Show simple item record

dc.rights.licenseUsers 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.
dc.contributorMagdon-Ismail, Malik
dc.contributorGupta, Aparna
dc.contributorSzymanśki, Bolesław
dc.contributor.authorBolin, Andrew
dc.date.accessioned2021-11-03T07:57:57Z
dc.date.available2021-11-03T07:57:57Z
dc.date.created2013-09-09T14:12:08Z
dc.date.issued2013-05
dc.identifier.urihttps://hdl.handle.net/20.500.13015/836
dc.descriptionMay 2013
dc.descriptionSchool of Science
dc.description.abstractPortfolio 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.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectComputer science
dc.titleEmpirical analysis of optimization algorithms for portfolio allocation
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid167018
dc.digitool.pid167019
dc.digitool.pid167020
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreeMS
dc.relation.departmentDept. of Computer Science


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record