Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/9639
Title: Enhanced indexation based on second-order stochastic dominance
Authors: Roman, D
Mitra, G
Zverovich, V
Keywords: Finance;Portfolio choice;Index tracking;Mean-risk;Stochastic dominance
Issue Date: 2013
Publisher: Elsevier
Citation: European Journal of Operational Research, 228: 1, pp. 273 - 281, 2013
Abstract: Second order Stochastic Dominance (SSD) has a well recognised importance in portfolio selection, since it provides a natural interpretation of the theory of risk-Averse investor behaviour. Recently, SSD-based models of portfolio choice have been proposed; these assume that a reference distribution is available and a portfolio is constructed, whose return distribution dominates the reference distribution with respect to SSD. We present an empirical study which analyses the effectiveness of such strategies in the context of enhanced indexation. Several datasets, drawn from FTSE 100, SP 500 and Nikkei 225 are investigated through portfolio rebalancing and backtesting. Three main conclusions are drawn. First, the portfolios chosen by the SSD based models consistently outperformed the indices and the traditional index trackers. Secondly, the SSD based models do not require imposition of cardinality constraints since naturally a small number of stocks are selected. Thus, they do not present the computational difficulty normally associated with index tracking models. Finally, the SSD based models are robust with respect to small changes in the scenario set and little or no rebalancing is necessary. In this paper we present a unified framework which incorporates (a) SSD, (b) downside risk (Conditional Value-At-Risk) minimisation and (c) enhanced indexation. © 2013 Elsevier B.V. All rights reserved.
URI: http://www.sciencedirect.com/science/article/pii/S0377221713000829
http://bura.brunel.ac.uk/handle/2438/9639
DOI: http://dx.doi.org/10.1016/j.ejor.2013.01.035
ISSN: 0377-2217
Appears in Collections:Dept of Computer Science Research Papers

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