Temporal and Heterogeneous Graph Neural Network for Financial Time
  Series Prediction
Sheng Xiang Dawei Cheng Chencheng Shang Ying Zhang Yuqi Liang
The price movement prediction of stock market has been a classical yet
challenging problem, with the attention of both economists and computer
scientists. In recent years, graph neural network has significantly improved
the prediction performance by employing deep learning on company relations.
However, existing relation graphs are usually constructed by handcraft human
labeling or nature language processing, which are suffering from heavy resource
requirement and low accuracy. Besides, they cannot effectively response to the
dynamic changes in relation graphs. Therefore, in this paper, we propose a
temporal and heterogeneous graph neural network-based (THGNN) approach to learn
the dynamic relations among price movements in financial time series. In
particular, we first generate the company relation graph for each trading day
according to their historic price. Then we leverage a transformer encoder to
encode the price movement information into temporal representations. Afterward,
we propose a heterogeneous graph attention network to jointly optimize the
embeddings of the financial time series data by transformer encoder and infer
the probability of target movements. Finally, we conduct extensive experiments
on the stock market in the United States and China. The results demonstrate the
effectiveness and superior performance of our proposed methods compared with
state-of-the-art baselines. Moreover, we also deploy the proposed THGNN in a
real-world quantitative algorithm trading system, the accumulated portfolio
return obtained by our method significantly outperforms other baselines.