While the MASA framework demonstrates significant potential in portfolio risk management, several limitations exist that could hinder its effectiveness in more complex market dynamics and investor behaviors:
Market Assumptions: The current framework may rely on certain assumptions about market efficiency and investor rationality, which do not always hold true in real-world scenarios. To address this, the framework can incorporate behavioral finance principles to model irrational investor behaviors, such as overconfidence and herd behavior, which can lead to market anomalies.
Scalability: As the number of assets in a portfolio increases, the computational complexity of the MASA framework may rise significantly. To improve scalability, the framework can implement parallel processing techniques and more efficient algorithms for the RL and solver-based agents, allowing them to handle larger datasets and more complex portfolios without compromising performance.
Data Quality and Availability: The effectiveness of the MASA framework is contingent on the quality and availability of market data. Inaccurate or incomplete data can lead to suboptimal decision-making. Enhancing the framework with robust data preprocessing techniques and incorporating alternative data sources (e.g., sentiment analysis, macroeconomic indicators) can improve the reliability of the market observer agent.
Risk Management Enhancements: The current risk management strategies may not fully capture extreme market events or tail risks. Implementing advanced risk measures, such as Value at Risk (VaR) and Conditional Value at Risk (CVaR), can provide a more comprehensive view of potential losses. Additionally, integrating stress testing and scenario analysis can help the framework prepare for adverse market conditions.
Real-time Adaptation: The MASA framework could benefit from real-time adaptation capabilities, allowing it to respond to sudden market changes more effectively. Incorporating online learning techniques can enable the agents to update their strategies dynamically based on incoming market data, enhancing their responsiveness to market shocks.