Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022)

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Stock Market Prediction Using Machine Learning

Authors
Qingyi Chen1, *
1Department of Statistics, University of Warwick, Coventry, CV4 7ES, UK
*Corresponding author. Email: Qingyi.Chen@warwick.ac.uk
Corresponding Author
Qingyi Chen
Available Online 20 December 2022.
DOI
10.2991/978-94-6463-030-5_47How to use a DOI?
Keywords
Stock Market; Machine Learning; Predictive Modeling; Comparative analysis
Abstract

Stock market analysis and prediction has always been a challenging problem for finance experts because it is so volatile and susceptible of external factors that deeply affect the sentiment of investors. Machine learning, which produces forecasts based on the values of current stock market indices by training on their prior values, is a recent trend in stock market prediction technologies and it shows great promise. However, the prediction methods and algorithms are still developing and the results seem to be volatile and unstable. Making the predictions fast and accurate can greatly impact the financial markets and it is necessary to further develop the models and expand the scope of machine learning approaches. This paper focuses on comparing the use and effectiveness of various models of stock market prediction including Support Vector Machine (SVM), Convolutional Neural Network (CNN), Regression based model and Long Short-Term Memory (LSTM) by summarizing qualitatively the results obtained from several existing sources and experiments. According to the research results of this paper, SVM and the combination of CNN and LSTM performed well in making accurate predictions in the stock market.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
20 December 2022
ISBN
978-94-6463-030-5
ISSN
2589-4919
DOI
10.2991/978-94-6463-030-5_47How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Qingyi Chen
PY  - 2022
DA  - 2022/12/20
TI  - Stock Market Prediction Using Machine Learning
BT  - Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022)
PB  - Atlantis Press
SP  - 458
EP  - 465
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-030-5_47
DO  - 10.2991/978-94-6463-030-5_47
ID  - Chen2022
ER  -