Search Results (138)

Bank operations are contingent upon investor confidence, particularly during periods of economic distress. If investor confidence drops, a bank faces difficulties obtaining money, higher borrowing costs, and lower stock values. Non-performing loans (NPLs) potentially jeopardize a bank’s long-term viability and short-term profitability, and investors are naturally wary of institutions that pose a high credit risk. The purpose of the study was to explore how non-performing loans influence investor confidence in banks. A purposive sampling technique was used to identify 253 New York Stock Exchange banks in the Thomson Reuters Eikon DataStream that satisfied all the inclusion and exclusion selection criteria. The Common Correlated Effects Mean Group (CCEMG) and Generalized Method of Moments (GMM) models were used to analyze the data, providing insight into the relationship between the variables. The study discovered that NPLs had a negative and significant influence on price–earnings (P/E) and price-to-book value (P/B) ratios. Furthermore, the bank’s age was found to have a positive and significant relationship with the P/E and P/B ratio. The moderating relationship between NPLs and bank age was found to have a negative and significant influence on price–earnings (P/E) and price-to-book value (P/B) ratios. The findings underscore the importance of asset quality and institutional reputation in influencing market perceptions. Bank managers should focus on managing non-performing loans effectively and leveraging institutional credibility to sustain investor confidence, particularly during financial distress. Full article

Breast cancer (BC) is the leading cancer diagnosis among women in Puerto Rico. Psychological distress is prevalent in this population, and social determinants may exacerbate this risk. This study examines whether sociodemographic characteristics, financial burden, and social support levels are associated with symptoms of anxiety and depression in Puerto Rican women with BC. A quantitative secondary analysis was conducted on a sample of 208 Hispanic women with BC, utilizing the Patient Health Questionnaire (PHQ-8) and the Generalized Anxiety Disorder (GAD-7) questionnaire. These scores were compared with sociodemographic values and Interpersonal Support Evaluation List (ISEL-12) scores, establishing statistical significance through association, parametric, and non-parametric tests, and regression models. 38.5% and 26.4% of participants showed clinically significant symptoms of depression and anxiety, respectively. Age and perceived income showed significant associations with psychological outcomes. However, regression analysis revealed perceived income as the only significant predictor for both depression and anxiety. Tangible and belonging support were significantly lower in participants with symptoms of depression, while appraisal support was significantly lower in participants with symptoms of anxiety. Findings highlight the influence of perceived financial stress on mental health and the need for psychosocial interventions tailored to the patients’ economic context. Full article

One of the key challenges in financial distress data is class imbalance, where the data are characterized by a highly imbalanced ratio between the number of distressed and non-distressed samples. This study examines eight resampling techniques for improving distress prediction using the XGBoost algorithm. The study was performed on a dataset acquired from the CSMAR database, containing 26,383 firm-quarter samples from 639 Chinese A-share listed companies (2007–2024), with only 12.1% of the cases being distressed. Results show that standard Synthetic Minority Oversampling Technique (SMOTE) enhanced F1-score (up to 0.73) and Matthews Correlation Coefficient (MCC, up to 0.70), while SMOTE-Tomek and Borderline-SMOTE further boosted recall, slightly sacrificing precision. These oversampling and hybrid methods also maintained reasonable computational efficiency. However, Random Undersampling (RUS), though yielding high recall (0.85), suffered from low precision (0.46) and weaker generalization, but was the fastest method. Among all techniques, Bagging-SMOTE achieved balanced performance (AUC 0.96, F1 0.72, PR-AUC 0.80, MCC 0.68) using a minority-to-majority ratio of 0.15, demonstrating that ensemble-based resampling can improve robustness with minimal impact on the original class distribution, albeit with higher computational cost. The compared findings highlight that no single approach fits all use cases, and technique selection should align with specific goals. Techniques favoring recall (e.g., Bagging-SMOTE, SMOTE-Tomek) are suited for early warning, while conservative techniques (e.g., Tomek Links) help reduce false positives in risk-sensitive applications, and efficient methods such as RUS are preferable when computational speed is a priority. Full article

