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Found 7 papers, 6 papers with code
Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs
Despite the success of GNNs, most existing GNNs are designed to learn node representations on the fixed and homogeneous graphs.
MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System
Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest.
Graph Transformer Networks
In this paper, we propose Graph Transformer Networks (GTNs) that are capable of generating new graph structures, which involve identifying useful connections between unconnected nodes on the original graph, while learning effective node representation on the new graphs in an end-to-end fashion.
HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
Methods that use relational data for stock market prediction have been recently proposed, but they are still in their infancy.
SAIN: Self-Attentive Integration Network for Recommendation
To deal with this problem, content based recommendation models which use the auxiliary attributes of users and items have been proposed.
Predicting Multiple Demographic Attributes with Task Specific Embedding Transformation and Attention Network
Second, they assumed that all transactions are equally important in predicting demographic attributes.
Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network
Moreover, the results show that future stock prices can be predicted even if the training and testing procedures are done in different countries.
