GraphCNNpred：使用基于图形的深度学习系统预测股市指数

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-04 DOI:arxiv-2407.03760

Yuhui Jin

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引用次数: 0

摘要

用于预测股市价格的深度学习技术是数据科学领域的一个热门话题。定制化特征工程是处理不同股市数据集的工具。在本文中，我们给出了一个基于图神经网络的卷积神经网络（CNN）模型，该模型可以应用于不同的数据源，试图提取特征来预测（text{S}\&text{P} 500、NASDAQ、DJI、NYSE 和 RUSSEL）指数的走势。

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GraphCNNpred: A stock market indices prediction using a Graph based deep learning system

Deep learning techniques for predicting stock market prices is an popular topic in the field of data science. Customized feature engineering arises as pre-processing tools of different stock market dataset. In this paper, we give a graph neural network based convolutional neural network (CNN) model, that can be applied on diverse source of data, in the attempt to extract features to predict the trends of indices of \text{S}\&\text{P} 500, NASDAQ, DJI, NYSE, and RUSSEL.

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arXiv - QuantFin - Computational Finance

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