The COVID-19 pandemic significantly disrupted higher education worldwide, imposing strict isolation measures, transitioning learning online, and exacerbating existing social and economic inequalities. This literature review examines the pandemic’s impact on the mental health of college students, with a focus on those belonging to minority groups, including racial, ethnic, migrant, gender, sexuality-based, and low-income populations. While elevated levels of anxiety, depression, and loneliness were observed across all students, findings indicate that LGBTQ+ and low-income students faced the highest levels of psychological distress, due to compounded stressors such as family rejection, unsafe home environments, and financial insecurity. Racial and ethnic minority students reported increased experiences of discrimination and reduced access to culturally competent mental healthcare. International and migrant students were disproportionately affected by travel restrictions, legal uncertainties, and social disconnection. These disparities underscore the need for higher education institutions to implement targeted, inclusive mental health policies that account for the unique needs of at-risk student populations during health crises. Full article

This study reframes closure intention in small- and medium-sized enterprises (SMEs) as an ex ante diagnostic signal rather than a post-mortem symptom of failure. The survey evidence from 385 Ecuadorian SMEs was analyzed in two stages; confirmatory factor analysis validated the scales capturing environmental pessimism and personal pressures, and a structural equation model confirmed that both latent constructs directly heighten exit propensity. A binomial logistic regression model correctly classified 71% of the cases and explained 30% of variance. Five variables proved decisive: low-level liquidity (OR = 0.84), a high debt-to-equity ratio (1.41), weak profitability (0.14), negative environmental perceptions (1.72), and a shorter operating tenure (0.91); the sector and the firm size were non-significant. The combined CFA-SEM-logit sequence yields practical early warning thresholds—debt-to-equity ratio > 1.4, current ratio < 1.0, and ROA < 0.15—that lenders, advisers, and entrepreneurs can embed in dashboards or credit screens. Recognizing closure intention as a rational, strategic step challenges the stigma surrounding exit and links financial distress and the strategic exit theory. Policymakers can use the findings to pair debt relief and liquidity programs with cognitive bias training that helps owners interpret risk signals realistically. For scholars, the results highlight closure intention as a dynamic learning process, especially pertinent in emerging economies characterized by informality and institutional fragility. Full article

Background/Objectives: Hypertension, diabetes, and cancer are three prevalent chronic conditions with distinct etiologies and significant global health impacts. This study aimed to explore the diverse impacts of different chronic illnesses on health behaviors and psychological well-being, with a focus on identifying and addressing the unique challenges faced by individuals with hypertension, diabetes, and cancer. It was hypothesized that health behaviors and psychological well-being would differ significantly among individuals with hypertension, diabetes, and cancer, reflecting the distinct demands and psychosocial impacts of each condition. Methods: The database of Americans’ Changing Lives, Wave 6, including 767 participants, was used (56.1% hypertension, 20.8% diabetes, and 19.9% cancer cases). Variables concerning physical and mental health issues were chosen. Descriptive statistics summarized the data. Chi-squared and t-tests assessed associations and group differences, with effect sizes reported. Logistic regression examined predictors of hypertension, diabetes, and cancer. Sensitivity analyses excluded outliers. Results: Hypertensive individuals are more likely to show cognitive impairment and unhealthy behaviors, including poor self-rated health, higher BMI, lower physical activity, and altered alcohol use. Risk increases with age, widowhood, retirement, hospital admissions, and poor mental health, while more emergency room or doctor visits slightly reduce it. People with diabetes experience greater depressive symptoms, hopelessness, and financial stress. They also tend to have poorer self-rated health, higher BMI, and less physical activity. Risk is higher for separated individuals and lower for females. Psychological distress is a key factor, while age, employment, and healthcare use show minimal influence. Cancer is linked to chronic stress, poorer perceived health, and mental health challenges. Risk is higher among older adults and those who keep house. Poor self-rated health, high BMI, low fruit and vegetable intake, and psychological distress increase risk, but healthcare use is not a strong predictor. Conclusions: While different chronic illnesses present distinct challenges to health behaviors and psychological well-being, they also share common features-such as increased stress and lifestyle disruptions-underscoring the importance of both tailored and cross-cutting interventions to effectively support individuals across conditions. Full article

