Linear programing models for portfolio optimization using a benchmark

We consider the problem of constructing a perturbed portfolio by utilizing a benchmark portfolio. We propose two computationally efficient portfolio optimization models, the mean-absolute deviation risk and the Dantzig-type, which can be solved using linear programing. These portfolio models push th...

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Veröffentlicht in:The European journal of finance Jg. 25; H. 5; S. 435 - 457
Hauptverfasser: Park, Seyoung, Song, Hyunson, Lee, Sungchul
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Routledge 24.03.2019
Taylor & Francis LLC
Schlagworte:
Benchmarks
Dantzig
Deviation
linear programing
Linear programming
Mean-absolute deviation risk
Optimization
perturbation
Portfolio management
portfolio optimization
Portfolios
sparsity
ISSN:1351-847X, 1466-4364
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Zusammenfassung:We consider the problem of constructing a perturbed portfolio by utilizing a benchmark portfolio. We propose two computationally efficient portfolio optimization models, the mean-absolute deviation risk and the Dantzig-type, which can be solved using linear programing. These portfolio models push the existing benchmark toward the efficient frontier through sparse and stable asset selection. We implement these models on two benchmarks, a market index and the equally-weighted portfolio. We carry out an extensive out-of-sample analysis with 11 empirical datasets and simulated data. The proposed portfolios outperform the benchmark portfolio in various performance measures, including the mean return and Sharpe ratio.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1351-847X
1466-4364
DOI:10.1080/1351847X.2018.1536070