oil futures, EUA futures, and natural gas futures. The study applies two main approaches—a conditional value-at-risk (CVaR)-based relative risk ratio (RRR) analysis and dynamic conditional correlation (DCC-GARCH) modeling—to assess tail risk mitigation and time-varying correlations. The results show that while green assets do not consistently act as safe havens during extreme market downturns, they can reduce the portfolio tail risk beyond certain allocation thresholds. Natural gas futures demonstrate significant volatility but offer diversification benefits when their portfolio weight exceeds 40%. EUA futures, although highly correlated with carbon emissions futures, show limited safe haven behavior. The findings challenge the assumption that green assets inherently provide downside protection and highlight the importance of strategic allocation. This research contributes to the literature by extending safe haven theory to environmental futures and offering empirical insights into the risk dynamics between green and dirty assets. Full article

Digital transformation (DT) has emerged as a key strategic lever for enhancing firm resilience and competitiveness, yet its influence on non-productive investment behaviors, such as corporate financial investment, remains underexplored. Existing studies have largely focused on DT’s role in innovation and operational efficiency, leaving a significant gap in understanding how DT reshapes firms’ financial asset allocation. Drawing on a unique panel dataset of A-share main board-listed firms in China from 2011 to 2023, this study provides novel empirical evidence that DT significantly restrains financial investment, with pronounced heterogeneity across ownership types. More importantly, this paper uncovers a multi-layered mechanism: DT enhances the corporate information environment, which subsequently reduces financial investment. In addition, the analysis reveals a moderated mediation mechanism wherein economic uncertainty dampens the information-enhancing effect of DT. Unlike previous research that treats corporate risk-taking as a parallel mediator, this study identifies a sequential mediation pathway, where improved information environments suppress financial investment indirectly by influencing firms’ risk-taking behavior. These findings offer new theoretical insights into the financial implications of DT and contribute to the broader understanding of enterprise behavior in the context of digitalization and economic volatility. Full article

This study compares two risk tolerance scales used in the Survey of Consumer Finances (SCF), namely the long-standing 4-point scale and the newer 11-point scale, to determine which better captures an individual’s investment risk preferences. The analysis includes exploring how each scale relates to household demographics, socioeconomic factors, and ownership of risky versus conservative investments. By utilizing prospect theory, the findings reveal that while both scales effectively measure risk tolerance, the 11-point scale provides a more detailed understanding of differences in asset ownership across risk levels. For financial professionals, these results highlight the value of using a more granular risk assessment tool to better align investment strategies with client preferences, leading to improved client relationships and outcomes. Full article

This paper introduces a novel multi-objective optimization framework for sustainable portfolio rebalancing under uncertainty. The model simultaneously targets return maximization, downside risk control, and liquidity preservation, addressing the complex trade-offs faced by investors in volatile markets. Unlike traditional static approaches, the framework allows for dynamic asset reallocation and explicitly incorporates nonlinear transaction costs, offering a more realistic representation of trading frictions. Key financial parameters—including expected returns, volatility, and liquidity—are modeled using interval arithmetic, enabling a flexible, distribution-free depiction of uncertainty. Risk is measured through semi-absolute deviation, providing a more intuitive and robust assessment of downside exposure compared to classical variance. A core innovation lies in the behavioral modeling of investor preferences, operationalized through three strategic configurations, pessimistic, optimistic, and mixed, implemented via convex combinations of interval bounds. The framework is empirically validated using a diversified cryptocurrency portfolio consisting of Bitcoin, Ethereum, Solana, and Binance Coin, observed over a six-month period. The simulation results confirm the model’s adaptability to shifting market conditions and investor sentiment, consistently generating stable and diversified allocations. Beyond its technical rigor, the proposed framework aligns with sustainability principles by enhancing portfolio resilience, minimizing systemic concentration risks, and supporting long-term decision-making in uncertain financial environments. Its integrated design makes it particularly suitable for modern asset management contexts that require flexibility, robustness, and alignment with responsible investment practices. Full article

The global crude oil market, known for its pronounced volatility and nonlinear dynamics, plays a pivotal role in shaping economic stability and informing investment strategies. Contrary to traditional research focused on price forecasting, this study emphasizes the more investor-centric task of predicting returns for West Texas Intermediate (WTI) crude oil. By spotlighting returns, it directly addresses critical investor concerns such as asset allocation and risk management. This study applies advanced machine learning models, including XGBoost, random forest, and neural networks to predict crude oil return, and for the first time, incorporates sustainability and external risk variables, which are shown to enhance predictive performance in capturing the non-stationarity and complexity of financial time-series data. To enhance predictive accuracy, we integrate 55 variables across five dimensions: macroeconomic indicators, financial and futures markets, energy markets, momentum factors, and sustainability and external risk. Among these, the rate of change stands out as the most influential predictor. Notably, XGBoost demonstrates a superior performance, surpassing competing models with an impressive 76% accuracy in direction forecasting. The analysis highlights how the significance of various predictors shifted during the COVID-19 pandemic. This underscores the dynamic and adaptive character of crude oil markets under substantial external disruptions. In addition, by incorporating sustainability factors, the study provides deeper insights into the drivers of market behavior, supporting more informed portfolio adjustments, risk management strategies, and policy development aimed at fostering resilience and advancing sustainable energy transitions. Full article

In the modern corporation, understanding sustainable cost management practices is essential for promoting economic resilience and resource efficiency. This study investigates how ownership structures influence the behavior of selling, and general and administrative (SG&A) costs during periods of sales fluctuations in South Korean firms, with particular attention to Chaebols. Drawing upon agency theory and corporate governance perspectives, we examine whether proxies for agency costs, namely, free cash flow, asset utilization ratios, and operating expense ratios, explain variations in SG&A cost responses to changes in revenue. Utilizing a panel dataset of 4279 firm-year observations from KOSPI-listed companies over the period 2011–2021, we employ Pooled Ordinary Least Squares (OLS), Fixed Effects, Random Effects, and Generalized Method of Moments (GMM) estimations to model SG&A cost behavior. The analysis incorporates regression-based interaction terms that capture asymmetric cost adjustments during sales declines, commonly referred to as cost stickiness. Our findings indicate that firms with concentrated ownership, such as Chaebols, exhibit significantly lower SG&A cost stickiness, reflecting stronger financial discipline and more efficient resource allocation. In contrast, firms with dispersed ownership demonstrate more pronounced cost stickiness, consistent with governance frictions and managerial discretion. These results emphasize the moderating role of ownership structure in cost behavior and highlight its implications for sustainable corporate governance. Our study contributes to the literature on cost management and financial sustainability by offering empirical insights from a distinctive institutional setting. Policy recommendations include enhancing internal controls, promoting transparent cost practices, and encouraging shareholder oversight to reinforce long-term efficiency. Full article