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| Content Provider | Springer Nature Link |
|---|---|
| Author | Valle, Cristia Arbex Roman, Diana Mitra, Gautam |
| Copyright Year | 2017 |
| 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 different benchmarks lead to very different 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 specified 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 (1) how this approach leads to superior portfolio performance (2) how significantly better performance is achieved for portfolios that include shorting of assets. |
| Starting Page | 257 |
| Ending Page | 280 |
| Page Count | 24 |
| File Format | |
| ISSN | 1619697X |
| Journal | Computational Management Science |
| Volume Number | 14 |
| Issue Number | 2 |
| e-ISSN | 16196988 |
| Language | English |
| Publisher | Springer Berlin Heidelberg |
| Publisher Date | 2017-02-02 |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Portfolio optimisation Stochastic dominance Reference distribution Left tail Downside risk Operation Research/Decision Theory Optimization |
| Content Type | Text |
| Resource Type | Article |
| Subject | Management Science and Operations Research Management Information Systems Information Systems Statistics, Probability and Uncertainty |
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