Issue #2 (Volume 23 2026)

Type of the article: Research Article

Abstract
Financial leasing has established itself as a key financing alternative for many companies in capital-intensive sectors, such as mining, due to its ability to improve profitability indicators without compromising liquidity. The objective of this study was to analyze the relationship between the use of financial leasing and business profitability in the mining sector companies listed on the Lima Stock Exchange (Peru). The methodology adopted a basic quantitative approach, with a correlational scope and a non-experimental cross-sectional design. The study sample consisted of three Peruvian mining companies active in the stock market, analyzed during the period 2017–2021. which generated a total of 15 annual observations used in the statistical analysis, using audited financial statements and the calculation of key profitability indicators as instruments. Given the non-parametric nature of the data, the Wilcoxon signed-rank test was used for hypothesis testing. The results show that companies that used financial leasing achieved an average ROE of 11.9% (±0.079), demonstrating favorable performance. Likewise, a significant relationship was identified with Gross Contribution Margin (GCM), whose average margin was 37.9% (p = 0.037). A significant correlation was also found between the tax shield associated with leasing and financial profitability (statistic = 119.00; p < 0.01), highlighting tax benefits as a relevant factor. Finally, the average ROA was 8.7% (±0.066), suggesting efficient management of assets obtained through leasing. Overall, the findings provide empirical evidence supporting the role of financial leasing as an effective financing mechanism that enhances profitability and operational efficiency in capital-intensive industries, particularly within emerging market contexts such as the Peruvian mining sector.

Type of the article: Research Article

Abstract
Bankruptcy forecasting in emerging markets is complicated by macroeconomic and regulatory volatility. This study evaluates whether a hybrid model that integrates firm financial ratios, macro indicators, and a Random Forest classifier outperforms traditional ratio-only approaches for Indian firms. Each bankrupt company is analyzed over a five-year window preceding its actual failure date, resulting in ten bankrupt firms paired with ten matched healthy peers. Using these firm-specific five-year pre-bankruptcy panels, we estimate logistic regression and Random Forest models with stratified 5-fold cross-validation and derive a parsimonious four-factor risk score.
Relative to ratio-only baselines, the hybrid design improves accuracy from 0.76→0.80 (logit) and 0.82→0.86 (Random Forest), and lifts the Area Under the ROC Curve (AUC) from 0.70→0.78, indicating that the model correctly ranks a bankrupt firm as riskier than a healthy firm 78% of the time. Debt-to-Equity, Current Ratio, Net Profit Margin, and GDP Growth dominate feature importance, and rising risk scores typically cross ~0.40 two to three years before failure.
Robustness checks, including alternative class-balance weights, sector dummies, and rolling-window estimation, yield comparable gains and stable feature rankings. The resulting bankruptcy Early-Warning System (EWS) is transparent, portfolio-scalable, and easily embedded into bank risk dashboards. The evidence shows that multidimensional hybrid models provide earlier and more reliable warnings than ratio-based formulas, offering practical value to lenders, investors, and regulators in volatile settings.

Type of the article: Research Article

Abstract
This paper investigates the impact of colonial ties, cultural proximity, and host-country market size on outward foreign direct investment from Arab Maghreb Union countries, focusing on both greenfield investments and cross-border mergers and acquisitions. Using the Generalized Method of Moments on a panel dataset of 556 transactions over the period 2004 to 2022, captured by the number of deals, we find that colonial ties and African cultural proximity positively influence both greenfield investments and cross-border mergers and acquisitions. However, Arab cultural proximity and host-country market size influence only greenfield investments. Among the variables studied, colonial ties have the greatest impact, followed by African cultural proximity. The estimated coefficients indicate that the magnitude of these effects is substantially larger for greenfield investments than for cross-border mergers and acquisitions, highlighting important differences in how firms respond to host-country characteristics across entry modes. This pattern is consistently observed across baseline estimations and robustness checks, reinforcing the presence of a clear entry-mode asymmetry in the determinants of outward foreign direct investment from Arab Maghreb Union countries. Taken together, the results integrate cultural proximity and historical ties into international business theories and provide new insights into the outward investment behaviors of emerging-market multinationals. Moreover, the findings reveal the relevance of leveraging shared history and cultural ties as instruments for attracting investment from Arab Maghreb Union countries, while adopting differentiated strategies for greenfield investments and cross-border mergers and acquisitions.

