A graph‐based convolutional neural network stock price prediction with leading indicators

Jimmy Ming‐Tai Wu · Zhongcui Li · Gautam Srivastava · Meng‐Hsiun Tasi · Jerry Chun‐Wei Lin

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Time Series Analysis and Forecasting

Abstract

Abstract The stock market is a capitalistic haven where the issued shares are transferred, traded, and circulated. It bases stock prices on the issue market, however, the structure and trading activities of the stock market are much more complicated than the issue market itself. Therefore, making an accurate prediction becomes an intricate as well as highly difficult task. On the other hand, because of the potential benefits of stock prediction, it attracts generation after generation of scholars as well as investors to continuously develop various prediction methods from different perspectives, a myriad of theories, a multitude of investment strategies, and different practical experiences. In this article, aiming at the task of time series (financial) feature extraction and prediction of price movements, a new convolutional novel neural network that can be called a framework to improve the prediction accuracy of stock trading is proposed. The method that is proposed is called SSACNN, a short form of stock sequence array convolutional neural network. SSACNN collects data including historical data of prices and its leading indicators (options/futures) for a stock to take an array as the input graph of the convolutional neural network framework. In our experimental results, five Taiwanese and American stocks were used as a benchmark to compare with the previous algorithms and proposed algorithm, the motion prediction performance of SSACNN has been improved significantly and proved that it has the potential to be applied in the real financial market.

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References

2017Deep learning with long short-term memory networks for financial market predictions 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory 1992The use of technical analysis in the foreign exchange market 2017Stock market's price movement prediction with LSTM neural networks 2000Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index 2015A LSTM-based method for stock returns prediction: A case study of China stock market 2019CNNpred: CNN-based stock market prediction using a diverse set of variables 2018Deep learning-based feature engineering for stock price movement prediction

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2022GCNET: Graph-based prediction of stock price movement using graph convolutional network 2022ML-GAT:A Multilevel Graph Attention Model for Stock Prediction 2023Intelligent forecasting model of stock price using neighborhood rough set and multivariate empirical mode decomposition

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