---
title: Hedge Fund Strategies
url: https://www.ml-quant.com/papers/repec/eee-ecofin-v-74-y-2024-i-c-s1062940824001657/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:eee:ecofin:v:74:y:2024:i:c:s1062940824001657
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1062940824001657%3Bh%3Drepec%3Aeee%3Aecofin%3Av%3A74%3Ay%3A2024%3Ai%3Ac%3As1062940824001657
featured: 2024-09-10
citations: unknown
topic: Derivatives & Volatility
---


# Hedge Fund Strategies

The study reveals that hedge funds with higher fees and minimum investments are better at hedging geopolitical risks, while global macro hedge funds excel at timing.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1062940824001657%3Bh%3Drepec%3Aeee%3Aecofin%3Av%3A74%3Ay%3A2024%3Ai%3Ac%3As1062940824001657
- Identifier: RePEc:eee:ecofin:v:74:y:2024:i:c:s1062940824001657
- Released: 2024-09-10
- First featured: Quant Letter No. 65 (2024-09-10): https://www.ml-quant.com/issues/2024-09-10/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [Hedge Fund Portfolio Construction Using PolyModel Theory and iTransformer](https://www.ml-quant.com/papers/arxiv/2408.03320/): The article explores the use of PolyModel theory and deep learning in creating hedge fund portfolios for high returns and low risks.
- [PolyModel for Hedge Funds' Portfolio Construction Using Machine Learning](https://www.ml-quant.com/papers/arxiv/2412.11019/): The use of machine learning and PolyModel feature selection in hedge fund investments improves returns and portfolio optimization, but also increases volatility, questioning the reliability of larger funds.
- [Common Ownership and Hedge Fund Activism: An Unholy Alliance?](https://www.ml-quant.com/papers/ssrn/4835079/): The study suggests that common ownership can lead to anticompetitive outcomes, such as reduced wages and wealth transfer to shareholders.
- [Short-Selling Hedge Funds](https://www.ml-quant.com/papers/ssrn/4764190/): Hedge funds involved in short-selling show superior performance and unique trading patterns, often trading against retail trading trends, contributing to their exceptional performance.
- [Compounding Effects in Leveraged ETFs: Beyond the Volatility Drag Paradigm](https://www.ml-quant.com/papers/arxiv/2504.20116/): The performance of leveraged ETFs is shown to depend on return autocorrelation and dynamics, with daily-rebalanced LETFs boosting returns in momentum-driven markets.
- [Optimal fees in hedge funds with first-loss compensation](https://www.ml-quant.com/papers/doi/10-1016-j-jbankfin-2020-105884/): The research suggests alternative fee schemes for hedge funds, arguing that traditional management and performance fees are suboptimal and that the recommended schemes reduce the fund's volatility.