Type of the article: Research Article

Abstract
Corporate real estate financing is a channel through which macro-financial volatility, regulation, and strategic orientation affect firms’ balance sheets. This study explains how firms in the United Arab Emirates, Saudi Arabia, and Singapore choose between leasing, owning, and hybrid property-financing structures and how these choices perform under uncertainty. The empirical framework combines Generalized Structural Equation Modeling with Monte Carlo simulation using macroeconomic and real estate data, latent constructs for strategic orientation, financial constraints, regulatory pressure, and perceived risk, and an outcome indicating the dominant property-financing structure. Measurement reliability is acceptable (Cronbach’s alpha 0.77–0.82, composite reliability 0.83–0.87, average variance extracted 0.57–0.62). Structural estimates show that strategic orientation (β = 0.36) and financial constraints (β = 0.41) have significant effects on property-financing choices, and regulatory pressure also contributes (β = 0.27), and perceived risk reduces the likelihood of ownership (β = −0.38) while mediating strategic and regulatory influences (indirect β = −0.13 and β = −0.17). Country context significantly moderates the impact of financial constraints (β = 0.12) and perceived risk (β = −0.10). Simulation results indicate net present values of 3.75, 2.80, and 4.10 million USD for the United Arab Emirates, Saudi Arabia, and Singapore. The study concludes that property-financing structure is a strategic decision and that the combined structural-simulation framework is a useful tool for analyzing corporate decisions in heterogeneous markets.

Type of the article: Research Article

Abstract
Cryptocurrency markets are highly volatile, making price prediction a complex yet essential task for investors, financial engineers, and institutions. The purpose of this study is to evaluate whether Bayesian optimization of technical indicator parameters significantly improves the forecasting performance of Long Short-Term Memory (LSTM) models compared to baseline configurations. The study used daily Bitcoin and Ethereum price data from January 2016 to September 2025. Six technical indicators representing trend, momentum, volatility, and volume-based technical indicators are constructed and dynamically optimized through Bayesian optimization. The optimized indicators are then used as inputs to an LSTM forecasting framework. The study found that the baseline LSTM model achieved moderate predictive accuracy, where Ethereum outperformed Bitcoin. After optimization, both models exhibited improved performance, reducing the forecasting error for Bitcoin by 36.4% and for Ethereum by 12.2%. LSTM model with Bayesian optimized indicators showed a higher forecasting accuracy as compared to the baseline model, with 32% and 18.6% improvements for Bitcoin and Ethereum, respectively. These findings suggest that combining optimized technical indicators with LSTM models enhances predictive power in cryptocurrency markets. The approach offers a robust forecasting framework for traders, analysts, and algorithmic systems in high-volatility environments.

Acknowledgment
“This work was funded by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. KFU261690].”

Type of the article: Research Article

Abstract
This study investigates the linear and nonlinear effects of ownership structure and financial leverage on corporate financial risk in Vietnam. Using panel data from publicly listed non-financial companies from 2014 to 2023, the analysis applies pooled, random-effects, and fixed-effects logit models, along with robustness checks based on linear probability and feasible generalized least squares estimations. The results reveal that financial leverage significantly raises the risk of financial distress, emphasizing the importance of capital structure in risk assessment. State ownership shows a nonlinear relationship with financial risk, with evidence suggesting a U-shaped pattern that becomes more evident in alternative model specifications. Conversely, institutional ownership and managerial ownership do not show statistically significant effects, indicating limited governance influence of these ownership types in the Vietnamese setting. Among control variables, profitability correlates with lower financial risk, while asset tangibility has a positive relationship; other firm characteristics do not display consistent impacts across models. These findings add to the literature by highlighting the role of ownership structure and leverage in influencing financial risk within institutional constraints. Policy-wise, the results suggest that firms should implement cautious leverage strategies, and regulators should carefully manage state ownership to balance its potential advantages and drawbacks.

