MATCC: A Novel Approach for Robust Stock Price Prediction Incorporating Market Trends and Cross-time Correlations
Pages 187 - 196
Abstract
Stock price prediction has been a challenging problem due to non-stationary dynamics and complex market dependencies. Existing work has two limitations: 1. Previous studies have underestimated the importance of market trends, relying solely on stock data to learn patterns and capture market regularities implicitly. However, due to random stock fluctuations and trading noise caused by market sentiment, it is difficult to learn underlying market trends, resulting in poor model performance. 2. Prior research has predominantly concentrated on time-aligned feature correlations, with limited exploration of cross-time stock correlations. To address these issues, we propose a novel framework, MATCC (Market Trend and Cross-time Correlation model). It explicitly extracts market trends as guiding information, decomposes stock data into trend and fluctuation components, and employs a carefully designed structure for mining cross-time correlation. Extensive experiments demonstrate that MATCC significantly outperforms previous works in both ranking and portfolio-based metrics. Additionally, we illustrate the influence of trends and correlations on stock prediction through visualization. We publish our code at https://github.com/caozhiy/MATCC.
References
[1]
Stefanos Bennett, Mihai Cucuringu, and Gesine Reinert. 2022. Lead--lag detection and network clustering for multivariate time series with an application to the US equity market. Machine Learning, Vol. 111, 12 (2022), 4497--4538.
[2]
John Y. Campbell and Albert S. Kyle. 1993. Smart Money, Noise Trading and Stock Price Behaviour. The Review of Economic Studies, Vol. 60, 1 (1993), 1--34. http://www.jstor.org/stable/2297810
[3]
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014).
[4]
Qianggang Ding, Sifan Wu, Hao Sun, Jiadong Guo, and Jian Guo. 2020. Hierarchical Multi-Scale Gaussian Transformer for Stock Movement Prediction. In IJCAI. 4640--4646.
[5]
Yuntao Du, Jindong Wang, Wenjie Feng, Sinno Pan, Tao Qin, Renjun Xu, and Chongjun Wang. 2021. Adarnn: Adaptive learning and forecasting of time series. In Proceedings of the 30th ACM international conference on information & knowledge management. 402--411.
[6]
Yuchen Duan, Weiyun Wang, Zhe Chen, Xizhou Zhu, Lewei Lu, Tong Lu, Yu Qiao, Hongsheng Li, Jifeng Dai, and Wenhai Wang. 2024. Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures. arXiv e-prints, Article arXiv:2403.02308 (March 2024), arXiv:2403.02308 pages. https://doi.org/10.48550/arXiv.2403.02308 arxiv: 2403.02308 [cs.CV]
[7]
Jinyong Fan and Yanyan Shen. 2024. StockMixer: A Simple Yet Strong MLP-Based Architecture for Stock Price Forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 8389--8397.
[8]
Fuli Feng, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun, and Tat-Seng Chua. 2018. Enhancing stock movement prediction with adversarial training. arXiv preprint arXiv:1810.09936 (2018).
[9]
Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, and Tat-Seng Chua. 2019. Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS), Vol. 37, 2 (2019), 1--30.
[10]
Yuan Gao, Haokun Chen, Xiang Wang, Zhicai Wang, Xue Wang, Jinyang Gao, and Bolin Ding. 2024. DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation. arXiv preprint arXiv:2402.06656 (2024).
[11]
Alex Graves and Alex Graves. 2012. Long short-term memory. Supervised sequence labelling with recurrent neural networks (2012), 37--45.
[12]
Albert Gu and Tri Dao. 2023. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023).
[13]
Norden E Huang, Zheng Shen, Steven R Long, Manli C Wu, Hsing H Shih, Quanan Zheng, Nai-Chyuan Yen, Chi Chao Tung, and Henry H Liu. 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, Vol. 454, 1971 (1998), 903--995.
[14]
Thanh Trung Huynh, Minh Hieu Nguyen, Thanh Tam Nguyen, Phi Le Nguyen, Matthias Weidlich, Quoc Viet Hung Nguyen, and Karl Aberer. 2023. Efficient integration of multi-order dynamics and internal dynamics in stock movement prediction. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 850--858.
[15]
Kelvin JL Koa, Yunshan Ma, Ritchie Ng, and Tat-Seng Chua. 2023. Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 1087--1096.
[16]
Tong Li, Zhaoyang Liu, Yanyan Shen, Xue Wang, Haokun Chen, and Sen Huang. 2024. Master: Market-guided stock transformer for stock price forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 162--170.
[17]
Zhige Li, Derek Yang, Li Zhao, Jiang Bian, Tao Qin, and Tie-Yan Liu. 2019. Individualized indicator for all: Stock-wise technical indicator optimization with stock embedding. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 894--902.
[18]
Hengxu Lin, Dong Zhou, Weiqing Liu, and Jiang Bian. 2021. Learning multiple stock trading patterns with temporal routing adaptor and optimal transport. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. 1017--1026.
