Name
Abstract | ||
|---|---|---|
In this paper, we study a new kind of portfolio problem, named trading point aware portfolio optimization (TPPO), which aims to obtain excess intraday profit by deciding the portfolio weights and their trading points simultaneously based on microscopic information. However, a strategy for the TPPO problem faces two challenging problems, i.e., modeling the ever-changing and irregular microscopic stock price time series and deciding the scattering candidate trading points. To address these problems, we propose a novel TPPO strategy named STrader based on normalizing flows. STrader is not only promising in reversibly transforming the geometric Brownian motion process to the unobservable and complicated stochastic process of the microscopic stock price time series for modeling such series, but also has the ability to earn excess intraday profit by capturing the appropriate trading points of the portfolio. Extensive experiments conducted on three public datasets demonstrate STrader's superiority over the state-of-the-art portfolio strategies. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.24963/ijcai.2022/557 | European Conference on Artificial Intelligence |
Keywords | DocType | Citations |
Multidisciplinary Topics and Applications: Finance,Machine Learning: Deep Reinforcement Learning,Machine Learning: Time-series, Data Streams | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mengyuan Yang | 1 | 0 | 0.68 |
| Xiaolin Zheng | 2 | 300 | 36.99 |
| Qianqiao Liang | 3 | 0 | 2.37 |
| Bing Han | 4 | 0 | 0.34 |
| Mengying Zhu | 5 | 0 | 0.34 |