Search Results (628)

This paper introduces a new framework for high-dimensional covariance matrix estimation, the Blockwise Exponential Covariance Model (BECM), which extends the traditional block-partitioned representation to the log-covariance domain. By exploiting the block-preserving properties of the matrix logarithm and exponential transformations, the proposed model guarantees strict positive definiteness while substantially reducing the number of parameters to be estimated through a blockwise log-covariance parameterization, without imposing any rank constraint. Within each block, intra- and inter-group dependencies are parameterized through interpretable coefficients and kernel-based similarity measures of factor loadings, enabling a data-driven representation of nonlinear groupwise associations. Using monthly stock return data from the U.S. stock market, we conduct extensive rolling-window tests to evaluate the empirical performance of the BECM in minimum-variance portfolio construction. The results reveal three main findings. First, the BECM consistently outperforms the Canonical Block Representation Model (CBRM) and the native 1/N benchmark in terms of out-of-sample Sharpe ratios and risk-adjusted returns. Second, adaptive determination of the number of clusters through cross-validation effectively balances structural flexibility and estimation stability. Third, the model maintains numerical robustness under fine-grained partitions, avoiding the loss of positive definiteness common in high-dimensional covariance estimators. Overall, the BECM offers a theoretically grounded and empirically effective approach to modeling complex covariance structures in high-dimensional financial applications. Full article

A Security Operation Center (SOC) is a security control center for monitoring, detecting, analyzing, and responding to cybersecurity threats. PT (Perseroan Terbatas) Non-Bank Financial Company (NBFC) has implemented an SOC to secure its information systems, but challenges remain to be solved. These include the absence of impact analysis on financial and regulatory requirements, cost, and effort estimation for recovery; established Key Performance Indicators (KPIs) and Key Risk Indicators (KRIs) for monitoring security controls; and an official program for insider threats. This study evaluates SOC effectiveness at PT NBFC using the ISO 27005:2018 and NIST SP 800-30 frameworks. The research results in a proposed SOC assessment framework, integrating risk assessment, risk treatment, risk acceptance, and monitoring. Additionally, a maturity level assessment was conducted for ISO 27005:2018, NIST SP 800-30, and the proposed framework. The proposed framework achieves good maturity, with two domains meeting the target maturity value and one domain reaching level 4 (Managed and Measurable). By incorporating domains from both ISO 27005:2018 and NIST SP 800-30, the new framework offers a more comprehensive risk management approach, covering strategic, managerial, and technical aspects. Full article

The capacity of the United Kingdom (UK) to prosecute technology-enabled financial and economic crime (FEC) is increasingly shaped by the Collingridge dilemma. Even though the dilemma was broadly conceptualized in technology governance, its application to prosecutorial and enforcement practice, evidentiary standards, and criminal liability attribution represents uncharted scholarly territory. Through socio-legal mixed methods combining doctrinal analysis, case studies, and comparative analysis, the paper shows how the dilemma’s two horns or pillars (i.e., early epistemic uncertainty and late institutional inertia) manifest in criminal law and regulatory contexts. The paper finds that just like the European Union and United States, the UK criminal enforcement ecosystem exhibits both horns across cryptocurrency, algorithmic trading, artificial intelligence (AI), and fintech domains. By integrating supplementary theories such as responsive regulation, precautionary principles and technological momentum, the study advances a socio-legal framework that explains enforcement inertia and doctrinal gaps in liability attribution for emerging technologies. The paper demonstrates how epistemic uncertainty and institutional entrenchment shape enforcement outcomes and proposes adaptive strategies for anticipatory governance including technology-literate capacity building, anticipatory legal reform, and data-driven public-private coordination. These recommendations balance ex-ante legal clarity (reducing uncertainty) with ex-post enforcement agility (overcoming entrenchment) to provide a normative framework for navigating the Collingridge dilemma in FEC prosecution. Full article

