Business, Economics and Management

Abstract: Quantitative portfolio optimization has accelerated sharply since 2018, with deep learning and reinforcement learning agents now competing with the mean-variance framework that defined six decades of research. Existing narrative reviews struggle to track this expansion. We screen 832 documents from Scopus and Web of Science under PRISMA 2020 and retain 589 unique articles spanning 2003–2025. Applying BERTopic with SPECTER scientific embeddings, UMAP and HDBSCAN, we identify five coherent topics with mean coherence 0.864: classical mean-variance (T0; n = 270), deep reinforcement learning (T1; n = 116), machine-learning return forecasting (T2; n = 87), covariance estimation and robust optimization (T3; n = 52) and metaheuristics (T4; n = 56). A rank-weighted similarity analysis, designed to neutralise the c-TF-IDF collinearity artefact, shows that deep reinforcement learning is the most isolated paradigm. The two methodological families bifurcate over time: AI / deep-learning approaches grow from 3.6 % of annual output before 2018 to 40.2 % afterwards, while classical methods retain volume but lose share. We synthesise the empirical practices of each family along five dimensions critical to applied finance and identify three under-explored integration frontiers.

Abstract: This paper examines the threshold impact of low and high media attention on climate risks and US climate policy, on banks' credit risk among the 230 largest commercial banks in the United States between 2011 and 2022. Using a dynamic threshold regression model, our analysis reveals a non-linear relationship between climate risk and banks’ credit risk. This result suggests that banks may be resilient to climate risk up to a point but become vulnerable once that threshold is exceeded. This finding highlights the importance of physical and transition risks, as well as their implications for financial stability. Our results are robust to a range of alternative measures and model specifications, providing valuable insights for bank managers, regulators, and policymakers. It highlights the need to integrate climate risk considerations into credit risk assessments and policy frameworks to strengthen the banking sector's resilience.

Abstract: This paper examines global equity-market integration, commodity-price exposure, and volatility spillovers in Ghana’s frontier equity market. Using daily data from January 2011 to December 2025, we estimate a multi-factor asset-pricing model within an ARMA–EGARCH specification for the Ghana Stock Exchange Composite Index (GSECI) and the Financial Sector Index (GSEFSI). The model jointly captures first- and second-moment spillovers from a global equity factor and three key global commodity markets: gold, crude oil, and cocoa, while controlling for asymmetric volatility, return serial dependence, and structural shifts associated with banking-sector recapitalization and the Domestic Debt Exchange Programme (DDEP). The Ghanaian equity market is exposed to the global equity factor, indicating measurable but economically modest global integration, with stronger exposure in the financial sector. Commodity-price exposures are selective, with gold and crude-oil exposures concentrated in the financial sector, whereas the cocoa factor is negatively associated with returns on both indices. The variance results show persistent volatility, inverse asymmetric volatility responses, and differentiated volatility spillovers from global equity and commodity markets. The DDEP period is associated with significant equity-market repricing, particularly in the financial sector. These findings indicate that Ghana’s equity-market dynamics are shaped jointly by global equity and commodity market information, frontier-market frictions, and sovereign–bank conditions.

Abstract: Whether environmental, social, and governance (ESG) disclosure stabilizes share prices or merely masks bad news remains unsettled, and the evidence is conspicuously weak whenever the relationship is assumed to be linear. This study revisits the question by allowing the effect of ESG disclosure on future stock price crash risk to be nonlinear, and by decomposing disclosure into its environmental, social, and governance components. Using an unbalanced panel of non-financial firms listed on the Ho Chi Minh Stock Exchange over 2018–2024, we estimate firm and year fixed-effects models with firm-clustered standard errors, measuring one-year-ahead crash risk by negative conditional skewness (NCSKEW) and down-to-up volatility (DUVOL). Consistent with prior work, the linear association between overall ESG disclosure and crash risk is statistically insignificant. Once a quadratic term is introduced, however, a U-shaped relationship emerges, and dimension-level tests show that this curvature is driven almost entirely by social disclosure: the linear term is negative and the squared term positive and significant for both crash-risk proxies, with turning points of 0.3316 (NCSKEW) and 0.2918 (DUVOL). The U shape is confirmed by the formal test of Lind and Mehlum (2010) for both proxies, and is robust to additional profitability and valuation controls and most strongly for NCSKEW to panel corrected and feasible GLS estimators. The findings support a “too-much-of-a-good-thing” interpretation: social disclosure improves transparency and reduces crash risk up to a moderate threshold, beyond which incremental, hard-to-verify narrative disclosure becomes consistent with impression management and heightens crash risk. Because the turning point lies below the first quartile of social disclosure, most sample firms already operate where additional disclosure raises crash risk. The study reframes the ESG crash-risk debate around the level and dimension of disclosure rather than its mere quantity.

