Abstract
Hedge funds have traditionally served wealthy individuals and institutional investors with the promise of delivering protection of capital and uncorrelated positive returns irrespective of market direction, allowing them to better manage portfolio risk. However, the financial crisis of 2008 has heightened investor sensitivity to the high fees, illiquidity, lack of transparency, and lockup periods typically associated with hedge funds. Hedge fund replication products, or clones, seek to answer these challenges by providing daily liquidity, transparency, and immediate exposure to a desired hedge fund strategy. Nonetheless, although lowering cost and adding simplicity by using a common set of factors, traditional replication products might offer lower risk-reward performance compared to hedge funds. This research explores hedge fund replication further by examining the importance of constructing clones with specific factors relevant to each hedge fund strategy, and then compares the strategy specific clone risk and reward performance against both actual hedge fund performance and hedge fund clones constructed using a more general set of common factors. Testing shows that using strategy specific factors to replicate common hedge fund strategies can offer superior risk-reward performance compared to previous general model clones.
Recommended Citation
S. Subhash and D. L. Enke, "Hedge Fund Replication using Strategy Specific Factors," Financial Innovation, vol. 5, no. 1, SpringerOpen, Dec 2019.
The definitive version is available at https://doi.org/10.1186/s40854-019-0127-3
Department(s)
Engineering Management and Systems Engineering
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Hedge fund replication; Hedge funds; Regression; Strategy specific factors; Trading strategies
International Standard Serial Number (ISSN)
2199-4730
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2019 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Dec 2019
Comments
The authors would like to acknowledge the Laboratory for Investment and Financial Engineering and the Department of Engineering Management and Systems Engineering at the Missouri University of Science and Technology for their financial support and the use of their facilities.