Forecasting returns in Real Estate Investment Trust (REIT) markets remains challenging because REIT performance is shaped by nonlinear and time-varying interactions with macro-financial conditions. This study evaluates the forecasting performance of Long Short-Term Memory (LSTM) neural networks relative to a TVP–VAR proxy implemented as an expanding window VAR for weekly U.S. U.S. REIT returns. All models are assessed within a harmonized experimental framework that applies consistent data preprocessing, feature construction, and strictly time-ordered out-of-sample evaluation. The results indicate that the baseline LSTM model delivers modest but more stable error-based performance than the TVP–VAR proxy, with improvements concentrated in RMSE and MAE, while evidence for directional predictability is weak and not consistently distinguishable from benchmark performance. To enhance transparency, SHapley Additive exPlanations (SHAPs) are used to interpret the LSTM forecasts. The attribution analysis highlights recent REIT returns, global equity indicators—particularly the Hang Seng Index—and crude oil prices as influential predictors, and shows that their contributions vary across volatility regimes, consistent with time-varying spillovers and changing risk transmission. Overall, the study positions LSTM forecasting combined with SHAP-based interpretation as a transparent and reproducible framework for comparative evaluation and driver analysis in weekly REIT returns, rather than as a strong directional timing tool.

This study investigates the cointegrating relationships among financial inclusion, institutional quality, and economic growth in 20 Sub-Saharan African nations from 2008 to 2024. Employing the Pooled Mean Group (PMG) estimator in an Autoregressive Distributed Lag (ARDL) panel, the analysis showed a significant and favourable long-term association between economic growth, financial inclusion and institutional quality. In particular, regardless of the proxy for economic growth, the long-term association between financial inclusion and economic growth is positive and statistically significant. Similarly, institutional quality demonstrates a favourable and significant long-run linkage to economic growth, suggesting that improvements in institutional frameworks are related to sustained economic expansion. In contrast, short-run dynamics differs. There is a short-term correlation between institutional quality and economic growth but not between financial inclusion and economic growth. These findings show the importance of institutional quality as a catalyst for economic growth in the region. Consequently, the study recommends that governments in Sub-Saharan Africa should prioritise setting up strong institutions and policies to foster financial inclusion, which has a correlation with sustainable economic growth. This is crucial for both overall economic development and the creation of job opportunities.

This study examines the False Reality Bias in treasury management, a cognitive distortion through which small and medium-sized enterprises (SMEs) infer financial stability from salient bank balances while overlooking pending obligations and cash-flow timing. Using a firm-level dataset of 50 Spanish meat-processing SMEs, the analysis develops two behavioral-finance indicators: the Liquidity Misperception Index (PEL), capturing the divergence between salient liquidity cues and effective short-term obligations, and the Liquidity Misconfidence Index (ICEL), measuring managerial overconfidence in liquidity assessments. Results show that 41% of firms overestimate liquidity (average PEL = 1.21), while 40% exhibit excessive confidence (ICEL > 1.3), both significantly associated with liquidity distress. Econometric estimates indicate that firms with PEL values above 1.2 are 4.48 times more likely to experience liquidity crises, even after controlling for bank balance levels. Predictive models are used in an exploratory capacity, achieving classification accuracies above 80% and supporting the robustness of the behavioral signals identified. In addition, AI-assisted cash-flow simulations reduce liquidity misperception by 34.7% (p < 0.01). Overall, the findings provide micro-level evidence that cognitive biases systematically distort SME treasury decisions but can be partially corrected through targeted decision-support tools, offering practical insights for managers, advisors, and policymakers.

This study investigates the financial and strategic implications of ESG implementation in the hotel sector, focusing on cost structures, stakeholder engagement, and risk-related outcomes. Empirical evidence remains limited in tourism-intensive economies, particularly regarding operational practices. Using a mixed-methods approach, we combine survey data from hotel managers in Crete, Greece, with a case study of a leading hotel enterprise. The findings reveal that environmental initiatives require substantial investment but can lead to operational efficiencies and regulatory alignment. Social and governance practices, while less capital intensive, play a key role in internal stakeholder trust. The study concludes that the integration of ESG into measurable key performance indicators, when strategically aligned with corporate objectives, contributes to long-term financial viability. These insights reinforce ESG’s growing role in enhancing resilience and governance effectiveness within the hospitality sector.

This paper investigates the relationship between institutional ownership and firm-level idiosyncratic volatility across European equity markets, with a particular focus on the moderating role of dividend policy. Using a sample of STOXX Europe 600 constituents from 2005 to 2025, we estimate idiosyncratic volatility via the Fama-French three-factor model and employ fixed-effects regressions with clustered standard errors. Our empirical results reveal a positive and statistically significant association between institutional ownership and idiosyncratic volatility, suggesting a destabilizing rather than stabilizing role in European markets. This volatility-enhancing effect is significantly more pronounced among dividend-paying firms and is primarily driven by transient institutional investors with high portfolio turnover. Furthermore, we find that: (1) larger firm size (market capitalization) and higher leverage (debt-to-capital ratio) are positively associated with heightened volatility; (2) growth-oriented firms (high market-to-book ratios) exhibit increased volatility, particularly among non-dividend payers; and (3) higher profitability (ROE) and favorable analyst coverage (buy recommendations) act as stabilizers, reducing idiosyncratic risk. These findings persist in both contemporaneous and lagged specifications. This study contributes to the literature by identifying dividend policy as a key channel through which institutional trading behavior amplifies firm-specific risk, providing novel evidence on the asset class effect within major European benchmark indices.

