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1. Qing Li, Jinghua Tan, Jun Wang, Hsinchu Chen (2020). A Multimodal Event-driven LSTM Model for Stock Prediction Using Online News. IEEE Transactions on Knowledge and Data Engineering, Accept.
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2. Yan Chen, Zhilong Xie, Wenjie Zhang, Rong Xing, Qing Li* (2020). Quantifying the Effect of Real Estate News on Chinese Stock Movements. Emerging Markets Finance and Trade, 2020: 1-26.
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3. Rong Xing, Qing Li, Jingmei Zhao, Xiaoqing Xu (2019). Media-based Corporate Network and Its Effects on Stock Market. Emerging Markets Finance and Trade,:1-26.
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4. Jun Wang, Zhilong Xie, Qing Li, Rong Xing, Yuanzhu Chen, Fengyun Wu (2019). Effect of Digitalized Rumor Clarification on Stock Markets. Emerging Markets Finance and Trade, 55(2), 450-474.
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5. Qing Li, Yan Chen, Jun Wang, Yuanzhu Chen, Hsinchu Chen (2018): Web Media and Stock Markets: A Survey and Future Directions from a Big Data Perspective. IEEE Transactions on Knowledge and Data Engineering (TKDE), 30 (2), 381-399.
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6. Qing Li, Yuanzhu Chen, Lilin Jiang, Ping Li, Hsinchu Chen (2016): A Tensor-Based Information Framework for Predicting the Stock Market. ACM Transactions on Information Systems (TOIS), 34(2),11:1- 11:31.
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7. Qing Li, Jun Wang, Feng Wang, Ping Li, Lin Liu, Yuanzhu Chen (2016): The role of social sentiment in stock markets: a view from joint effects of multiple information sources. Multimedia Tools and Applications, 1-31.
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8. Ling Liu, Jing Wu, Ping Li,Qing Li* (2015): A social-media-based approach to predicting stock comovement. Expert Systems with Applications, 42(8):3893-3901. (*Corresponding Author)
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9. Qing Li, Tiejun Wang, Ping Li, Ling Liu, Qixu Gong, