Kisiel, Damian;
Gorse, Denise;
(2022)
A Meta-Method for Portfolio Management Using Machine Learning for Adaptive Strategy Selection.
In:
Proceedings of the 4th International Conference on Computational Intelligence and Intelligent Systems.
Association for Computing Machinery (ACM)
(In press).
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 - 4th International Conference on Computational Intelligence and Intelligent Systems |
| Location: | Tokyo, Japan |
| Dates: | 20th-22nd November 2021 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://dl.acm.org/ |
| Language: | English |
| Additional information: | Meta-methods, Machine learning, Supervised learning, Portfolio management, Adaptive strategy selection |
| UCL classification: | 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 UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10144863 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
