Portfolio diversification based on stochastic dominance under incomplete probability information
Portfolio diversification based on stochastic dominance under incomplete probability information
Identifying efficient portfolio diversification strategies subject to stochastic dominance (SD) criteria usually assumes that the state-space of future asset returns can be captured by a fixed sample of equally probable historical returns. This paper relaxes this assumption by developing SD criteria under incomplete information on state probabilities. Specifically, we identify portfolios that dominate a given benchmark for any state probabilities in a given set. The proposed approach is applied to analyze if industrial diversification can be utilized to outperform the market portfolio. The results from this application demonstrate that the use of set-valued state probabilities can help to improve out-of-sample performance of SD-based portfolio optimization.
755-768
Liesiö, Juuso
6c56f7bb-1dc3-4a11-af88-df6e9c542173
Xu, Peng
4a72430c-992e-40e5-be0b-e8d9d83d6f3d
Kuosmanen, Timo
fad22eda-2142-4e8f-8573-9d376530a960
23 May 2020
Liesiö, Juuso
6c56f7bb-1dc3-4a11-af88-df6e9c542173
Xu, Peng
4a72430c-992e-40e5-be0b-e8d9d83d6f3d
Kuosmanen, Timo
fad22eda-2142-4e8f-8573-9d376530a960
Liesiö, Juuso, Xu, Peng and Kuosmanen, Timo
(2020)
Portfolio diversification based on stochastic dominance under incomplete probability information.
European Journal of Operational Research, 286 (2), .
(doi:10.1016/j.ejor.2020.03.042).
Abstract
Identifying efficient portfolio diversification strategies subject to stochastic dominance (SD) criteria usually assumes that the state-space of future asset returns can be captured by a fixed sample of equally probable historical returns. This paper relaxes this assumption by developing SD criteria under incomplete information on state probabilities. Specifically, we identify portfolios that dominate a given benchmark for any state probabilities in a given set. The proposed approach is applied to analyze if industrial diversification can be utilized to outperform the market portfolio. The results from this application demonstrate that the use of set-valued state probabilities can help to improve out-of-sample performance of SD-based portfolio optimization.
Text
EJOR_LiesioXuKuosmanen2020
- Accepted Manuscript
More information
Accepted/In Press date: 11 March 2020
e-pub ahead of print date: 19 March 2020
Published date: 23 May 2020
Identifiers
Local EPrints ID: 497817
URI: http://eprints.soton.ac.uk/id/eprint/497817
ISSN: 0377-2217
PURE UUID: 217826cc-ff20-48f3-aa05-c0483ad17bfb
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Date deposited: 31 Jan 2025 18:10
Last modified: 04 Feb 2025 03:14
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Contributors
Author:
Juuso Liesiö
Author:
Peng Xu
Author:
Timo Kuosmanen
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