Search Results (223)

The current research undertook a comprehensive examination of global research related to the use of measurement, reporting, and verification (MRV) techniques for quantifying and tracking greenhouse gas (GHG) emissions from agriculture and livestock farming. Data were collected using a bibliometric analysis of 5340 studies published in the period (1990–2025) and a systematic literature review of 100 studies published in the period (2020–2025). The insights from the findings showed that four MRV techniques were broadly adopted across different regions: (1) inventory techniques (IPCC Tiers, national systems), (2) accounting at the project/product level (LCA, carbon footprint protocols), (3) MRV based on measurement and models (chambers, remote sensing, farm models, AI/ML), and (4) frameworks for governance and standardization (UNFCCC, Paris ETF, PAS 2050, etc.). The findings further revealed the impact of the MRV techniques on agriculture and livestock farming, showing that they facilitated the uptake of low-carbon practices. In agriculture, the MRV techniques showed that lower emissions emerged from mixed cropping, while in livestock farming, the emissions varied based on the feeding stage and type of diet used. However, various challenges arose in the adoption of MRV techniques where there was limited data related to GHG emissions, thereby reducing generalizability. In future work, there is a need for scholars to consider integrating the different MRV techniques to develop an understanding of the problem area. Full article

This study proposes a hierarchical signal-to-policy learning framework for risk-aware portfolio optimization that integrates model-based return forecasting, explainable machine learning, and deep reinforcement learning (DRL) within a unified architecture. In the first stage, next-period returns are estimated using gradient-boosted tree models, and SHAP-based feature attributions are extracted to provide transparent, factor-level explanations of the predictive signals. In the second stage, a Proximal Policy Optimization (PPO) agent incorporates both predictive forecasts and explanatory signals into its state representation and learns dynamic allocation policies under a mean–CVaR reward function that explicitly penalizes tail risk while controlling trading frictions. By separating signal extraction from policy learning, the proposed architecture allows the use of economically interpretable predictive signals to incorporate into the policy’s state representation while preserving the flexibility and adaptability of reinforcement learning. Empirical evaluations on U.S. sector ETFs and Dow Jones Industrial Average constituents show that the hierarchical framework delivers higher and stable out-of-sample risk-adjusted returns relative to both a single-layer DRL agent trained solely on technical indicators, a mean–CVaR optimized portfolio using the same parameters used in the proposed hierarchical model and standard equal weight as well as index-based benchmarks. These results demonstrate that integrating explainable predictive signals with risk-sensitive reinforcement learning improves the robustness and stability of data-driven portfolio strategies. Full article

We propose Adaptive Inference, a portfolio management framework extending Active Inference to non-stationary financial environments. The framework integrates inference, control, and execution under endogenous uncertainty, modeling investment decisions as coupled dynamics of belief updating, preference encoding, and action selection rather than optimization over fixed objectives. In this approach, portfolio behavior is governed by the expected free energy (EFE) minimization, showing that classical valuation models emerge as limiting cases when epistemic components vanish. Using train–test evaluation on the ARKK Innovation ETF (2015–2025), we identify a Passivity Paradox: frozen belief transfer outperforms naive adaptive learning. A Professional Agent achieves a Sharpe ratio of 0.39 while its adaptive counterpart degrades to $-0.28$, reflecting belief contamination when learning from policy-dependent signals. Crucially, the architecture is not designed to generate alpha but to perform endogenous risk management that mitigates overtrading under regime ambiguity and distributional shift. Adaptive Inference Agents maintain long exposure most of the time while tactically reducing positions during high-entropy periods, implementing uncertainty-aware passive investing. All agents reduce realized volatility relative to ARKK Buy-and-Hold (43.0% annualized). Cross-asset validation on the S&P 500 ETF (SPY) shows that inference-guided risk shaping achieves a positive Entropic Sharpe Ratio (ESR), defined as excess return per unit of informational work, thereby quantifying the economic value of information under thermodynamic constraints on inference. Full article

This study investigates the impact of sustainability-related uncertainty (SRU)—captured via the Sustainability-related Uncertainty Index in equal-weighted (ESGUI_EQ) and GDP-weighted (ESGUI_GDP) forms—on the volatility of green financial assets, focusing on decentralized finance (DeFi) protocols and Environmental, Social, and Governance (ESG)-focused Exchange-Traded Funds (ETFs). Employing a fuzzy logic framework, complemented by 3D surface visualization, Rule Viewer analysis, diagnostic validation, and Granger causality tests, the study uncovers non-linear, asymmetric, and time-varying responses of these assets to sustainability ambiguity. Empirical results reveal a structural divergence: DeFi protocols amplify volatility due to fragmented governance, speculative investor behavior, and sensitivity to policy-driven signals, often exhibiting bidirectional predictive feedback with SRU, whereas ESG ETFs maintain stability through diversification, regulatory oversight, and rigorous ESG screening, primarily absorbing sustainability shocks. These findings extend sustainable finance theory by integrating governance, technology, and policy dimensions, and illustrate the value of fuzzy logic combined with Granger causality in modeling complex, ambiguous markets. From a practical standpoint, the study provides actionable guidance for investors, fund managers, and policymakers, emphasizing the importance of technology-informed governance, standardized ESG disclosures, regulatory sandboxes, and continuous monitoring of SRU. Full article

