A. Kumar and A. Segev, "Bayesian Ensembled Knowledge Extraction Strategy for Online Portfolio Selection," 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, pp. 4148-4156, doi: 10.1109/BigData55660.2022.10020708.
Bayesian ensembled knowledge extraction strategy for online portfolio selection
|Author:||Kumar, Abhishek1,2; Segev, Aviv3|
1Center for Ubiquitous Computing University of Oulu Oulu, Finland
2Department of Computer Science University of Helsinki Helsinki, Finland
3Department of Computer Science University of South Alabama Mobile, USA
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023031431529
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-03-14
Online portfolio selection, one of the major fundamental problems in finance, has been explored quite extensively in recent years by machine learning and artificial intelligence communities. Recent state-of-the-art methods have focused on Mean Reversion significantly and have demonstrated outstanding performance. Another version of the same phenomenon, Median Reversion, has also performed well and demonstrated its ability to be robust against noises and outliers. Another important characteristic is Momentum. In this paper, a Bayesian ensembling approach to extract knowledge from both Mean Reversion and Median Reversion simultaneously based on the momentum associated with each one is proposed for the online portfolio selection task. The proposed method demonstrates its effectiveness by outperforming current state-of-the-art algorithms on several datasets.
|Pages:||4148 - 4156|
2022 IEEE International Conference on Big Data (Big Data)
IEEE International Conference on Big Data
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
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