Search Results (469)

This study examines Average True Range (ATR)-based take-profit (TP) and stop-loss (SL) rules as components of adaptive risk-management design within robust algorithmic trading systems in the USD/JPY foreign exchange market. Rather than simply evaluating whether volatility-adaptive exit rules improve trading performance, the study investigates the conditions under which they contribute to trading outcomes. To this end, the study adopts a systematic framework combining a broad Moving Average Convergence Divergence (MACD) parameter space with ATR-based TP/SL multiplier settings and distinguishes heterogeneous outcome patterns before and after ATR implementation. The results show that the effectiveness of ATR-based exit rules is conditional. Performance improvements occur only for specific combinations of model structures, exit-rule specifications, and market conditions. A notable finding is that optimizing trading-model parameters plays a primary role in determining the effectiveness of exit rules, while ATR-based exit rules function as complementary components that reinforce well-specified trading models. Moreover, under mildly mean-reverting market conditions, profit-enhancement cases expand across a broader range of optimized MACD parameter configurations, suggesting a previously underexplored interaction between market dynamics, model structure, and volatility-adaptive exit-rule design. Overall, the findings provide new evidence that the effectiveness of adaptive risk-management mechanisms depends on the interaction between model structure, exit-rule design, and market conditions, thereby offering broader insights into adaptive trading-system design under changing market conditions. For practitioners, the results suggest that adaptive ATR-based exit rules are most effective when combined with appropriately optimized trading models. Full article

In an increasingly complex financial landscape, individual financial behavior is shaped by a range of cognitive, technological, and psychological factors. Existing research on financial behavior often examines financial literacy, FinTech adoption, and financial attitude separately, with limited attention to their combined effects or to whether these relationships remain consistent across contrasting economic environments. To address this gap, this study examines the associations of financial literacy and FinTech adoption with financial behavior, both directly and indirectly through the mediating role of financial attitude. Grounded in the Theory of Planned Behavior and the Technology Acceptance Model, the study proposes an integrated behavioral model using primary data from two contrasting contexts: Lebanon, a financially constrained and unstable environment, and the United Arab Emirates (UAE), a stable, high-income country with advanced FinTech infrastructure. Data were collected through a survey of 400 respondents and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that financial literacy and FinTech adoption are positively associated with financial behavior in both countries. Moreover, financial attitude significantly mediates both relationships. Measurement invariance was established prior to cross-country comparisons, and the multi-group analysis indicated that the structural relationships were generally comparable across Lebanon and the UAE despite descriptive differences in several path coefficients. The study contributes to the behavioral finance and sustainable finance literature by integrating cognitive, technological, and psychological predictors within a unified framework, validating the mediating role of financial attitude, and providing cross-national evidence from two contrasting economic contexts. The findings suggest that strengthening financial literacy alongside responsible FinTech adoption may support more sustainable and inclusive financial behaviors, particularly in environments characterized by economic instability and unequal access to financial services. Practical and policy implications are offered for educators, FinTech providers, financial institutions, and policymakers, emphasizing the importance of context-sensitive initiatives that promote financial resilience, financial inclusion, and the development of sustainable financial systems. Full article

Machine-readable financial reporting changes how corporate disclosures become visible, verified, and answerable. Inline eXtensible Business Reporting Language (Inline XBRL) combines a human-readable report with embedded structured data, while artificial intelligence expands automated extraction and screening. The study develops Machine-Readable Accountability as a bounded socio-technical construct organised around dynamic visibility, distributed judgement, and responsibility displacement. A qualitative documentary analysis examines four primary archives: regulatory rules, official filing-evidence records, verification materials, and algorithmic-governance documents. Peer-reviewed studies serve as contextual framing and external corroboration. They do not form primary documentary observations. The corpus covers the mature United States Securities and Exchange Commission regime and the European Single Electronic Format from 2020 to July 2026, with earlier sources retained for historical grounding. A seven-family codebook guides analysis of classification, visibility, validation, judgement, audit, artificial intelligence, and accountability. Results arise from coded rules, official filing observations, assurance requirements, and cross-archive role mapping. Structured reporting reduces extraction costs, supports comparison, and permits automated quality checks. It also places institutional weight on taxonomy fit, extension design, validation logic, and software-mediated review. Documentary evidence supports the relocation of judgement across preparers, taxonomy designers, software vendors, auditors, and regulators. Evidence for deliberate narrative optimisation aimed at artificial intelligence remains indirect, so algorithmic answerability remains a bounded theoretical proposition. The framework links market-efficiency research with studies of quantification, professional judgement, and digital governance, and specifies controls for data lineage, extension approval, model documentation, human review, and responsibility assignment. Full article

