| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Matrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfolios |
| Autor/-in: | Papenbrock, Jochen Schwendner, Peter Jaeger, Markus Krügel, Stephan |
| et. al: | No |
| DOI: | 10.3905/jfds.2021.1.056 |
| Erschienen in: | The Journal of Financial Data Science |
| Band(Heft): | 3 |
| Heft: | 2 |
| Seite(n): | 51 |
| Seiten bis: | 69 |
| Erscheinungsdatum: | 13-Mär-2021 |
| Verlag / Hrsg. Institution: | Portfolio Management Research |
| ISSN: | 2640-3943 |
| Sprache: | Englisch |
| Schlagwörter: | Statistical method; Big data/machine learning; Performance measurement; Portfolio construction |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 332.6: Investition |
| Zusammenfassung: | In this article, the authors present a novel and highly flexible concept to simulate correlation matrixes of financial markets. It produces realistic outcomes regarding stylized facts of empirical correlation matrixes and requires no asset return input data. The matrix generation is based on a multiobjective evolutionary algorithm, so the authors call the approach matrix evolutions. It is suitable for parallel implementation and can be accelerated by graphics processing units and quantum-inspired algorithms. The approach is useful for backtesting, pricing, and hedging correlation-dependent investment strategies and financial products. Its potential is demonstrated in a machine learning case study for robust portfolio construction in a multi-asset universe: An explainable machine learning program links the synthetic matrixes to the portfolio volatility spread of hierarchical risk parity versus equal risk contribution. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/22348 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Management and Law |
| Organisationseinheit: | Institut für Wealth & Asset Management (IWA) |
| Enthalten in den Sammlungen: | Publikationen School of Management and Law |
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Papenbrock, J., Schwendner, P., Jaeger, M., & Krügel, S. (2021). Matrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfolios. The Journal of Financial Data Science, 3(2), 51–69. https://doi.org/10.3905/jfds.2021.1.056
Papenbrock, J. et al. (2021) ‘Matrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfolios’, The Journal of Financial Data Science, 3(2), pp. 51–69. Available at: https://doi.org/10.3905/jfds.2021.1.056.
J. Papenbrock, P. Schwendner, M. Jaeger, and S. Krügel, “Matrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfolios,” The Journal of Financial Data Science, vol. 3, no. 2, pp. 51–69, Mar. 2021, doi: 10.3905/jfds.2021.1.056.
PAPENBROCK, Jochen, Peter SCHWENDNER, Markus JAEGER und Stephan KRÜGEL, 2021. Matrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfolios. The Journal of Financial Data Science. 13 März 2021. Bd. 3, Nr. 2, S. 51–69. DOI 10.3905/jfds.2021.1.056
Papenbrock, Jochen, Peter Schwendner, Markus Jaeger, and Stephan Krügel. 2021. “Matrix Evolutions : Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios.” The Journal of Financial Data Science 3 (2): 51–69. https://doi.org/10.3905/jfds.2021.1.056.
Papenbrock, Jochen, et al. “Matrix Evolutions : Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios.” The Journal of Financial Data Science, vol. 3, no. 2, Mar. 2021, pp. 51–69, https://doi.org/10.3905/jfds.2021.1.056.
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