Publication Search

This qualitative literature review explores the advances and challenges in predicting SME failures, focusing on methodological trends, data imbalance solutions, and model validation practices. Over recent years, machine learning techniques have gained prominence, replacing traditional statistical models and improving predictive accuracy. Key strategies for overcoming data imbalance, such as Synthetic Minority Over-sampling Technique (SMOTE) and cost-sensitive learning, have also been highlighted. However, challenges persist, particularly in model interpretability, generalization, and overfitting. The review emphasizes the need for continuous refinement of predictive models and validation practices to ensure real-world applicability. The findings suggest that while considerable progress has been made, future research should aim to enhance model transparency and address limitations in data representation to improve SME failure prediction across diverse contexts.

This research explores the relationship between public information precision, borrower risk-taking behavior, and financial reporting regulations. It examines how varying levels of accounting disclosures influence creditor-borrower dynamics in financial markets. Enhanced precision in public information, such as accounting earnings, promotes market efficiency by reducing information asymmetry and improving creditors' ability to accurately assess borrower creditworthiness. While higher precision generally mitigates borrower risk-shifting tendencies, regulatory context and economic conditions modulate these effects. This literature review systematically identifies and analyzes peer-reviewed articles on forecast dispersion, accuracy, and their implications for cross-sectional return anomalies in financial markets. The findings reveal that higher forecast dispersion is linked to greater uncertainty and perceived risk, leading to higher expected returns, while accurate forecasts reduce information asymmetry and improve market efficiency. Differences in forecast precision significantly contribute to market anomalies. In conclusion, forecast dispersion and accuracy are critical in explaining cross-sectional return anomalies. Future research should refine models, explore behavioral biases, and evaluate technological advancements, emphasizing balanced financial reporting regulations to harness transparency benefits while mitigating potential costs during economic expansions.