This study addresses the increasing emphasis on sustainability and the importance of understanding how environmental risk influences business failure, a factor unexplored in traditional financial prediction models. Environmental risk, or environmental financial exposure, refers to the potential percentage of a company’s revenue at risk due to the environmental damage it causes. Previous research has not sufficiently integrated environmental variables into failure prediction models. This study aims to determine whether environmental risk significantly predicts business failure and how it interacts with conventional financial indicators. Utilizing data from 971 Spanish cooperative companies in 2022, including financial ratios, the VADIS bankruptcy propensity indicator, and the TRUCAM environmental risk score, the study employs the Extreme Gradient Boosting (XGBoost) machine learning algorithm, chosen for its robustness in handling multicollinearity and nonlinear relationships. The methodology involves training and validation samples, cross-validation for hyperparameter tuning, and interpretability techniques such as variable importance analysis and partial dependence plots. Results demonstrate that the variable related to environmental risk (TRUCAM) ranks among the top predictors, alongside liquidity, profitability, and labor costs, with higher TRUCAM values correlating positively with failure risk, underscoring the importance of sustainable cost management. These findings suggest that firms facing substantial environmental risk are more prone to financial distress. By incorporating this environmental variable into a machine learning framework, this work contributes to the interaction between sustainability practices and corporate viability. Full article

In the context of new quality productive forces, enterprises must leverage technological innovation and intelligent management to enhance financial risk resilience. This article proposes a financial distress prediction model based on deep learning, combined with a CNN, BiLSTM, and attention mechanism, using SMOTE for sample imbalance and Hyperband for hyperparameter optimization. Among four CNN-BiLSTM-AT model structures and seven mainstream models (CNN, BiLSTM, CNN-BiLSTM, CNN-AT, BiLSTM-AT, CNN-GRU, and Transformer), the 1CNN-1BiLSTM-AT model achieved the highest validation accuracy and relatively faster training speed. We conducted 100 repeated experiments using data from two companies, with validation on 2025 data, confirming the model’s stability and effectiveness in real-world scenarios. This article lays a solid empirical foundation for further optimization of financial distress warning models. Full article

Background/Objectives: Financial distress from the direct and indirect costs of cancer treatment is a critical issue for many patients with breast cancer, particularly those from underserved populations who may be more vulnerable to financial hardship and its negative impacts on quality of life and clinical outcomes (i.e., financial toxicity). Few investigations, however, focus on protective factors that safeguard against financial toxicity. This study explores how social support might reduce financial toxicity among patients with breast cancer who are at high risk for financial hardship. Methods: We analyzed interviews with 41 adult women treated for stage I-IV breast cancer that had been conducted between December 2021 and March 2022. Our study specifically sampled women considered to be at elevated risk for financial toxicity: young adults aged 18–40 years old, Black women, women with lower incomes, and those residing in rural communities. We used deductive and inductive coding to identify themes related to social support. Results: Interviewees reported receiving support from family, friends, and their communities during their treatments. They noted how this social support helped with direct and indirect costs, encouraged emotional wellbeing, and safeguarded against economizing behaviors that offset spending (e.g., financial tradeoffs that jeopardize their treatment plan). Conclusions: Patients with breast cancer from groups vulnerable to financial toxicity often rely on the support of family, friends, and their communities to help buffer financial distress from the costs of treatment. These data highlight social support as an area for future studies exploring strategies to mitigate financial toxicity. Full article

