Using portfolio optimisation models to enhance decision making and prediction

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.

Citation

Chau Li, W., Wu, Y. and Ojiako, U. (2014), "Using portfolio optimisation models to enhance decision making and prediction", Journal of Modelling in Management, Vol. 9 No. 1, pp. 36-57. https://doi.org/10.1108/JM2-11-2011-0057