Bayesian portfolio analysis

Doron Avramov*, Guofu Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


This paper reviews the literature on Bayesian portfolio analysis. Information about events, macro conditions, asset pricing theories, and security-driving forces can serve as useful priors in selecting optimal portfolios. Moreover, parameter uncertainty and model uncertainty are practical problems encountered by all investors. The Bayesian framework neatly accounts for these uncertainties, whereas standard statistical models often ignore them. We review Bayesian portfolio studies when asset returns are assumed both independently and identically distributed as well as predictable through time. We cover a range of applications, from investing in single assets and equity portfolios to mutual and hedge funds. We also outline challenges for future work.

Original languageAmerican English
Pages (from-to)25-47
Number of pages23
JournalAnnual Review of Financial Economics
StatePublished - 2010


  • Informative prior beliefs
  • Learning
  • Model uncertainty
  • Parameter uncertainty
  • Portfolio choice
  • Return predictability


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