Yingmei Chen’s research while affiliated with Shanghai University and other places

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Publications (1)


Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction
  • Conference Paper

October 2018

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361 Reads

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209 Citations

Yingmei Chen

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Zhongyu Wei

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Xuanjing Huang

In this paper, we propose to incorporate information of related corporations of a target company for its stock price prediction. We first construct a graph including all involved corporations based on investment facts from real market and learn a distributed representation for each corporation via node embedding methods applied on the graph. Two approaches are then explored to utilize information of related corporations based on a pipeline model and a joint model via graph convolutional neural networks respectively. Experiments on the data collected from stock market in Mainland China show that the representation learned from our model is able to capture relationships between corporations, and prediction models incorporating related corporations' information are able to make more accurate predictions on stock market.

Citations (1)


... This shift reflects investors' primary interest: identifying stocks that can bring higher returns in the future [3]. Traditional approaches to stock prediction, such as Auto Regressionbased methods [4] and Support Vector Regression [5], often fall short of capturing the dynamic, nonlinear nature of financial markets. Deep learning methods, including Recurrent Neural Networks [6,7,8] and attention-based models [9,10,11,12], have shown promise in learning sequential patterns from longitudinal data. ...

Reference:

Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphs
Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction
  • Citing Conference Paper
  • October 2018