• DocumentCode
    2504814
  • Title

    Portfolio selection via constrained stochastic gradients

  • Author

    Bean, Andrew J. ; Singer, Andrew C.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA
  • fYear
    2011
  • fDate
    28-30 June 2011
  • Firstpage
    37
  • Lastpage
    40
  • Abstract
    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.
  • Keywords
    approximation theory; gradient methods; investment; optimisation; stochastic processes; constrained stochastic gradient based approximations; online portfolio selection problem; sequential regularized optimizations; universal portfolios; Approximation algorithms; Approximation methods; Mathematical model; Optimization; Portfolios; Signal processing algorithms; Stochastic processes; exponentiated gradient; portfolios; stochastic gradient; universal;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Statistical Signal Processing Workshop (SSP), 2011 IEEE
  • Conference_Location
    Nice
  • ISSN
    pending
  • Print_ISBN
    978-1-4577-0569-4
  • Type

    conf

  • DOI
    10.1109/SSP.2011.5967709
  • Filename
    5967709
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2504814