Search Results (525)

During financial crises, markets do not only fall or become more volatile. They may also become more dynamically coupled, with the behaviour of one market becoming more recoverable from another. The purpose of this study is to examine whether crisis periods strengthen nonlinear coupling between major US and European equity indices, using Convergent Cross-Mapping as a state-space reconstruction method. Daily log returns for the Dow Jones Industrial Average, S&P 500, FTSE 100 and DAX are analysed across pre-crisis, crisis and post-crisis windows for the COVID-19 market shock and the Global Financial Crisis. Pairwise bidirectional Convergent Cross-Mapping is used to estimate cross-map skill, convergence and directional asymmetry, with a focused lagged analysis of key transatlantic pairs during COVID-19. Cross-map skill is interpreted as the strength of the recoverable dynamical footprint between markets. The results show that nonlinear coupling, measured as recoverable dynamical information between reconstructed market states, increases during crisis phases. During COVID-19, mean late-library cross-map skill rises from the pre-crisis to the crisis period, and all tested directional relationships satisfy the convergence criterion. The Global Financial Crisis also shows increased crisis-period coupling, with stronger persistence into the post-crisis phase. Lagged COVID-19 results indicate that the strongest recoverability occurs over a short contemporaneous-to-three-trading-day alignment window. The findings position Convergent Cross-Mapping as a complementary mathematical modelling framework for identifying recoverable dynamical information between markets during financial stress. Full article

Although employed women represent one of the fastest-growing segments of the investor population in emerging economies, their investment decision-making is still largely modelled through fragmented, single-determinant frameworks that treat women as a homogeneous group. This study develops and tests an integrated structural model of financial-goal-directed investment orientation among employed women, drawing together Behavioural Finance Theory, the Theory of Planned Behaviour, and the Life-Cycle Hypothesis. Primary data were collected through a structured questionnaire from 951 employed women across the four administrative zones of Bengaluru, India, using stratified random sampling. The measurement model was validated through exploratory and confirmatory factor analysis, and four competing structural specifications were estimated by maximum likelihood; the best-fitting model was selected on the basis of the corrected Akaike Information Criterion and approximate fit indices. The results indicate that risk tolerance is the strongest direct correlate of financial-goal-directed investment orientation, that confidence and self-efficacy operates as the pivotal psychological mediator linking macroeconomic perception to risk-taking, and that market sentiments are the strongest external correlate of investor confidence. Peer influence shows a theoretically important dual association, positively related to risk tolerance while negatively related to confidence. A serial mediation pathway running from market sentiments through confidence and risk tolerance to financial goals is supported. Because the design is cross-sectional, the associations are interpreted as structural relationships consistent with the proposed theoretical framework rather than as established causal effects. This study is exploratory and hypothesis-generating in character. The findings reframe financial-inclusion interventions for employed women around confidence-building rather than information provision, with implications for product design, advisory practice, and policy. Full article

We develop an operator-theoretic framework for extreme events in reflexive financial systems, identifying inverse evolution—the deterministic contraction of the manifold of admissible futures—as the structural mechanism underlying crashes and melt-ups. The interpolation constraint, which forces all analytical continuations to match the terminal empirical price, resolves the apparent continuum of stochastic paths into a discrete, countable spectrum of metastable futures. This countable manifold is stabilized by a spectral regularizer that preserves dimensionality through a “wait-and-adjust” re-categorization logic. Within this unified structure, we distinguish three pathways to collapse: (i) the Black Swan, a crisis of spectral weight; (ii) the projection operator, a rank-reducing projection that restores symmetry by exclusion; and (iii) the reactivation operator, a breakdown of spectral truncation that reactivates suppressed behaviour with large emergent return (Heavy) modes and forces the system into a regime of manifold resumption. Central to all modalities is the emergent return, an effective mass parameter whose sign determines whether collapse manifests as reflexive contraction (crash) or reflexive amplification (melt-up). The resulting dynamics exhibit cross-domain universality. The same operator grammar governs geopolitical choke-points, institutional purges, technological monopolies, retail-driven short squeezes, and other macrosystems in which dimensionality is either forcibly reduced or abruptly restored. Full article

