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

by   Junran Wu, et al.

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.


page 1

page 2

page 3

page 4


Financial Time Series Forecasting using CNN and Transformer

Time series forecasting is important across various domains for decision...

A Stochastic Time Series Model for Predicting Financial Trends using NLP

Stock price forecasting is a highly complex and vitally important field ...

Temporal Relational Ranking for Stock Prediction

Stock prediction aims to predict the future trends of a stock in order t...

Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series

Volatility is a quantity of measurement for the price movements of stock...

Lagged correlation-based deep learning for directional trend change prediction in financial time series

Trend change prediction in complex systems with a large number of noisy ...

DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis

Modern machine learning models (such as deep neural networks and boostin...

Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification

Deep Learning models have become dominant in tackling financial time-ser...

Please sign up or login with your details

Forgot password? Click here to reset