Publication Type
Journal Article
Version
submittedVersion
Publication Date
7-2023
Abstract
Developing an excellent quantitative trading strategy to obtain a high Sharpe ratio requires optimizing several parameters at the same time. Example parameters include the window length of a moving average sequence, the choice of trading instruments, and the thresholds used to generate trading signals. Simultaneously optimizing all these parameters to seek a high Sharpe ratio is a daunting and time-consuming task, partly because of the unknown mechanism determining the Sharpe ratio. This article proposes using Bayesian optimization to systematically search for the optimal parameter configuration that leads to a high Sharpe ratio. The author shows that the proposed intelligent search strategy performs better than manual search, a common practice that proves to be inefficient. The author’s framework also can easily be extended to other parameter selection tasks in portfolio optimization and risk management.
Discipline
Finance | Finance and Financial Management
Research Areas
Quantitative Finance
Publication
Journal of Portfolio Management
Volume
49
Issue
7
First Page
1
Last Page
9
ISSN
0095-4918
Identifier
10.3905/jpm.2023.1.497
Publisher
Institutional Investor Inc
Citation
LIU, Peng.
Seeking better Sharpe ratio via Bayesian optimization. (2023). Journal of Portfolio Management. 49, (7), 1-9.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/7472
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.3905/jpm.2023.1.497
