Search Results (1,038)

This study examines quantitative investment strategies for Brazilian equity funds, integrating traditional financial performance indicators with machine learning techniques to enhance fund selection. The main objective was to construct and validate predictive models for fund selection. The methodology involved collecting daily data from 2019 to 2025, computing a range of return and risk measures, and trained models to classify 1- and 3-month shifted windows. The 3-month models achieved the strongest predictive accuracy, exceeding 91%, with the Sharpe Ratio emerging as the most influential feature. A 12-month backtest (October/2024–September/2025) showed that ML-constructed portfolios delivered cumulative returns between 14.65% and 91.86%, depending on the selection criterion, substantially outperforming Brazil’s CDI risk-free benchmark (12.70%) and the Ibovespa (11.46%). These findings highlight the practical potential of ML-based fund selection, though successful implementation requires careful risk management and ongoing model validation. Full article

In the uni-directional conversion problem, the objective is to convert wealth from one asset into another while maximizing its value at the end of the investment horizon. In the k-preemptive variant of this problem, also known as the k-search problem, the wealth is divided into k equally-sized units that cannot be converted simultaneously. In this work the weighted k-search problem is introduced. The weighted k-search problem is a generalization of the k-search problem, since the problem setting is changed in a way in which the given number of units to convert is not limited to one. In the weighted k-search problem, the k units are grouped into l groups of variable size. Instead of one unit, each group has to be converted at once, and each group has to be converted separately. The online algorithm lRPP is presented and its competitive ratio is determined. It is shown that no deterministic algorithm can achieve a lower competitive ratio. Thus, lRPP solves the weighted k-search problem optimally. Both variants of the weighted k-search problem, i.e., min-search and max-search, are solved separately. Full article

Marine plastic pollution poses severe ecological and economic threats, while people with disabilities (PwDs) often face limited meaningful employment opportunities. This study evaluated a unique social enterprise in Japan that addresses both challenges through upcycling marine plastic waste into accessories while providing employment for PwDs. Using the Social Return on Investment (SROI) methodology, we assessed the project’s social and environmental impacts over one year (2020). Data was collected through stakeholder surveys, interviews, and operational records. The analysis identified 15 outcomes across six stakeholder groups, including income generation, environmental awareness-raising, and sustained volunteer engagement. The project achieved an SROI ratio of 3.50, indicating that every JPY 1 invested generated JPY 3.50 in social value. Media exposure (30.5%), employment income (25.6%), and volunteer motivation (18.5%) comprised 74% of the total value. Despite processing only 50 kg of marine plastic annually, the project demonstrated significant symbolic impact through behavior change and public awareness. Key challenges include limited production capacity, wage constraints, and gender-biased consumer demographics. This case illustrates how small-scale, community-based upcycling initiatives can generate multidimensional social value by integrating environmental conservation with social inclusion objectives. Full article

Establishing a mechanism for ecological product value realizing (EPVR) is a critical component of China’s ecological civilization strategy, aimed at translating the concept that “lucid waters and lush mountains are invaluable assets” into actionable economic policies. Although central government investments in the form of project for EPVR have increased significantly, surpassing CNY 700 billion by 2024, studies rarely focus on these projects and how to evaluate them. Evaluating the performance of EPVR projects is essential for optimizing resource allocation, enhancing project accountability, and ensuring the sustainable realization of ecological, economic, and social values. This study innovatively defines the conceptual connotation of EPVR projects and constructs a comprehensive performance evaluation system based on a “benefit-cost” analysis, comprising a multi-dimensional indicator system, quantifiable calculation methods, and explicit evaluation criteria. As water source protection projects are typical EPVR projects, the comprehensive water environment management project of Hongfeng Lake is selected for an in-depth empirical study. The results reveal that (1) the total annual benefits amount to CNY 923.66 million, dominated by ecological benefits (84.04%); (2) with an investment of CNY 1194.66 million, the project yields a net loss and a moderate performance index (PCPI = 0.77); (3) the project performance is primarily affected by weak economic value conversion stemming from restrictive zoning policies and underdeveloped market mechanisms for ecological services; and (4) integrated development pathways—such as ecotourism, eco-aquaculture, and ecological branding—are proposed to enhance the long-term sustainability of the project. The Hongfeng Lake case establishes a replicable framework for global assessment of analogous projects and delivers actionable insights for enhancing benefit–cost ratios in public ecological initiatives, with costs confined to data collection, modeling, and validation. Therefore, this study contributes a quantifiable and reproducible tool for the full lifecycle management of EPVR projects, thereby facilitating more informed government decision-making. Key findings reveal the following: (1) A comprehensive “Benefit-Cost” performance evaluation framework, pioneered in this study and tailored specifically for individual EPVR projects, surpasses regional-scale accounting methodologies like Gross Ecosystem Product (GEP). (2) A novel consolidated metric (PCPI) is introduced to integrate ecological, economic, and social dimensions with cost input, thus enabling direct cross-project comparison and classification. (3) The framework operationalizes evaluation by providing a detailed, adaptable indicator system with explicit monetization methods for 26 distinct benefits, thereby bridging the gap between theoretical value accounting and practical project assessment. (4) The empirical application to a drinking water source protection project addresses a critical yet understudied category of EPVR projects, offering insights into “protection-oriented” models. Full article