Gold’s price reflects currency, opportunity-cost, and safe-haven channels whose strength shifts across regimes, motivating an empirical, data-driven forecasting approach. This study develops a monthly gold price forecasting system for ASM sales-timing decisions in Peru (January 2020–June 2026) using macro-financial predictors including a geopolitical risk index and three U.S. monetary indicators, none of which were Granger-causal and were therefore excluded from the production set. After confirming non-stationarity and Johansen cointegration (four vectors), thirty-two model-feature-set combinations, including Elastic Net, Bayesian Ridge, and a PCA factor, were compared under strict temporal validation with bounded hyperparameter search. The selected model, Ridge regression on the CONTROL feature set, achieved a cross-validation MAPE of 2.29% and test MAPE of 3.62% (official)/3.15% (extended sensitivity window). It was benchmarked against random walk, historical mean, and exponential smoothing and evaluated via the Diebold–Mariano, Clark–West, encompassing, and Model Confidence Set tests (low-power caveats given the small sample). A dual-horizon Monte Carlo simulation, robust to heavy-tailed shocks, projected USD 4482/oz (December 2026) and USD 5106/oz (December 2027). A sales-timing backtest showed a statistically significant result (−0.67%) versus a passive strategy, indicating calibrated price information alone does not yet yield a reliable trading edge, supporting the model’s role as decision support rather than an autonomous trading signal. Full article

Regulatory permissiveness is widely prescribed as the primary institutional lever for digital asset adoption. This study challenges that prescription. Analyzing NFT and DeFi adoption across 105 countries using Principal Component Analysis (PCA)-constructed composite indices and multivariate Ordinary Least Squares (OLS) regression, we find that the Frontier Technology Readiness Index (FTRI) is the dominant structural correlate across all specifications, consistently outperforming competing explanatory variables. Regulatory environments neither independently explain adoption nor are associated with it linearly: both permissive and restrictive environments outperform mostly prohibited jurisdictions, suggesting that regulatory clarity rather than permissiveness is the operative institutional dimension. NFT and DeFi markets follow empirically distinct pathways: NFT adoption shows stronger associations with digital marketplace maturity while DeFi is more closely associated with technological infrastructure, suggesting that treating Web3 as a homogeneous policy category is unwarranted. National income conditions how effectively technological readiness is associated with adoption gains, with structural determinants exhibiting considerably reduced explanatory power in lower-middle-income economies. For policymakers, these findings reframe the debate: the primary structural correlate of digital asset adoption is technological capacity, not regulatory stance, and below a development threshold, neither intervention is reliably associated with adoption gains. Full article

Many investors have relied on Bitcoin as a hedge against inflation. However, differences in inflation measurement and monetary policies across countries make it difficult to determine whether Bitcoin effectively serves as an inflation hedge. This study examined Bitcoin’s effectiveness as an inflation hedge in the United States and India using monthly data on Bitcoin returns and Consumer Price Index (CPI) changes from January 2015 to December 2024 (N = 118, after first-differencing and lag alignment). The study employed Ordinary Least Squares (OLS) models, bivariate Vector Autoregression (VAR) Granger causality tests, Bai–Perron Structural Break Analysis, Impulse Response Functions (IRFs), and Quantile Regression analyses. The findings revealed no significant relationship between CPI and Bitcoin returns in either the United States or India, providing no empirical support for the Fisher Hypothesis. However, Granger causality results showed that lagged Bitcoin returns significantly predicted future U.S. CPI values, while no such relationship was observed for India. The predictive power of the model for the U.S. was lost after October 2022 due to the crypto winter phenomenon. This indicates that Bitcoin is not used as a hedge against inflation but is rather considered a financially driven information asset, which is subject to market influences. Full article

This study presents a systematic literature review of 68 peer-reviewed articles (2015–2025) on artificial intelligence in tax compliance and evasion mitigation. Using the PRISMA 2020 protocol and textometric analysis via IRAMUTEQ software, we map publication trends, geographic distribution, and three research paradigms: machine learning and predictive modeling; artificial intelligence, technology and tax compliance; and government, financial development and revenue administration. The CIMO (Context–Intervention–Mechanism–Outcome) framework structures our synthesis of how institutional conditions shape intervention design and why identical technologies produce divergent outcomes across settings. While existing reviews have focused primarily on detection metrics without theorizing institutional boundary conditions, behavioral dynamics without addressing governance capacity, or ethical deficits without a theoretical framework, this study constructs the Adaptive AI Tax Compliance Framework (AAITCF), a context-sensitive implementation roadmap differentiated across three institutional maturity tiers. The results indicate that AI achieves high detection accuracies in digitally mature economies, yet effectiveness is contingent on data quality, governance capacity, and organizational readiness. Developing countries face structural asymmetries, infrastructural deficits, and human capital gaps that constrain algorithmic performance even where technical sophistication is high. The AAITCF treats context as constitutive of intervention effectiveness and identifies underexplored areas regarding causal pathways from AI deployment to long-term institutional change, taxpayer trust, and equitable fiscal governance. Full article

This paper examines the association between two Financial Technology (FinTech)-related policy windows and the profitability of Qatari commercial banks over a twenty-year horizon (2005–2024). The analysis is anchored by two structural breaks: in 2017, the Qatar Central Bank (QCB) established its FinTech task force and lifted restrictions on the implementation of a regulatory sandbox and centralised electronic know your customer (e-KYC) framework; and during the digital-acceleration period in 2020 in response to the COVID-19 pandemic and the issuance of digital banking licences. FinTech adoption is not measured directly at the bank level; the two policy windows are used as intent-to-treat proxies. Using return on assets (ROA) and return on equity (ROE), bank performance is measured and influenced by bank size (log of total assets), bank age and type (Islamic and conventional). Multiple diagnostics of Hausman and Breusch–Pagan support the use of fixed-effects (FE) panel regressions with cluster-robust standard errors on an unbalanced panel of 125 bank–year observations. The results show a positive coefficient on the post-2017 dummy in the ROE model (β = 0.0306, p = 0.054, cluster-robust) and no detectable change in ROA (β = −0.00058, p = 0.868). For the post-2020 phase, both coefficients are positive but do not reach conventional significance (ROA: β = 0.00218, p = 0.539; ROE: β = 0.0224, p = 0.150). There is no systematic difference between Islamic and conventional banks that is offered by the interaction terms in either phase. Given the small sample (nine banks, eight effective clusters after the FE singleton drop; 125 observations) and the use of policy-window proxies rather than direct bank-level FinTech measures, the design cannot isolate the effect of the FinTech-related reforms from concurrent macroeconomic, sectorial or pandemic-related developments, and the cluster-robust p-values should be read as approximate. The results are therefore presented as preliminary and indicative. Read in light of these design constraints, the results are consistent with incremental rather than transformative change around the FinTech-related policy windows in Qatar, with results influenced more by timing, scale economies, and regulatory saturation than by bank type. The country-specific empirical findings can help restore context to the literature on the Gulf Cooperation Council (GCC) average, and provide measured guidance for bank managers and regulators working toward the Qatar National Vision 2030 digital aspiration. Full article

Digital transformation is increasingly viewed as a strategic driver of operational efficiency, financial performance, and organisational resilience in rural transition economies. Existing research, however, largely assumes homogeneous digitalisation effects across firms while overlooking the structural conditions shaping integration efficiency. This study investigates the threshold relationship between supply chain integration and financial–economic performance using a threshold regression framework. The analysis is based on firm-level data from 80 agricultural, agritourism, and tourism-related firms operating in rural Northern Albania. Methodologically, the study combines Hansen’s threshold estimation with robust OLS and threshold logistic regression models, complemented by exploratory macro-level threshold analysis for Western Balkan economies. The findings reveal significant regime-dependent dynamics. Below the estimated socio-economic integration threshold, supply chain integration generates weak and statistically insignificant effects. Above the threshold, integration mechanisms produce substantially stronger financial and operational outcomes, indicating that digital transformation becomes economically productive primarily under sufficiently integrated organisational conditions. Additional diagnostics further show that highly integrated firms achieve superior coordination efficiency, resource allocation, and financial resilience. The study contributes to the literature by advancing a managerial-financial and coordination-based interpretation of digital transformation and its threshold performance effects in rural transition economies. Full article

