Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 5th International Conference on Computing and Data Science

Series Vol. 17 , 23 October 2023


Open Access | Article

Comparative analysis of machine learning and deep learning techniques for prediction of the stock market

Xuyang Zheng * 1
1 Nanjing University of Posts and Telecommunications

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 17, 139-149
Published 23 October 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Xuyang Zheng. Comparative analysis of machine learning and deep learning techniques for prediction of the stock market. ACE (2023) Vol. 17: 139-149. DOI: 10.54254/2755-2721/17/20230926.

Abstract

In recent years, there has been a continuing search for reliable instruments that can predict trends in financial markets and activities related to investments. In the past, academics have used traditional methods to forecast the investment worth of equities by analyzing metrics such as the financial records of companies from both a fundamental and technical point of view. The effectiveness of these strategies could decrease as market information asymmetry continues to rise and high-frequency trading becomes increasingly prevalent. Researchers have developed novel methodologies as a result of the progress that has been made in the field of artificial intelligence technology. One of these methodologies is the application of neural networks for forecasting. In the meantime, data visualization is becoming increasingly common, which could make it easier to conduct an in-depth analysis of the advantages and disadvantages presented by various models. The purpose of this research is to evaluate the performance of machine learning and deep learning strategies, including logistic regression, support vector machine, multi-layer perceptron and convolution neural networks, in forecasting stock market prices where various data visualization techniques are utilized for investigation. The findings from error analysis demonstrate that convolutional neural networks operate superbly.

Keywords

market prediction, machine learning, neural networks, data visualization

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 5th International Conference on Computing and Data Science
ISBN (Print)
978-1-83558-025-7
ISBN (Online)
978-1-83558-026-4
Published Date
23 October 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/17/20230926
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated