TY - GEN
T1 - An adaptive news-driven method for CVaR-sensitive online portfolio selection in non-stationary financial markets
AU - Liang, Qianqiao
AU - Zhu, Mengying
AU - Zheng, Xiaolin
AU - Wang, Yan
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85125456774&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2021/373
DO - 10.24963/ijcai.2021/373
M3 - Conference proceeding contribution
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 2708
EP - 2715
BT - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021
A2 - Zhou, Zhi-Hua
PB - International Joint Conferences on Artificial Intelligence
CY - Freiburg, Germany
T2 - 30th International Joint Conference on Artificial Intelligence, IJCAI 2021
Y2 - 19 August 2021 through 27 August 2021
ER -