Background and Objectives: Periodontal diseases are highly prevalent worldwide and have been associated with impaired oral health-related quality of life (OHRQoL). However, the strength and direction of this association remain inconsistent, particularly when sociodemographic and behavioural factors are considered. The aim of this study was to evaluate the independent association between periodontal status and OHRQoL in Bulgarian adults attending a university dental clinic, while accounting for sociodemographic factors and sleep quality, which was included as a clinically relevant behavioural determinant of oral health-related quality of life. Materials and Methods: This cross-sectional study included 504 adult participants (≥18 years) who underwent comprehensive periodontal examination and completed validated questionnaires. OHRQoL was assessed using the culturally adapted Bulgarian version of the Oral Health Impact Profile (OHIP-14), and sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI). Due to the skewed distribution of OHIP-14 scores, the outcome was dichotomized at the sample median. Multivariable logistic regression analysis was performed to assess independent associations, adjusting for age, sex, education, financial status, place of residence, and sleep quality. Results: Periodontal status, sex, and sleep quality were independently associated with impaired OHRQoL. Females demonstrated lower odds of impaired OHRQoL compared to males (OR = 0.636; 95% CI: 0.428–0.924; p = 0.025). Poor sleep quality was associated with increased odds of impaired OHRQoL (OR = 1.554; 95% CI: 1.041–2.321; p = 0.031). Periodontitis was significantly associated with impaired OHRQoL (OR = 3.526; 95% CI: 2.073–5.998; p < 0.001), reflecting a complex relationship between clinical periodontal status and subjective health perception. Conclusions: OHRQoL is influenced by a multifactorial framework integrating periodontal status, behavioural factors, and sociodemographic characteristics. These findings highlight the importance of incorporating patient-reported outcomes into periodontal assessment and support a biopsychosocial approach to oral health research. Full article

Background: Generative artificial intelligence (GenAI) is diffusing among consumers at exceptional speed, yet little is known about how its adoption relates to households’ prior engagement with digital financial services, or about the barriers associated with non-adoption. Methods: Using individual microdata from the 2025 Spanish Survey on ICT Equipment and Use in Households (INE; 14,642 internet users aged 16 and older), we estimate survey-weighted logistic regressions of GenAI adoption on financial digital engagement, digital skills and sociodemographics, with autonomous community fixed effects and cluster-robust standard errors, complemented by average marginal effects, alternative variance estimators, and an analysis of all stated reasons for non-use. Results: GenAI adoption is 37.3% among internet users. Online banking users have 73% higher adjusted odds of adopting GenAI (OR = 1.73; 95% CI 1.53–1.97), and adoption rises monotonically from 18% to 79% across a 0–4 financial digital engagement index, although this gradient is driven mainly by the online banking (extensive) margin. A steep age gradient is present, but the financial digital advantage does not significantly widen with age. Among non-adopters, the most frequently stated reason for non-use is a lack of perceived need (67%); privacy or security concerns are also prominent (43%) and are cited disproportionately by online banking users, women, and older individuals. Conclusions: Prior financial digital engagement is a strong correlate, rather than a demonstrated cause, of consumer GenAI adoption, consistent with experience and facilitating condition constructs in UTAUT2. Among capable non-adopters, for whom access and skills are not the obstacle, privacy- and security-related concerns, rather than a broader deficit of trust in AI, emerge as the most salient barriers to adoption, underscoring these concerns as priorities for consumer-facing AI in financial services. Full article

This work is devoted to the practical application of the geometric theory of dynamical systems to find invariants of a set of symmetric time series. Such series are often encountered in information systems processing large volumes of financial data, where spikes, outliers, and fluctuations are visually similar to each other, which can be explained, for example, by identical seasonal cycles or the type of economic activity. Although absolute values, amplitudes, and deviations may differ, the qualitative behaviour of such series is the same, as in oscillatory physical systems under different initial conditions and parameters. The discovered geometric invariant represents a structuring model that can be applied to the entire group of symmetric time series, providing a foundation for robust interval forecasting and the clustering of structurally similar dynamical systems. A theorem is proved establishing that the curvature of the jet space curve is a complete invariant under the group of affine transformations of the jet space coordinates and rotations. The use of translation, scaling, and rotation transformations applied to the locus of points of integral curves makes it possible to identify series that have the same qualitative behaviour up to a weak violation of symmetry. Experimental validation is performed on 25 daily bank account balance series. The proposed method achieves consistent alignment across all series. Full article