The construction sector is central to achieving the objectives of the European Green Deal (EGD). While existing research on transition risks predominantly focuses on project- or firm-level challenges, less is known about the transition risks implied by high-level EU policy documents. This study addresses this gap by systematically analysing 101 EGD-related policy and guidance documents published between 2019 and February 2025. A mixed human–AI content analysis approach was applied, combining human expert manual coding with automated validation using large language models (Kimi K2 and GLM 4.6). The final dataset contains 2752 coded risk references organised into eight main categories and twenty-six subcategories. Results show that transition risks are most frequently associated with environmental, economic, and legislative domains, with Climate Change Impact, Cost of Transition, Pollution, Investment Risks, and Implementation Variability emerging as the most prominent risks across the corpus. Technological and social risks appear less frequently but highlight important systemic and contextual vulnerabilities. Overall, analysis of the EGD policy texts reveals the green transition as being constrained not only by environmental pressures but also by financial feasibility and execution capacity. The study provides a structured, policy-level risk profile of the EGD and demonstrates the value of hybrid human–LLM analysis for large-scale policy content analysis and interpretation. These insights support policymakers and industry stakeholders to anticipate structural uncertainties that may affect the construction sector’s transition toward a low-carbon, circular economy. Full article

Background/Objectives: Socioeconomic stressors, such as financial strain, rising living costs, and perceived price burden, have gained relevance in the post-pandemic period and may adversely affect adolescent mental health. This study examined the association between subjective financial stress and symptoms of depression, anxiety, and emotional/behavioral difficulties among adolescents in Northern Italy. Methods: Data were obtained from the 2025 Corona and Psyche South Tyrol (COP-S) population survey. A total of 2554 adolescents aged 11–19 years and their parents participated; 1598 adolescents provided complete data for analyses of socioeconomic stressors (parent-reported Family Affluence Scale III, adolescent self-reported and parent proxy and self-reported burden due to price increases). Mental health outcomes included depressive symptoms (PHQ-2), generalized anxiety (SCARED-GAD), and emotional/behavioral difficulties (SDQ). Associations were assessed using chi-square tests, Kendall’s tau correlations, and two-factor ANOVA models. Results: Elevated depressive symptoms were present in 10.7% of adolescents, emotional/behavioral difficulties in 13.9%, and anxiety symptoms in 27.9% of adolescents. Female adolescents consistently showed higher symptom levels in all domains. Self-reported financial burden was the strongest and most consistent correlate of mental health problems, demonstrating small-to-moderate positive correlations with depressive symptoms (τ = 0.20, p < 0.001), emotional/behavioral difficulties (τ = 0.14, p < 0.001), and anxiety (τ = 0.25, p < 0.001). Parent-reported burden showed weaker and less consistent associations, and the Family Affluence Scale III was not significantly related to any of the mental health outcomes. ANOVA models indicated that adolescents’ own perception of financial burden significantly predicted anxiety levels in both age groups (11–14 and 15–19 years), whereas discrepancies between adolescent and parent burden perceptions were particularly relevant among younger adolescents. Conclusions: In this affluent European region, subjective financial strain, especially adolescents’ perception of burden due to rising prices, is a stronger determinant of depressive symptoms, anxiety, and psychosocial difficulties than parental burden reports or structural affluence indicators. Adolescents, especially females, appear to be particularly vulnerable. These findings underscore the importance of addressing subjective financial stress in adolescent mental health and public health strategies. Full article

Accurate forecasting in low-liquidity futures markets is essential for effective trading. This study introduces a hybrid decision-support framework that combines Mini-TAIEX (MTX) futures data with sentiment signals extracted from 13 financial news sources and PTT forum discussions. Sentiment features are generated using three domain-adapted large language models—FinGPT-internLM, FinGPT-llama, and FinMA—trained on more than 360,000 finance-related texts. These features are integrated with technical indicators in four deep learning models: LSTM, GRU, Informer, and PatchTST. Experiments from June 2024 to June 2025 show that sentiment-augmented models consistently outperform baselines. Backtesting further demonstrates that the sentiment-enhanced PatchTST achieves a 526% cumulative return with a Sharpe ratio of 0.407, highlighting the value of incorporating sentiment into AI-driven futures trading systems. Full article

