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

TL;DR

This paper introduces a novel graph-based framework that transforms financial time series into complex networks to better capture long-range dependencies and chaotic properties, improving stock prediction accuracy and profitability.

Contribution

The study presents a new method converting stock prices into graphs to extract structural information, enhancing deep learning models for stock trend forecasting.

Findings

Achieved superior prediction performance compared to state-of-the-art models.

Obtained highest cumulative profits in trading simulations.

Validated effectiveness on real-world stock data.

Abstract

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.