Debt levels are a crucial factor when assessing the financial stability of agricultural firms, and excessive indebtedness is usually the most important indicator of financial distress. As agriculture is a capital-intensive sector with a high reliance on borrowed funds, firms in this sector are more vulnerable to insolvency. This study examines the performance of artificial neural networks (ANNs) and decision trees (DTs) in predicting the bankruptcy of Slovak agricultural enterprises. In an attempt to compare the models’ performances, the most consequential indebtedness ratios are investigated through machine learning approaches. ANN and DT models are found to perform significantly better than traditional forecast methods. ANN achieved an AUC of 0.9500, accuracy of 96.37%, precision of 96.60%, recall of 99.68%, and an F1-score of 98.12%, determining its robust predictive ability. DT performed a little better on AUC (0.9550) and achieved an accuracy of 97.78%, precision of 98.69%, recall of 99.01%, and an F1-score of 98.85%, determining its predictive ability and interpretability. These findings confirm the potential for applying AI-based models to enhance financial risk assessment. This study provides informative results for financial analysts, policymakers, and corporate managers in support of early intervention strategies. Additional research would be required to explore state-of-the-art AI techniques to further refine bankruptcy forecasting and financial decision-making in vulnerable sectors like agriculture. Full article

Understanding the time dynamics of systemic risk in banking networks is crucial for preventing financial crises and ensuring economic stability. This paper aims to quantify key transition times in the evolution of distress within a banking system using a mathematical framework. We investigate the dynamics of systemic risk in a hypothetical, homogeneous banking network using the Undistressed–Exposed–Distressed–Recovered (UEDR) model. The UEDR model, inspired by compartmental epidemic frameworks, captures how financial distress propagates and recedes through interactions between banks. It is selected because of its tractability and its ability to distinguish between different stages of bank vulnerability. We focus on two critical times, denoted as $t_1$ and $t_2$, which play a fundamental role in understanding the behavior of the distressed compartment (representing the number of distressed banks) over time. The time $t_1$ represents the first instance of a decrease in the number of distressed banks, indicating the containment of systemic risk. On the other hand, the time $t_2$ marks the onset when the number of undistressed banks falls below a specified threshold, signifying the restoration of financial stability. We examine these time dependencies by considering the initial conditions of the UEDR model and assess their characteristics using partial differential equations. We establish the continuity, smoothness, and uniqueness of solutions for $t_1$ and $t_2$, along with their corresponding boundary conditions. Furthermore, we provide explicit representation formulas for $t_1$ and $t_2$, allowing for precise estimation when the initial population compartments are large. Our results provide practical insights for financial regulators and policymakers in determining time-sensitive interventions for mitigating systemic risk and accelerating recovery in banking systems. The findings highlight how mathematical modeling can inform real-time risk management strategies in financial networks. Full article

Background: The COVID-19 pandemic, coupled with social distancing measures and economic disruptions, has been associated with increased experiences of loneliness and financial strain. While prior research has examined their separate associations with suicidal ideation, limited attention has been given to their joint relationship. Methods: We used data from the 2022 Mental Health and Access to Care Survey (MHACS) (n = 9861; ages 15+ in Canada) to assess whether financial strain modifies the association between loneliness or emotional distress and suicidal ideation. Multivariable survey-weighted logistic regression was conducted, adjusting for sociodemographic, economic, psychosocial, and health-related characteristics, including mental health and substance use conditions. Results: Among the 9743 respondents who answered the question on suicidal ideation, 355 (3.65%) reported suicidal ideation. Compared to individuals with neither stressor, those who experienced loneliness or emotional distress alone had 1.54 times higher odds of suicidal ideation (aOR = 1.54, 95% CI: 1.29–1.84, p < 0.001), while those who reported financial strain alone had 0.58 times the odds (aOR = 0.58, 95% CI: 0.43–0.80, p = 0.001). The highest odds were observed among individuals who experienced both loneliness/emotional distress and financial strain, with an adjusted odds ratio of 2.05 (95% CI: 1.71–2.45, p < 0.001), indicating an interaction between these stressors. Conclusion: The co-occurrence of loneliness or emotional distress and financial strain was associated with higher odds of suicidal ideation during the COVID-19 pandemic, compared to individuals experiencing neither stressor. These findings highlight the importance of considering both social and economic stressors when assessing mental health risks. Given the cross-sectional nature of this study, further longitudinal research is needed to explore the temporal relationships and potential causal pathways linking these experiences to suicidal ideation. Full article

