Business, Economics and Management

Abstract: Virtual currencies have become an increasingly prominent feature of the modern financial system, yet their public perception and adoption remain insufficiently understood. This study examines the perception and use of virtual currencies in Slovakia. Its primary objective is to assess public awareness, attitudes, and patterns of use. Data were collected through a structured questionnaire covering knowledge, perception, and engagement with virtual currencies. The responses were analysed using graphical methods and statistical techniques, including the t-test and Pearson’s chi-square test of independence, which were also employed to test the proposed hypotheses. The findings indicate high levels of awareness among younger respondents, but also a limited understanding of the underlying technologies and broader potential of virtual currencies. The results further suggest that many individuals hold only one virtual currency, reflecting either insufficient information or uncertainty about these assets. On the basis of these findings, the study proposes measures to improve the current situation, including targeted educational initiatives, investment in research and development, wider practical applications, enhanced security and trust, and continuous monitoring of market trends. The study offers evidence-based insights into the Slovak context and may also inform policymakers, educators, and financial institutions seeking to promote informed and responsible engagement with virtual currencies.

Abstract: The accurate prediction of credit default risk remains a significant challenge for financial institutions operating within increasingly complex data environments. This study pro-poses a hybrid Long Short-Term Memory (LSTM) and eXtreme Gradient Boosting (XGBoost) model that integrates deep learning and ensemble machine learning techniques to enhance predictive performance while preserving interpretability. The LSTM component effectively captures temporal patterns in borrower behavior, and its output is utilized as a meta-feature within the XGBoost framework. The model is evaluated using a bench-mark credit dataset and is compared with conventional machine learning approaches. The results indicate that the proposed hybrid model outperforms standalone models across key evaluation metrics, achieving high accuracy, F1-score, and ROC–AUC. To enhance transparency, Shapley Additive Explanations (SHAP) are employed to analyse feature contributions and directional effects. The findings reveal that repayment behavior, particularly recent delinquency, serves as the most influential predictor of default risk, followed by indicators of financial capacity. The feature derived from the LSTM demonstrates the strongest overall impact, thereby confirming the significance of temporal dependencies in credit risk prediction. This study illustrates that the integration of deep learning with ensemble techniques establishes a robust and interpretable framework for credit risk assessment, thereby providing practical value for enhancing financial decision-making and risk management.

Abstract: This paper presents a full implementation of data-driven modelling of the dynamics of the options on Bitcoin, using high-frequency data from the Deribit exchange. To this end, we provide a synthesis of methods established in prior papers, namely the works involving “neural SDE market models,” to build a pipeline to go from raw options quotes to a functioning non-parametric model. The options surface is decomposed into a low-dimensional latent space designed to minimise arbitrage in reconstruction and the temporal evolution of these factors are modelled with a stochastic differential equation (SDE). The drift and diffusion of the SDE are learnt from data using neural networks, thereby forming a ’Neural SDE’. These networks are constrained in order to guarantee the absence of static arbitrage and to minimise dynamic arbitrage in the resulting model. The networks are trained using a likelihood-based objective function in an SDE transition discretisation. The framework produces arbitrage-free simulations of option surfaces and enables risk management applications such as Value-at-Risk estimation and hedging applications.

Abstract: U.S. banks are investing unprecedented amounts in artificial intelligence, with annual spending at institutions like JPMorgan Chase, Bank of America, and Citigroup now exceeding $2–$4 billion each. Yet a critical national financial resilience problem persists: most U.S. banks cannot confidently determine whether these massive AI investments generate positive risk-adjusted returns, creating capital allocation inefficiency and potential systemic vulnerability. This research proposal outlines a comprehensive mixed-methods research design for investigating how senior executives in U.S. global banks govern enterprise AI investments, manage emerging financial risks, and measure return on investment when scaling AI across national banking operations. Drawing on the Resource-Based View, Paradox Theory, and the Technology-Organization-Environment framework, this proposal develops an integrated conceptual framework linking AI governance mechanisms, operating model configurations, and multi-dimensional ROI measurement specifically calibrated to the U.S. regulatory environment (Federal Reserve, OCC, FDIC). The proposed study would employ an embedded multiple-case design with semi-structured interviews of 30–40 C-Suite executives across 6–8 U.S.-headquartered global banks, supplemented by secondary analysis of SEC filings, FRED economic data, FDIC call reports, and Model Risk Management documentation. We propose a novel risk-adjusted ROI calculation framework incorporating direct financial benefits, indirect value creation, strategic option pricing, and probabilistic risk adjustments aligned with U.S. banking stress testing practices. Anticipated methodological barriers include organizational resistance, access constraints to senior executives, and causal attribution challenges—each addressed with specific mitigation strategies outlined in this proposal. This proposal aims to contribute empirically validated ROI measurement tools for executive decision-making at U.S. systemically important financial institutions and demonstrates a scholar-practitioner approach to bridging academic rigor with national financial stability priorities.

