Unveiling Taiwan Stock Market Dynamics: A Graph Neural Network Approach to Automatic Stock Clustering for Enhanced Predictions

In this paper, we introduce graph neural networks (GNNs) and automatic stock clustering to improve stock market prediction accuracy in the market of Taiwan Stock Exchange. GNNs capture intricate inter-stock relationships, enhancing prediction accuracy. Automatic clustering based on market behavior e...

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Main Authors: Lee, Lin-Sheng, Chen, Jenhui
Format: Conference Proceeding
Language:English
Subjects:
automatic clustering
Cause effect analysis
financial forecasting
Fitting
Graph neural networks
Predictive models
stock market prediction
Stock markets
Task analysis
Technological innovation
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Chen, Jenhui
description In this paper, we introduce graph neural networks (GNNs) and automatic stock clustering to improve stock market prediction accuracy in the market of Taiwan Stock Exchange. GNNs capture intricate inter-stock relationships, enhancing prediction accuracy. Automatic clustering based on market behavior ensures adaptability. Results demonstrate significant improvements, with potential applicability beyond Taiwan's market, advancing financial prediction methodologies.
doi_str_mv 10.1109/ICASI60819.2024.10547817
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subjects automatic clustering
Cause effect analysis
financial forecasting
Fitting
Graph neural networks
Predictive models
stock market prediction
Stock markets
Task analysis
Technological innovation
title Unveiling Taiwan Stock Market Dynamics: A Graph Neural Network Approach to Automatic Stock Clustering for Enhanced Predictions
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