  The importance of considering related stocks data for the prediction of stock
price movement has been shown in many studies, however, advanced graphical
techniques for modeling, embedding and analyzing the behavior of interrelated
stocks have not been widely exploited for the prediction of stocks price
movements yet. The main challenges in this domain are to find a way for
modeling the existing relations among an arbitrary set of stocks and to exploit
such a model for improving the prediction performance for those stocks. The
most of existing methods in this domain rely on basic graph-analysis
techniques, with limited prediction power, and suffer from a lack of generality
and flexibility. In this paper, we introduce a novel framework, called GCNET
that models the relations among an arbitrary set of stocks as a graph structure
called influence network and uses a set of history-based prediction models to
infer plausible initial labels for a subset of the stock nodes in the graph.
Finally, GCNET uses the Graph Convolutional Network algorithm to analyze this
partially labeled graph and predicts the next price direction of movement for
each stock in the graph. GCNET is a general prediction framework that can be
applied for the prediction of the price fluctuations of interacting stocks
based on their historical data. Our experiments and evaluations on a set of
stocks from the NASDAQ index demonstrate that GCNET significantly improves the
performance of SOTA in terms of accuracy and MCC measures.
