Portfolio selection via constrained stochastic gradients

Andrew J. Bean, Andrew Carl Singer

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we consider the online portfolio selection problem. We develop several algorithms for portfolio selection based on sequential regularized optimizations and constrained stochastic gradient based approximations to this. We relate these methods to related results in stochastic gradients and universal portfolios, and compare results of simulations using historical data. We also demonstrate that these results compare favorably with respect to so-called universal portfolios.

Original languageEnglish (US)
Title of host publication2011 IEEE Statistical Signal Processing Workshop, SSP 2011
Number of pages4
StatePublished - Sep 5 2011
Event2011 IEEE Statistical Signal Processing Workshop, SSP 2011 - Nice, France
Duration: Jun 28 2011Jun 30 2011

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings


Other2011 IEEE Statistical Signal Processing Workshop, SSP 2011


  • exponentiated gradient
  • portfolios
  • stochastic gradient
  • universal

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications


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