Purpose: Despite growing research on green fintech and sustainable finance individually, no systematic theoretical framework explains how AI-driven green fintech solutions can be adopted in banking for sustainable investment purposes. This paper addresses this demonstrated gap by developing the first bibliometrically grounded, TOE-based conceptual framework for AI-driven green fintech adoption in banking. Design/Methodology/Approach: A two-phase approach is employed. First, a bibliometric analysis of 79 Scopus-indexed documents (2020–2026) using bibliometrix in R provides quantitative evidence of the research gap through keyword co-occurrence networks, thematic mapping, and trend topic analysis. Second, building on this evidence, a conceptual framework integrating the Technology–Organization–Environment (TOE) framework with three mediating constructs, technological readiness, sustainability culture, and regulatory support is developed and five theoretical propositions are derived. Findings: The bibliometric analysis reveals an annual growth rate of 78.4% in the field and confirms that the TOE framework has never occupied the motor themes quadrant of the green fintech literature. The proposed framework theorizes three mediated pathways through which technological, organizational, and environmental conditions translate into improved sustainable investment outcomes including enhanced ESG transparency, increased green investment allocation, and SDG alignment. Practical Implications: The framework provides bank executives with three actionable intervention points: technological infrastructure investment, sustainability culture embedding, and regulatory engagement and offers policymakers evidence-based guidance for designing supportive green fintech adoption frameworks. Originality/Value: This study presents a conceptual framework that is, to the authors’ knowledge, the first to combine TOE theory, AI-driven green fintech, a banking context, an explicit three-mediator architecture (technological readiness, sustainability culture, regulatory support), and sustainable investment outcomes as the dependent variable, grounded in reproducible bibliometric evidence. Existing studies address subsets of these dimensions; none integrates all six simultaneously. Full article

Predicting merger withdrawals is challenging because failed deals can impose large costs on firms and investors, while withdrawals are rare and difficult to identify using structured deal variables alone. We propose MergerLLM, a large-language-model-based framework that predicts merger completion using serialized deal characteristics and text-based firm information. Using a sample of U.S. mergers and acquisitions (M&A) deals, we compare MergerLLM with machine-learning and deep-learning benchmarks. MergerLLM improves detection of withdrawn deals, especially in recall and F1-score, while maintaining competitive ranking performance. Its predicted merger-success probabilities also contain economic information in post-announcement return tests. We then construct HAIDiff, a proxy for model–market disagreement, defined as the difference between MergerLLM’s predicted merger-success probability and a transformed announcement-return-based market signal. Climate exposure is positively associated with this disagreement measure, and the result is robust to an alternative measure based on target cumulative abnormal returns. The evidence suggests that climate-exposed deals are settings in which model-implied completion probabilities and market-based signals diverge more strongly. Full article

This study investigates whether FinTech thematic assets achieve dynamic variance reduction for regional ESG market risk under clean-energy equity stress regimes, using daily data from March 2019 to August 2024 across five S&P ESG LargeMidCap markets spanning developed and emerging economies (North America, Europe, Asia Pacific Developed, Latin America Emerging, and Asia Pacific Emerging). Employing a DCC-GARCH framework with GJR-GARCH univariate specifications across four S&P Kensho FinTech channels—Democratized Banking, Alternative Finance, Future Payments, and Distributed Ledger—we estimate time-varying correlations and hedging effectiveness, and assess safe-haven properties via the Baur–Lucey framework. The most robust finding is that North America ESG shows the strongest dynamic variance reduction (59–76%), improving further during high clean-energy equity stress regimes (p < 0.01, bootstrap permutation test); Asia Pacific Developed ESG shows the weakest (7–9%) despite its developed-market status, while Latin America Emerging ESG’s comparatively high variance reduction (28–40%) is tempered by residual ARCH effects that point to incompletely modeled volatility rather than structural hedging capacity. All FinTech channels remain positive diversifiers rather than safe havens across every market and regime. Hedging capacity thus tracks market-specific volatility and correlation dynamics rather than a simple developed–emerging divide. The analysis is bounded by a single five-year sample window and two transition-risk proxies, warranting continued monitoring as FinTech and ESG regulatory frameworks evolve. Full article