Despite significant advances in bank regulation and the widespread adoption of macroprudential frameworks, emerging market economies remain persistently vulnerable to global financial shocks. Episodes such as the Global Financial Crisis, the COVID-19 market turmoil, and recent monetary tightening cycles reveal that financial stress originating in core markets continues to transmit rapidly and forcefully to emerging economies. This paper argues that such vulnerability reflects structural features of contemporary financial systems rather than deficiencies in domestic banking regulation alone. Adopting a conceptual and analytical approach, the article develops an integrated framework of macro-financial blind spots that links global financial cycles, non-bank financial intermediation, and regulatory fragmentation. The analysis highlights how funding liquidity, collateral valuation, margin dynamics, and market-based leverage amplify global shocks through channels that lie largely outside traditional, bank-centric macroprudential frameworks. As market-based finance expands, systemic risk increasingly originates in activities rather than institutions, limiting the effectiveness of entity-based regulation and reinforcing emerging markets’ role as price-takers in global portfolios. The paper contributes to the literature by synthesizing insights from macroprudential policy, market liquidity, and non-bank finance to explain the persistence of emerging market vulnerability in an era of globalized funding. It further derives policy implications for macro-financial governance, emphasizing the need for system-wide, activity-based approaches, improved data and transparency, and stronger domestic and international regulatory coordination. These findings are relevant for policymakers seeking to reconcile financial integration with systemic resilience in emerging markets.

Using a quantile–frequency connectedness framework, this study analyzes the regime-contingent and horizon-specific transmission of shocks among AI assets, FinTech markets, and Blue Economy financial instruments. The empirical results reveal a distinctly asymmetric connectedness structure, whereby high-frequency spillovers intensify in upper-quantile states associated with liquidity stress and sentiment-driven trading, while low-frequency connectedness remains comparatively muted, thereby preserving cross-segment diversification potential. AI assets emerge as dominant net transmitters in short-horizon dynamics, reflecting rapid innovation cycles and speculative adjustments. FinTech markets exhibit stabilizing properties under median regimes but transition into net propagation roles when risk conditions escalate. Blue finance instruments act as conditional net absorbers, attenuating volatility originating from digital innovation-driven markets, particularly during adverse market states. By decomposing spillover intensities across quantiles and spectral bands, the analysis highlights a structural differentiation between innovation-sensitive digital assets and the comparatively stable behavior of blue-themed financial assets. These findings advance the understanding of nonlinear dependence, asymmetric contagion, and state-dependent co-movements in emerging financial ecosystems. The results provide actionable insights for systemic-risk measurement, cross-market shock diagnostics, and multi-asset portfolio construction in an increasingly interconnected global financial system.

This research aims to evaluate the effectiveness of monetary circulation in Cyprus by applying the Index of the Cycle of Money, derived from the Theory of the Cycle of Money. The analysis focuses exclusively on the Cypriot economy over the period of 2012–2017, a timeframe marked by severe financial stress following a domestic banking crisis. Using national GDP and bank deposits as core inputs, the study computes the country-specific cycle-of-money index and compares it to the global benchmark. The results show that Cyprus consistently exhibits a cycle-of-money index significantly above the global average, indicating a highly efficient internal redistribution and reuse of money. This finding suggests that the Cypriot economic structure possesses strong resilience and recovery capacity, even under adverse monetary and fiscal conditions. The analysis contributes to the comparative literature on monetary circulation by providing a clearly delimited single-country application and by reinforcing the explanatory power of the cycle-of-money framework in crisis contexts.

This study examines quantitative investment strategies for Brazilian equity funds, integrating traditional financial performance indicators with machine learning techniques to enhance fund selection. The main objective was to construct and validate predictive models for fund selection. The methodology involved collecting daily data from 2019 to 2025, computing a range of return and risk measures, and trained models to classify 1- and 3-month shifted windows. The 3-month models achieved the strongest predictive accuracy, exceeding 91%, with the Sharpe Ratio emerging as the most influential feature. A 12-month backtest (October/2024–September/2025) showed that ML-constructed portfolios delivered cumulative returns between 14.65% and 91.86%, depending on the selection criterion, substantially outperforming Brazil’s CDI risk-free benchmark (12.70%) and the Ibovespa (11.46%). These findings highlight the practical potential of ML-based fund selection, though successful implementation requires careful risk management and ongoing model validation.

Using a method inspired by quantum principles, this study estimates the composition of various types of tax contributions expected from foreign trade operations. The estimation approach is proposed considering the superposition of expectations and disturbances—fundamental elements of quantum methods—that add complexity to the forecasts of tax collections. For instance, the contributions of international trade-related taxes may be determined not only by the country’s degree of regional integration but also by the composition of tax revenue that depends on the kind and use of merchandise. Using the case of Mexico’s imports, the methodology illustrates how the expectations of collecting certain taxes—like the General Import Tariff (GIT) and the Value Added Tax (VAT)—would be impacted by fluctuations in others—such as the Special Tax on Production and Services (STPS). The hypothesis of this study is that, through the proposed quantum-inspired methodology, it is possible to establish future scenarios of tax revenue compositions while maintaining fiscal consistency by anticipating potential outcomes in the adjustments of contributions if the recently proposed fiscal reform is approved by the Mexican Government. This work contributes to the academic literature on public finance management by advancing a methodology that can support the strategic formulation of fiscal expectations and policy.