ST-Trader: A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement

Document Type

Article

Publication Date

5-1-2021

Abstract

Stocks that are fundamentally connected with each other tend to move together. Considering such common trends is believed to benefit stock movement forecasting tasks. However, such signals are not trivial to model because the connections among stocks are not physically presented and need to be estimated from volatile data. Motivated by this observation, we propose a framework that incorporates the inter-connection of firms to forecast stock prices. To effectively utilize a large set of fundamental features, we further design a novel pipeline. First, we use variational autoencoder (VAE) to reduce the dimension of stock fundamental information and then cluster stocks into a graph structure (fundamentally clustering). Second, a hybrid model of graph convolutional network and long-short term memory network (GCN-LSTM) with an adjacency graph matrix (learnt from VAE) is proposed for graph-structured stock market forecasting. Experiments on minute-level U.S. stock market data demonstrate that our model effectively captures both spatial and temporal signals and achieves superior improvement over baseline methods. The proposed model is promising for other applications in which there is a possible but hidden spatial dependency to improve time-series prediction.

Identifier

85104096084 (Scopus)

Publication Title

IEEE Caa Journal of Automatica Sinica

External Full Text Location

https://doi.org/10.1109/JAS.2021.1003976

e-ISSN

23299274

ISSN

23299266

First Page

1015

Last Page

1024

Issue

5

Volume

8

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