  Market prediction plays a major role in supporting financial decisions. An
emerging approach in this domain is to use graphical modeling and analysis to
for prediction of next market index fluctuations. One important question in
this domain is how to construct an appropriate graphical model of the data that
can be effectively used by a semi-supervised GNN to predict index fluctuations.
In this paper, we introduce a framework called NETpred that generates a novel
heterogeneous graph representing multiple related indices and their stocks by
using several stock-stock and stock-index relation measures. It then thoroughly
selects a diverse set of representative nodes that cover different parts of the
state space and whose price movements are accurately predictable. By assigning
initial predicted labels to such a set of nodes, NETpred makes sure that the
subsequent GCN model can be successfully trained using a semi-supervised
learning process. The resulting model is then used to predict the stock labels
which are finally aggregated to infer the labels for all the index nodes in the
graph. Our comprehensive set of experiments shows that NETpred improves the
performance of the state-of-the-art baselines by 3%-5% in terms of F-score
measure on different well-known data sets.
