Online Portfolio Selection with Cardinality Constraint and Transaction Costs based on Contextual Bandit
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Special Track on AI in FinTech. Pages 4682-4689. https://doi.org/10.24963/ijcai.2020/646
Online portfolio selection (OLPS) is a fundamental and challenging problem in financial engineering, which faces two practical constraints during the real trading, i.e., cardinality constraint and non-zero transaction costs. In order to achieve greater feasibility in financial markets, in this paper, we propose a novel online portfolio selection method named LExp4.TCGP with theoretical guarantee of sublinear regret to address the OLPS problem with the two constraints. In addition, we incorporate side information into our method based on contextual bandit, which further improves the effectiveness of our method. Extensive experiments conducted on four representative real-world datasets demonstrate that our method significantly outperforms the state-of-the-art methods when cardinality constraint and non-zero transaction costs co-exist.
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