Type of the article: Research Article

Abstract
To enhance risk management techniques, this study attempts to evaluate the feasibility of treating volatility as a distinct asset class in portfolio diversification strategies. In particular, it examines dynamic conditional correlations and spillover effects between equity returns across Johannesburg Stock Exchange (JSE) indices and volatility (as determined by the VIX and the South African Volatility Index – SAVI), both before and during the COVID-19 pandemic (2010 to 2022). By extending the analysis beyond the global VIX to also include the local SAVI, the study provides insights into the role of volatility in emerging economies. The study employed the Multivariate GARCH models: the Dynamic Conditional Correlation (DCC) GARCH of Engle and the Baba, Engle, Kraft, and Kroner (BEKK) model proposed by Engle and Kroner. We found evidence of volatility spillovers from the VIX to the South African equity indices. However, the VIX did not exhibit a significant response to volatility in the South African market. The study revealed consistent and significant negative correlations between volatility (VIX & SAVI) and JSE broad market indices, with these correlations further decreasing during the pandemic. Additionally, the SAVI showed notably lower correlations with the JSE market compared to the VIX, suggesting its distinct role in conveying risk perception and market expectations specific to the JSE.

Type of the article: Research Article

Abstract
This study examines the funding gap between AI and non-AI startups using a cross-sectional dataset of 2,850 global startups drawn from Kaggle Public Domain. The sample was processed in four stages: text normalization, median imputation, outlier screening, and keyword-based classification. This produced 1,156 AI startups (40.6%) and 1,694 non-AI startups (59.4%), identified through keywords such as "Artificial Intelligence," "Machine Learning," "Deep Learning," "Natural Language Processing," "Computer Vision," and "Generative AI." The dependent variable was each company's last disclosed funding amount in millions of US dollars. Independent variables included AI classification (binary), founding year, employee count, market size in billions of USD, and industry dummy variables. The analysis used multivariable OLS regression with HC3 robust standard errors and Welch's t-tests across 15 industries. The results showed remarkably similar funding levels: $115.18 million for AI startups versus $117.98 million for non-AI startups. Regression analysis found no statistically significant relationship between AI classification and funding (β = 0.89, p = .869), with the model explaining 38.7% of funding variance. Employee count was the strongest predictor (β = 0.13, p < .001), while founding year and market size had no significant effects. These findings challenge the widely held belief that AI startups attract premium investment. As AI matures from a novel technology into standard infrastructure, its signalling power in venture capital markets appears to be fading. What matters most to investors is not a technology label, but how well a business is built and run.

Type of the article: Research Article

Abstract
The study explored the impact of FDI on economic growth in emerging markets (Argentina, China, Colombia, Indonesia, Mexico, Peru, Republic of Korea, Turkey, Thailand, South Africa, Philippines, Malaysia, India, Czech Republic, and Brazil). Panel data ranging from 2007 to 2021 were used in this study. The impact of the interaction term (FDI x technology) on economic growth was also investigated using panel-corrected standard errors. The panel threshold approach was employed to examine the FDI threshold level that enables significant positive economic growth. Available literature shows that the nexus between FDI, technology, and economic growth remains a virgin academic and research area because existing empirical results are divergent, mixed, and quite conflicting. Panel-corrected standard errors show that FDI significantly reduces economic growth, whilst higher levels of FDI above the threshold level of 3.16 significantly enhance economic growth in emerging markets. Both econometric approaches employed noted that technology significantly improved economic growth, consistent with theoretical predictions. The conclusion is that higher levels of FDI and technology are of paramount importance in promoting economic growth. This economic grouping was selected because it experienced a rapid surge in FDI, economic growth, and technology in the last two decades, yet research on this topic remains very limited. Emerging markets are therefore encouraged to implement FDI and technology-enhancing policies to promote sustainable economic growth and development.