Abstract: The most widely used bankruptcy predictor, Altman’s Z-Score, assigns a positive coefficient to asset turnover: faster firms are rated safer. Under crisis conditions, that assumption reverses. We introduce the Solvency Margin (SM), a diagnostic calculable from standard financial statements that measures, in dollars, how far an organization is from the threshold where operations become impossible. Unlike static liquidity ratios, the SM yields a concrete speed limit: the maximum operating velocity at which an organization can survive a defined shock. We validate the SM against pre-crisis financial data across three crises in two domains. In the automotive sector, SM computed from FY2019 filings showed directional predictive power among ten major automakers in both the 2021 semiconductor shortage (ρ = 0.50, p = 0.14) and the 2020 COVID-19 pandemic (ρ = 0.53, p = 0.12; ρ = 0.70, p = 0.036 excluding one governance-driven outlier). In the 2023 U.S. banking crisis, SM augmented with a Deposit Stability Factor predicted crisis outcomes among eighteen regional banks (Spearman ρ = 0.62, p = 0.006), correctly ranking three of four failed institutions in the bottom three positions. Monte Carlo simulation (450,000+ runs) confirms threshold behavior across a wide range of conditions. We present a five-step calculation method and a three-lever decision framework for practitioners.

Abstract: This study examines the relationship between insurance market development and economic growth in 33 OECD countries over the period 2011–2021, with particular emphasis on life insurance markets and structural characteristics. To capture the multidimensional nature of insurance development, the analysis distinguishes between insurance depth (density), size (penetration), and structure (retention and foreign participation). Using a two-way fixed effects panel framework with country and year effects and insurance-market controls, the results reveal a differentiated pattern. Insurance density—both total and life—is positively and statistically significantly associated with GDP per capita, indicating that the intensity of insurance usage remains economically relevant in advanced economies. In contrast, life insurance penetration is negatively associated with economic growth. Life insurance retention is also negatively associated with economic growth, highlighting the role of risk allocation in mature insurance systems. Foreign insurer participation does not exhibit a statistically significant effect. The findings suggest that in OECD countries, the economic contribution of insurance markets depends more on efficiency and structure than on scale.

Abstract: Valuation methodologies vary across industries because firms differ in capital intensity, asset life, earnings stability, and exposure to risk. This study examines the valuation approaches used by South African equity analysts across diversified mining, platinum group metals mining, gold mining, retail, and banking sectors over the period 2018-2026, with non-financial firm coverage extending to 2024 and banking sector coverage extending to 2026. Using qualitative document analysis of 201 equity research reports covering 24 Johannesburg Stock Exchange-listed companies, including 19 non-financial firms and the five largest South African banks, the study identifies clear clustering of valuation methods by industry. The findings show that resource-based sectors are predominantly valued using intrinsic approaches such as life-of-mine discounted cash flow (DCF) and risk-adjusted net present value (NPV), while retail firms are primarily valued using earnings-based multiples. Gold mining exhibits a hybrid valuation pattern, and banking institutions are valued using balance-sheet- and profitability-based approaches anchored on book value, return on equity, and dividend flows. Overall, the results indicate that analyst valuation practice in the South African equity market is strongly industry-specific and aligned with the underlying economic characteristics of the sectors being analysed. The study contributes to the limited empirical literature on professional valuation practice in African capital markets and provides insights relevant to analysts, investors, and regulators.

Abstract: This study examines the dynamic relationship between deposits, credit and sales across Ecuador’s provinces over the period 2019-2025 using a Panel VAR model estimated by two-step GMM. Sales declared to the Internal Revenue Service are employed as a high-frequency administrative indicator of provincial economic activity. The results are consistent with a predominantly supply-leading structure, in which deposits and credit exhibit predictive capacity over provincial sales, with no robust evidence in the reverse direction. The speed of transmission differs between the two financial channels. De-posits affect sales with a one-period lag, whereas credit does so with two, suggesting that liquidity is channelled toward commercial activity more immediately than credit financing. During the pandemic period, an increase in deposits, a contraction in credit, and a decline in sales are observed. The study provides subnational evidence for a dol-larized Latin American economy and covers a recent period marked by an extraordinary shock. The findings indicate that the relevance of financial intermediation for territorial economic activity depends not only on the direction of the linkage but also on the dif-ferentiated speed of its components.

