Overview of Machine Learning for Stock Selection Based on Multi-Factor Models

Overview of Machine Learning for Stock Selection Based on Multi-Factor Models

In recent years, many scholars have used different methods to predict and select stocks. Empirical studies have shown that in multi-factor models, machine learning algorithms perform better on stock selection than traditional statistical methods. This article selects six classic machine learning alg...

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Main Authors: Li Haoxuan, Zhang Xueyan, Li Ziyan, Zheng Chunyuan
Format: Article
Language:English
Published: EDP Sciences 2020-01-01
Series:E3S Web of Conferences
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/74/e3sconf_ebldm2020_02047.pdf
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spelling doaj-e33fdcda2ab34b8d8b55d48852f1cb132021-04-02T16:29:00ZengEDP SciencesE3S Web of Conferences2267-12422020-01-012140204710.1051/e3sconf/202021402047e3sconf_ebldm2020_02047Overview of Machine Learning for Stock Selection Based on Multi-Factor ModelsLi HaoxuanZhang XueyanLi ZiyanZheng ChunyuanIn recent years, many scholars have used different methods to predict and select stocks. Empirical studies have shown that in multi-factor models, machine learning algorithms perform better on stock selection than traditional statistical methods. This article selects six classic machine learning algorithms, and takes the CSI 500 component stocks as an example, using 19 factors to select stocks. In this article, we introduce four of these algorithms in detail and apply them to select stocks. Finally, we back-test six machine learning algorithms, list the data, analyze the performance of each algorithm, and put forward some ideas on the direction of machine learning algorithm improvement.https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/74/e3sconf_ebldm2020_02047.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Li Haoxuan
Zhang Xueyan
Li Ziyan
Zheng Chunyuan
spellingShingle Li Haoxuan
Zhang Xueyan
Li Ziyan
Zheng Chunyuan
Overview of Machine Learning for Stock Selection Based on Multi-Factor Models
E3S Web of Conferences
author_facet Li Haoxuan
Zhang Xueyan
Li Ziyan
Zheng Chunyuan
author_sort Li Haoxuan
title Overview of Machine Learning for Stock Selection Based on Multi-Factor Models
title_short Overview of Machine Learning for Stock Selection Based on Multi-Factor Models
title_full Overview of Machine Learning for Stock Selection Based on Multi-Factor Models
title_fullStr Overview of Machine Learning for Stock Selection Based on Multi-Factor Models
title_full_unstemmed Overview of Machine Learning for Stock Selection Based on Multi-Factor Models
title_sort overview of machine learning for stock selection based on multi-factor models
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2020-01-01
description In recent years, many scholars have used different methods to predict and select stocks. Empirical studies have shown that in multi-factor models, machine learning algorithms perform better on stock selection than traditional statistical methods. This article selects six classic machine learning algorithms, and takes the CSI 500 component stocks as an example, using 19 factors to select stocks. In this article, we introduce four of these algorithms in detail and apply them to select stocks. Finally, we back-test six machine learning algorithms, list the data, analyze the performance of each algorithm, and put forward some ideas on the direction of machine learning algorithm improvement.
url https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/74/e3sconf_ebldm2020_02047.pdf
work_keys_str_mv AT lihaoxuan overviewofmachinelearningforstockselectionbasedonmultifactormodels
AT zhangxueyan overviewofmachinelearningforstockselectionbasedonmultifactormodels
AT liziyan overviewofmachinelearningforstockselectionbasedonmultifactormodels
AT zhengchunyuan overviewofmachinelearningforstockselectionbasedonmultifactormodels
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