A Meta-Method for Portfolio Management Using Machine Learning for Adaptive Strategy Selection
| Autor: | Damian Kisiel, Denise Gorse |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computational Engineering
Finance and Science (cs.CE) FOS: Economics and business FOS: Computer and information sciences Computer Science - Machine Learning Quantitative Finance - Computational Finance Portfolio Management (q-fin.PM) Risk Management (q-fin.RM) Computational Finance (q-fin.CP) Computer Science - Computational Engineering Finance and Science Quantitative Finance - Portfolio Management Machine Learning (cs.LG) Quantitative Finance - Risk Management |
| Popis: | 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 Na\"ive 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. Comment: 5 pages |
| Databáze: | OpenAIRE |
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