Multi-stock portfolio optimization under prospect theory

We study how a behavioral agent allocates her portfolio. We consider a cumulative prospect theory investor in a single period setting with one riskless bond and multiple risky stocks, which follow a multivariate elliptical distribution. Our main result is a two-fund separation between the riskless b...

Full description

Saved in:
Bibliographic Details
Published in:Mathematics and financial economics 2012-08, Vol.6 (4), p.337-362
Main Authors: Pirvu, Traian A., Schulze, Klaas
Format: Article
Language:English
Subjects:
Applications of Mathematics
Behavior
Economic models
Economic theory
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Expected utility
Finance
Institutional investments
Insurance
Investment policy
Macroeconomics/Monetary Economics//Financial Economics
Management
Mathematics
Mathematics and Statistics
Optimization techniques
Original Article
Portfolio investments
Portfolio management
Power
Quantitative Finance
Statistics for Business
Stocks
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We study how a behavioral agent allocates her portfolio. We consider a cumulative prospect theory investor in a single period setting with one riskless bond and multiple risky stocks, which follow a multivariate elliptical distribution. Our main result is a two-fund separation between the riskless bond and a mean–variance-portfolio, up to an exogenous benchmark portfolio. The mean–variance-portfolio, which we derive explicitly, is the same for all agents. Individual risk preferences are mirrored only in the participation in this portfolio. This dependence is illustrated by considering empirical returns. Furthermore we solve ill-posed optimization problems by imposing a regulatory risk constraint. Finally we address specific parameterizations of the value function by studying power, linear, and exponential utility.
ISSN:1862-9679
1862-9660
DOI:10.1007/s11579-012-0079-0