Empirical analysis of optimization algorithms for portfolio allocation

Authors
Bolin, Andrew
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Other Contributors
Magdon-Ismail, Malik
Gupta, Aparna
Szymanśki, Bolesław
Issue Date
2013-05
Keywords
Computer science
Degree
MS
Terms of Use
Attribution-NonCommercial-NoDerivs 3.0 United States
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
Portfolio optimization algorithms were tested using historical S&P100 data. A traditional Mean-Var algorithm is tested as well as two alternative risk methods. The alternative risk methods used the maximum drawdown (MDD) as the measure of risk rather than the standard deviation of returns. The long term performance of each portfolio produced by these algorithms was compared to various benchmarks. It was found that the algorithms often outperformed the benchmarks in real rate of return. However, when return was adjusted for risk, the algorithms generally underperformed the benchmarks. A hierarchical method involving clustering stocks before allocation was also tested, but was found to underperform allocation without clustering. MDD-minimization subject to a return constraint was found to be the only optimization algorithm that outperformed the benchmarks in each measure of performance. Closer examination revealed that this algorithm closely matched the benchmarks during periods of continuous market growth, but significantly outperformed them during periods of continuous market decline. This result supports the use of maximum drawdown as an alternative measure of risk in portfolio optimization when investors are assumed to be risk-averse.
Description
May 2013
School of Science
Department
Dept. of Computer Science
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
Access
CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.