GCN-based stock relations analysis for stock market prediction

Most stock price predictive models merely rely on the target stock's historical informa-tion to forecast future prices, where the linkage effects between stocks are neglected. However, a group of prior studies has shown that the leverage of correlations between stocks could significantly improve the predictions. This article proposes a unified time-series relational multi-factor model (TRMF), which composes a self-generating relations (SGR) algorithm that can extract relational features automatically. In addition, the TRMF model integrates stock relations with other multiple dimensional features for the price prediction compared to extant works. Experimental validations are performed on the NYSE and NASDAQ data, where the model is compared with the popular methods such as attention Long Short-Term Memory network (Attn-LSTM), Support Vector Regression (SVR), and multi-factor framework (MF). Results show that compared with these extant methods, our model has a higher expected cumulative return rate and a lower risk of return volatility.

Automated summary

Most stock price predictive models neglect the correlation effects between stocks, while this article proposes a unified time-series relational multi-factor model that can automatically extract relational features and integrate them with other multiple dimensional features, resulting in significantly improved prediction accuracy and stability.