Prospect Utility Portfolio Optimization
(2016) NEKN01 20161Department of Economics
 Abstract
 Portfolio choice theory have in the last decades seen a rise in utilising
more advanced utility functions for finding optimal portfolios. This is
partly a consequence of the relatively simplistic nature of the quadratic
utility, which is often assumed in the classical meanvariance framework.
There have been some suggestions on how to find optimal portfolios in ac
cordance to more realistic utility functions gathered from Prospect theory.
However, some of these methods suffer from practical drawbacks.
This paper proposes a method consisting of a mixture between two op
timization techniques, in order to find a portfolio allocation that is optimal
in relation to the first four moments. In the empirical implementation, we
utilise... (More)  Portfolio choice theory have in the last decades seen a rise in utilising
more advanced utility functions for finding optimal portfolios. This is
partly a consequence of the relatively simplistic nature of the quadratic
utility, which is often assumed in the classical meanvariance framework.
There have been some suggestions on how to find optimal portfolios in ac
cordance to more realistic utility functions gathered from Prospect theory.
However, some of these methods suffer from practical drawbacks.
This paper proposes a method consisting of a mixture between two op
timization techniques, in order to find a portfolio allocation that is optimal
in relation to the first four moments. In the empirical implementation, we
utilise the Sshaped and Bilinear utility functions gathered from Prospect
Theory. Results hold in an insample testing environment. Improving ex
pected utility, and the first three moments when tested against a standard
benchmark method, as well as in measurement of the Sharpe Ratio. (Less)  Popular Abstract
 Portfolio choice theory have in the last decades seen a rise in utilising
more advanced utility functions for finding optimal portfolios. This is
partly a consequence of the relatively simplistic nature of the quadratic
utility, which is often assumed in the classical meanvariance framework.
There have been some suggestions on how to find optimal portfolios in ac
cordance to more realistic utility functions gathered from Prospect theory.
However, some of these methods suffer from practical drawbacks.
This paper proposes a method consisting of a mixture between two op
timization techniques, in order to find a portfolio allocation that is optimal
in relation to the first four moments. In the empirical implementation, we
utilise... (More)  Portfolio choice theory have in the last decades seen a rise in utilising
more advanced utility functions for finding optimal portfolios. This is
partly a consequence of the relatively simplistic nature of the quadratic
utility, which is often assumed in the classical meanvariance framework.
There have been some suggestions on how to find optimal portfolios in ac
cordance to more realistic utility functions gathered from Prospect theory.
However, some of these methods suffer from practical drawbacks.
This paper proposes a method consisting of a mixture between two op
timization techniques, in order to find a portfolio allocation that is optimal
in relation to the first four moments. In the empirical implementation, we
utilise the Sshaped and Bilinear utility functions gathered from Prospect
Theory. Results hold in an insample testing environment. Improving ex
pected utility, and the first three moments when tested against a standard
benchmark method, as well as in measurement of the Sharpe Ratio. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/studentpapers/record/8890111
 author
 Lindeke, Niklas ^{LU}
 supervisor

 Birger Nilsson ^{LU}
 organization
 course
 NEKN01 20161
 year
 2016
 type
 H1  Master's Degree (One Year)
 subject
 keywords
 Utility Maximization, Portfolio Choice, Gradient Ascent, Sparse Group LASSO
 language
 English
 id
 8890111
 date added to LUP
 20160909 14:04:56
 date last changed
 20160909 14:04:56
@misc{8890111, abstract = {Portfolio choice theory have in the last decades seen a rise in utilising more advanced utility functions for finding optimal portfolios. This is partly a consequence of the relatively simplistic nature of the quadratic utility, which is often assumed in the classical meanvariance framework. There have been some suggestions on how to find optimal portfolios in ac cordance to more realistic utility functions gathered from Prospect theory. However, some of these methods suffer from practical drawbacks. This paper proposes a method consisting of a mixture between two op timization techniques, in order to find a portfolio allocation that is optimal in relation to the first four moments. In the empirical implementation, we utilise the Sshaped and Bilinear utility functions gathered from Prospect Theory. Results hold in an insample testing environment. Improving ex pected utility, and the first three moments when tested against a standard benchmark method, as well as in measurement of the Sharpe Ratio.}, author = {Lindeke, Niklas}, keyword = {Utility Maximization,Portfolio Choice,Gradient Ascent,Sparse Group LASSO}, language = {eng}, note = {Student Paper}, title = {Prospect Utility Portfolio Optimization}, year = {2016}, }