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DC Field | Value | Language |
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dc.contributor.author | Roman, D | - |
dc.contributor.author | Arbex Valle, C | - |
dc.contributor.author | Mitra, G | - |
dc.date.accessioned | 2017-01-19T15:08:20Z | - |
dc.date.available | 2017-01-19T15:08:20Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Computational Management Science, (2017) | en_US |
dc.identifier.issn | 1619-697X | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/13899 | - |
dc.description.abstract | In the last decade, a few models of portfolio construction have been proposed which apply Second Order Stochastic Dominance (SSD) as a choice criterion. SSD approach requires the use of a reference distribution which acts as a benchmark. The return distribution of the computed portfolio dominates the benchmark by the SSD criterion. The benchmark distribution naturally plays an important role since di erent benchmarks lead to very di erent portfolio solutions. In this paper we describe a novel concept of reshaping the benchmark distribution with a view to obtaining portfolio solutions which have enhanced return distributions. The return distribution of the constructed portfolio is considered enhanced if the left tail is improved, the downside risk is reduced and the standard deviation remains within a speci ed range. We extend this approach from long only to long-short strategies which are used by many hedge fund and quant fund practitioners. We present computational results which illustrate (i) how this approach leads to superior portfolio performance (ii) how signi cantly better performance is achieved for portfolios that include shorting of assets. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer Verlag | en_US |
dc.subject | Portfolio optimisation | en_US |
dc.subject | Stochastic dominance | en_US |
dc.subject | Reference distribution | en_US |
dc.subject | Left tail | en_US |
dc.subject | Downside risk | en_US |
dc.title | Novel approaches for portfolio construction using second order stochastic dominance | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/s10287-017-0274-9 | - |
dc.relation.isPartOf | Computational Management Science | - |
pubs.publication-status | Accepted | - |
Appears in Collections: | Dept of Mathematics Research Papers |
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File | Description | Size | Format | |
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Fulltext.pdf | 1.6 MB | Adobe PDF | View/Open |
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