Business and Social Sciences
Business and social sciences | Online ISSN 3067-8919
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Volume 2 Number 1 2024
RESEARCH ARTICLE (Open Access)
Innovative Quantitative Models for Enhancing Financial Resilience in U.S. Capital Markets
Mitu Akter1*, Md. Rezaul Haque2
Business and Social Sciences 2 (1) 1-8 https://doi.org/10.25163/business.2110488
Submitted: 04 July 2025 Revised: 02 September 2024 Accepted: 07 September 2024 Published: 09 September 2024
Abstract
Background: Systemic shocks in capital markets have become more frequent because of rising economic instability and unpredictable geopolitical events and fast technological advancements. The standard risk assessment systems fail to identify institutional relationships between financial entities which results in poor vulnerability prediction capabilities.
Methods: The study obtained data from 277 participants who worked at banks and investment firms and regulatory agencies through structured questionnaire surveys about their risk management procedures. The evaluation process for the model performance included predictive accuracy and false-positive rates and root mean square error and correlation coefficients with their respective p-values.
Results: The novel framework showed superior performance than standard models through its 22% better predictive accuracy at 88% and 19% lower false positive rate at p = 0.01 and 33% reduced RMSE at 0.28. The research demonstrated that predicted stress results showed a strong connection with actual stress results since their correlation coefficient reached 0.81 at p = 0.001. The machine learning clustering process divided the data into three distinct risk categories which included high-leverage derivatives (n = 92), moderate-liquidity (n = 104) and diversified portfolios (n = 81) while network analysis revealed that the top 10% of nodes held 68% of the contagion potential.
Conclusion: The integration of machine learning algorithms with stress testing and network mapping methods results in statistically proven enhanced performance for early-warning detection systems and systemic-risk mitigation which strengthens the resilience of U.S. capital markets.
Keywords: Financial resilience, Quantitative modeling, Systemic risk, capital markets, Machine learning
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