The valuation of companies has long been a cornerstone of financial analysis and investment decision-making, offering critical frameworks for investors to gauge a firm’s worth and evaluate the relative value of future income streams within a specific industry or sector. In this work we propose a new valuation framework by integrating traditional and modern valuation approaches, providing actionable insights for investors and analysts seeking to optimize asset allocation and portfolio performance. We introduce a novel framework (TARFA) to comparable company valuation by identifying investor-preferred return-driving points for accounting-based factors. Through an analysis of 68 commonly used accounting measures, the study identifies three key factors that drive superior returns. The results of the TARFA framework demonstrate that both general and sector-specific models consistently outperformed population returns, with the general model showing superior performance in broader market contexts. The study also highlights the stability of key financial ratios over time and introduces the Relative Equity Score, further enhancing the model’s ability to identify undervalued equities. Full article

Time-of-Use (TOU) tariffs are a primary driver for deploying demand-side energy storage, yet their specific structural characteristics, such as peak-to-valley ratios, and the presence of critical-peak pricing, can significantly influence the economic viability of hybrid storage systems. In addition, the continuous decrease in storage capacity costs also constitutes a major influencing factor on storage investment portfolios. This study investigates the sensitivity of optimal hybrid storage portfolios to varying TOU tariffs and storage costs. We develop a multi-scenario optimization framework that models diverse, realistic TOU tariff structures and evaluates their impact on the life cycle economic performance of hybrid storage in a representative office building. The methodology leverages a refined daily operation optimization model that accounts for storage degradation and system efficiencies, applied across a set of typical operational days. The impacts of specific tariff parameters (e.g., peak-to-valley ratio, critical-peak pricing) and storage costs on the optimal allocation of investment between battery and cooling storage are investigated. The thresholds of tariff and capacity cost that trigger a shift in investment preference are identified. The findings provide actionable insights for policymakers on designing effective dynamic tariffs to incentivize specific storage technologies and for building owners formulating future-resilient storage investment strategies. Full article

This study investigates how U.S. Federal Reserve interest rate cuts during the 2019–2020 easing cycle influenced the performance of equity mutual funds, with a particular emphasis on contrast between growth and value investment styles. Using an event study framework, we examine abnormal returns, cumulative abnormal returns, and risk-adjusted performance metrics, including those based on both 30 days static and rolling Jensen’s alpha and Sharpe ratios, across short-term (30-day) and long-term (6-month and 1-year) windows surrounding three major rate cut events. Our empirical results show that growth funds significantly outperform value funds following rate reductions, especially over longer horizons. This performance advantage is more pronounced in risk-adjusted measures and strengthens when incorporating rolling dynamics, indicating that and asymmetric sensitivity of fund styles to interest rate changes, shaped by differences in duration exposure and investor sentiment. Overall, this study offers novel insights into how monetary policy influences fund-level dynamics beyond broad market movements and deepens the understanding of monetary transmission in asset management by incorporating time-varying performance metrics. Full article

With global climate changing, numerous cities have a rise in the frequency of heavy rainfall events. Concurrently, the rapid urbanization is increasing the impermeable surfaces, heightening the vulnerability to cope with flooding of urban stormwater drainage systems. This work compared the different retention strategies to mitigate flooding risks by simulating various scenarios using StormDesk 2.0. Additionally, it conducts multi-objective optimization of retention volume reduction, overflow volume reduction, and cost constraints through NSGA-II to obtain adaptation schemes across diverse scenarios. The findings demonstrate that, compared with the maximum area and overflow reduction ratio schemes, the drainage capacity can increase 15% under the adaptation scheme. Furthermore, the investment of the adaptation scheme is the most economical, at 10.59% of the maximum area scheme, and the overflow reduction surpasses that of the maximum area scheme by 45.8%. The most economical unit control cost in the adaptation scheme was USD 64.2/m³, while the full cost reached USD 277,337.9, highlighting its superior cost-benefit. The above results can provide a paradigmatic reference for enhancing stormwater drainage capacity in urban built-up areas. Full article