Type of the article: Research Article

Abstract
This study examines how medium-sized enterprises improve financial performance through environmental awareness and digital financial literacy, with accounting information-based decision making as a mediating mechanism. The study focuses on medium-sized enterprises operating in East Kalimantan, particularly in the cities of Samarinda, Balikpapan, and Bontang, Indonesia, as this region represents a rapidly developing economic area with increasing environmental and digitalization challenges. Data were collected from 244 business owners and managers using stratified random sampling from June 2025 to September 2025. Partial Least Squares Structural Equation Modeling (PLS-SEM) is employed for analysis. The empirical results reveal that digital financial literacy has a stronger influence on financial performance than environmental awareness, emphasizing the dominant role of digital capability in driving firm outcomes. Furthermore, accounting information-based decision making significantly mediates these relationships, confirming its role as a crucial mechanism through which internal capabilities are translated into improved financial outcomes. The model also demonstrates strong explanatory and predictive power, indicating that the proposed framework effectively captures the key determinants of financial performance. The findings show that environmental awareness and digital financial literacy significantly enhance financial performance both directly and indirectly through accounting information-based decision making. The mediating role of decision making emerges as a key mechanism linking internal capabilities to firm performance, while government support does not significantly moderate these relationships. These results highlight the importance of internal behavioral and cognitive capabilities in strengthening the financial performance of medium-sized enterprises.

Type of the article: Research Article

Abstract
The growing financialization of non-financial firms has raised concerns about resource allocation and the effectiveness of monetary policy, particularly in emerging economies where financial markets remain bank-oriented. This study analyzes the impact of monetary policy on the financial investment behavior of listed non-financial firms in Vietnam, with a specific focus on the moderating role of state ownership. Using an unbalanced panel of 545 firms observed over the period 2007–2024, the generalized method of moments is employed to address endogeneity and dynamic adjustment in firms’ investment decisions. Monetary policy is proxied by key interest rate measures, while state ownership is captured using both continuous ownership ratios and a dummy variable for controlling state ownership. The empirical results show that a one percentage point reduction in interest rates increases financial investment by about 0.08–0.10 percentage points. However, this effect diminishes as state ownership rises and becomes insignificant at moderate ownership levels. For firms with controlling state ownership, monetary easing exerts a negative effect on financial investment, indicating a reversal in the transmission mechanism. These findings point to a structural divergence in financial behavior across ownership types and highlight the importance of ownership structure in shaping firms’ responses to monetary policy. From a policy perspective, the results underscore the need to consider heterogeneity in ownership when evaluating monetary policy effectiveness and to closely monitor corporate financialization, which may crowd out productive investment.

Acknowledgment
This study is part of Nguyen Hoai Diem Lam’s doctoral dissertation at Ho Chi Minh University of Banking, carried out under the academic supervision of Van Dan Dang and Hoang Chung Nguyen.

Type of the article: Research Article

Abstract
The intersection of Sharia governance practices and Islamic microfinance institutions’ (IMFi) financial resilience in Indonesia is critical to understand, especially given the unique nature and growing importance of Sharia-compliant financial systems in modern economies. This study examines the relationship between Sharia governance practices and IMFi’s financial resilience in Indonesia. The exploratory factor analysis and a Partial Least Squares Structural Equation Model were employed. The results of the exploratory factor analysis identified three distinct components of financial resilience for IMFi: financial performance reflecting the institution’s profitability and efficiency; financial adaptability, indicating its capacity to adjust to economic changes and shocks; and financial robustness, measuring the strength of its capital base and risk management systems to absorb losses. Structural equation modeling using Partial Least Squares reveals a complex relationship between Sharia governance practices and financial resilience; specifically, a strong Sharia governance framework and an institution’s transparency and confidentiality were found to have a significant positive impact on overall financial resilience. Rigid consistency procedures and a more prominent role of the Sharia board were unexpectedly found to negatively influence financial resilience, suggesting that excessive procedural rigidity or overly conservative board oversight might hamper an institution’s ability to respond flexibly to financial challenges. The findings extend contingency- and resource-based theory by evidencing the differential impact of sharia governance practices on financial resilience. This study recommends that managers prioritize transparent reporting systems and regulators mandate a clear governance structure for Indonesia’s Islamic microfinance institutions.