The main purpose of this study was to analyze the dynamics of the conditional correlation between Bitcoin and BOVA11 (a Brazilian stock market ETF that has seen a significant increase in foreign investors) across the pre-, during, and post-COVID-19 pandemic periods. This analysis allowed us to investigate the Bitcoin characteristics as a diversifier, hedge, or safe haven relative to the ETF. The study employed a DCC-GARCH model using daily closing prices from 2 January 2015 to 26 September 2025. A robustness check was conducted using Large Language Models (LLMs). Results indicated that in the pre- and post-pandemic periods, Bitcoin showed no significant correlation with the ETF, potentially acting as a weak hedge. Conversely, during the pandemic, Bitcoin behaved as a diversifier for the ETF rather than a safe haven. This finding may surprise market participants, particularly given the widespread narrative of Bitcoin as “digital gold” and, therefore, a natural protection in scenarios of high uncertainty. The results suggest that, during the pandemic, Bitcoin’s behavior aligned more closely with risk assets than with safe havens, underscoring the need for cautious, context-specific empirical assessments of its protective properties. Full article

This paper presents a hybrid framework for multi-stock trading that combines the decision-making ability of Deep Q-Networks (DQN) with the allocation precision of portfolio optimization models. Realistic markets are noisy and non-stationary, and complex action spaces can hinder reinforcement learning (RL) performance. The DQN generates buy/sell signals based on market conditions. The framework passes buy-listed assets to an optimizer, which computes portfolio weights. Five allocation strategies are examined: naïve 1/N, Markowitz Mean–Variance, Global Minimum Variance, Risk Parity, and Sharpe Ratio Maximization. Empirical evaluations on emerging-market exchange-traded funds (ETFs), as well as additional tests on U.S. equities, show that even the baseline DQN outperforms traditional technical indicators. Furthermore, integrating any of the optimization approaches with DQN yields measurable improvements in return-risk performance metrics. Among the hybrid frameworks, DQN combined with Sharpe Ratio Maximization delivers the most consistent gains. The findings highlight the value of decomposing stock selection from capital allocation and demonstrate the effectiveness of the proposed DQN-optimization framework on our testbed. Full article

Portfolio management (PM) is a broad investment strategy aimed at risk mitigation through diversified financial product investments. Acknowledging the significance of dynamic adjustments after establishing a portfolio to enhance stability and returns, we propose employing reinforcement learning (RL) to address dynamic decision-making challenges. However, traditional RL methods often struggle to adapt to significant market volatility, primarily by focusing on adjusting existing asset weights. Different from traditional RL methods, the multi-agent reinforcement learning correctable strategy (MAC) developed in this study detects and replaces potentially harmful assets with familiar alternatives, ensuring a resilient response to market crises. Utilizing the multi-agent reinforcement learning model, MAC empowers individual agents to maximize portfolio returns and minimize risk separately. During training, MAC strategically replaces assets to simulate market changes, allowing agents to learn risk-identification through uncertainty estimation. During testing, MAC detects potentially harmful assets and replaces them with more reliable alternatives, enhancing portfolio stability. Experiments conducted on a real-world US Exchange-Traded Fund (ETF) market dataset demonstrate MAC’s superiority over standard RL-based PM methods and other baselines, underscoring its practical efficacy for real-world applications. Full article

We investigate the underlying risk exposures of ETFs compared with their indices using a Principal Component Analysis approach. Then, we test whether ETFs’ tracking errors can capture the risk exposure difference between ETFs and their underlying benchmarks. We document a significant positive relation between tracking error and differences in risk exposure between ETFs and their corresponding indices. Even modest increases in tracking error are associated with economically meaningful divergences in risk exposure between an ETF and its benchmark. These findings suggest that comparisons of tracking error across index ETFs when making investment decisions may be misleading for investors seeking benchmark-consistent risk exposure. Full article

