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.