Acknowledgment
The authors gratefully acknowledge the support of the Ministry of Higher Education, Research, and Technology of the Republic of Indonesia for providing the necessary research funding (SP DIPA-0139.04.1.693320/2025 and Contract number: KPt/001/LPPM-UNES/VI/2025).

Type of the article: Research Article

Abstract
Voluntary disclosure theory predicts that managers delay bad-news disclosure and accelerate good-news disclosure when firm-specific uncertainty rises. However, this prediction may not hold in low-trust frontier markets. This study aims to determine whether lagged idiosyncratic volatility changes the timing of good- and bad-news voluntary disclosure in Vietnam and whether environmental, social, and governance performance, analyst coverage, and state ownership moderate that relation. The study uses hand-collected voluntary disclosures from 210 Vietnamese non-financial listed firms over 2018–2024 and estimates probit models with firm and year-month fixed effects on 5,122 firm-month observations. The results show a reversal of the developed-market pattern. A one-standard-deviation increase in lagged idiosyncratic volatility raises the probability of bad-news disclosure by about 12 percentage points and lowers the probability of good-news disclosure by more than 17 percentage points. Higher environmental, social, and governance performance does not weaken this asymmetry and instead amplifies it; analyst coverage provides no mitigating effect, and the reversal is stronger in state-owned enterprises. Robustness tests using two-way clustering, paired cluster bootstrap, random subsample splits, and placebo volatility confirm the pattern. The findings indicate that disclosure incentives under uncertainty depend on institutional trust and ownership structure, so governance mechanisms effective in mature markets cannot be assumed to operate similarly in Vietnam.

Acknowledgment(s)
This research was funded by Ho Chi Minh City University of Technology and Engineering (HCMUTE), Vietnam, under grant number T2025-143.

Type of the article: Research Article

Abstract
Behavioral theories, established in sociological and psychological models, provide intriguing descriptions and explanations of anomalies in the market and market inefficiencies. India represents itself as one of the fastest-growing economies on a global scale. The present article explores the role of nine behavioral biases in investment behavior, particularly by addressing the mediating effect of neuroticism among Indian investors. This research framework was developed by an in-depth literature analysis; hypotheses were tested experimentally using SPLS (smart partial least squares) and SEM (structural equation modeling) on a sample of 450 participants from October 1, 2024, to December 30, 2025, and utilized a structured adopted questionnaire to acquire data from retail investors. Anchoring β = 0.267, hindsight β = 0.088, mental accounting β = 0.249, and overconfidence β = 0.164 display a noticeably positive impact on investment behavior. Conversely, self-attribution β = -0.283 shows a significantly adverse impact. However, the disposition effect, emotional bias, herding behavior, and representativeness appear to exert an insignificant impact on investment behavior. The neuroticism trait β = 0.157 has a significantly positive impact on investment behavior. The findings show that anchoring β = -0.023, the disposition effect β = 0.030, emotional bias β = 0.023, herding β = 0.032, mental accounting β = -0.019, and overconfidence β = -0.031 in behavioral finance significantly impact investment behavior indirectly through neuroticism. This model explains 31.1% of the variance in biases; hence, it enhances the mediating role of neuroticism in shaping investment behavior.

Type of the article: Theoretical Article

Abstract
A common assumption in corporate finance is that firms maximize profits. However, in countries with weak tax administration and limited contract enforcement, firms may understate reported pre-tax earnings to reduce tax liabilities through evasion. This paper revisits the canonical threshold-based earnings management framework and extends it by incorporating an additional (often illegal) incentive to reduce corporate income payments. Simulation results indicate that manipulation does not significantly change when latent earnings are negative. In contrast, when latent earnings are moderately positive, firms combine legal earnings management with illegal underreporting to reduce reported earnings for two purposes: (i) to shift earnings forward to increase the likelihood of meeting next-period benchmark and (ii) to lower current tax payments. At higher earnings levels, both channels plateau as manipulation costs, marginal legal costs, and detection risk increase. Using distributional (histogram-based) diagnostics, discontinuity tests, and a Probit regression model, we find that Ukrainian companies in 2024 were more focused on reducing taxable income than on beating the zero-earnings benchmark. Excess mass is concentrated in the first positive bin, and it is associated with a higher effective tax rate and lower discretionary accruals. Overall, the results suggest that in weak-enforcement settings, tax-motivated earnings suppression can dominate classic threshold-beating incentives.