[19]
Jintao Liu, Hongfei Lin, Xikai Liu, Bo Xu, Yuqi Ren, Yufeng Diao, and Liang Yang. 2019. Transformer-based capsule network for stock movement prediction. In Proceedings of the first workshop on financial technology and natural language processing. 66--73.
[20]
Ilya Loshchilov and Frank Hutter. 2016. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016).
[21]
Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, Kranthi Kiran GV, et al. 2023. Rwkv: Reinventing rnns for the transformer era. arXiv preprint arXiv:2305.13048 (2023).
[22]
Hao Qian, Hongting Zhou, Qian Zhao, Hao Chen, Hongxiang Yao, Jingwei Wang, Ziqi Liu, Fei Yu, Zhiqiang Zhang, and Jun Zhou. 2024. MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction. arXiv preprint arXiv:2402.06633 (2024).
[23]
Yao Qin, Dongjin Song, Haifeng Cheng, Wei Cheng, Guofei Jiang, and Garrison W Cottrell. 2017. A dual-stage attention-based recurrent neural network for time series prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2627--2633.
[24]
Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, Tyler Derr, and Rajiv Ratn Shah. 2021. Stock selection via spatiotemporal hypergraph attention network: A learning to rank approach. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 497--504.
[25]
Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, and Rajiv Ratn Shah. 2020. Spatiotemporal hypergraph convolution network for stock movement forecasting. In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 482--491.
[26]
Pushpendra Singh. 2018. Novel Fourier quadrature transforms and analytic signal representations for nonlinear and non-stationary time-series analysis. Royal Society open science, Vol. 5, 11 (2018), 181131.
[27]
David So, Wojciech Ma'nke, Hanxiao Liu, Zihang Dai, Noam Shazeer, and Quoc V Le. 2021. Searching for efficient transformers for language modeling. Advances in neural information processing systems, Vol. 34 (2021), 6010--6022.
[28]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017).
[29]
Petar Velivcković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).
[30]
Heyuan Wang, Shun Li, Tengjiao Wang, and Jiayi Zheng. 2021. Hierarchical Adaptive Temporal-Relational Modeling for Stock Trend Prediction. In IJCAI. 3691--3698.
[31]
Haiyao Wang, Jianxuan Wang, Lihui Cao, Yifan Li, Qiuhong Sun, and Jingyang Wang. 2021. A stock closing price prediction model based on CNN-BiSLSTM. Complexity, Vol. 2021 (2021), 1--12.
[32]
Heyuan Wang, Tengjiao Wang, Shun Li, Jiayi Zheng, Shijie Guan, and Wei Chen. 2022. Adaptive long-short pattern transformer for stock investment selection. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 3970--3977.
[33]
Yunong Wang, Yi Qu, and Zhensong Chen. 2022. Review of graph construction and graph learning in stock price prediction. Procedia Computer Science, Vol. 214 (2022), 771--778.
[34]
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in neural information processing systems, Vol. 34 (2021), 22419--22430.
[35]
Hongjie Xia, Huijie Ao, Long Li, Yu Liu, Sen Liu, Guangnan Ye, and Hongfeng Chai. 2024. CI-STHPAN: Pre-trained Attention Network for Stock Selection with Channel-Independent Spatio-Temporal Hypergraph. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 9187--9195.
[36]
Sheng Xiang, Dawei Cheng, Chencheng Shang, Ying Zhang, and Yuqi Liang. 2022. Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 3584--3593.
[37]
Wentao Xu, Weiqing Liu, Lewen Wang, Yingce Xia, Jiang Bian, Jian Yin, and Tie-Yan Liu. 2021. Hist: A graph-based framework for stock trend forecasting via mining concept-oriented shared information. arXiv preprint arXiv:2110.13716 (2021).
[38]
Can Yang, Junjie Zhai, Guihua Tao, et al. 2020. Deep learning for price movement prediction using convolutional neural network and long short-term memory. Mathematical Problems in Engineering, Vol. 2020 (2020).
[39]
Linyi Yang, Jiazheng Li, Ruihai Dong, Yue Zhang, and Barry Smyth. 2022. NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-Task Financial Forecasting. In AAAI. AAAI Press, 11604--11612.
[40]
Xiao Yang, Weiqing Liu, Dong Zhou, Jiang Bian, and Tie-Yan Liu. 2020. Qlib: An ai-oriented quantitative investment platform. arXiv preprint arXiv:2009.11189 (2020).
[41]
Jaemin Yoo, Yejun Soun, Yong-chan Park, and U Kang. 2021. Accurate multivariate stock movement prediction via data-axis transformer with multi-level contexts. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2037--2045.
[42]
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121--11128.
[43]
Liheng Zhang, Charu Aggarwal, and Guo-Jun Qi. 2017. Stock Price Prediction via Discovering Multi-Frequency Trading Patterns (KDD '17). Association for Computing Machinery, New York, NY, USA.
[44]
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International conference on machine learning. PMLR, 27268--27286.
Index Terms
- MATCC: A Novel Approach for Robust Stock Price Prediction Incorporating Market Trends and Cross-time Correlations
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Published: 21 October 2024
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