Proceedings

An Adaptive News-Driven Method for CVaR-sensitive Online Portfolio Selection in Non-Stationary Financial Markets

Qianqiao Liang, Mengying Zhu, Xiaolin Zheng, Yan Wang

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 2708-2715.

https://doi.org/10.24963/ijcai.2021/373

Keywords

  • Multidisciplinary Topics and Applications: Economic and Finance
  • Machine Learning: Online Learning

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.

Qianqiao Liang, Mengying Zhu, Xiaolin Zheng, and Yan Wang. 2021. An Adaptive News-Driven Method for CVaR-sensitive Online Portfolio Selection in Non-Stationary Financial Markets. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 2708–2715. https://doi.org/10.24963/ijcai.2021/373
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