ABSTRACT
Securities play a crucial role in the modern economy. However, securities price movement prediction, a type of time-series forecasting problem, remains challenging. This paper suggests that the underutilization of temporal structural information in the data hinders the improvement of accuracy in securities price movement prediction. Therefore, this paper proposes a securities price movement prediction method based on graph computing. By abstracting trading days and the temporal relationships between them as nodes and edges, the method transforms historical trading data into graph data. Subsequently, graph neural networks (GNN) are used to process the graph data and make predictions about stock price movements. Experiments show that the proposed method effectively improves the performance of price movement prediction. Thus, the proposed method is a simple, effective way to utilize time-series data and holds substantial value in securities price movement prediction.
- Fama E F. Random walks in stock market prices [J]. Financial analysts journal, 1995, 51(1): 75-80.Google Scholar
- Bollen J, Mao H, Zeng X. Twitter mood predicts the stock market [J]. Journal of computational science, 2011, 2(1): 1-8.Google Scholar
- Kalyanaraman V, Kazi S, Tondulkar R, Sentiment analysis on news articles for stocks[C]//2014 8th Asia Modelling Symposium. IEEE, 2014, 10-15.Google Scholar
- Yoshihara A, Fujikawa K, Seki K, Predicting stock market trends by recurrent deep neural networks[C]//PRICAI 2014: Trends in Artificial Intelligence: 13th Pacific Rim International Conference on Artificial Intelligence, Gold Coast, QLD, Australia, December 1-5, 2014. Proceedings 13. Springer International Publishing, 2014, 759-769.Google Scholar
- Shah D, Isah H, Zulkernine F. Stock market analysis: A review and taxonomy of prediction techniques [J]. International Journal of Financial Studies, 2019, 7(2): 26.Google ScholarCross Ref
- Devi B U, Sundar D, Alli P. An effective time series analysis for stock trend prediction using ARIMA model for nifty midcap-50 [J]. International Journal of Data Mining & Knowledge Management Process, 2013, 3(1): 65.Google ScholarCross Ref
- Ariyo A A, Adewumi A O, Ayo C K. Stock price prediction using the ARIMA model[C]//2014 UKSim-AMSS 16th international conference on computer modelling and simulation. IEEE, 2014, 106-112.Google Scholar
- Siami-Namini S, Tavakoli N, Namin A S. A comparison of ARIMA and LSTM in forecasting time series[C]//2018 17th IEEE international conference on machine learning and applications (ICMLA). IEEE, 2018, 1394-1401.Google Scholar
- Milosevic N. Equity forecast: Predicting long term stock price movement using machine learning [J]. arXiv preprint arXiv:1603.00751, 2016.Google Scholar
- Babu M S, Geethanjali N, Satyanarayana B. Clustering approach to stock market prediction[J]. International Journal of Advanced Networking and Applications, 2012, 3(4): 1281.Google Scholar
- Ding X, Zhang Y, Liu T, Deep learning for event-driven stock prediction[C]//Twenty-fourth international joint conference on artificial intelligence. 2015.Google Scholar
- Di Persio L, Honchar O. Recurrent neural networks approach to the financial forecast of Google assets [J]. International journal of Mathematics and Computers in simulation, 2017, 11: 7-13.Google Scholar
- Zhang L, Aggarwal C, Qi G J. Stock price prediction via discovering multi-frequency trading patterns[C]//Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. 2017, 2141-2149.Google Scholar
- Chen Y, Wei Z, Huang X. Incorporating corporation relationship via graph convolutional neural networks for stock price prediction[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018, 1655-1658.Google Scholar
- Scarselli F, Gori M, Tsoi A C, The graph neural network model [J]. IEEE transactions on neural networks, 2008, 20(1): 61-80.Google Scholar
- Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks [J]. arXiv preprint arXiv:1609.02907, 2016.Google Scholar
- Hamilton W, Ying Z, Leskovec J. Inductive representation learning on large graphs[J]. Advances in neural information processing systems, 2017, 30.Google Scholar
- Veličković P, Cucurull G, Casanova A, Graph attention networks [J]. arXiv preprint arXiv:1710.10903, 2017.Google Scholar
- Zhang J, Shi X, Xie J, GaAN: Gated attention networks for learning on large and spatiotemporal graphs [J]. arXiv preprint arXiv:1803.07294, 2018.Google Scholar
- Wu Z, Pan S, Chen F, A comprehensive survey on graph neural networks[J]. IEEE transactions on neural networks and learning systems, 2020, 32(1): 4-24.Google Scholar
- Feng F, He X, Wang X, Temporal relational ranking for stock prediction[J]. ACM Transactions on Information Systems (TOIS), 2019, 37(2): 1-30.Google ScholarDigital Library
- Matsunaga D, Suzumura T, Takahashi T. Exploring graph neural networks for stock market predictions with rolling window analysis[J]. arXiv preprint arXiv:1909.10660, 2019.Google Scholar
- Sawhney R, Agarwal S, Wadhwa A, Spatiotemporal hypergraph convolution network for stock movement forecasting[C]//2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020, 482-491.Google Scholar
- Ye J, Zhao J, Ye K, Multi-graph convolutional network for relationship-driven stock movement prediction[C]//2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021, 6702-6709.Google Scholar
- Zhao Y, Du H, Liu Y, Stock movement prediction based on bi-typed hybrid-relational market knowledge graph via dual attention networks [J]. IEEE Transactions on Knowledge and Data Engineering, 2022.Google ScholarDigital Library
- Cheng D, Yang F, Xiang S, Financial time series forecasting with multi-modality graph neural network[J]. Pattern Recognition, 2022, 121: 108218.Google ScholarDigital Library
- Hu Z, Dong Y, Wang K, Heterogeneous graph transformer[C]//Proceedings of the web conference 2020. 2020, 2704-2710.Google Scholar
- Cerqueira V, Torgo L, Mozetič I. Evaluating time series forecasting models: An empirical study on performance estimation methods[J]. Machine Learning, 2020, 109: 1997-2028.Google ScholarDigital Library
Index Terms
- Securities Price Movement Prediction Based on Graph Neural Networks
Recommendations
Conceptual-temporal graph convolutional neural network model for stock price movement prediction and application
AbstractStock price movement prediction is an important problem for trading decision-making. But it is a challenging task due to the nonlinearity and complexity of the stock trading data. This paper analyzes the linkage effect of price movement among ...
Deep Learning-based Integrated Framework for stock price movement prediction
AbstractStock market prediction is a very important problem in the economics field. With the development of machine learning, more and more algorithms are applied in the stock market to predict the stock price movement. However, stock market ...
Highlights- The combination of public opinions and sentiments, SA-DLSTM can provide robust and accurate predictions for the stock market trends.
Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction
CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge ManagementIn this paper, we propose to incorporate information of related corporations of a target company for its stock price prediction. We first construct a graph including all involved corporations based on investment facts from real market and learn a ...
Comments