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Using portfolio optimisation models to enhance decision making and prediction

Using portfolio optimisation models to enhance decision making and prediction
Using portfolio optimisation models to enhance decision making and prediction
Purpose
– The purpose of this paper is to analyse and compare the performances of portfolio optimisation models including Markowitz's mean-variance model (MV model), Konno and Yamazaki's mean-absolute deviation portfolio optimisation model (MAD model), Young's minimax portfolio model and the VaR model.

Design/methodology/approach
– Historical data on 43 constituent shares listed on the Hong Kong Hang Seng Index (HSI) covering a four-year period are obtained. The paper then tests the performance of each model under different scenarios and against different sets of historical data.

Findings
– The paper finds that different levels of required annual returns impact on portfolio composition, historical data have a major impact on the determination of portfolio composition and the level of required annual return impacts on how optimisation models perform.

Practical implications
– The paper posits that with a comprehensive understanding of the performance of each of these performance optimisation models, investors may be able to develop a better understanding of how to adjust investment risk strategies, thus preventing serious losses.

Originality/value
– There are two major points of value to this paper. In the first place, the paper presents an original review of portfolio optimisation models. Second, using “real” data, the paper utilises five different scenarios to test the performance of each model under different situations.
performance management, modelling
1746-5664
36-57
Li, Wan Chau
97c0d20a-3a38-4845-a25d-054b9b4a4834
Wu, Yue
e279101b-b392-45c4-b894-187e2ded6a5c
Ojiako, U
57665e2d-6fe4-4585-ad0f-e76a55d81539
Li, Wan Chau
97c0d20a-3a38-4845-a25d-054b9b4a4834
Wu, Yue
e279101b-b392-45c4-b894-187e2ded6a5c
Ojiako, U
57665e2d-6fe4-4585-ad0f-e76a55d81539

Li, Wan Chau, Wu, Yue and Ojiako, U (2012) Using portfolio optimisation models to enhance decision making and prediction. Journal of Modelling in Management, 9 (1), 36-57. (doi:10.1108/JM2-11-2011-0057).

Record type: Article

Abstract

Purpose
– The purpose of this paper is to analyse and compare the performances of portfolio optimisation models including Markowitz's mean-variance model (MV model), Konno and Yamazaki's mean-absolute deviation portfolio optimisation model (MAD model), Young's minimax portfolio model and the VaR model.

Design/methodology/approach
– Historical data on 43 constituent shares listed on the Hong Kong Hang Seng Index (HSI) covering a four-year period are obtained. The paper then tests the performance of each model under different scenarios and against different sets of historical data.

Findings
– The paper finds that different levels of required annual returns impact on portfolio composition, historical data have a major impact on the determination of portfolio composition and the level of required annual return impacts on how optimisation models perform.

Practical implications
– The paper posits that with a comprehensive understanding of the performance of each of these performance optimisation models, investors may be able to develop a better understanding of how to adjust investment risk strategies, thus preventing serious losses.

Originality/value
– There are two major points of value to this paper. In the first place, the paper presents an original review of portfolio optimisation models. Second, using “real” data, the paper utilises five different scenarios to test the performance of each model under different situations.

Full text not available from this repository.

More information

Accepted/In Press date: 9 October 2012
Keywords: performance management, modelling
Organisations: Centre of Excellence in Decision, Analytics & Risk Research

Identifiers

Local EPrints ID: 378994
URI: https://eprints.soton.ac.uk/id/eprint/378994
ISSN: 1746-5664
PURE UUID: 13a65e3b-e286-4ec4-9aab-9a31a843e33e

Catalogue record

Date deposited: 20 Jul 2015 15:25
Last modified: 17 Jul 2017 20:47

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