Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/151362
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Xing, Frank Z. | en_US |
dc.contributor.author | Cambria, Erik | en_US |
dc.contributor.author | Welsch, Roy E. | en_US |
dc.date.accessioned | 2021-06-23T05:24:55Z | - |
dc.date.available | 2021-06-23T05:24:55Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Xing, F. Z., Cambria, E. & Welsch, R. E. (2019). Growing semantic vines for robust asset allocation. Knowledge-Based Systems, 165, 297-305. https://dx.doi.org/10.1016/j.knosys.2018.11.035 | en_US |
dc.identifier.issn | 0950-7051 | en_US |
dc.identifier.uri | https://hdl.handle.net/10356/151362 | - |
dc.description.abstract | The vine structure has been widely studied as a graphical representation for high-dimensional dependence modeling, depicting complicated probability density functions, and robust correlation estimation. However, specification of the best vine structure is challenging as the number of candidate vine structures grows combinatorially when the number of elements increases. In this article, we propose to leverage semantic prior knowledge of assets extracted from their descriptive documents to find a suitable vine structure for financial portfolio optimization. A vine growing algorithm is provided and the robust covariance matrix estimation process is performed on this vine structure. Our construction of a semantic vine improves the state-of-the-art arbitrary vine-growing method in the context of robust correlation estimation and multi-period asset allocation. The effectiveness of our methods on a large scale is also demonstrated by experiments. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Knowledge-Based Systems | en_US |
dc.rights | © 2018 Elsevier B.V. All rights reserved. | en_US |
dc.subject | Engineering::Computer science and engineering | en_US |
dc.title | Growing semantic vines for robust asset allocation | en_US |
dc.type | Journal Article | en |
dc.contributor.school | School of Computer Science and Engineering | en_US |
dc.identifier.doi | 10.1016/j.knosys.2018.11.035 | - |
dc.identifier.scopus | 2-s2.0-85058386370 | - |
dc.identifier.volume | 165 | en_US |
dc.identifier.spage | 297 | en_US |
dc.identifier.epage | 305 | en_US |
dc.subject.keywords | Vine | en_US |
dc.subject.keywords | Dependence Modeling | en_US |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
Appears in Collections: | SCSE Journal Articles |
SCOPUSTM
Citations
50
9
Updated on Mar 27, 2024
Web of ScienceTM
Citations
20
5
Updated on Oct 24, 2023
Page view(s)
212
Updated on Mar 27, 2024
Google ScholarTM
Check
Altmetric
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.