Growing Semantic Vines for Robust Asset Allocation

Knowledge-Based Systems, 165 pp 297-305

Posted: 21 Nov 2018 Last revised: 21 Jan 2019

See all articles by Frank Xing

Frank Xing

Nanyang Technological University (NTU)

Erik Cambria

Nanyang Technological University (NTU)

Roy E. Welsch

Massachusetts Institute of Technology (MIT); National Bureau of Economic Research (NBER)

Date Written: February 1, 2019

Abstract

The vine structure has been widely studied as a graphical representation for high-dimensional dependence modeling, depiction of complicated probability density functions, and robust correlation estimation. However, the number of candidate vine structures grows exponentially as the number of elements increases, making the specification of the best vine structure a challenging issue. 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. The experiments show that our construction of a semantic vine is superior to the state-of-the-art arbitrary vine-growing method. The effectiveness of using semantic vines for robust correlation estimation for the classic asset allocation model on a large scale is also demonstrated.

Keywords: Vine, Dependence Modeling, Semantic Analysis, Asset Allocation, Robust Estimation, Financial Text Mining

JEL Classification: C44, C63, G11

Suggested Citation

Xing, Frank and Cambria, Erik and Welsch, Roy, Growing Semantic Vines for Robust Asset Allocation (February 1, 2019). Knowledge-Based Systems, 165 pp 297-305 . Available at SSRN: https://ssrn.com/abstract=3275132

Frank Xing (Contact Author)

Nanyang Technological University (NTU) ( email )

S3 B2-A28 Nanyang Avenue
Singapore, 639798
Singapore

Erik Cambria

Nanyang Technological University (NTU) ( email )

S3 B2-A28 Nanyang Avenue
Singapore, 639798
Singapore

Roy Welsch

Massachusetts Institute of Technology (MIT) ( email )

E53-383
Cambridge, MA 02139
United States
617-253-6601 (Phone)
617-253-6601 (Fax)

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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