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In the realm of financial decision-making, predicting stock prices is pivotal.
1908
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2015
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H. Li, Y. Shen, and Y. Zhu, “Stock price prediction using attention-based multi-input lstm,” in Proceedings of The 10th Asian Conference on Machine Learning , ser. Proceedings of Machine Learning Research, J. Zhu and I. Takeuchi, Eds., vol. 95. PMLR, 14–16 Nov 2018, pp. 454–469. [Online]. Available: https://proceedings.mlr.press/v95/li18c.html
2018
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J. Kim, J. Seo, M. Lee, and J. Seok, “Stock price prediction through the sentimental analysis of news articles,” in 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN) , 2019, pp. 700–702
2019
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S. Mohan, S. Mullapudi, S. Sammeta, P. Vijayvergia, and D. C. Anastasiu, “Stock price prediction using news sentiment analysis,” in 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService) , 2019, pp. 205–208
2019
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L. Sayavong, Z. Wu, and S. Chalita, “Research on stock price prediction method based on convolutional neural network,” in 2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS) , 2019, pp. 173–176
Y. Guo, “Stock price prediction based on lstm neural network: the effectiveness of news sentiment analysis,” in 2020 2nd International Conference on Economic Management and Model Engineering (ICEMME) , 2020, pp. 1018–1024
2020
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K. J, H. E, M. S. Jacob, and D. R, “Stock price prediction based on lstm deep learning model,” in 2021 International Conference on System, Computation, Automation and Networking (ICSCAN) , 2021, pp. 1–4
2021
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H. Huang and T. Zhao, “Stock market prediction by daily news via natural language processing and machine learning,” in 2021 International Conference on Computer, Blockchain and Financial Development (CBFD) , 2021, pp. 190–196
2021
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P. Bhargava, A. Drozd, and A. Rogers, “Generalization in nli: Ways (not) to go beyond simple heuristics,” 2021
2021
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2019
Cited alongside, same era.
M. A. Istiake Sunny, M. M. S. Maswood, and A. G. Alharbi, “Deep learning-based stock price prediction using lstm and bi-directional lstm model,” in 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) , 2020, pp. 87–92
2020
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F. Colasanto, L. Grilli, D. Santoro, and G. Villani, “Albertino for stock price prediction: a gibbs sampling approach,” Information Sciences , vol. 597, pp. 341–357, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S002002552200264X
2022
Later among the works it cites.