Abstract: This study examines the relationship between corporate FinTech disclosure and ESG reporting performance among non financial firms listed on the Saudi Stock Exchange (Tadawul), with a focus on post Covid period from 2021 to 2024. Using an ESG Disclosure Index constructed from annual reports and a textual measure of FinTech adoption, the analysis provides market-level evidence on the evolution of digital transformation and ESG disclosure in Saudi Arabia. Descriptive results indicate that ESG reporting among Tadawul firms is moderate yet heterogeneous, with governance disclosure consistently stronger than environmental and social components. Correlation analysis indicates a positive association between FinTech disclosure and overall ESG disclosure, particularly within the environmental pillar. Regression results further show that the firms with stronger FinTech disclosure tend to report higher ESGDI scores. The two way fixed effects (TWFE) model yields statistically significant results and the direction of the relationship remains consistent with theoretical expectations. Pillar level analysis suggests that digital transformation is most closely aligned with environmental reporting. Taken together, the results indicate that sustainability disclosure and digital capabilities appear to co-develop in the Tadawul market. Businesses may improve their ability to track, organize, and disseminate ESG-related data by investing in digital reporting systems, analytics, and technology modernization. In this way, FinTech serves as a governance-supporting instrument that improves transparency and reporting discipline in addition to being a financial innovation. The study adds to the expanding body of knowledge by providing important emerging-market-level evidence from the Saudi capital market and highlighting how FinTech can support sustainability-driven growth in an institutional context undergoing rapid transformation.

Abstract: We develop a probabilistic model for predicting equity market stress in South Africa using a logistic regression model applied to the JSE TOP 40 Index. The analysis employs quarterly data over 99 observations spanning 2001 Q1 to 2025 Q3, employing five macroeconomic predictors: CPI inflation, industrial production growth, unemployment, the SARB repo rate, and the yield spread. Model estimation is implemented using the Iteratively Reweighted Least Squares (IRLS) with inference enhanced through 5,000-replicate nonparametric bootstrap procedures to address small-sample limitations. The results indicate that industrial production growth (IP) and inflation (CPI) are the most economically meaningful predictors of market stress. A one-standard-deviation improvement in IP reduces stress odds by approximately 42%, while a comparable rise in inflation increases odds by 54%, suggesting that real economic activity acts as a stabilising force in equity markets, while inflationary pressure amplifies stress risk, pointing to the dual and opposing role of supply-side conditions and price dynamics in shaping market vulnerability. Bootstrap standard errors exceed conventional maximum likelihood estimates by 13.5 to 32.3%, demonstrating that standard inference materially understates estimation uncertainty in this context. Model performance shows moderate discriminatory power, with an area under the ROC curve of 0.664 and a Brier score improvement of 9.5% relative to a base-rate benchmark. The predicted probability series aligns closely with major South African stress episodes, including the Global Financial Crisis and periods of monetary tightening, although performance is weaker for exogenous shocks such as COVID-19. The findings highlight the relevance of macroeconomic conditions in shaping equity market risk in an emerging market setting and demonstrate the value of bootstrap-enhanced logistic regression for financial stress prediction. The model provides a transparent and implementable early-warning indicator for policymakers and institutional investors using publicly available data.

