The Effect of Financial Market Regulation on Investor Protection and Systemic Risk Mitigation Frameworks

Posted: Jul 23, 2018

• Sophia Hill
• Harper Torres
• Zoey Martin

Abstract

This research presents a novel computational framework for analyzing the complex interplay between financial market regulation, investor protection mechanisms, and systemic risk mitigation. Unlike traditional approaches that examine these elements in isolation, we develop a multi-agent simulation environment that models the financial ecosystem as an adaptive complex system. Our methodology integrates machine learning techniques with network theory to capture the emergent behaviors and feedback loops that characterize modern financial markets. The framework incorporates heterogeneous agents including retail investors, institutional traders, regulatory bodies, and financial intermediaries, each operating with distinct behavioral patterns and decision-making algorithms. We introduce a novel regulatory impact metric that quantifies the effectiveness of various regulatory interventions across different market conditions. Our results demonstrate that conventional regulatory approaches often create unintended consequences, including regulatory arbitrage and risk migration to unregulated sectors. The simulation reveals that adaptive, data-driven regulatory frameworks that dynamically adjust to market conditions provide superior investor protection while more effectively containing systemic risk compared to static regulatory regimes. Furthermore, we identify critical thresholds in regulatory intensity beyond which additional regulation yields diminishing returns and may even increase systemic fragility. This research contributes to the financial regulation literature by providing a computational laboratory for testing regulatory proposals before implementation, offering policymakers a powerful tool for designing more resilient financial systems.