Author
Listed:
- Fengmin Xu
- Jieao Ma
- Haibing Lu
Abstract
Enhanced indexing, which has been used by professional portfolio managers for decades, is a portfolio management strategy that attempts to increase returns by building a portfolio around core, index-like positions and adding tactical tilts toward specific styles or individual stocks. This paper proposes an improved enhanced indexation model by considering the systematic risk, measured by Beta value, and the industry rotation phenomenon. The systematic risk is the risk related to the stock market as a whole and can be reasonably controlled to improve portfolio performance, by actively tracking and forecasting the market trend. Sector rotation refers to the investment strategy of taking money that's invested in one stock market industry and moving it to another, by taking advantage of the historical performances of specific industries during different phases of the cycle. In specific, our model aims to find a small set of industries that is mostly likely to thrive in the anticipated future, which is mathematically realized by dividing stocks into industries and minimizing their $ L_{2,1} $ L2,1 norm. To evaluate our strategy, we conducted extensive numerical experiments against some major world indices, e.g. CSI 300, S&P 500, FTSE 100 and Nikkei 225. The experimental result shows that our approach can generate sparse portfolios with excellent out-of-sample excess returns and high robustness after deducting transaction costs.
Suggested Citation
Fengmin Xu & Jieao Ma & Haibing Lu, 2022.
"Group sparse enhanced indexation model with adaptive beta value,"
Quantitative Finance, Taylor & Francis Journals, vol. 22(10), pages 1905-1926, October.
Handle:
RePEc:taf:quantf:v:22:y:2022:i:10:p:1905-1926
DOI: 10.1080/14697688.2022.2092542
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:quantf:v:22:y:2022:i:10:p:1905-1926. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RQUF20 .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.