An adaptive news-driven method for CVaR-sensitive online portfolio selection in non-stationary financial markets

Qianqiao Liang, Mengying Zhu, Xiaolin Zheng*, Yan Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

CVaR-sensitive online portfolio selection (CS-OLPS) becomes increasingly important for investors because of its effectiveness to minimize conditional value at risk (CVaR) and control extreme losses. However, the non-stationary nature of financial markets makes it very difficult to address the CS-OLPS problem effectively. To address the CS-OLPS problem in non-stationary markets, we propose an effective news-driven method, named CAND, which adaptively exploits news to determine the adjustment tendency and adjustment scale for tracking the dynamic optimal portfolio with minimal CVaR in each trading round. In addition, we devise a filtering mechanism to reduce the errors caused by the noisy news for further improving CAND's effectiveness. We rigorously prove a sub-linear regret of CAND. Extensive experiments on three real-world datasets demonstrate CAND’s superiority over the state-of-the-art portfolio methods in terms of returns and risks.
Original languageEnglish
Title of host publicationThirtieth International Joint Conference on Artificial Intelligence (IJCAI 2021)
EditorsZhi-Hua Zhou
Place of PublicationFreiburg, Germany
PublisherInternational Joint Conferences on Artificial Intelligence
Pages2708-2715
Number of pages8
ISBN (Electronic)9780999241196
DOIs
Publication statusPublished - 2021
Event30th International Joint Conference on Artificial Intelligence, IJCAI 2021 - Montreal, Canada
Duration: 19 Aug 202127 Aug 2021

Conference

Conference30th International Joint Conference on Artificial Intelligence, IJCAI 2021
CountryCanada
CityMontreal
Period19/08/2127/08/21

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