Type of the article: Research Article

Abstract
Rising global uncertainty and rapid digital transformation have significantly altered volatility dynamics and cross-market linkages in Southeast Asian equity markets, raising critical questions regarding market efficiency and regional financial integration. This study investigates weak-form market efficiency and examines short-term and long-term integration dynamics across the capital markets of Indonesia, Malaysia, Singapore, the Philippines, Thailand, and Vietnam during the digital disruption period. Monthly stock index data spanning January 2020 to July 2025 are analyzed using quantitative time-series techniques to assess return behavior, information transmission, and long-run market relationships.
The empirical results indicate that all sampled markets broadly exhibit weak-form efficiency, as evidenced by random return behavior and stationary price series. Nevertheless, statistically significant short-term dependence persists, suggesting partial and time-varying inefficiencies. The findings further reveal strong long-term integration among ASEAN capital markets, while short-term interactions remain asymmetric. In particular, the Indonesian market functions as a unidirectional information transmitter to the Thai market. Additional analysis shows that shocks originating from the Philippine market account for approximately 25–28 percent of fluctuations in the Indonesian market, whereas the contribution of the Singapore market is limited to around 5–6 percent, reflecting a higher degree of market independence. Overall, the results suggest that ASEAN capital markets are structurally integrated in the long run, although uneven short-term information transmission continues to limit regional diversification opportunities.

Type of the article:

Abstract
The changing economy, macroeconomic factors, political decisions, and investor sentiment contribute to the dynamic nature of any financial market. Conventional econometric models are constrained by linear assumptions and rigid structures. This study aims to comparatively evaluate the predictive performance of selected deep learning models for forecasting stock index movements in the Indian equity market using multiple evaluation metrics, including Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Directional Accuracy (DA). NSE index daily closing values from January 2017 to June 2025 are analyzed using Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM) networks. The findings reveal significant disparities in forecasting precision among the models. The CNN model achieves the lowest error, with a Mean Absolute Percentage Error (MAPE) of 0.63, followed by LSTM at 0.72, while ANN records a higher error of 0.89. When benchmarked against ARIMA and Random Walk models, which exhibit substantially higher errors (MAPE: 1.21 and 1.47), the findings indicate improved predictive capability beyond trend-following behavior. Statistical validation using the Diebold–Mariano test confirms that deep learning models significantly outperform benchmark approaches (p < 0.05). Confidence interval analysis indicates that CNN and LSTM provide stable predictions. These results suggest that CNN models are particularly effective in capturing short-term market dynamics, whereas LSTM models perform better in modeling temporal dependencies. Overall, deep learning approaches demonstrate superior capability in handling the nonlinear characteristics of financial time series compared to conventional econometric models.

Acknowledgment / Funding Statement
The authors acknowledge Kingdom University, Bahrain, for funding article processing charges through research grant number KU-SRU-BA-01.

Type of the article: Research Article

Abstract
The increasing importance of intangible resources and narrative disclosure in a knowledge-based economy has raised critical questions regarding their role in shaping corporate investment behavior, particularly within state-owned enterprises operating under complex institutional constraints. This study examined the direct effect of intellectual capital on investment decisions and evaluated the mediating roles of annual and audit report readability in this context. The empirical analysis is based on a balanced pooled data set of 20 non-financial Indonesian State-Owned Enterprises (SOEs) listed on the Indonesia Stock Exchange for the period 2018–2023, totaling 120 firm-year observations. These firms, representing strategic sectors such as infrastructure, energy, and telecommunications, were selected due to their high asset intensity and pivotal role in national development under bureaucratic oversight. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the results reveal that intellectual capital has a significant positive effect on investment decisions (β = 0.401; t = 7.075), explaining 18.7% of the variance, which increases to 53.6% when incorporating firm-level control variables. Conversely, intellectual capital does not significantly influence report readability, and neither readability measure affects investment decisions, indicating an absence of mediation effects. Firm size emerges as the primary determinant, while profitability and leverage remain insignificant. In conclusion, investment decisions in Indonesian SOEs are driven by internal resource efficiency and organizational scale rather than narrative clarity. These findings suggest that in bureaucratic environments, report readability reflects compliance-oriented disclosure rather than effective informational signaling, highlighting a transparency paradox.

