Model-Free Market Risk Hedging Using Crowding Networks
Paper discusses model-free market risk hedging using crowding networks and graph analysis to construct long-short portfolios.
What it examines
This paper analyzes stock crowding using network analysis of fund holdings to compute crowding scores for stocks. These scores are used to construct costless long-short portfolios that provide market risk hedging, including tail risk, without requiring costly option-based strategies or complex numerical optimization.
What it concludes
The research offers a costless, model-free method for hedging market risk using a long-short portfolio based on crowding signals from graph analysis. Potential applications include portfolio risk management and alternative to option-based strategies. Future research could explore dynamic graph models and machine learning for nowcasting fund holdings.
Evidence objects
The research offers a costless, model-free method for hedging market risk using a long-short portfolio based on crowding signals from graph analysis. Potential applications include portfolio risk management and alternative to option-based strategies. Future research could explore dynamic graph models and machine learning for nowcasting fund holdings.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: Crowding is widely regarded as one of the most important risk factors in designing portfolio strategies. In this paper, we analyze stock crowding using network analysis of fund holdings, which is used to compute crowding scores for stocks. These scores are used to construct costless long-short portfolios, computed in a distribution-free (model-free) way and without using any numerical optimization… ▽ More Crowding is widely regarded as one of the most important risk factors in designing portfolio strategies. In this paper, we analyze stock crowding using network analysis of fund holdings, which is used to compute crowding scores for stocks. These scores are used to construct costless long-short portfolios, computed in a distribution-free (model-free) way and without using any numerical optimization, with desirable properties of hedge portfolios. More specifically, these long-short portfolios provide protection for both small and large market price fluctuations, due to their negative correlation with the market and positive convexity as a function of market returns. By adding our long-short portfolio to a baseline portfolio such as a traditional 60/40 portfolio, our method provides an alternative way to hedge portfolio risk including tail risk, which does not require costly option-based strategies or complex numerical optimization. The total cost of such hedging amounts to the total cost of rebalancing the hedge portfolio. △ Less
Source row: 1357 · abstract type: unknown