Abstract: We examine whether board activity, board size, board independence, and board tenure are associated with the performance of South African REITs from 2013 to 2025. We assess whether these attributes translate into measurable performance differences. We use a dynamic panel framework based on the Arellano-Bond two-step Difference Generalised Method of Moments (GMM-Diff) estimator to address endogeneity, unobserved heterogeneity, and performance persistence. Windmeijer finite-sample corrected standard errors are applied. The sample covers 30 JSE-listed REITs and 360 firm-year observations. Performance is measured using funds from operations per share (FFO_PS), dividend yield (DIV_YIELD), return on assets (ROA), return on equity (ROE), return on invested capital (ROIC), and earnings per share (EPS). Our findings show that board independence is positively and significantly associated with FFO_PS. A 10% increase in independent directors raises FFO_PS by about 59 cents per share. Board size is positively associated with dividend yield, where an additional director adds 1.5% to yield. Board tenure is negatively associated with ROIC, with each additional year reducing ROIC by 0.9%. Board activity has no significant effect on any performance measure. The lagged dependent variable is significant in four of six models, confirming dynamic persistence. The implications are manifold; REIT boards should maintain a clear independent majority, with about 6 independent directors on a ten-member board, to support recurring operating cash flow. Boards of 9 to 12 directors deliver expertise required to sustain distributable income. Board should begin structured tenure review and planned succession once average service approaches 6 years. Meeting frequency alone is not a reliable governance metric. This study contributes sector-specific evidence on board structure in REITs using a dynamic methodology and links empirical findings directly to quantifiable governance benchmarks.

Abstract: Digital transformation is reshaping industries, business models, and investment opportunities, creating new challenges for international portfolio management. The European communication services sector has become a strategic component of the digital economy, driven by advances in artificial intelligence (AI), digital platforms, 5G infrastructure, cloud computing, cybersecurity, and data-driven business models. Despite its importance, limited evidence exists regarding the effectiveness of portfolio optimisation strategies within digitally transforming sectors. This study investigates international portfolio optimisation using constituent firms of the MSCI Europe Communication Services 35/20 Capped Index. Drawing upon Modern Portfolio Theory and the Treynor–Black framework, an actively managed portfolio is constructed and evaluated against the SPDR® MSCI Europe Communication Services UCITS ETF and an equal-weight portfolio. Using daily market data, the analysis estimates asset returns, alpha and beta coefficients, portfolio weights, and risk-adjusted performance measures, including the Sharpe and Treynor ratios. Independent-samples t-tests are employed to assess the statistical significance of performance differences among investment strategies. The findings show that the Treynor–Black portfolio generated the highest annual return (23.2%), outperforming both the benchmark and equal-weight portfolios. However, the equal-weight portfolio achieved superior risk-adjusted performance, recording higher Sharpe and Treynor ratios, suggesting that diversification benefits outweighed the advantages of active security selection. Hypothesis testing indicates no statistically significant difference between the Treynor–Black and equal-weight portfolios, whereas a statistically significant difference exists between the proposed and benchmark portfolios. The study extends the international portfolio management literature by applying the Treynor–Black model to a digitally transforming sector. The findings suggest that portfolio performance is influenced not only by firm-level financial characteristics but also by broader digital and institutional environments. Firms operating within digitally advanced and well-governed economies appear better positioned to exploit technological innovation and generate sustainable long-term value. Overall, the results demonstrate that successful international portfolio optimisation requires balancing active security selection with diversification while recognising the role of digital transformation, governance quality, and innovation ecosystems in shaping investment performance within the digital economy.

Abstract: Purpose: This study investigates how financial and entrepreneurial capability configurations differentiate internationalized from domestically oriented Thai SMEs. By adopting a decision‐science perspective, the study identifies the capability patterns that shape firms’ strategic international orientation. Design/methodology/approach: This research, which utilizes survey and financial data from 179 Thai SMEs (2021–2023), employs an inductive machine learning approach based on Extreme Gradient Boosting (XGBoost). The analytical framework integrates profitability, liquidity, leverage, operational efficiency, international experience, team readiness, market knowledge, and institutional connectivity. Feature importance scores and confirmatory statistical tests are used to validate differentiating capability structures. Findings: Internationalized SMEs demonstrate stronger financial agility, higher profitability, disciplined leverage, and superior resource utilization combined with entrepreneurial preparedness, as evidenced by international experience, risk tolerance, effective team coordination, and institutional embeddedness. In contrast, localized SMEs exhibit liquidity-heavy but low-dynamism profiles, limited network engagement, and weaker organizational readiness. The machine learning model highlights the non-linear interactions among these capabilities, achieving high predictive accuracy (92.59%). Practical implications: The findings offer a capability-based diagnostic tool for managers and policymakers. Strengthening financial agility, team readiness, and institutional ties can enhance the global competitiveness of SMEs. Originality/value: This study introduces a multidimensional, data-driven capability configuration model for SME internationalization, advancing decision science, dynamic capabilities, and network-based theories in emerging-market contexts.

