<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the development of artificial intelligence technology, machine learning has achieved very good results in the field of stock selection. This paper mainly studies the application of linear model, clustering, support vector machine, random forest, neural network and deep learning methods in the field of stock selection. The main contribution of this paper is to provide a new idea for traditional quantitative investors, so that they can build a more efficient stock selection model in practical application. The experimental results show that the stock selection model constructed by these six machine learning methods can obtain higher return and stability.</p>
</abstract>

With the development of artificial intelligence technology, machine learning has achieved very good results in the field of stock selection. This paper mainly studies the application of linear model, clustering, support vector machine, random forest, neural network and deep learning methods in the field of stock selection. The main contribution of this paper is to provide a new idea for traditional quantitative investors, so that they can build a more efficient stock selection model in practical application. The experimental results show that the stock selection model constructed by these six machine learning methods can obtain higher return and stability.

Application of machine learning in stock selection

University of Chinese Academy of Sciences

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Application of machine learning in stock selection"}

With the development of artificial intelligence technology, machine learning has achieved very good results in the field of stock selection. This paper mainly...

Application of machine learning in stock selection

Pubblicato online: 20 set 2022

Pagine: 2413 - 2424

Ricevuto: 22 feb 2022

Accettato: 15 mag 2022

DOI: https://doi.org/10.2478/amns.2022.1.00025

Parole chiave
Stock selection, machine learning, clustering, SVM, random forest, neural network, deep learning

© 2023 Pengfei Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of machine learning in stock selection

Pubblicato online: 20 set 2022

Pagine: 2413 - 2424

Ricevuto: 22 feb 2022

Accettato: 15 mag 2022

DOI: https://doi.org/10.2478/amns.2022.1.00025

Parole chiaveStock selection, machine learning, clustering, SVM, random forest, neural network, deep learning

© 2023 Pengfei Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Stock selection, machine learning, clustering, SVM, random forest, neural network, deep learning