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A Short Survey on Graph Neural Networks Based Stock Market Prediction Models

Authors:
Lei Zhou
Yuqi Zhang
Jian Yu
Sira Yongchareon
Samaneh Madanian
Guiling Wang

Keywords: graph neural networks, stock prediction, deep learning.

Abstract:
Stock market predictions are challenging due to the complexity and volatility of markets across the globe. Influential factors include, but are not limited to, economic conditions, political events, investor sentiment, and even natural calamities. In this survey, we review the current literature on stock market predictions using various approaches and propose a framework that facilitates the categorization and analysis of existing works. A novel taxonomy is also proposed within this framework for Graph Neural Network (GNN)-based stock market prediction methods. Potential research gaps are identified, and future research directions are discussed towards the end of this survey.

Pages: 10 to 16

Copyright: Copyright (c) IARIA, 2024

Publication date: April 14, 2024

Published in: conference

ISSN: 2308-3972

ISBN: 978-1-68558-147-3

Location: Venice, Italy

Dates: from April 14, 2024 to April 18, 2024