Abstract:

This study investigates whether impact investing through Environmental, Social, and Governance (ESG) indices listed on India’s National Stock Exchange (NSE) can generate abnormal returns relative to the broader market benchmark. Using data from ESG indices listed on the National Stock Exchange between April 2011 and June 2023, the analysis evaluates the risk–return performance of the Nifty100 ESG and Nifty Enhanced ESG indices relative to the Nifty 100 benchmark. We applied a comprehensive suite of time-series methodologies encompassing unit root testing, month-of-the-year dummy regressions, ARIMA residual modelling, and, critically, Generalised Autoregressive Conditional Heteroscedasticity (GARCH) family models to test the impact investing hypothesis. Conditional volatility surged in April 2020 during the COVID-19 market shock, however the ESG indices exhibited slightly lower peak volatility than the Nifty 100. Results show that both ESG indices outperformed the conventional benchmark over the full sample period, achieving cumulative gains of about 272–274% compared with 240% for the Nifty 100. A distinct March effect—analogous to the January effect in developed markets—is detected at the 10% significance level for ESG indices. Our findings underscore the growing importance of responsible investing and time-varying risk premia in the Indian equity market.

Abstract: This study investigates the impact of jointly modeling jumps in asset prices and mortality rates on the valuation of insurance guarantees. Mortality dynamics are specified using two extended frameworks based on the classical Lee–Carter model, with and without the inclusion of jump components. Financial asset returns are modeled using Merton jump–diffusion processes. In the proposed specification, asset prices evolve according to a two–regime Merton model, where the regimes correspond to pandemic and non–pandemic market conditions. Using historical mortality data for the U.S. population and financial market data from the S&P 500 index, we evaluate the pricing implications for a Guaranteed Minimum Death Benefit (GMDB) rider. Contract values and Greeks are computed across multiple issue ages and policy maturities. The empirical results highlight the importance of accounting for simultaneous mortality and market jumps, and demonstrate that their interaction has a material effect on the valuation of GMDB products.

Abstract: This study introduces the R-index as a novel framework for quantifying the economic impact of risk through realized deviations from expected performance. In contrast to traditional risk measures that rely on probabilistic or volatility-based approaches, the proposed index captures risk as an outcome-based phenomenon directly linked to firm-level performance. The R-index is constructed as a normalized measure of deviation between actual and expected values and is further extended to a multidimensional setting, allowing for aggregation across different performance indicators. The empirical analysis is conducted using longitudinal financial data from three firms operating in distinct sectors of the Montenegrin economy—telecommunications, retail, and tourism—over the period 2015–2024. The results reveal substantial heterogeneity in the realization of risk across firms, even under identical macroeconomic conditions. While some firms exhibit stable performance and limited deviations, others demonstrate pronounced volatility and sensitivity to external shocks, particularly during the COVID-19 period. These findings suggest that risk is not uniformly transmitted but is instead shaped by firm-specific characteristics, including operational structure and adaptive capacity. The study contributes to the literature by redefining risk as a realized economic phenomenon and by proposing a scalable and interpretable metric that bridges risk measurement and performance evaluation. The R-index offers practical relevance for managerial decision-making and provides a foundation for future research on the relationship between risk and firm value.

Abstract: We develop a queueing-organized framework for within-venue monitoring of BTC/USDT liquidity, signed-flow pressure, and resiliency on Binance. The model treats latent buy and sell pressure as occupancy processes and uses that state space to organize three empirical diagnostics: the variance-per-BTC liquidity measure R_r, the effective mean-reversion rate θ_eff, and the companion signed-flow proxy beta^proxy_eff. Using Binance trade data from 2020 to 2025, we find a pooled first-order variance-volume regularity away from the highest-volume tail and substantial time variation in rolling liquidity and resiliency. In overlapping 30-day windows, θ_eff is positive by point estimate in roughly two-thirds of windows but clearly positive in only about two-fifths under a simple uncertainty buffer, implying that local recovery is often fragile or ambiguous. The intended users are short-horizon risk managers, execution desks, market makers, and exchange surveillance teams that need auditable venue-level indicators of when liquidity is thinning, recovery is weakening, and signed flow is turning one-sided. Queueing is useful here because it turns those signals into one coherent monitoring dashboard for venue-level market quality and short-horizon risk.

