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Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods.
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Using structured events to predict stock price movement: An empirical investigation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) . 1415–1425
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News impact on stock price return via sentiment analysis
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Stock trends prediction based on hypergraph modeling clustering algorithm. In 2014 IEEE International Conference on Progress in Informatics and Computing . IEEE, 27–31
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Decision support system for stock trading using multiple indicators decision tree. In 2014 The 1st International Conference on Information Technology, Computer, and Electrical Engineering . IEEE, 291–296
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Short and long term stock trend prediction using decision tree. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) . IEEE, 1371–1375
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The role of social sentiment in stock markets: a view from joint effects of multiple information sources
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Stock price prediction using LSTM, RNN and CNN-sliding window model. In 2017 international conference on advances in computing, communications and informatics (icacci) . IEEE, 1643–1647
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Stock price prediction via discovering multi-frequency trading patterns. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining . 2141–2149
Liheng Zhang, Charu Aggarwal, and Guo-Jun Qi. 2017 · 2017
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Time-weighted LSTM model with redefined labeling for stock trend prediction. In 2017 IEEE 29th international conference on tools with artificial intelligence (ICTAI) . IEEE, 1210–1217
Zhiyong Zhao, Ruonan Rao, Shaoxiong Tu, and Jun Shi. 2017 · 2017
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João Carapuço, Rui Neves, and Nuno Horta. 2018 · 2018
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Incorporating corporation relationship via graph convolutional neural networks for stock price prediction. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management . 1655–1658
Yingmei Chen, Zhongyu Wei, and Xuanjing Huang. 2018 · 2018
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A systematic review of fundamental and technical analysis of stock market predictions
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Improving stock prediction accuracy using cnn and lstm. In 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI) . IEEE, 1–5
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Deep attentive learning for stock movement prediction from social media text and company correlations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 8415–8426
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Spatiotemporal hypergraph convolution network for stock movement forecasting. In 2020 IEEE International Conference on Data Mining (ICDM) . IEEE, 482–491
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Learning target-specific representations of financial news documents for cumulative abnormal return prediction. In Proceedings of the 27th international conference on computational linguistics . 2823–2833
Junwen Duan, Yue Zhang, Xiao Ding, Ching Yun Chang, and Ting Liu. 2018 · 2018
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Enhancing stock movement prediction with adversarial training
Fuli Feng, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun, and Tat-Seng Chua. 2018 · 2018
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Addressing function approximation error in actor-critic methods. In International conference on machine learning . PMLR, 1587–1596
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Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction. In Proceedings of the eleventh ACM international conference on web search and data mining . 261–269
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Financial trading as a game: A deep reinforcement learning approach
Chien Yi Huang. 2018 · 2018
Cited alongside, same era.
Learning to trade with deep actor critic methods. In 2018 11th International Symposium on Computational Intelligence and Design (ISCID) , Vol. 2. IEEE, 66–71
Jinke Li, Ruonan Rao, and Jun Shi. 2018a · 2018
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Adversarial deep reinforcement learning in portfolio management
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Cited alongside, same era.
Price trailing for financial trading using deep reinforcement learning
Avraam Tsantekidis, Nikolaos Passalis, Anastasia-Sotiria Toufa, Konstantinos Saitas-Zarkias, Stergios Chairistanidis, and Anastasios Tefas. 2020 · 2020
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Incorporating expert-based investment opinion signals in stock prediction: A deep learning framework. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34. 971–978
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A CNN-based stock price trend prediction with futures and historical price. In 2020 International Conference on Pervasive Artificial Intelligence (ICPAI) . IEEE, 134–139
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Modeling the Momentum Spillover Effect for Stock Prediction via Attribute-Driven Graph Attention Networks. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 55–62
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Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 4417–4422
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Multi-model generative adversarial network hybrid prediction algorithm (MMGAN-HPA) for stock market prices prediction
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Quantitative day trading from natural language using reinforcement learning. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 4018–4030
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Forecasting Stock Prices Using Stock Correlation Graph: A Graph Convolutional Network Approach. In 2021 International Joint Conference on Neural Networks (IJCNN) . 1–8
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Deep Reinforcement Learning for Quantitative Trading: Challenges and Opportunities
Bo An, Shuo Sun, and Rundong Wang. 2022 · 2022
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Guided Attention Multimodal Multitask Financial Forecasting with Inter-Company Relationships and Global and Local News. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 6313–6326
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HGNN: Hierarchical Graph Neural Network for Predicting the Classification of Price-Limit-Hitting Stocks
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