Meta Algorithms for Portfolio Selection
2010-09-20
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Meta Algorithms for Portfolio Selection
Authors
Published Date
2010-09-20
Publisher
Type
Report
Abstract
We consider the problem of sequential portfolio selection in the stock market. There are theoretically well grounded algorithms for the problem, such as Universal Portfolio (UP), Exponentiated Gradient (EG) and Online Newton Step (ONS). Such algorithms enjoy the property of being universal, i.e., having low regret with the best constant rebalanced portfolio. However, the practical performance of such popular algorithms is sobering compared to heuristics such as Anticor, which have no theoretical guarantees but perform surprisingly well in practice. Motivated by such discrepancies, in this paper we focus on designing meta algorithms for portfolio selection which can leverage the best of both worlds. Such algorithms work with a pool of base algorithms and use online learning to redistribute wealth among the base algorithms. We develop two meta-algorithms: MA_EG which uses online gradient descent following EG and MA_ONS which uses online Newton step following ONS. If one of the base algorithms is universal, it follows that the meta-algorithm is universal. Through extensive experiments on two real stock market datasets, we show that the meta-algorithms are competitive and often better than the best base algorithm, including heuristics, while maintaining the guarantee of being an universal algorithm.
Keywords
Description
Related to
Replaces
License
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Suggested citation
Das, Puja; Banerjee, Arindam. (2010). Meta Algorithms for Portfolio Selection. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215840.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.