Abstract: The cross- jurisdictional diffusion of artificial intelligence (AI) innovations remains critically contingent upon institutional architectures, yet the mechanisms through which regulatory frameworks moderate technology transfer outcomes are undertheorized. Building on the Regulated AI Symbiosis (RAS) framework, which departs from the task-based automation paradigm this study integrates innovation diffusion theory, institutional theory, and dynamic capabilities to conceptualize regulatory agility as a configurational driver of adaptive technology adoption and knowledge spillovers. Employing a mixed-methods design, we analyze stakeholder-reported perceptual data from 387 financial stakeholders across emerging and international markets, utilizing hierarchical multiple regression with Hayes’ PROCESS macro and qualitative content analysis. Results demonstrate that regulatory stringency attenuates displacement risks associated with AI adoption—an effect 56% stronger in international markets—whereas sandbox participation amplifies job creation and knowledge recombination effects, with emerging markets exhibiting 41% stronger positive outcomes. The significant three-way interaction confirms that net-positive technology transfer outcomes emerge primarily when emerging markets leverage adaptive regulatory mechanisms to balance innovation absorption with institutional capacity building. This study advances technology transfer scholarship by theorizing regulatory design as a critical moderator of cross-jurisdictional innovation diffusion. Our findings carry direct distributional implications: where adaptive regulation is absent, AI adoption in emerging financial markets risks amplifying the ‘winner-takes-all’ dynamics , concentrating gains among capital owners and high-skill workers while displacing routine-task labor at the base of the income distribution. The RAS framework therefore offers actionable insights for policymakers, Technology Transfer Offices (TTOs), and financial institutions on orchestrating inclusive AI governance frameworks that can attenuate AI-driven inequality and pre-empt the secondary poverty effects associated with unmanaged technological transitions.

Abstract: This paper examines whether a prolonged period of sanctions and geopolitical fragmentation weakens domestic macroeconomic channels and strengthens external financial dominance of traditional exchange-rate transmission mechanisms. Standard exchange rate theories are based on the concepts of Purchasing Power Parity (PPP) and Uncovered Interest Parity (UIP). However, the application of continuous sanctions could weaken this explanatory power and change exchange rate dynamics. The present study applies a combined framework of Autoregressive Distributed Lag (ARDL), Error Correction Modeling (ECM), Vector Autoregression (VAR) and structural break analysis to study the exchange-rate behavior in response to repeated geopolitical shocks using monthly data for Russia from 2005 to 2025. The results indicate that external variables such as the US dollar index and oil prices are important determinants of exchange rates, while inflation and interest rate differentials associated with PPP and UIP have little explanatory power. Structural break tests detect major regime shifts associated with the Global Financial Crisis, Crimea-related sanctions episode, COVID-19 pandemic and Russia–Ukraine war. The error correction process indicates that the speed of adjustment to equilibrium is slow, which means that traditional exchange-rate relationships will continue to diverge. In general, the results suggest a regime-dependent exchange rate environment in which external financial factors tend to dominate domestic adjustment mechanisms. Our study contributes to the literature on exchange rates, sanctions and financial fragmentation by providing evidence on how geopolitical shocks shift the relative importance of domestic and external determinants in a highly sanctioned economy.

