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.