Equity Drawdown Reduction
Exploring how quantitative methods and AI can contribute to protecting investors from behavioral pitfalls while maintaining market participation through academic research and practical application.
Downside Protection with Upside Participation
Our equity drawdown reduction strategies employ dynamic hedging techniques to protect against significant market declines while preserving the ability to capture positive returns.
Avoid Large Drawdowns
Losses lead to difficult behavioral decisions. Research shows investors often sell at the worst times, missing subsequent recoveries and compounding their losses.
Keep the Upside
Staying fully invested in favorable market conditions. Academic studies demonstrate the importance of maintaining market exposure to capture long-term equity premiums.
Use AI to Combine Signals
Make AI work for your protection by systematically combining multiple data sources to identify market stress periods and behavioral risk factors.
Our Approach
QI developed the QI Equity Guard Mechanism which combines liquid derivatives with an in-house trained deep learning model, integrating a large number of data sources known to have predictive value for market stress periods.
Deep Learning Integration
Our in-house trained deep learning model processes multiple alternative data sources to identify early warning signals of market stress. This allows us to save on protection premiums during calm periods while being over-protected during risky times.
Liquid Derivatives Framework
Long-standing experience with derivatives enables sophisticated protection strategies that balance cost efficiency with downside protection. Our approach leverages institutional-grade execution and risk management practices.
Behavioral Finance Application
Working with academics and practitioners, we focus on the most critical challenge: shielding investors from selling at the wrong time and buying into the market too late. Our research addresses systematic behavioral biases in investment decision-making.
Academic Collaboration
Our approach combines academic research in behavioral finance with practical market experience. We collaborate with researchers and practitioners to make AI work for the investor, particularly in avoiding emotionally-driven investment decisions.
Research Framework & Key Metrics
Our research focuses on measurable behavioral and risk metrics that academic literature has identified as critical for long-term investment success.
Maximum Drawdown Management
Historical studies suggest that limiting maximum drawdowns can significantly improve investor behavior and long-term outcomes. Academic research indicates that large losses often trigger emotional selling decisions that harm long-term performance.
Past performance does not predict future results. Market conditions vary significantly.
Upside Participation
Academic research demonstrates the importance of maintaining substantial market exposure during positive periods. Historical analysis shows that missing the best market days can significantly impact long-term returns.
Market timing involves substantial risks. Historical patterns may not repeat.
Downside Protection
Behavioral finance research indicates that investors experience losses more intensely than equivalent gains. Systematic protection strategies aim to reduce the emotional impact of market volatility on investment decisions.
Protection strategies involve costs and may reduce returns in rising markets.
Risk-Adjusted Returns
Academic literature suggests that risk-adjusted return metrics provide better insight into strategy effectiveness than absolute returns. Our research examines how systematic approaches can improve these metrics over time.
Risk metrics are statistical estimates and do not guarantee future performance.