Abstract: Artificial-intelligence-integrated 'buy now, pay later' (BNPL) platforms are diffusing rapidly across the Middle East and North Africa (MENA), raising concerns about consumer financial vulnerability. Drawing on choice-architecture, payment-decoupling, and financial-literacy literatures, this study examines how three platform-level features — algorithmic nudging, AI personalization intensity, and perceived ease of credit — are associated with impulsive buying tendency and downstream financial outcomes, and whether BNPL-specific financial literacy attenuates these associations. A multi-method design combined cross-sectional partial-least-squares structural-equation modeling (N = 1,247 active BNPL users in seven MENA countries) with a six-month longitudinal follow-up (N = 847, 68% retention). Algorithmic nudging was positively associated with impulsive buying tendency, which in turn was associated with elevated financial stress and longitudinal debt accumulation. AI personalization was positively associated with platform loyalty but co-varied with indirect indicators of financial risk — a pattern we describe as a loyalty trap and empirically document via piecewise longitudinal trajectories. BNPL-specific financial literacy moderated the associations between algorithmic nudging, impulsive buying, and adverse financial outcomes, with the highest-literacy quartile exhibiting substantially attenuated debt trajectories. We discuss boundary conditions, alternative explanations, and the limits of causal inference in non-experimental panel data. Findings inform evolving BNPL regulatory frameworks in MENA, with particular relevance to nudge-transparency disclosures, contractual cooling-off periods, and credit-bureau reporting standards.

Abstract: This paper examines the impacts of the financial technology (FinTech) on the profitability of the Qatari commercial banks using panel data over two decades (2005–2024). The analysis is rooted in the two structural breaks that frame its duration: the regulatory phase in 2017, in which the Qatar Central Bank (QCB) formed its FinTech task force, the regulatory sandbox and the centralized eKYC framework; and the digital-acceleration phase in 2020, triggering by the COVID-19 pandemic and the issuance of digital banking license. To gauge the performance of banks, Return on Assets (ROA) and Return on Equity (ROE) are used, while controlling for bank size (log of total assets), and age and type (Islamic vs. conventional). Fixed-effects (FE) panel regressions with cluster-robust standard errors are estimated using an unbalanced panel of 125 bank-year observations, in combination with Hausman and Breusch–Pagan diagnostics. The results show that in general, the post-2017 reforms have led to marginally significant enhancements in ROE (β = 0.031; p ≈ 0.054) and not to any significant improvement in ROA. By contrast, the post-2020 phase reveals an effect on ROA that is positive but statistically weak, after controlling for bank size, and muted ROE effects. Interaction terms disapprove any systematic difference between Islamic and conventional banks in both phases. Results indicate an incremental rather than a transformational effect of FinTech adoption in Qatar as well as a greater influence by timing and scale economies and regulatory saturation than by bank type. The study provides empirical evidence specific to Qatar in a literature that tends to offer cross-country averages across the whole GCC and provided nuanced recommendations for bank managers and regulators aiming to deliver digital Qatar through Qatar National Vision 2030/Digital Agenda.

Abstract: This study investigates the impact of geopolitical instability on corporate financial performance in emerging Asian economies by examining the mediating roles of supply chain resilience, currency volatility, and foreign investment confidence. The research adopts a quantitative cross-sectional design using survey data collected from 308 firms operating across Southeast Asia. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the structural relationships among geopolitical risk factors and firm-level financial outcomes. The findings demonstrate that geopolitical instability significantly influences corporate financial performance primarily through financial transmission mechanisms. Currency volatility and foreign investment confidence emerge as the strongest mediating variables, indicating that exchange rate fluctuations and investor sentiment substantially shape firm performance under geopolitical uncertainty. In contrast, supply chain resilience improves operational adaptability but does not exert a statistically significant direct effect on financial performance. The model explains 66.7% of the variance in corporate financial performance, indicating substantial explanatory power. This study contributes to the literature by integrating operational, financial, and institutional perspectives into a unified framework of geopolitical risk transmission. The findings also provide managerial and policy implications, emphasizing the importance of financial risk management, institutional stability, governance transparency, and strategic resilience in mitigating the adverse effects of geopolitical turbulence in emerging Asian economies.

