Category
Applied
Description
This study evaluates regime-based allocation strategies for downside risk management in long-only portfolios. Traditional portfolio risk management relies heavily on derivative instruments like protective puts and futures to mitigate downside. However, student-managed portfolios operate under strict regulatory and educational constraints, and lack continuous oversight required for active derivative management. Academic literature on regime-switching models and tactical asset allocation demonstrates that economic regimes influence portfolio returns, yet practical implementation studies for long-only investors remain underdeveloped. Limited empirical work examines whether systematic allocation adjustments based on regime identification provide downside protection comparable to derivative-based hedging strategies. We seek to address the research question: Can a regime-analysis hedging framework, combining economic regime classification with market stress monitoring, generate greater alpha for a long-only student investment fund compared to hedge fund derivative-based protective put strategies, and can this approach provide actionable allocation rules for future portfolio management? We hypothesize that portfolio allocation adjustments based on regime classification and market stress signals will achieve downside protection comparable to protective put hedging strategies while maintaining alpha, demonstrating that dynamic allocation serves as an effective alternative to derivative-based hedging for long-only investors. This study employs quantitative backtesting to develop a regime-based allocation framework. Building upon our ridge regression model analyzing sector sensitivity to macroeconomic indicators, we will implement a Hidden Markov Model to classify economic regimes from 1991 to the present, and construct a stress index from yield curve dynamics and momentum indicators. These inputs generate a decision matrix analyzing the optimal sector allocation during the regime. Historical backtesting will analyze portfolio performance compared to the protective put strategies, modeled using Black-Scholes pricing. This research provides a hedging alternative for long-only investors. Future research could extend this analysis to evaluate performance against additional hedging strategies and offers a replicable risk management approach for investors.
Hedging Without Derivatives: A Regime-Aware Framework for Managing Downside Risk in Long-Only Portfolios
Applied
This study evaluates regime-based allocation strategies for downside risk management in long-only portfolios. Traditional portfolio risk management relies heavily on derivative instruments like protective puts and futures to mitigate downside. However, student-managed portfolios operate under strict regulatory and educational constraints, and lack continuous oversight required for active derivative management. Academic literature on regime-switching models and tactical asset allocation demonstrates that economic regimes influence portfolio returns, yet practical implementation studies for long-only investors remain underdeveloped. Limited empirical work examines whether systematic allocation adjustments based on regime identification provide downside protection comparable to derivative-based hedging strategies. We seek to address the research question: Can a regime-analysis hedging framework, combining economic regime classification with market stress monitoring, generate greater alpha for a long-only student investment fund compared to hedge fund derivative-based protective put strategies, and can this approach provide actionable allocation rules for future portfolio management? We hypothesize that portfolio allocation adjustments based on regime classification and market stress signals will achieve downside protection comparable to protective put hedging strategies while maintaining alpha, demonstrating that dynamic allocation serves as an effective alternative to derivative-based hedging for long-only investors. This study employs quantitative backtesting to develop a regime-based allocation framework. Building upon our ridge regression model analyzing sector sensitivity to macroeconomic indicators, we will implement a Hidden Markov Model to classify economic regimes from 1991 to the present, and construct a stress index from yield curve dynamics and momentum indicators. These inputs generate a decision matrix analyzing the optimal sector allocation during the regime. Historical backtesting will analyze portfolio performance compared to the protective put strategies, modeled using Black-Scholes pricing. This research provides a hedging alternative for long-only investors. Future research could extend this analysis to evaluate performance against additional hedging strategies and offers a replicable risk management approach for investors.
