  Great research efforts have been devoted to exploiting deep neural networks
in stock prediction. While long-range dependencies and chaotic property are
still two major issues that lower the performance of state-of-the-art deep
learning models in forecasting future price trends. In this study, we propose a
novel framework to address both issues. Specifically, in terms of transforming
time series into complex networks, we convert market price series into graphs.
Then, structural information, referring to associations among temporal points
and the node weights, is extracted from the mapped graphs to resolve the
problems regarding long-range dependencies and the chaotic property. We take
graph embeddings to represent the associations among temporal points as the
prediction model inputs. Node weights are used as a priori knowledge to enhance
the learning of temporal attention. The effectiveness of our proposed framework
is validated using real-world stock data, and our approach obtains the best
performance among several state-of-the-art benchmarks. Moreover, in the
conducted trading simulations, our framework further obtains the highest
cumulative profits. Our results supplement the existing applications of complex
network methods in the financial realm and provide insightful implications for
investment applications regarding decision support in financial markets.