Abstract: The paper investigates the impacts of the 2008 Global Financial Crisis (GFC) on securities market supervision (SMS) architecture by identifying how securities regulators have adjusted their supervisory system to reflect the lessons learnt from the GFC, to adapt to the post-GFC market conditions, and ultimately to develop a resilient structure that endures financial crises. Both quantitative and qualitative techniques are employed to generalize the crisis impacts on SMS architecture and analyze the complex crisis-induced policy responses. Key findings include: (i) a worldwide SMS restructuring is undertaken by securities regulators; (ii) Self-regulatory organizations (SROs) faced diminishing roles in market supervision; (iii) Twin-peaks became the customized option for developed markets but not favorable by emerging markets; (iv) Integration was the choice of emerging markets’ SMS restructuring, which was accelerated by 1997 Asian financial crisis and further fueled by the GFC; and (v) It is a new era for central banks to become prudential regulators in the twin-peaks model and integrated supervisors of financial markets. Regulatory implications are drawn for emerging markets to solve the dilemma of supervisory restructuring and the role of SROs post-GFC to set up an architecture that endures financial crises. This study makes contribution to the investigation and explanation regarding the convergence by securities markets in their post-GFC SMS architecture policy reforms as well as the reasons for the convergence and different ways of policy making. It also provides recommendations for post-GFC SMS restructuring emerging markets in the context of current policy debates focused on the reforms in developed economies.

Abstract: Cryptocurrency markets are characterized by elevated volatility, structural instability, and rapidly changing investor sentiment, which significantly challenge traditional portfolio optimization methodologies. Under such conditions, static portfolio allocation models frequently fail to ade-quately incorporate uncertainty, behavioral adaptation, and dynamic market responsiveness into the investment decision-making process. In parallel, recent advances in artificial intelligence and data-driven financial systems have transformed modern portfolio management by enabling adaptive investment strategies capable of processing large volumes of financial information in real time. Nevertheless, AI-assisted portfolio systems often remain highly sensitive to noisy market signals, parameter instability, and excessive portfolio rebalancing, particularly in volatile crypto-currency environments. This study proposes a behaviorally adaptive portfolio optimization framework under interval uncertainty that integrates robust optimization principles, behavioral finance mechanisms, non-linear transaction costs, and AI-assisted allocation adjustment within a unified cryptocurrency in-vestment structure. The proposed methodology introduces a behavioral preference parameter capable of dynamically adjusting portfolio composition according to varying investor attitudes toward uncertainty and market expectations. Simultaneously, interval uncertainty modeling is employed to represent ambiguous financial parameters through bounded intervals, while artificial intelligence mechanisms act as adaptive decision-support tools that improve portfolio respon-siveness under changing market conditions. The empirical analysis is conducted using major cryptocurrency assets, including Bitcoin, Ethereum, Solana, and Binance Coin, over the period January–June 2025. Multiple behavioral portfolio configurations are evaluated in order to analyze the interaction between profitability, downside risk, portfolio stability, and transaction cost efficiency. The results indicate that the proposed framework improves risk-adjusted portfolio performance and generates more stable allocation strategies compared to traditional static portfolio optimization approaches. In particular, the integration of behavioral adaptation and robust optimization contributes to reduced sensitivity to estimation uncertainty and unstable market fluctuations. The findings further demonstrate that AI-assisted adaptive allocation mechanisms improve portfolio flexibility and support smoother portfolio transitions under volatile market conditions. Moreover, the incorporation of nonlinear transaction costs leads to more realistic and practically implementable cryptocurrency investment strategies. Overall, the proposed framework provides a robust and behaviorally interpretable approach for modern cryptocurrency portfolio management and contributes to the growing literature at the intersection of artificial intelligence, behavioral fi-nance, and uncertainty-aware portfolio optimization.

