Develop an Online Portfolio Model for Optimal Trading Strategies
- DOI
- 10.2991/978-94-6463-044-2_119How to use a DOI?
- Keywords
- Long Short-Term Memory; Online Portfolio Strategy; Risk assessment factors; Improved nonparametric kernel-based log optimal algorithm
- Abstract
Combined with the modeling and simulation concept, this paper considers the impact of transaction costs, long and short term risks and efficiency on the portfolio. Based on the past asset prices, this paper refers to the price trend and analyzes the buying and selling risks, and establishes a long and short term memory neural network model to predict the prices of various assets in the future. An improved non-parametric kernel-based logarithmic optimal algorithm is designed to solve the optimal online portfolio strategy. The model is tested on the dataset of gold daily price from London Bullion Market Association, 9/11/2021 and Bitcoin daily price from NASDAQ, 9/11/2021. The results show that: The long short term memory neural network model predicts the known asset prices with an average root mean square error of only 1742.0421, and under the influence of Sharpe ratio of 0.8231, our algorithm can earn $214,764.78 in the future period of investment.
- Copyright
- © 2022 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Junyu Xiong AU - Zhaoyi Li AU - Yuyao Zhang AU - Guoyan Chen AU - Xuesong Liu PY - 2022 DA - 2022/12/27 TI - Develop an Online Portfolio Model for Optimal Trading Strategies BT - Proceedings of the 2022 3rd International Conference on Modern Education and Information Management (ICMEIM 2022) PB - Atlantis Press SP - 947 EP - 954 SN - 2667-128X UR - https://doi.org/10.2991/978-94-6463-044-2_119 DO - 10.2991/978-94-6463-044-2_119 ID - Xiong2022 ER -