Exchange-traded funds (ETFs) provide low-cost, liquid access to broad equity and fixed-income exposures, including rapidly growing Asian and Asia-focused markets. Yet the academic evidence on Asian ETF portfolio construction remains fragmented, often limited to narrow country samples and centered on mean–variance trade-offs and standard performance statistics, with comparatively less emphasis on downside tail risk and on implementable long-only versus long–short designs under leverage constraints. This study examines the performance and risk characteristics of 29 Asian and Asia-focused ETFs over 2014–2025 and evaluates whether optimization using variance-based and tail-sensitive risk measures improves portfolio outcomes relative to a simple, implementable benchmark. We construct Markowitz mean–variance and conditional value-at-risk (CVaR) efficient frontiers and implement six optimized portfolios at the 95% and 99% tail levels under long-only and long–short configurations with leverage up to 30%. Performance is evaluated relative to an equally weighted Asian ETF benchmark using the Sharpe ratio and tail-sensitive measures, including the Rachev ratio and the stable tail adjusted return (STARR), complemented by fat-tail diagnostics based on the Hill tail-index estimator. The empirical results show that optimization improves efficiency relative to equal weighting in risk-adjusted terms and that moderate leverage can increase returns but typically amplifies volatility, dispersion, and drawdowns. Taken together, the evidence indicates that risk-measure choice materially affects portfolio composition and realized outcomes, with tail-based optimization generally producing more robust allocations than mean–variance approaches when downside risk is a primary concern. Full article

The accurate estimation of helicopter engine power is crucial for ensuring operational performance and maintaining safety. Current methods, such as Maximum Power Checks (MPCs), are effective but resource-intensive and infrequent. This paper presents a novel machine learning-based framework tailored for operational helicopter fleets to estimate Engine Torque Factor (ETF) values from routine flight data obtained via Health and Usage Monitoring Systems (HUMS). The novelty lies in combining a statistically validated labeling strategy that links MPC-derived ETF values to regular flights with a dual-stage preprocessing pipeline, consisting of steady-state filtering and data consolidation, which is designed to produce high-quality, representative training data from noisy operational logs. Regression models, including XGBoost, CatBoost, and Random Forest, were trained and evaluated using HUMS data from AH-64A helicopters. Results demonstrate that focusing on specific ETF ranges significantly improves model performance, achieving R² values of up to 0.94. While the current implementation operates post-flight, the approach enables continuous monitoring between scheduled MPCs, potentially reducing unnecessary checks and providing earlier indications of power degradation. Full article

Set in the vocational education and training sector in England, this article draws attention to how top-down, centre–periphery approaches to curriculum design and development in vocational education fail for at least three reasons. First, they misconstrue the nature of knowledge. Second, they lead to perfunctory and fragmented approaches to curriculum design, coupled with mechanistic measures of quality and achievement, which often require little more than “one-off” and superficially assessed demonstrations of performance. Finally, they underplay the role and importance of the teacher as researcher and artist in putting the cultural resources of society to work in creative curriculum design and pedagogy. Teacher artistry is pivotal in animating and heightening the vitality of vocational curricula. It is through this artistry that teachers make theories, ideas and concepts in vocational subjects and disciplines accessible and meaningful to all learners in coherent ways in the contexts of their learning and their lives. The consequences of the epistemic faux pas underpinning centre-to-periphery models of curriculum design and development are highlighted in this article in vocational tutors’ accounts of experiences of problems and issues in curriculum design, development and assessment encountered in their practice. Participants in the research teach in a variety of vocational education settings, including Apprenticeships and Higher-Level Technical Education; English Language at General Certificate of Secondary Education (GCSE) level; Health and Social Care; Information and Communications Technology; Construction (Plumbing); Digital Production, Design and Development and High-Tech Precision Engineering. Data are analysed and reported through systematic, thematic analysis This article draws upon qualitative data derived from a study funded by the Education and Training Foundation (ETF) in England over a two-year period from 2021 to 2023. The research population consists of a group of eight practitioner–researchers working in three colleges of Further Education (FE) and one Industry Training Centre (ITC) in England. All of the teachers of vocational education reported here volunteered to participate in the study. Research methods include semi-structured interviews, analysis of critical incidents and case studies produced by practitioner–researchers from across the FE and Skills sector in England. Full article