Against the dual backdrop of China’s rapid economic growth and the continuous expansion of the income gap between urban and rural areas, income mobility, as a dynamic indicator for measuring social opportunity equity, is of great significance for breaking through class solidification and promoting common prosperity. Based on the tracking data of the China Household Finance Survey (CHFS) from 2015 to 2019, this paper systematically examines the mechanism and heterogeneity of the impact of financial literacy on household income mobility from the perspective of urban–rural comparison by constructing the Markov transition matrix and the Ordered Probit model. The findings are as follows: first, financial literacy significantly enhances household income mobility in both urban and rural areas, but there is a significant urban–rural difference. The baseline regression shows that financial literacy has a stronger promoting effect on urban households, while the endogeneity test further reveals that there is an underestimation of urban–rural heterogeneity in its impact; second, the mechanism test shows that financial literacy promotes household income mobility by influencing financial behaviour; third, from the perspective of heterogeneity analysis, regional heterogeneity, the effect of urban households and the eastern and northeastern regions is more prominent. Income heterogeneity, the role of financial literacy in promoting income mobility, is particularly prominent among low-income groups in both urban and rural areas. This study not only provides an analytical framework from static to dynamic for understanding the economic empowerment effect of financial literacy, but also deepens the academic discussion on how financial capacity promotes opportunity equity, and its findings on the heterogeneity between urban and rural areas, regions, and income groups offer crucial micro-evidence for formulating more targeted differentiated policies that can effectively avoid the Matthew effect. Full article

This study examines the determinants of investment decision quality among individual investors in the United Arab Emirates (UAE) financial market. As digital investment platforms continue to expand access to financial information and investment opportunities, understanding the drivers of effective investment decisions has become increasingly important for investors. Integrating insights from financial literacy, behavioral finance, trust and disclosure, and technology acceptance perspectives, the study develops a unified framework to examine how financial literacy and awareness, trust, transparency and disclosure, as well as digital platform support jointly shape the quality of investment decisions. Drawing on survey data from 208 individual investors with varying levels of investment experience, the study employs correlation and multiple regression analyses to examine the proposed relationships. The results reveal positive bivariate associations between investment decision quality and all three explanatory factors. In the multivariate model, however, only financial literacy and awareness and digital platform support remain statistically significant, with digital platform support emerging as exerting the largest statistically significant effect on decision quality. The independent effect of trust, transparency, and disclosure attenuates to non-significance, suggesting that its effect largely overlaps with financial literacy and digital platform support rather than representing a distinct independent contribution. The findings indicate that investor outcomes depend not only on access to information but also on investors’ ability to understand, interpret, and apply that information through effective digital support mechanisms. The study contributes to the investment decision-making literature by providing an integrated empirical framework and new evidence from the UAE as an underexplored market context. The findings provide indicative implications for investor education initiatives, financial institutions, and digital platform providers within the sampled context of an increasingly digital financial environment. Full article

Artificial intelligence (AI)-driven fraud detection systems in FinTech ecosystems increasingly face a governance tension between high predictive accuracy and limited regulatory transparency, a gap that existing reviews have not addressed through an integrated behavioural, technical, and institutional lens. This study synthesises 99 Scopus-indexed, ABDC-ranked journal articles (2015–2026) using PRISMA 2020 and the SPAR-4-SLR protocol, integrating the Theory of Planned Behaviour (TPB) within an Antecedents–Decisions–Outcomes (ADO) framework to examine organisational adoption of explainable AI (XAI) in financial fraud detection. Three antecedent clusters are identified: attitudinal (algorithmic complexity, model opacity, data imbalance), normative (regulatory compliance, ethical expectations), and control-based (technical self-efficacy, organisational readiness)—which drive decision mechanisms including post hoc interpretability tools (SHapley Additive exPlanations [SHAP], Local Interpretable Model-Agnostic Explanations [LIME]), ethical governance protocols, and human-in-the-loop oversight. These produce outcomes across precision (reduced false positives, improved decision accuracy), compliance (audit transparency, institutional legitimacy), and cognitive (user acceptance, procedural justice) dimensions. The study introduces the Stability–Transparency–Reliability (STR) model, which advances TPB, Socio-Technical Systems Theory, and the Dynamic Capabilities View by reframing XAI from a static interpretability output into a recursive governance capability, formalised through the concept of Interpretative Agility, with direct implications for financial institutions operating under the EU AI Act. Full article

