The Progress and Challenges of Machine Learning Technology in Stock Analysis

Anlan Wang

2024

Abstract

With the advancement of human society and technology, economic and financial connections have become increasingly intertwined, and technological development has significantly transformed business practices. The analysis of the stock market, widely regarded as an important investment and financial market barometer of the substantial economy, is crucial to financial activities. While traditional methods such as fundamental and technical analysis remain prevalent, machine learning algorithms have gained substantial traction in stock market forecasting over the past decade, enhancing both the accuracy and efficiency of analysis. In order to gain a better understanding of the development and application of machine learning approaches, this paper provides a review of the progress and challenges related to applying machine learning techniques in stock analysis and prediction. Despite the considerable progress and growing adoption of these methods, there is still a long way to go in further advancing their application in stock prediction.

Paper Citation

in Harvard Style

Wang A. (2024). The Progress and Challenges of Machine Learning Technology in Stock Analysis. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 205-208. DOI: 10.5220/0013212900004568

in Bibtex Style

@conference{ecai24,
author={Anlan Wang},
title={The Progress and Challenges of Machine Learning Technology in Stock Analysis},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={205-208},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013212900004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - The Progress and Challenges of Machine Learning Technology in Stock Analysis
SN - 978-989-758-726-9
AU - Wang A.
PY - 2024
SP - 205
EP - 208
DO - 10.5220/0013212900004568
PB - SciTePress