Kisiel, Damian;
Gorse, Denise;
(2022)
A Meta-Method for Portfolio Management Using Machine Learning for Adaptive Strategy Selection.
In:
CIIS '21: Proceedings of the 2021 4th International Conference on Computational Intelligence and Intelligent Systems.
(pp. pp. 67-71).
Association for Computing Machinery (ACM): New York, NY, US.
Preview |
Text
2111.05935.pdf - Accepted Version Download (5MB) | Preview |
Abstract
This work proposes a novel portfolio management technique, the Meta Portfolio Method (MPM), inspired by the successes of meta approaches in the field of bioinformatics and elsewhere. The MPM uses XGBoost to learn how to switch between two risk-based portfolio allocation strategies, the Hierarchical Risk Parity (HRP) and more classical Naïve Risk Parity (NRP). It is demonstrated that the MPM is able to successfully take advantage of the best characteristics of each strategy (the NRP’s fast growth during market uptrends, and the HRP’s protection against drawdowns during market turmoil). As a result, the MPM is shown to possess an excellent out-of-sample risk-reward profile, as measured by the Sharpe ratio, and in addition offers a high degree of interpretability of its asset allocation decisions.
| Type: | Proceedings paper |
|---|---|
| Title: | A Meta-Method for Portfolio Management Using Machine Learning for Adaptive Strategy Selection |
| Event: | CIIS 2021: The 4th International Conference on Computational Intelligence and Intelligent Systems |
| ISBN-13: | 9781450385930 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3507623.3507635 |
| Publisher version: | https://doi.org/10.1145/3507623.3507635 |
| Language: | English |
| Additional information: | This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Adaptive strategy selection, Supervised learning, Portfolio management, Meta-methods, Machine learning. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10172664 |
Archive Staff Only
 |
View Item |
