Growth-Optimal Portfolio Selection under CVaR Constraints

Guy Uziel, Ran El-Yaniv
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR 84:48-57, 2018.

Abstract

Online portfolio selection research has so far focused mainly on minimizing regret defined in terms of wealth growth. Practical financial decision making, however, is deeply concerned with both wealth and risk. We consider online learning of portfolios of stocks whose prices are governed by arbitrary (unknown) stationary and ergodic processes, where the goal is to maximize wealth while keeping the conditional value at risk (CVaR) below a desired threshold. We characterize the asymptomatically optimal risk-adjusted performance and present an investment strategy whose portfolios are guaranteed to achieve the asymptotic optimal solution while fulfilling the desired risk constraint. We also numerically demonstrate and validate the viability of our method on standard datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v84-uziel18a, title = {Growth-Optimal Portfolio Selection under CVaR Constraints}, author = {Uziel, Guy and El-Yaniv, Ran}, booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics}, pages = {48--57}, year = {2018}, editor = {Storkey, Amos and Perez-Cruz, Fernando}, volume = {84}, series = {Proceedings of Machine Learning Research}, month = {09--11 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v84/uziel18a/uziel18a.pdf}, url = {https://proceedings.mlr.press/v84/uziel18a.html}, abstract = { Online portfolio selection research has so far focused mainly on minimizing regret defined in terms of wealth growth. Practical financial decision making, however, is deeply concerned with both wealth and risk. We consider online learning of portfolios of stocks whose prices are governed by arbitrary (unknown) stationary and ergodic processes, where the goal is to maximize wealth while keeping the conditional value at risk (CVaR) below a desired threshold. We characterize the asymptomatically optimal risk-adjusted performance and present an investment strategy whose portfolios are guaranteed to achieve the asymptotic optimal solution while fulfilling the desired risk constraint. We also numerically demonstrate and validate the viability of our method on standard datasets. } }
Endnote
%0 Conference Paper %T Growth-Optimal Portfolio Selection under CVaR Constraints %A Guy Uziel %A Ran El-Yaniv %B Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2018 %E Amos Storkey %E Fernando Perez-Cruz %F pmlr-v84-uziel18a %I PMLR %P 48--57 %U https://proceedings.mlr.press/v84/uziel18a.html %V 84 %X Online portfolio selection research has so far focused mainly on minimizing regret defined in terms of wealth growth. Practical financial decision making, however, is deeply concerned with both wealth and risk. We consider online learning of portfolios of stocks whose prices are governed by arbitrary (unknown) stationary and ergodic processes, where the goal is to maximize wealth while keeping the conditional value at risk (CVaR) below a desired threshold. We characterize the asymptomatically optimal risk-adjusted performance and present an investment strategy whose portfolios are guaranteed to achieve the asymptotic optimal solution while fulfilling the desired risk constraint. We also numerically demonstrate and validate the viability of our method on standard datasets.
APA
Uziel, G. & El-Yaniv, R.. (2018). Growth-Optimal Portfolio Selection under CVaR Constraints. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 84:48-57 Available from https://proceedings.mlr.press/v84/uziel18a.html.

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