Maximizing equity market sector predictability in a Bayesian time-varying parameter model

Lorne D. Johnson and Georgios Sakoulis

Computational Statistics & Data Analysis, 2008, vol. 52, issue 6, 3083-3106

Abstract: The Kalman filter methodology is employed to develop a dynamic sector allocation model for US equities. Bayesian parameter estimation and model selection criteria result in significantly improved sector return predictability over static or rolling parameter specifications. A simple trading strategy illustrates how widely tested financial and economic variables can be used as inputs in for a potentially profitable investment strategy.

Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(07)00359-3
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:52:y:2008:i:6:p:3083-3106

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().