Hybrid AC/DC transmission expansion planning must balance investment cost, supply reliability and AC/DC stability, which challenges conventional mathematical programming and heuristic methods. This paper proposes a multi-objective planning framework based on an improved multi-step backtracking α-Q(λ) reinforcement learning algorithm with eligibility traces and an adaptive learning factor. A tri-objective model minimises annual economic cost, expected power shortage and a comprehensive electrical index that combines electrical betweenness, commutation-failure margin and effective short-circuit ratio. The mixed-integer planning problem is reformulated as an interactive learning process, where the state encodes candidate line construction decisions, the action builds or cancels lines, and the eligibility-trace matrix is used to quantify line importance. Case studies on the Garver-6 system, the IEEE 24-bus reliability test system and a 500 kV regional hybrid AC/DC grid show that, compared with classical Q-learning, the proposed method yields lower annual cost, reduced expected power shortage and improved AC/DC stability; in the 500 kV system, the expected annual power shortage is reduced from 70,810 MWh to 28,320 MWh. Full article

The effect of nitrogen (N) deposition on soil organic carbon (SOC) and the underlying mechanisms in grassland ecosystems remain a topic of debate. Moreover, previous research has primarily concentrated on interaction between carbon (C) and N cycles in response to N deposition, with less attention paid to how N-induced phosphorus (P) deficits impact SOC sequestration. To further investigate whether soil microbial stoichiometry influences SOC sequestration under N enrichment, we conducted a field experiment involving N and P additions. The soil properties, nutrients within plant leaves and microbial biomass, and the potential activity of eco-enzymes related to microbial nutrient acquisition were measured. Results showed that SOC did not significantly change with N addition, and SOC significantly increased with addition of N and P together, which suggested that the SOC was depleted with N addition. Soil available phosphorus and microbial biomass phosphorus (MBP) did not significantly decrease alongside N addition, which suggested that microbes alleviated P limitation. Microbial metabolic limitation analysis showed microbial P limitation was enhanced by N10 treatment. At the same time, microbial P limitation enhanced microbial C limitation. Consequently, microbes also required more C as an energy resource to invest in enzyme production. Microbial P and C limitations were both significantly negatively correlated with SOC. Results from SEM analysis also showed that the MBC:MBP ratio was significantly negatively correlated with SOC. These results support the idea that consumer-driven nutrient recycling shapes the dynamics of SOC. Therefore, nitrogen deposition-induced MBC:MBP imbalance may regulate SOC in alpine grassland ecosystems. Full article

As a form of long-term asset allocation, pension fund investment necessitates accurate estimation of both asset returns and associated risks over extended time horizons. However, long-term asset returns are significantly influenced by macroeconomic factors, whereas variance-based risk measures cannot account for the directional nature of deviations from expected returns. To address these issues, we propose a novel CVaR-based Black–Litterman model incorporating macroeconomic cycle views (CVaR-BL-MCV) for optimal asset allocation of pension funds. This approach integrates macroeconomic cycle dynamics to quantify their impact on asset returns and utilizes Conditional Value-at-Risk (CVaR) as a coherent measure of downside risk. We employ a Markov-switching model to identify and forecast the phases of economic and monetary cycles. By analyzing the economic cycle with PMI and CPI, economic conditions are categorized into three distinct phases: stable, transitional, and overheating. Similarly, by analyzing the monetary cycle with M2 and SHIBOR, monetary conditions are classified into expansionary and contractionary phases. Based on historical asset return data across these cycles, view matrices are constructed for each cycle state. CVaR is used as the risk measure, and the posterior distribution of the Black–Litterman (BL) model is derived via generalized least squares (GLS), thereby extending the traditional BL framework to a CVaR-based approach. The experimental results demonstrate that the proposed CVaR-BL-MCV model outperforms the benchmark models. When the risk aversion coefficient is 1, 1.5, and 3, the Sharpe ratio of pension asset allocation using the CVaR-BL-MCV model is 21.7%, 18.4%, and 20.5% higher than that of the benchmark models, respectively. Moreover, the BL model incorporating CVaR improves the Sharpe ratio of pension asset allocation by an average of 19.7%, while the BL model with MCV achieves an average improvement of 14.4%. Full article

This study examines the economic performance of residential photovoltaic systems combined with battery storage (PV-BESS) under Poland’s net-billing regime for a single-family household without subsidy support in 10-year operational horizon. These insights extend existing European evidence by demonstrating how net-billing fundamentally alters investment incentives. The analysis incorporates real production data from selected locations and realistic household consumption profiles. Results demonstrate that optimal system configuration (6 kWp PV with 15 kWh storage) achieves 64.3% reduction in grid electricity consumption and positive economic performance with NPV of EUR 599, IRR of 5.32%, B/C ratio of 1.124 and discounted payback period of 9.0 years. The optimized system can cover electricity demand in the summer half-year by over 90% and reduce local network stress by shifting surplus solar generation away from midday peaks. Residential PV-BESS systems can achieve economic efficiency in Polish conditions when properly optimized, though marginal profitability requires careful risk assessment regarding component costs, durability and electricity market conditions. For Polish energy policy, the findings indicate that net-billing creates strong incentives for regulatory instruments that promote higher self-consumption, which would enhance the economic role of residential storage. Full article