This study examines South Africa’s financial inclusion landscape, with a focus on how regulatory design, consumer capability, and community-based financial ecosystems shape equitable access to financial services. Despite sustained policy commitments, financial inclusion outcomes remain suboptimal, with approximately 3.9 million low-income adults lacking access to basic financial services such as savings and credit accounts. The paper contributes to the financial inclusion discourse by assessing whether government and regulatory institutions have enabled access for low-income groups or reinforced existing patterns of exclusion. Using a qualitative thematic analysis of academic literature, national policy documents, and regulatory frameworks, the study identifies key structural barriers and enabling factors influencing financial inclusion in South Africa. Adopting an institutional-capability approach, the analysis integrates regulatory theory with insights from behavioural finance and community-based financial systems. The findings indicate that while the Financial Sector Regulation Act represents a significant milestone by establishing financial inclusion as a statutory objective, meaningful progress depends on a stronger emphasis on financial literacy, differentiated consumer education, and context-responsive product design. The study further highlights that fintech innovation presents both opportunities and risks, expanding access while intensifying regulatory asymmetries and operational vulnerabilities. A holistic policy approach that promotes inclusive institutional design, revised credit risk assessment frameworks, and gender-responsive financial products is therefore essential to advancing South Africa’s financial inclusion agenda. Full article

This study examines the modelling and forecasting of South African macroeconomic and financial time series using a comparative framework based on Vector Autoregressive (VAR), Vector Autoregressive Moving Average (VARMA), and GARCH-type models. Quarterly data spanning 1970 to 2024 were analysed to determine GDP growth, exchange rates, interest rates, and household consumption expenditure. VAR and VARMA models were employed to capture conditional mean dynamics, while GARCH, EGARCH, and GJR-GARCH models, including ARMA-GARCH extensions, were used to model volatility behaviour. Optimal model specifications were selected using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Hannan–Quinn Criterion (HQ), and the Extended Cross-Correlation Matrix (ECCM), resulting in the estimation of VAR (4) and VARMA (1,1) models. The results reveal strong dynamic interdependencies among the variables. However, diagnostic tests indicate that the VAR (4) and VARMA (1,1) models do not fully capture the underlying data-generating process, as evidenced by residual autocorrelation, heteroskedasticity, and non-normality. Although the VARMA (1,1) model improved forecasting performance relative to the VAR (4) model, important nonlinear and higher-order dynamics remained unexplained. Volatility modelling revealed substantial persistence and clustering, particularly in exchange rates and interest rates. Initial GARCH, EGARCH, and GJR-GARCH specifications exhibited residual autocorrelation and remaining ARCH effects, suggesting model misspecification. The incorporation of an ARMA (1,1) term into the asymmetric GARCH models significantly improved model adequacy by eliminating residual autocorrelation and heteroskedasticity. Limited evidence of asymmetric volatility effects was found. Overall, the findings demonstrate that GARCH-ARMA specifications provide a more robust framework for modelling South Africa’s macroeconomic and financial dynamics. This study recommends future research incorporating nonlinear, regime-switching, and exogenous-variable models to enhance forecasting accuracy and policy relevance. Full article

Background: price dynamics in financial markets exhibit scale-invariant volatility, quantized liquidity and collective behaviour that resist single-paradigm models; Multiscale Relativistic Quantum Finance (MRQF) reconciles these facets on an energy–entropy $(E,S)$ plane, but its translation into a deployable decision system has remained open. Methods: we propose MRQF-MAS, a cooperative multi-agent system (MAS) in which institutional, commercial and retail operators become first-class agents, each decomposed into signal, energy, entropy, risk and execution sub-agents that share beliefs through a horizontal cooperation layer and a shared knowledge base (SKB) of $(E,S)$ trajectories. The framework is benchmarked as a high-volatility regime classifier on 6978 daily EUR/USD reference rates published by the European Central Bank (ECB) over 1999–2026 against four baselines including Generalized Autoregressive Conditional Heteroscedasticity (GARCH)(1,1). Results: on the full official ECB EUR/USD series, MRQF-MAS attains 83.0% accuracy, precision 0.552 and Matthews correlation coefficient (MCC) 0.479 with 95% bootstrap CI [0.46, 0.51] and a one-day median detection latency, improving slightly on a rolling-volatility baseline while remaining below a GARCH(1,1) reference. Conclusions: MRQF-MAS delivers a structurally interpretable, agent-traceable regime decomposition complementary to scalar volatility estimators. Full article

