Vector Autoregressive Weighting Reversion Strategy for Online Portfolio Selection
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. Special Track on AI in FinTech. Pages 4469-4475.
https://doi.org/10.24963/ijcai.2020/616
Keywords
- AI for trading: AI for portfolio analytics
- AI for trading: AI for algorithmic trading
- AI for trading: AI for strategic trading and strategy design
- AI for trading: General
Abstract
Aiming to improve the performance of existing reversion based online portfolio selection strategies, we propose a novel multi-period strategy named “Vector Autoregressive Weighting Reversion” (VAWR). Firstly, vector autoregressive moving-average algorithm used in time series prediction is transformed into exploring the dynamic relationships between different assets for more accurate price prediction. Secondly, we design the modified online passive aggressive technique and advance a scheme to weigh investment risk and cumulative experience to update the closed-form of portfolio. Theoretical analysis and experimental results confirm the effectiveness and robustness of our strategy. Compared with the state-of-the-art strategies, VAWR greatly increases cumulative wealth, and it obtains the highest annualized percentage yield and sharp ratio on various public datasets. These improvements and easy implementation support the practical applications of VAWR.
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@inproceedings{ijcai2020p616,
title = {Vector Autoregressive Weighting Reversion Strategy for Online Portfolio Selection},
author = {Cai, Xia},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere},
pages = {4469--4475},
year = {2020},
month = {7},
note = {Special Track on AI in FinTech},
doi = {10.24963/ijcai.2020/616},
url = {https://doi.org/10.24963/ijcai.2020/616},
}
TY - CPAPER TI - Vector Autoregressive Weighting Reversion Strategy for Online Portfolio Selection AU - Cai, Xia T2 - Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2020 SP - 4469 EP - 4475 DO - 10.24963/ijcai.2020/616 UR - https://doi.org/10.24963/ijcai.2020/616 ER -Copied
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