The financial services sector is experiencing unprecedented transformation through the adoption of virtualization technologies, encompassing cloud computing and edge computing digitalization initiatives that fundamentally alter operational paradigms and competitive dynamics within the industry. This systematic literature review employed a comprehensive methodology, analyzing peer-reviewed articles, systematic reviews, and industry reports published between 2016 and 2025 across three primary technological domains, utilizing thematic content analysis to synthesize findings and identify key implementation patterns, performance outcomes, and emerging challenges. The analysis reveals consistent evidence of positive long-term performance outcomes from virtualization technology adoption, including average transaction processing time reductions of 69% through edge computing implementations, substantial operational cost savings and efficiency improvements through cloud computing adoption, while simultaneously identifying critical challenges related to regulatory compliance, security management, and organizational transformation requirements. Virtualization technology offers transformative potential for financial services through improved operational efficiency, enhanced customer experience, and competitive advantage creation, though successful implementation requires sophisticated approaches to standardization, regulatory compliance, and change management, with future research needed to develop integrative frameworks addressing technology convergence and emerging applications in decentralized finance and digital currency systems. Full article

Background/Objectives: Family members are the principal providers of home-based care for migrant older adults. Linguistic, cultural, and structural barriers within health systems exacerbate the caregiver burden across emotional, physical and financial domains. Although home healthcare services may alleviate this burden, variability in access, cultural safety, and care coordination can also intensify it. This scoping review maps the evidence on the burden experienced by family caregivers who deliver home-based healthcare to migrant older adults and examines how these arrangements affect caregivers’ health and well-being. It synthesizes the literature on facilitators and barriers—including access, cultural-linguistic fit, coordination with formal services, and legal/immigration constraints—and distills implications for policy and practice to strengthen equitable, culturally responsive home care. Method: The Joanna Briggs Institute (JBI) scoping review framework was used to conduct the review. A comprehensive search was performed across six databases (CINAHL, Scopus, Web of Science, PsycINFO, MEDLINE and Sociological Abstracts) for articles published between 2000 and 2025. Studies were selected based on predefined inclusion criteria focusing on the family caregiver burden in providing home healthcare for migrant older adults. Data extraction and thematic analysis were conducted to identify key themes. Results: The review identified 20 studies across various geographical regions, highlighting four key themes: (1) Multidimensional Caregiver Burden, (2) The Influence of Gender, Family Hierarchy, and Migratory Trajectories on Caregiving, (3) Limited Access to Formal and Culturally Appropriate Support, and (4) Health Outcomes, Coping, and the Need for Community-Based Solutions. Conclusions: System-level reforms are required to advance equity in home healthcare for aging migrants. Priorities include establishing accountable cultural-safety training for providers; expanding multilingual access across intake, assessment, and follow-up; and formally recognizing and resourcing family caregivers (e.g., navigation support, respite, training, and financial relief). Investment in community-driven programs, frameworks and targeted outreach—co-designed with migrant communities—can mitigate isolation and improve uptake. While home healthcare is pivotal, structural inequities and cultural barriers continue to constrain equitable access. Addressing these gaps demands coordinated policy action, enhanced provider preparation, and culturally responsive care models. Future research should evaluate innovative frameworks that integrate community partnerships and culturally responsive practices to reduce the caregiver burden and improve outcomes for migrant families. Full article

Deep reinforcement learning (DRL) plays a pivotal role in decision-making within financial markets. However, DRL models are highly reliant on raw market data and often overlook the impact of future trends on model performance. To address these challenges, we propose a novel framework named Cluster Embedding-Proximal Policy Optimization (CE-PPO) for trading decision-making in financial markets. Specifically, the framework groups feature channels with intrinsic similarities and enhances the original model by leveraging clustering information instead of features from individual channels. Meanwhile, zero-shot prediction for unseen samples is achieved by assigning them to appropriate clusters. Future Open, High, Low, Close, and Volume (OHLCV) data predicted from observed values are integrated with actually observed OHLCV data, forming the state space inherent to reinforcement learning. Experiments conducted on five real-world financial datasets demonstrate that the time series model integrated with Cluster Embedding (CE) achieves significant improvements in predictive performance: in short-term prediction, the Mean Absolute Error (MAE) is reduced by an average of 20.09% and the Mean Squared Error (MSE) by 30.12%; for zero-shot prediction, the MAE and MSE decrease by an average of 21.56% and 31.71%, respectively. Through data augmentation using real and predicted data, the framework substantially enhances trading performance, achieving a cumulative return rate of 137.94% on the S&P 500 Index. Beyond its empirical contributions, this study also highlights the conceptual relevance of symmetry in the domain of algorithmic trading. The constructed deep reinforcement learning framework is capable of capturing the inherent balanced relationships and nonlinear interaction characteristics embedded in financial market behaviors. Full article

