Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Papenbrock, Jochen | - |
| dc.contributor.author | Schwendner, Peter | - |
| dc.contributor.author | Jaeger, Markus | - |
| dc.contributor.author | Krügel, Stephan | - |
| dc.date.accessioned | 2021-04-29T08:27:41Z | - |
| dc.date.available | 2021-04-29T08:27:41Z | - |
| dc.date.issued | 2021-03-13 | - |
| dc.identifier.issn | 2640-3943 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/22348 | - |
| dc.description.abstract | 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. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | Portfolio Management Research | de_CH |
| dc.relation.ispartof | The Journal of Financial Data Science | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject | Statistical method | de_CH |
| dc.subject | Big data/machine learning | de_CH |
| dc.subject | Performance measurement | de_CH |
| dc.subject | Portfolio construction | de_CH |
| dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
| dc.subject.ddc | 332.6: Investition | de_CH |
| dc.title | Matrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfolios | de_CH |
| dc.type | Beitrag in wissenschaftlicher Zeitschrift | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Management and Law | de_CH |
| zhaw.organisationalunit | Institut für Wealth & Asset Management (IWA) | de_CH |
| dc.identifier.doi | 10.3905/jfds.2021.1.056 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.issue | 2 | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.pages.end | 69 | de_CH |
| zhaw.pages.start | 51 | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.volume | 3 | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen School of Management and Law | |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.