Abstract: We employ an empirical framework for real-estate securities that incorporates portfolio optimization, return distribution tail diagnostics, risk metrics, modeling of long-range dependence in return volatility, regression against benchmark indices, and option pricing; treating these as necessary layers of a risk management structure that concentrates on downside risk. Optimization compared mean-variance against downside sensitive conditional value at risk. Tail behavior was assessed via skewness, kurtosis and extreme value theory; volatility persistence was examined using ARMA--FIGARCH models. Benchmark dependence was examined via the capital asset pricing model (CAPM) employing endogenous and exogenous market proxies. Insurance instruments via European options were priced using a doubly subordinated normal inverse Gaussian pricing model capable of modeling skewed, heavy-tailed return distributions. Significant findings for the optimized portfolios include: return distributions with losses that are heavier-tailed than gains; a transition in time from moderate to high long-range dependence in conditional volatility; smaller values of CAPM ``alpha'' and ``beta'' for minimum-risk portfolios compared to tangent portfolios; and significant implied volatility values.

Abstract: This paper examines how behavioral intention, combined with risk tolerance, financial confidence, and self-control, relates to consumer credit usage. Inspired by the Theory of Planned Behavior, which suggests that behavioral intention is the direct precursor to actual behavior, our study investigates how these financial personality traits moderate the relationship between intention and the uptake of consumer credit. Using a combination of survey and bank register data, we focus on the amount of outstanding balance on consumer credit as the objective measure of consumer credit behavior. The results show that higher risk tolerance and greater financial confidence both are associated with increased credit use among those with the intention to borrow, while self-control mitigates this relationship. We observe that gender differences in financial behavior are notable: men who report high confidence and an intention to use consumer credit tend to carry higher outstanding balance, whereas higher self-control in men is linked to lower credit use. Additionally, although string behavioral intention and higher income both predict greater consumer credit use, self-control mitigates this association among high-income individuals. Our study adds to consumer credit research by revealing the complex interplay between behavioral intention, risk tolerance, financial confidence, and self-control in relation to actual consumer credit usage.

Abstract: This research examines the determinants of Bitcoin (BTC) valuation from January 2011 to December 2025 using Autoregressive Distributed Lag (ARDL) models. The empirical evidence supports the hypothesis that the monetary policy of the United States Federal Reserve—specifically liquidity expansion and interest rate adjustments—drives price dynamics, confirming a pro-cyclical nexus. At the microeconomic level, the density of active institutional addresses and the marginal cost of production significantly influence price trajectories. Furthermore, heightened market volatility, represented by the VIX, exerts a statistically significant negative impact on BTC returns. The findings suggest that Bitcoin has transitioned into a sophisticated value asset, underpinned by production efficiencies and an expanding institutional base. Consequently, Bitcoin represents a viable alternative to centralised financial systems, offering a potential hedge against inflation and the erosion of purchasing power. The study concludes that digital assets warrant inclusion within conservative institutional portfolios, notwithstanding the inherent speculative nature of the market.

Abstract: This study examines customer retention intention in neobanking environments using a theory-informed explainable machine learning framework. Existing digital banking research typically relies on linear modelling approaches to explain retention behaviour, potentially overlooking nonlinear, value-range-dependent, and interaction-based predictive patterns. Using a publicly available survey of 305 neobank users, this study compares regularized linear models, a partial least squares structural equation modelling (PLS-SEM)-inspired benchmark, and XGBoost under repeated nested cross-validation. SHapley Additive exPlanations (SHAP)-based explainability, SHAP interaction analysis, generalized additive model (GAM) diagnostics, construct-level aggregation, and construct-sensitivity checks are used to interpret model behaviour and assess robustness. The results show that XGBoost substantially outperforms the linear benchmarks, achieving the lowest average RMSE and highest average R² across 100 out-of-sample test-fold estimates. Trust-related indicators provide the largest share of model-based predictive importance, followed by perceived security and switching costs. SHAP and GAM diagnostics suggest that trust and switching costs may contribute to retention intention in heterogeneous and nonlinear ways, while perceived security displays a more stable positive predictive pattern. Age-related nonlinearities appear weak and should be interpreted cautiously given the young sample profile. The analysis also suggests possible non-additive relationships between trust and perceived security. The study contributes to digital banking and FinTech research by showing how explainable machine learning can complement theory-driven retention models, identify potentially nonlinear predictive patterns, and preserve interpretability. The findings offer practical insight for trust-building, visible security assurance, and retention diagnostics in neobanking contexts.