Prediction of stock closing price plays a critical role in financial planning, risk management, and informed investment decision-making. In this study, we propose a novel model that synergistically amalgamates Bidirectional GRU (BiGRU) with three complementary attention techniques—Top-k Sparse, Global, and Bahdanau Attention—to tackle the complex, intricate, and non-linear temporal dependencies in financial time series. The proposed Fused Attention Model is validated on two highly volatile, non-linear, and complex- patterned stock indices: NIFTY 50 and S&P 500, with 80% of the historical price data used for model learning and the remaining 20% for testing. A comprehensive analysis of the results, benchmarked against various baseline and hybrid deep learning architectures across multiple regression performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R² Score, demonstrates the superiority and noteworthiness of our proposed Fused Attention Model. Most significantly, the proposed model yields the highest prediction accuracy and generalization capability, with R² scores of 0.9955 on NIFTY 50 and 0.9961 on S&P 500. Additionally, to mitigate the issues of interpretability and transparency of the deep learning model for financial forecasting, we utilized three different Explainable Artificial Intelligence (XAI) techniques, namely Integrated Gradients, SHapley Additive exPlanations (SHAP), and Attention Weight Analysis. The results of these three XAI techniques validated the utilization of three attention techniques along with the BiGRU model. The explainability of the proposed model named as BiGRU based Fused Attention (BiG-FA), in addition to its superior performance, thus offers a robust and interpretable deep learning model for time-series prediction, making it applicable beyond the financial domain. Full article

In the context of advancing agricultural and rural modernization in China, digital agriculture has gained significant governmental attention. However, existing research has predominantly focused on examining the relationship from digital inclusive finance to digital agriculture, while in-depth investigations into their bidirectional coupled coordination, spatiotemporal evolution, and underlying obstacle factors remain limited. To address this research gap, this study aims to construct innovative evaluation index systems for both domains and to establish a coupling coordination degree model alongside an obstacle degree model. This methodological framework is designed to examine the bidirectional coupled coordination, reveal its spatiotemporal evolution patterns, and identify key obstacle factors across 30 Chinese provinces. Results indicate a consistent annual improvement in the coupling coordination level across provinces. Many regions have progressed from moderate or mild dysfunction to marginal or primary coordination, with coordination degrees ranging between 0.5 and 0.6 by 2022. Specifically, the eastern region recorded 0.586, the central region 0.562, and the western region 0.531. Regional disparities are identified as the primary source of variation. Key obstacles include insufficient support from digital finance to agriculture, the east–west development gap, low actual usage of digital financial services, volatility in agricultural production price indices, and high agricultural carbon emissions. Recommendations focus on bridging regional gaps, strengthening financial support, and addressing these impediments, which are crucial for promoting sustainable development. Full article

This paper presents a PRISMA-ScR scoping review of 20 digital maturity assessment tools in the architecture, engineering, and construction (AEC) sector and wider business domains. The objective is to compare key features including assessment matrices, maturity dimensions, completion time, accessibility, and platform availability. The review follows predefined eligibility criteria and a structured screening process to identify and analyse tool frameworks in terms of scope, metrics, platform type, usability, and focus areas. Most tools primarily target the AEC sector, while some address broader organisational digital transformation. Common maturity areas include technology, organisation, data management, and processes. The analysis highlights limitations such as underemphasis on people, strategy, policy, skills development, standards, and financial resources, as well as the fragmented integration of maturity components. A combined bottom-up (scoring) and top-down (dimension structuring) approach is recommended for future tool development. The review provides insights for practitioners when selecting tools and proposes guidelines for creating more comprehensive and integrated maturity models. Full article