Corporate performance is increasingly impacted by environmental issues, but their specific role in business failure remains underexplored, which leads to a gap in research that is often focused exclusively on financial metrics. By investigating the relationship between environmental financial exposure and business failure, this study addresses this gap, integrating financial ratios and environmental variables to understand how environmental performance affects financial viability. A novel dual-stage methodology was employed, first using Extreme Gradient Boosting (XGBoost) for feature selection to identify the most significant predictors of failure from a dataset of Spanish companies (N = 38,456) using 2022 ORBIS data. Next, a fuzzy-set qualitative comparative analysis (fsQCA) was applied to analyze the sufficient causal configurations leading to a high propensity for business failure. The analysis identified three distinct causal configurations associated with failure. All highlighted poor financial performance indicators, such as low results per employee and low profit per employee. Notably, one configuration identified high environmental risk (measured by TRUCAM) as a core condition contributing significantly to financial distress. These findings highlight the critical link between environmental responsibility and financial health, demonstrating the benefits of combining fsQCA with machine learning to identify intricate causal configurations and providing information to companies and governments who want to support long-term financial stability and corporate sustainability. Full article

Financial distress poses a significant risk to companies worldwide, irrespective of their nature or size. It refers to a situation where a company is unable to meet its financial obligations on time, potentially leading to bankruptcy and liquidation. Predicting distress has become a crucial application in business classification, employing both Statistical approaches and Artificial Intelligence techniques. Researchers often compare the prediction performance of different techniques on specific datasets, but no consistent results exist to establish one model as superior to others. Each technique has its own advantages and drawbacks, depending on the dataset. Recent studies suggest that combining multiple classifiers can significantly enhance prediction performance. However, such ensemble methods inherit both the strengths and weaknesses of the constituent classifiers. This study focuses on analyzing and comparing the financial status of Indian automobile manufacturing companies. Data from a sample of 100 automobile companies between 2013 and 2019 were used. A novel Firm-Feature-Wise three-step missing value imputation algorithm was implemented to handle missing financial data effectively. This study evaluates the performance of 11 individual baseline classifiers and all the 11 baseline algorithm’s combinations by using ensemble method. A manual ranking-based approach was used to evaluate the performance of 2047 models. The results of each combination are inputted to hard majority voting mechanism algorithm for predicting a company’s financial distress. Eleven baseline models are trained and assessed, with Gradient Boosting exhibiting the highest accuracy. Hyperparameter tuning is then applied to enhance individual baseline classifier performance. The majority voting mechanism with hyperparameter-tuned baseline classifiers achieve high accuracy. The robustness of the model is tested through k-fold Cross-Validation, demonstrating its generalizability. After fine-tuning the hyperparameters, the experimental investigation yielded an accuracy of 99.52%, surpassing the performance of previous studies. Furthermore, it results in the absence of Type-I errors. Full article

This article investigates the effectiveness of various classification techniques in predicting financial distress for Albanian firms. The dataset includes 16 financial ratios from the financial statements of 187 of the largest non-financial businesses operating in Albania, covering the period from 2011 up to 2014, and ranked by 2014 revenues. The methods used in predicting financial distress are logistic regression, Ada Boost, Naïve Bayes, decision trees, support vector machine (SVM), neural network, and random forest. To compare the effectiveness of the models applied we used Classification Accuracy (CA), confusion matrix, and area under the curve (AUC) as evaluation criteria. The results demonstrate the superior predictive ability of ensemble methods, with random forest achieving more accurate forecasts than other methods, followed by Ada Boost. The research contributes to the literature by showing the added value of machine learning models in emerging markets with unique practice and economic conditions and proposing an alternative classification approach for the classification of financial distress when lacking bankruptcy data. Finally, the empirical findings evidence that the strengths of ensemble learning methods are reinforced in unbalanced not-big datasets of a unique emerging economy. These insights are relevant for lending institutions and researchers aiming to refine credit risk models in unique markets where access to relevant data is a challenge. Full article