This paper proposes a new model with the goal of improving several aspects of the modern portfolio theory: (i) investor behavior, (ii) depiction of behavior in the fractal stochastic differential equations about price efficiency in chaotic dynamics (Tsallis statistics) and the fractal market hypothesis, (iii) the introduction of the novel Evolving Returns Integrated System (ERIS) in portfolio selection in the fractal behavioral convolution, and (iv) the selection of an accurate classifier (ERIS) among three neuro-genetic hybrids of 66 models: 22 modular, 22 Jordan–Elman and 22 generalized feedforward networks. Our model demonstrates superior classification performance across the Greek (1996–1998) and NYSE (2008–2010) equity market datasets examined in this study. Full article

This study maps the intellectual structure and thematic evolution of buy now, pay later (BNPL) research published between 2010 and 2025, with particular attention to how impulsive buying and post-purchase regret are positioned within the broader BNPL knowledge domain. Drawing on an integrated bibliometric science mapping and focused behavioral synthesis approach, the study first mapped a broad Scopus dataset of BNPL-related digital consumer credit and deferred payment research published between 2010 and 2025. This dataset was used for performance analysis and VOSviewer-based science mapping. A second, narrower PRISMA-guided screening process was then applied to identify empirical studies that directly examined BNPL-related behavioral and psychological outcomes, resulting in 13 studies retained for focused qualitative synthesis. The bibliometric findings show that BNPL scholarship expanded sharply after 2020, with research concentrated in marketing, consumer behavior, fintech, and digital commerce outlets. The science mapping results reveal a fragmented field structured around digital finance adoption, impulsive consumption, consumer vulnerability, and emerging ethical and regulatory concerns. The systematic synthesis further indicates that BNPL-related mechanisms, including installment framing, urgency cues, perceived affordability, and reduced payment salience, are consistently associated with impulsive buying tendencies. However, post-purchase regret remains underexamined and is rarely modeled as a distinct emotional outcome. By integrating bibliometric evidence with behavioral synthesis, this study clarifies how BNPL research has developed, where conceptual fragmentation remains, and why future studies should connect digital payment design, cognitive distortions, impulsive purchasing, and post-purchase emotional consequences within more comprehensive theoretical models. The study contributes by offering a structured research agenda for advancing responsible BNPL scholarship, consumer protection, and future digital finance research. Full article

Tax systems worldwide face a compliance gap that OECD data places at USD 100–240 billion annually in corporate avoidance alone, before accounting for the shadow economy and crypto-asset transactions. FinTech mandatory e-invoicing, real-time transaction matching, and machine-learning audit selection is narrowing the informational conditions that enable evasion, while simultaneously introducing governance risks: opaque algorithmic audit targeting, contested blockchain forensic evidence, and the surveillance potential of programmable money. This article presents a PRISMA 2020 systematic literature review of 59 peer-reviewed articles (Scopus, Web of Science, and ScienceDirect), complemented by IRAMUTEQ lexicometric analysis and an extension of the Allingham Sandmo compliance model to incorporate algorithmic detection probabilities, bomb-crater belief dynamics, and Zero-Knowledge Proof verification. Four thematic clusters emerge: tax compliance behaviour and FinTech adoption (19.92%), digital transformation and corporate performance (35.34%), bibliometric and emerging-technology research (16.54%), and cryptocurrency markets and regulatory challenges (28.20%). Across them, FinTech reduces evasion where institutional and technical conditions allow but generates distributional, evidentiary, and constitutional risks that existing legal frameworks have yet to resolve. In response, we propose the Techno-Legal Due Process Framework (TLDPF) three pillars (Techno-Proportionality, Cryptographic Burden of Proof, and Algorithmic Constitutionalism) grounded in EU/OECD constitutional doctrine as a normative design proposal awaiting empirical validation. Full article