Purpose: This study examines whether finance-adapted (FA) phishing detection models improve the detection of finance-themed (FT) attacks, whether improvements differ across email and webpage modalities, and whether finance adaptation creates a specialisation–generalisation trade-off. Design/Methodology/Approach: A domain-aware framework is developed using email (82,486 instances) and webpage (11,430 instances) datasets. FT and non-finance-themed (NFT) instances are identified using weighted lexicon-based labelling. Generic models are compared with FA models across Logistic Regression, Linear SVC, and Random Forest using F1-score, MCC, balanced accuracy, ROC-AUC, and PR-AUC. Statistical validation employs bootstrap confidence intervals and McNemar’s test, while SHAP and permutation importance interpret webpage model behaviour. Findings: FA models outperform generic models in FT email classification, confirming that finance-specific semantic cues improve detection. However, gains are weaker and less consistent in webpage classification, where models rely mainly on structural indicators (page rank, Google index, hyperlinks). The results reveal a specialisation–generalisation trade-off: FA models improve in-domain detection but do not consistently outperform generic models on NFT instances, with F1-score declines of −0.0057 to −0.0151 on non-finance subsets. Practical Implications: Financial institutions and fintech platforms should deploy domain-adapted detection for email-based threats, where finance-specific linguistic cues yield measurable gains, while maintaining generic or ensemble models for broader webpage phishing coverage. Originality/Value: This study introduces a finance-themed, multi-modal, explainable AI framework for phishing detection, demonstrating that domain adaptation depends critically on data modality and feature representation. It provides a novel systematic comparison of generic versus FA phishing detection across both modalities with statistical validation and explainability analysis. Full article

Agricultural production is increasingly threatened by climate variability, limited access to quality inputs, and market shocks in developing countries. Insurance–input bundles, which integrate crop insurance with inputs like fertiliser and seed, have emerged as a promising tool for improving productivity and resilience among smallholder farmers. This study adopts a structured (systematic narrative) literature review approach, synthesising evidence from 152 studies to examine farmers’ awareness, attitudes, willingness to pay, participation, satisfaction, and productivity outcomes associated with insurance–input bundles. The findings show that awareness remains uneven and often limited by weak extension systems and low financial literacy, while farmers’ attitudes are strongly shaped by past experiences, cultural perceptions, and institutional trust. Furthermore, affordability constraints and risk misinterpretation reduce willingness to pay, whereas perceived value and institutional credibility significantly enhance demand for bundled products. Across the reviewed literature, adoption is shown to be a non-linear and interdependent process influenced by behavioural, economic, and institutional factors, where breakdowns in trust, affordability, or information can limit participation. Evidence further indicates that insurance–input bundles promote the adoption of improved inputs, increase yields, and enhance income stability, although these impacts are highly context-dependent and mediated by implementation quality, including timely payouts and effective service delivery. The review contributed to the literature by advancing a systems-based understanding of bundled insurance adoption, highlighting the central role of institutional reliability, behavioural responses, and implementation quality. Lastly, the review underscores the need for strong institutions, integrated extension systems and farmer-centred design to ensure sustainable scaling. Full article

The efficient market hypothesis, which holds that prices completely reflect available information, is commonly used in financial market analysis. However, emerging empirical evidence shows that market efficiency develops with time, as posited by the adaptive market hypothesis (AMH), with predictability varying across shifting economic and behavioural regimes. Despite the increasing use of deep learning in financial forecasting, there has been little systematic investigation into whether neural network topologies can successfully identify time-varying efficiency trends across diverse markets. Furthermore, the relevance of transfer learning in studying adaptive behaviour between foreign exchange markets and extremely volatile cryptocurrency markets has received little attention. Using these data, we investigate the AMH by comparing the forecasting performance of various deep learning architectures and determining whether knowledge transfer from a relatively stable fiat currency market, Botswana Pula/US Dollar (BWP/USD), improves the predictive accuracy in a highly volatile cryptocurrency market, Bitcoin/US Dollar (BTC/USD). We use daily data from 1 January 2015 to 11 January 2026 to develop deep neural networks (DNNs) and alpha-recurrent neural networks, and, for generalisation, we benchmark using a recurrent temporal neural network (RTNN), a domain-adversarial neural network (DANN), and KLIEP-based importance-weighted regression. A transfer learning technique is used, in which models are initially trained on BWP/USD and then re-estimated on BTC/USD without freezing any network layers, ensuring complete flexibility and enabling parameters to respond to changing market dynamics. Out-of-sample accuracy measures and rolling long-memory diagnostics are used to evaluate forecast performance in terms of time-varying efficiency. The findings reveal that the RTNN regularly outperforms other forecasting models across marketplaces. Predictive accuracy fluctuates with time, and rolling long-memory measurements show persistent departures from random walk behaviour, which supports the AMH. Transfer learning improves predictive stability in the cryptocurrency market by identifying the existence of transferable informational structures between fiat and digital asset markets. Overall, our results support the idea that market efficiency is dynamic rather than static, and they show that adaptive deep learning systems are an excellent way to test the AMH. The paper suggests that cross-market transfer mechanisms and adaptive modelling methodologies be investigated further in growing foreign exchange and cryptocurrency markets. Full article