Stock market prediction using Altruistic Dragonfly Algorithm

Bitanu Chatterjee, Sayan Acharya, Trinav Bhattacharyya, Seyedali Mirjalili and Ram Sarkar

PLOS ONE, 2023, vol. 18, issue 4, 1-20

Abstract: Stock market prediction is the process of determining the value of a company’s shares and other financial assets in the future. This paper proposes a new model where Altruistic Dragonfly Algorithm (ADA) is combined with Least Squares Support Vector Machine (LS-SVM) for stock market prediction. ADA is a meta-heuristic algorithm which optimizes the parameters of LS-SVM to avoid local minima and overfitting, resulting in better prediction performance. Experiments have been performed on 12 datasets and the obtained results are compared with other popular meta-heuristic algorithms. The results show that the proposed model provides a better predictive ability and demonstrate the effectiveness of ADA in optimizing the parameters of LS-SVM.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0282002 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 82002&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0282002

DOI: 10.1371/journal.pone.0282002

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().