Financial institutions face increased reputational, regulatory, and ethical risks as the frequency and complexity of Environmental, Social, and Governance (ESG) controversies increase. Whistleblowing mechanisms are essential in the context of institutional resilience and the mitigation of internal governance failures. This study quantifies the exposure of 364 European financial institutions to a variety of ESG controversies to assess the effectiveness of whistleblowing during the fiscal year 2024. A whistleblowing performance index that captures the relative influence of ESG-related risk factors—such as corruption allegations, environmental violations, and executive misconduct—is constructed using a hybrid Multi-Criteria Decision-Making (MCDM) framework that is based on Entropy Weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results emphasize that the perceived efficacy of whistleblower systems is substantially influenced by the frequency of media-reported controversies and the presence of robust anti-bribery policies. The study provides a data-driven, replicable paradigm for assessing internal governance capabilities in the face of ESG risk pressure. Our findings offer actionable insights for regulators, compliance officers, and ESG analysts who are interested in evaluating and enhancing ethical accountability systems within the financial sector by connecting the domains of financial risk management, corporate ethics, and sustainability governance. Full article

The loss of urban nature and declining biodiversity pose significant challenges to the sustainability of cities and the well-being of their inhabitants. Existing initiatives such as the Taskforce on Nature-related Financial Disclosures (TNFD) have begun to address ecological risks in real estate, but they still address mental health, biodiversity, and social equity only partially as non-financial values. This article adopts an integrative review and conceptual framework approach. It develops a nature-based accounting framework for urban real estate that combines principles of traditional Chinese architecture with contemporary sustainability metrics. The study reviews ecological theory, nature-related accounting, and evidence on biodiversity and mental health, and then undertakes an operational mapping from classical site planning, courtyard design, water management, and community structures to measurable indicators that remain compatible with TNFD-aligned reporting. The framework groups indicators into three main domains: nature-related conditions, ecosystem service pathways, and human well-being outcomes. It also outlines simple procedures for normalising and combining these indicators at the project scale to support assessments of biodiversity, microclimate, mental health, and basic aspects of cost-effectiveness and social accessibility in urban real estate projects. The paper provides a structured, heritage-informed basis for future applications and empirical testing, helping to incorporate biodiversity, mental health, and equity into urban real estate assessment. Full article

The estimation of correlation matrices in high-dimensional data streams presents a fundamental conflict between computational efficiency and statistical robustness. Moment-based estimators, such as Pearson’s correlation, offer linear $O\left(N\right)$ complexity but lack robustness. In contrast, high-breakdown methods like the minimum covariance determinant (MCD) are computationally prohibitive ($O(N{p}^2+p^3)$) for real-time applications. This paper introduces Fast Outlier-Robust Correlation Estimation (FORCE), a streaming algorithm that performs adaptive coordinate-wise trimming using the $P^2$ algorithm for streaming quantile approximation, requiring only $O\left(p\right)$ memory independent of stream length. We evaluate FORCE against six baseline algorithms—including exact trimmed methods (TP-Exact, TP-TER) that use $O(NlogN)$ sorting with $O\left(Np\right)$ storage—across five benchmark datasets spanning synthetic, financial, medical, and genomic domains. FORCE achieves speedups of approximately 470× over FastMCD and 3.9× over Spearman’s rank correlation. On S&P 500 financial data, coordinate-wise trimmed methods substantially outperform FastMCD: TP-Exact achieves the best RMSE (0.0902), followed by TP-TER (0.0909) and FORCE (0.1186), compared to FastMCD’s 0.1606. This result demonstrates that coordinate-wise trimming better accommodates volatility clustering in financial time series than multivariate outlier exclusion. FORCE achieves 76% of TP-Exact’s accuracy while requiring ${10}^4\times$ less memory, enabling robust estimation in true streaming environments where data cannot be retained for batch processing. We validate the 25% breakdown point shared by all IQR-based trimmed methods using the ODDS-satellite benchmark (31.7% contamination), confirming identical degradation for FORCE, TP-Exact, and TP-TER. For memory-constrained streaming applications with contamination below 25%, FORCE provides the only viable path to robust correlation estimation with bounded memory. Full article