Price graphs: Utilizing the structural information of financial time series for stock prediction

Great research efforts have been devoted to exploiting deep neural networks in stock prediction. However, long-term dependencies and chaotic properties 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 temporal point associations and node weights, is extracted from the mapped graphs to resolve the problems regarding long-term dependencies and chaotic properties. 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.(c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This study proposes a novel framework to address the issues of long-term dependencies and chaotic properties in stock prediction. By transforming time series into complex networks and extracting structural information from the mapped graphs, the performance of the prediction model is improved. The effectiveness of the framework is validated through real-world stock data and trading simulations.