Abstract: This paper examines the short-run dynamics of monetary policy transmission in a bank-dominated emerging economy, with a particular focus on the relative timing of adjustments across bank equity valuations, balance-sheet aggregates, and inflation. Using monthly data over the period 2018–2024, the analysis relies on a reduced-form VAR framework. The results indicate that monetary policy innovations, interpreted as informational signals, are more visibly reflected in bank equity valuations—proxied by the MASI banking index—at short horizons, while balance-sheet aggregates exhibit more limited and less persistent adjustments. Inflation dynamics remain difficult to identify clearly within the short-run horizon, consistent with the slow-moving nature of price adjustments. These findings are consistent with a configuration in which policy-related information is first incorporated into financial valuations before being gradually reflected in credit and macroeconomic variables. This pattern is interpreted as reflecting heterogeneous adjustment speeds rather than a causal transmission sequence. The contribution of the paper is to document these heterogeneous short-run adjustment patterns within a unified empirical framework, highlighting the importance of temporal dynamics in the analysis of monetary transmission.

Abstract: This paper draws on survey data from 585 family farms in Jiangsu Province, China, in 2023. It endeavors to examine how farmers' utilization of information and communication technologies (ICT) in agricultural production and management affects their access to agricultural production credit. The results demonstrate that farmers who apply ICT more comprehensively in agricultural production and management are more inclined to obtain agricultural production credit. Intriguingly, these outcomes persist resilient even when taking into account selection bias and endogeneity issues.In terms of transmission mechanisms, agricultural digital transformation can facilitate farmers' access to agricultural production credit. Specifically, it does so by reducing the credit transaction costs related to bank loans and enhancing the efficiency of agricultural resource allocation. Furthermore, the heterogeneity analysis reveals that agricultural digital transformation is more conducive for smallholder farmers to acquire agricultural production credit from large banks. Finally, it is evident that the application of ICT in areas such as agricultural product sales and the management of agricultural digital equipment is more beneficial for farmers in attaining agricultural production credit.

Abstract: Climate transition risk is emerging as a critical determinant of value in real estate finance as cities adopt increasingly stringent decarbonization policies, adding to the pressure of physical risk. New York City’s Local Law 97 (LL97), which imposes binding emissions caps and financial penalties on large buildings, offers a salient case to examine how capital markets respond when building-sector climate regulation becomes financially consequential. This paper investigates whether and how U.S. equity Real Estate Investment Trusts (REITs) with exposure to New York City assets are impacted by climate transition policies like LL97. Using a standard event study framework, the analysis examines abnormal returns around two key milestones: the policy’s approval as part of the Climate Mobilization Act in April 2019 and the onset of its enforcement phase in January 2024. Results show that the initial announcement generated statistically insignificant cumulative abnormal returns, suggesting that investors did not price LL97’s long-term horizon implications at the time of the vote. By contrast, the enforcement milestone coincided with economically meaningful negative abnormal returns across most sampled REITs, particularly those with substantial New York City office exposure, although these effects are not statistically significant and can be attributed to broader sectoral stress. Cross-sectional tests reveal no significant differences between highly and moderately exposed groups. Overall, while isolating the impact of transition risk alone is empirically challenging, the findings suggest that climate-related transition risk is priced gradually, potentially non-material in the short term and can become more salient as implementation approaches.

Abstract: This study investigates the role of market volatility, proxied by the CBOE Volatility Index (VIX), as a potential amplifier of corporate leverage risk within the S&P 100. Addressing the limitations of traditional financial distress models in capturing non-linear and regime-dependent dynamics, we employ XGBoost combined with SHAP-based explainable AI (XAI) on a longitudinal dataset spanning 2000-2025. The results show that total debt remains the dominant predictor of financial distress, while the influence of risk-related variables such as the VIX and equity returns increases during crises periods. Monetary policy indicators become more important during pandemic conditions, whereas inflation dominates in stable environment. This finding highlights the regime-dependent nature of financial risk drivers and demonstrates the value of explainable machine learning in developing interpretable early warning systems. By integrating predictive accuracy with interpretability, this study provides new insights into the interaction between firm-level leverage and external market volatility.

