Journal
CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT
Volume -, Issue -, Pages 1655-1658Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3269206.3269269
Keywords
Node Embedding; Corporation Similarity; Graph Convolutional Neural Networks; Stock Price Prediction
Funding
- Libo Wu in Fudan University
- National Natural Science Foundation of China [61702106]
- Shanghai Science and Technology Commission [17JC1420200, 17YF1427600, 16JC1420401]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available