1 research outputs found
REST: Relational Event-driven Stock Trend Forecasting
Stock trend forecasting, aiming at predicting the stock future trends, is
crucial for investors to seek maximized profits from the stock market. Many
event-driven methods utilized the events extracted from news, social media, and
discussion board to forecast the stock trend in recent years. However, existing
event-driven methods have two main shortcomings: 1) overlooking the influence
of event information differentiated by the stock-dependent properties; 2)
neglecting the effect of event information from other related stocks. In this
paper, we propose a relational event-driven stock trend forecasting (REST)
framework, which can address the shortcoming of existing methods. To remedy the
first shortcoming, we propose to model the stock context and learn the effect
of event information on the stocks under different contexts. To address the
second shortcoming, we construct a stock graph and design a new propagation
layer to propagate the effect of event information from related stocks. The
experimental studies on the real-world data demonstrate the efficiency of our
REST framework. The results of investment simulation show that our framework
can achieve a higher return of investment than baselines