A Study on Stock Price Prediction Based on Attention GCN-BiLSTM Modeling

Xiaofang Zhao

doi:10.62051/ijgem.v4n1.17

Authors

Xiaofang Zhao

DOI:

https://doi.org/10.62051/ijgem.v4n1.17

Keywords:

GCN, Bi-LSTM, Vine-Copula, Stock price prediction

Abstract

It is of great significance to accurately predict stock price movements. By combining the R-Vine-Copula structure with the relationship between the conceptual sectors to which the stocks belong, the price correlation between stocks is measured and the adjacency matrix is constructed, and the model AGLSTM, which is constructed by combining the temporal attention mechanism, Graph Convolutional Neural Network and Bi-directional Long and Short-Term Memory Neural Network, is proposed to predict the price of 46 stocks from the constituent stocks of the SSE 50 by modeling them. The experimental results show that, compared with the baseline, the AGLSTM can predict the stock price. The experimental results show that compared with the baseline model, the MAE and RMSE results of the AGLSTM model predicted one step forward outperform all the comparison models, and the MAPE results are close to the best baseline model results. The results of MAPE are close to those of the best baseline model. Moreover, good results are also achieved in the experiment of multi-step forward prediction, which demonstrates the ability of AGLSTM model in long-term prediction and can provide some reference for investors to make investment decisions.

References

[1] Roll R. R2 [J]. Journal of finance, 1988, 43(3): 541-566.

[2] Wang Y, Qu Y, Chen Z. Review of graph construction and graph learning in stock price prediction [J]. Procedia computer science, 2022, 214: 771-778.

[3] Ghosh P, Neufeld A, Sahoo J K. Forecasting directional movements of stock prices for intraday trading using LSTM and random forests [J]. Finance research letters, 2022, 46: 102280.

[4] Lohrmann C, Luukka P. Classification of intraday S&P500 returns with a Random Forest [J]. International journal of forecasting, 2019, 35(1): 390-407.

[5] Yun K K, Yoon S W, Won D. Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process [J]. Expert systems with applications, 2021, 186: 115716.

[6] Tang H, Dong P, Shi Y. A new approach of integrating piecewise linear representation and weighted support vector machine for forecasting stock turning points [J]. Applied soft computing, 2019, 78: 685-696.

[7] Fischer T, Krauss C. Deep learning with long short-term memory networks for financial market predictions [J]. European journal of operational research, 2018, 270(2): 654-669.

[8] Wang J, Sun T, Liu B, et al. CLVSA: A convolutional LSTM based variational sequence-to-sequence model with attention for predicting trends of financial markets [J]. arXiv preprint arXiv:2104.04041, 2021.

[9] Wu J, Xu K, Chen X, et al. Price graphs: Utilizing the structural information of financial time series for stock prediction [J]. Information sciences, 2022, 588: 405-424.

[10] Wang T, Guo J, Shan Y, et al. A knowledge graph–GCN–community detection integrated model for large-scale stock price prediction [J]. Applied soft computing, 2023, 145: 110595.

[11] Shi Y, Wang Y, Qu Y, et al. Integrated gcn-lstm stock prices movement prediction based on knowledge-incorporated graphs construction [J]. International journal of machine learning and cybernetics, 2024, 15(1): 161-176.

[12] Ma Y, Mao R, Lin Q, et al. Multi-source aggregated classification for stock price movement prediction [J]. Information fusion, 2023, 91: 515-528.

[13] Xu C, Huang H, Ying X, et al. HGNN: Hierarchical graph neural network for predicting the classification of price-limit-hitting stocks [J]. Information sciences, 2022, 607: 783-798.

[14] Jafari A, Haratizadeh S. GCNET: graph-based prediction of stock price movement using graph convolutional network [J]. Engineering applications of artificial intelligence, 2022, 116: 105452.

[15] Song G, Zhao T, Wang S, et al. Stock ranking prediction using a graph aggregation network based on stock price and stock relationship information [J]. Information sciences, 2023, 643: 119236.

[16] Feng S, Xu C, Zuo Y, et al. Relation-aware dynamic attributed graph attention network for stocks recommendation [J]. Pattern recognition, 2022, 121: 108119.

[17] Aas K, Czado C, Frigessi A, et al. Pair-copula constructions of multiple dependence [J]. Insurance: mathematics and economics, 2009, 44(2): 182-198.

[18] Bedford T, Cooke R M. Probability density decomposition for conditionally dependent random variables modeled by vines [J]. Annals of mathematics and artificial intelligence, 2001, 32: 245-268.

[19] Guo S, Lin Y, Feng N, et al. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting [C]//Proceedings of the AAAI conference on artificial intelligence. 2019, 33(01): 922-929.

[20] Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering [J]. Advances in neural information processing systems, 2016, 29.

[21] Chen K, Zhou Y, Dai F. A LSTM-based method for stock returns prediction: A case study of China stock market [C]//2015 IEEE international conference on big data (big data). IEEE, 2015: 2823-2824.

[22] Li S, Jin X, Xuan Y, et al. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting [J]. Advances in neural information processing systems, 2019, 32.

[23] 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.

[24] Chaolong L, Zhen C, Wenming Z, et al. Spatio-temporal graph convolution for skeleton based action recognition[C]//Thirty-second AAAI conference on artificial intelligence. 2018, 2.