Abstract: The study sought to examine the intervening influence of financial development on the relationship between sustainability practices and sustainable development of the Sub-Saharan African countries. The study used a longitudinal panel design and incorporated both the descriptive and explanatory elements. The study adopted a positivist research philosophy. It examined data from 49 Sub-Saharan African countries over a 24-year period from 2000 to 2023 to analyse sustainability practices, financial development and their influence on sustainable development. The study relied on secondary data from the World Bank Data Bank, UNDP and Sustainable Development Reports. Descriptive analysis and regression models were used for analysis. The study found that financial development does not serve as an effective transmission channel through which sustainability practices influence sustainable development outcomes. The research concluded that policy interventions should include developing sustainable banking regulations, creating green finance incentives, establishing sustainability-linked lending criteria, and strengthening financial inclusion policies that target sustainable development sectors.

Abstract: This paper has presented a combined empirical framework for measuring the risk-return profile of listed real-estate securities in a non-Gaussian market situation. By leveraging daily data for 30 U.S. and international listed real estate securities from 2021 to 2024, we probe how portfolio outcomes vary according to the optimization criterion and distributional aspects of returns obscured by conventional mean-variance summaries. We build long-only and long-short portfolios using classical Markowitz mean–variance optimization methods and conditional value-at-risk (CVaR) optimization techniques, and compare their realized dynamics cumulative growth and efficient frontiers under alternative risk-free benchmarks. By applying extreme value theory, we quantify extreme-risk exposure via generalized Pareto modeling and Hill tail-index estimation, which is compared to the broader behavior of the equity market. We analyze portfolio stability and reward efficiency using volatility, Sharpe, Sortino, and Rachev ratios, as well as maximum drawdown and information ratio. In addition, robust single-factor regressions are estimated on a sector benchmark, and residual diagnostics are analyzed to define common factor dependence while minimizing the impact of outliers. To introduce a forward-looking dimension, we calibrate the double-subordinated normal inverse Gaussian specification and extract NDIG model-implied option prices and volatility surfaces. We also investigate volatility persistence through ARFIMA-GARCH modeling to assess whether listed real-estate security returns exhibit long-memory features beyond standard volatility clustering. Results indicate that listed real-estate security returns exhibit heavy tails, pronounced downside sensitivity, and persistent volatility, supporting the use of tail-aware optimization, robust estimation, and long-memory-consistent volatility diagnostics beyond standard Gaussian benchmarks.

Abstract: The Ghanaian foreign exchange (FX) market has experienced substantial transformation over the past decade, marked by rising trading volumes and several episodes of exchange rate turbulence. Building on the pioneering work of Duffour et al. (2011) on order flow and exchange rate dynamics in Ghana, this study employs high‑frequency daily data from 2018 to 2023—capturing both stable and volatile market conditions. Using the BK‑18 spillover index, our findings show that order flows and exchange rates are tightly interconnected through bidirectional causality. Moreover, the EUR/GHS exchange rate emerges as a dominant transmitter of shocks within the multivariate system of order flows and exchange rates. These insights carry important implications for foreign exchange (FX) policy design, regulatory oversight, market monitoring, and trading strategies in Ghana.

Abstract: This study empirically explores the digital determinants of financial account ownership of 152 economies using the Global Findex Database 2025, which is the most recent cross-country repository on financial inclusion produced by the World Bank. By using ordinary least squares (OLS) regressions, together with descriptive, regional and bivariate analytical procedures, the authors examine the extent to which digital financial access, ownership of a mobile money account and the use of the internet predict financial account ownership at country level. The results show that digital financial access is the strongest and statistically significant determinant of financial account ownership in the world with a coefficient of 0.911 (p < 0.001), while the full model explains 76.7% of the cross-country variation in account ownership. Mobile money account ownership is added to the model with a negative and statistically significant coefficient of -0.236 (p = 0.006) indicating a substitution effect in economies where mobile money becomes the primary avenue to financial access in lieu of the traditional infrastructure of a banking sector. While internet usage is positively correlated with account ownership in bivariate tests, in the multivariate model, internet usage is not found statistically significant in driving financial inclusion without the presence of functioning digital financial infrastructure alongside with it. The regional analysis shows significant disparities in the results of financial inclusion. High income economies have an average account ownership rate of 92.03%, compared with 42.41% in the Middle East and North Africa and 55.60% in Sub-Saharan Africa. These results have some interesting implications for policymakers and development-finance institutions interested in designing inclusive digital financial ecosystems; they highlight the importance of investing in digital financial infrastructure in a targeted way rather than simply expanding internet access as a tool of financial inclusion. This study is the first cross-country empirical analysis of the factors driving digital financial inclusion based on the Global Findex 2025 dataset and offers timely and original evidence that can be relevant for policy formulation and practice concerning digital financial inclusion.