Maintaining prior distributions across evolving eigenspaces: An application to portfolio construction

Kevin R. Keane, Jason J. Corso

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Temporal evolution in the generative distribution of nonstationary sequential data is challenging to model. This paper presents a method for retaining the information in prior distributions of matrix variate dynamic linear models (MVDLMs) as the eigenspace of sequential data evolves. The method starts by constructing sliding windows ' matrices composed of a fixed number of columns containing the most recent point-in-time multivariate observation vectors. Characteristic time series, the right singular vectors, are extracted from a window using singular value decomposition (SVD). Then, a sequence of matrices capturing the rotation and scaling of the eigenspace is specified as a function of adjacent windows characteristic time series. The method is tested on observations derived from daily US stock prices spanning 25 years. The results indicate that models constructed using sliding window SVD and MVDLMs, as extended in this paper, are resistant to over-fitting and perform well when used in portfolio construction applications.

Original languageEnglish
Title of host publicationProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Pages422-427
Number of pages6
DOIs
StatePublished - 2012
Event11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012 - Boca Raton, FL, United States
Duration: 12 Dec 201215 Dec 2012

Publication series

NameProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Volume2

Conference

Conference11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
Country/TerritoryUnited States
CityBoca Raton, FL
Period12/12/1215/12/12

Keywords

  • Online regression methods
  • applications of dynamic online incremental learning
  • unsupervised methods

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