A quantitative fusion strategy of stock picking and timing based on Particle Swarm Optimized-Back Propagation Neural Network and Multivariate Gaussian-Hidden Markov Model-Reference-Cited by-同舟云学术

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A quantitative fusion strategy of stock picking and timing based on Particle Swarm Optimized-Back Propagation Neural Network and Multivariate Gaussian-Hidden Markov Model

Published:2023-12-08 Issue: Volume:7 Page:185-192
ISSN:
Container-title:Proceedings of the 2023 4th International Conference on Big Data Economy and Information Management
language:
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Author:

Li Huajian¹^ORCID,Li Longjian²^ORCID,Liang Jiajian¹^ORCID,Dai Weinan³^ORCID

Affiliation:

1. Guangzhou University, China

2. Guangdong University of Technology, China

3. Trine University, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3659211.3659243

Reference26 articles.

1. Portfolio selection;Markowits Harry M;Journal of finance,1952

2. Modeling Corporate Bond Returns

3. Huajian Li, Longjian Li, and Jiajian Liang. Construction of stock molecular system and the popularization of density functional theory in stock market. arXiv preprint arXiv:2310.05677, 2023.

4. Quantification of risk in classical models of finance

5. Iuliia Shpak, Ben Human, and Andrea Nardon. Idiosyncratic momentum in commodity futures. Available at SSRN 3035397, 2017.

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