Wildfires are a recurring dry-season hazard in northern Thailand, contributing to severe air pollution and trans-boundary haze. However, the region lacks the ground-based measurements necessary for monitoring Live Fuel Moisture Content (LFMC), a key variable influencing vegetation flammability. This study presents a preliminary framework for near-real-time (NRT) LFMC estimation using Sentinel-2 multispectral imagery. The system integrates normalized vegetation and moisture-related indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Infrared Index (NDII), and the Moisture Stress Index (MSI) with an NDVI-derived evapotranspiration fraction (ETf) within a heuristic modeling approach. The workflow includes cloud and shadow masking, weekly to biweekly compositing, and pixel-wise normalization to address the persistent cloud cover and heterogeneous land surfaces. Although currently unvalidated, the LFMC estimates capture the relative spatial and temporal variations in vegetation moisture across northern Thailand during the 2024 dry season (January–April). Evergreen forests maintained higher moisture levels, whereas deciduous forests and agricultural landscapes exhibited pronounced drying from January to March. Short-lag responses to rainfall suggest modest moisture recovery following precipitation, although the relationship is influenced by additional climatic and ecological factors not represented in the heuristic model. LFMC-derived moisture classes reflect broad seasonal dryness patterns but should not be interpreted as direct fire danger indicators. This study demonstrates the feasibility of generating regional LFMC indicators in a data-scarce tropical environment and outlines a clear pathway for future calibration and validation, including field sampling, statistical optimization, and benchmarking against global LFMC products. Until validated, the proposed NRT LFMC estimation product should be used to assess relative vegetation dryness and to support the refinement and development of future operational fire management tools, including early warnings, burn-permit regulation, and resource allocation. Full article

Forecasting stock prices remains a central challenge in financial modelling, as markets are influenced by market sentiment, firm-level fundamentals and complex interactions between macroeconomic and microeconomic factors, for example. This study evaluates the predictive performance of both classical statistical models and advanced attention-based deep learning architectures for daily stock price forecasting. Using a dataset of major U.S. equities and Exchange Traded Funds (ETFs) covering 2012–2024, we compare traditional statistical approaches, Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing (ES) in the Error, Trend, Seasonal (ETS) framework, with deep learning architectures such as the Temporal Fusion Transformer (TFT), and a novel hybrid model, the TFT-Graph Neural Network (TFT-GNN), which incorporates relational information between assets. All models are assessed under consistent experimental conditions in terms of forecast accuracy, computational efficiency, and interpretability. Our results indicate that while statistical models offer strong baselines with high stability and low computational cost, the TFT outperforms them in capturing short-term nonlinear dependencies. The hybrid TFT-GNN achieves the highest overall predictive accuracy, demonstrating that relational signals derived from inter-asset connections provide meaningful enhancements beyond traditional temporal and technical indicators. These findings highlight the advantages of integrating relational learning into temporal forecasting frameworks and emphasise the continued relevance of statistical models as interpretable and efficient benchmarks for evaluating deep learning approaches in high-frequency financial prediction. Full article

This study conducts a bibliometric review of Bitcoin research in the Business and Economics domains, using VOSviewer to visualize network structures and Bidirectional Encoder Representations from Transformers Topic (BERTopic) to derive semantically coherent topic clusters. The analysis identifies five major research themes: (1) Diversification, hedging, and safe-haven properties; (2) Market dynamics, efficiency, and investor behavior; (3) Bitcoin price and volatility prediction attempts; (4) Environmental impact of Bitcoin; and (5) Financial impact of Central Bank Digital Currency (CBDC). Based on these themes, the study recommends further investigation into the influence of Exchange-Traded Fund (ETF) approvals, regulatory frameworks, and institutional investor participation on Bitcoin’s safe-haven potential; the role of market dynamics and regulatory interventions; early detection of herding behavior and price bubbles; the integration of machine learning and deep-learning models for price prediction; the environmental costs associated with mining; and the evolving regulatory and implementation challenges of CBDCs. Overall, this review synthesizes existing scholarship and outlines future research directions for the rapidly evolving cryptocurrency ecosystem. Full article

The cryptocurrency landscape underwent significant changes in 2024 with the regulatory approval of spot Bitcoin ETFs, opening the market to institutional investors and millions of new clients. As Bitcoin reached new price peaks, the market attracted many retail traders using speculative approaches, evidenced by the surge in meme coins at the end of 2024. In such an environment, properly examining trading strategies can offer substantial advantages over the majority of market participants. Many traders, however, fail to test their strategies adequately, limiting evaluations to selected time periods and risking overfitting. This paper introduces the Rolling Strategy–Hold Ratio (RSHR), which uses a rolling-window approach to evaluate how strategies would perform from thousands of different starting points. This method helps mitigate recency bias and provides a more comprehensive understanding of strategy performance across diverse market conditions and cycles. By comparing strategies results against buy-and-hold results, traders can make informed decisions about whether to refine their strategies further or opt for index-based investing or alternative analytical methods. This study demonstrates the RSHR’s applications across technical, on-chain, sentiment analysis, and dollar cost averaging strategies, with initial research suggesting potential applications in traditional markets. Full article