This paper develops a robust portfolio optimization framework that integrates Weighted Shannon Entropy (WSE) into the classical mean–variance paradigm, offering a distribution-free approach to diversification suited for volatile and heavy-tailed markets. While traditional variance-based models are highly sensitive to estimation errors and instability in covariance structures—issues that are particularly acute in cryptocurrency markets—entropy provides a structural mechanism for mitigating concentration risk and enhancing resilience under uncertainty. By incorporating informational weights that reflect asset-specific characteristics such as volatility, market capitalization, and liquidity, the WSE model generalizes classical Shannon entropy and allows for more realistic, data-driven diversification profiles. Analytical solutions derived from the maximum entropy principle and Lagrange multipliers yield exponential-form portfolio weights that balance expected return, variance, and diversification. The empirical analysis examines two case studies: a four-asset cryptocurrency portfolio (BTC, ETH, SOL, and BNB) over January–March 2025, and an extended twelve-asset portfolio over April 2024–March 2025 with rolling rebalancing and proportional transaction costs. The results show that WSE portfolios achieve systematically higher entropy scores, more balanced allocations, and improved downside protection relative to both equal-weight and classical mean–variance portfolios. Risk-adjusted metrics confirm these improvements: WSE delivers higher Sharpe ratios and less negative Conditional Value-at-Risk (CVaR), together with reduced overexposure to highly volatile assets. Overall, the findings demonstrate that Weighted Shannon Entropy offers a transparent, flexible, and robust framework for portfolio construction in environments characterized by nonlinear dependencies, structural breaks, and parameter uncertainty. Beyond its empirical performance, the WSE model provides a theoretically grounded bridge between information theory and risk management, with strong potential for applications in algorithmic allocation, index construction, and regulatory settings where diversification and stability are essential. Moreover, the integration of informational weighting schemes highlights the capacity of WSE to incorporate both statistical properties and market microstructure signals, thereby enhancing its practical relevance for real-world investment decision-making. Full article

In the context of the growing prominence of socially responsible investment, the debate over whether sustainable corporate practices translate into sustained shareholder value has intensified. As a key tool for aligning their investment portfolios with responsible/sustainable corporate practices, investors rely on listed companies’ Environmental, Social, and Governance (ESG) ratings. This study aims to investigate the long-term impact of ESG practices on the stock performance of listed companies. We perform a Q1 2000–Q1 2025 backtest to analyse the comparative performance of a Best-in-Class ESG portfolio, constructed by the top 30 listed companies with market capitalisations above USD 2 billion ranked by Morningstar Sustainalytics’ ESG Risk Ratings as of 31 March 2025 against the S&P 500 Total Return index. We found that ESG leaders exhibited superior risk-adjusted performance, outperforming the S&P 500 Total Return Index. The BiC portfolios achieved a substantially higher CAGR and Sharpe ratio, while maintaining maximum drawdowns that remained comparable to the benchmark S&P 500 Total Return index. We also found that ESG advantages were more pronounced in market downturns, with the Best-in-Class ESG portfolio showing better CAGR and Sortino ratios. The findings of this study demonstrate that responsible governance and management create benefits for all stakeholders, including investors, society and nature, in the broadest sense of these terms. Full article

Background: Digital technologies can contribute to healthcare democratization in an ethical, safe, and sustainable context. The remote monitoring of implantable cardiac devices enables the follow-up of patients from a distance. Patients with anti-bradycardia pacemakers represent the largest group and have the least access to this technology due to the controversial cost–benefit ratio and barriers to its widespread implementation, such as equipment costs and organizational challenges. In contrast, reimbursed transportation in Portugal reached approximately 82 million euros in 2024. Objectives: The aim of the present study was to assess the financial viability of remote pacemaker follow-up in a Portuguese district hospital, comparing the non-urgent transportation costs and the investment in remote monitoring equipment while measuring user acceptance and satisfaction. Methods: A total of 41 surveys were conducted with patients who received a pacemaker and used publicly reimbursed transportation. The projected costs were calculated for two simulated protocols: the first involved in-person visits every six months, while the second involved in-person visits every two years with remote consultations every six months, over the expected lifespan of the devices. EZR, version 1.61, was used. Results: Our data showed a 74% overall reduction in face-to-face visits. The implementation of remote follow-up would result in a cost saving of EUR 373/patient (21.2%), with total reimbursement (p = 0.01151). The savings increased to 33.3%, reaching EUR 764/patient (p = 0.0002742) for distances greater than 60 km (round trip) for ambulance users with total reimbursement. Acceptance and satisfaction achieved 88%. Conclusions: Remote monitoring of pacemakers can be a financially viable alternative with high acceptance and satisfaction. Full article