Acknowledgment
This research was made possible through the full support of Universitas Riau, particularly via its Institute for Research and Community Service.

Type of the article: Research Article

Abstract
The study provides a comparative empirical assessment of the financial stability of waste management companies in Ukraine and the EU under geopolitical fragmentation and war-induced shocks. The sample includes 2,371 firms from the Orbis database operating in waste collection, recycling, and disposal from 2019 to 2023. The methodology combines correlation analysis, K-means clustering, and robustness checks using hierarchical and DBSCAN algorithms. Financial stability is measured by liquidity, solvency, and profitability. Wartime data gaps are addressed using median and k-NN imputation. The results identify three financial profiles (resilient, balanced, at-risk) in both regions but reveal profound structural asymmetries. In the EU, even at-risk companies maintain positive solvency (13.9%) and marginal ROA (0.81%), reflecting stable institutional conditions. In contrast, Ukrainian at-risk firms exhibit critical financial distress, with near-zero solvency (0.09%) and deeply negative profitability (ROA (–11.2%), ROE (–15.9%)), indicating capital destruction due to war-related shocks. A key finding is the weakening of financial coherence in Ukraine, with a significantly lower correlation between ROA and liquidity (0.60) than in the EU (0.78), confirming a structural break after 2022. Additionally, resilient Ukrainian firms show abnormally high liquidity (Current Ratio 7.91 vs. 3.51 in the EU), indicating precautionary cash hoarding and investment paralysis under extreme uncertainty. The findings confirm that geopolitical shocks transform financial behavior, whereby EU companies maintain efficiency-driven models and Ukrainian firms shift toward survival-oriented strategies. The study offers micro-level evidence of war-induced disruption to financial stability and proposes policy measures for recapitalization, green finance, and alignment with EU sustainability frameworks.

Type of the article: Research Article

Abstract
The rapid growth of cryptocurrency has generated significant debate in Muslim-majority societies, where financial transactions are expected to conform to Islamic legal principles. In Indonesia, the Indonesian Ulema Council (MUI) issued a fatwa in 2021 declaring the use of cryptocurrency as a means of exchange unlawful (haram) on the grounds that it contains elements of excessive uncertainty and harm. Despite this religious prohibition, the government continues to permit cryptocurrency trading as a regulated commodity, creating a complex normative environment that shapes public discourse. This study analyzes the sentiment of Indonesian Muslims toward cryptocurrency by applying Natural Language Processing (NLP) techniques to a corpus of 1,065 Indonesian-language tweets collected from the X (formerly Twitter) platform over six months (October 2024–March 2025). The IndoBERT-base-p1 model, a state-of-the-art transformer-based language model pre-trained on Indonesian text, was employed for three-class sentiment classification (positive, neutral, negative). The results indicate that neutral sentiment is dominant, accounting for 80.8% of the corpus (861 tweets), while positive sentiment represents 12.9% (137 tweets) and negative sentiment 6.4% (67 tweets). The predominance of neutral sentiment suggests that the majority of Indonesian Muslim social media users maintain a cautious and observational stance, shaped by ongoing scholarly debate, limited digital financial literacy, and regulatory ambiguity. The study contributes to the literature by providing the first application of a deep learning NLP model to the analysis of Indonesian Muslim discourse on cryptocurrency, integrating perspectives from Islamic finance and computational linguistics.

Acknowledgment
The substantial financial support for this study is sponsored by the Ministry of Religious Affairs (MoRA) and the Indonesia Endowment Fund for Education (LPDP) of the Ministry of Finance of the Republic of Indonesia.