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 mean-variance 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 mean-variance 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 S-shaped and Bilinear utility functions gathered from Prospect
Theory. Results hold in an in-sample 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 mean-variance 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 mean-variance 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 S-shaped and Bilinear utility functions gathered from Prospect
Theory. Results hold in an in-sample 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/student-papers/record/8890111
- author
- Lindeke, Niklas LU
- supervisor
- 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
- 2016-09-09 14:04:56
- date last changed
- 2016-09-09 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 mean-variance 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 S-shaped and Bilinear utility functions gathered from Prospect Theory. Results hold in an in-sample 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}}, language = {{eng}}, note = {{Student Paper}}, title = {{Prospect Utility Portfolio Optimization}}, year = {{2016}}, }