Portfolio selection via constrained stochastic gradients

Andrew J. Bean; Andrew C. Singer

doi:10.1109/SSP.2011.5967709

Portfolio selection via constrained stochastic gradients

Andrew J. Bean
, Andrew C. Singer

Office of the Dean of the College of Engineering and Applied Sciences

Stony Brook University

University of Illinois at Urbana-Champaign

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

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.

Original language	English
Title of host publication	2011 IEEE Statistical Signal Processing Workshop, SSP 2011
Pages	37-40
Number of pages	4
DOIs	https://doi.org/10.1109/SSP.2011.5967709
State	Published - 2011
Event	2011 IEEE Statistical Signal Processing Workshop, SSP 2011 - Nice, France Duration: Jun 28 2011 → Jun 30 2011

Publication series

Name	IEEE Workshop on Statistical Signal Processing Proceedings

Conference

Conference	2011 IEEE Statistical Signal Processing Workshop, SSP 2011
Country/Territory	France
City	Nice
Period	06/28/11 → 06/30/11

Keywords

exponentiated gradient
portfolios
stochastic gradient
universal

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10.1109/SSP.2011.5967709

Cite this

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title = "Portfolio selection via constrained stochastic gradients",

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 = "exponentiated gradient, portfolios, stochastic gradient, universal",

author = "Bean, \{Andrew J.\} and Singer, \{Andrew C.\}",

year = "2011",

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language = "English",

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series = "IEEE Workshop on Statistical Signal Processing Proceedings",

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booktitle = "2011 IEEE Statistical Signal Processing Workshop, SSP 2011",

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AU - Bean, Andrew J.

AU - Singer, Andrew C.

PY - 2011

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AB - 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.

KW - exponentiated gradient

KW - portfolios

KW - stochastic gradient

KW - universal

UR - https://www.scopus.com/pages/publications/80052227140

U2 - 10.1109/SSP.2011.5967709

DO - 10.1109/SSP.2011.5967709

M3 - Conference contribution

SN - 9781457705700

T3 - IEEE Workshop on Statistical Signal Processing Proceedings

SP - 37

EP - 40

BT - 2011 IEEE Statistical Signal Processing Workshop, SSP 2011

T2 - 2011 IEEE Statistical Signal Processing Workshop, SSP 2011

Y2 - 28 June 2011 through 30 June 2011

ER -

Portfolio selection via constrained stochastic gradients

Abstract

Publication series

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Keywords

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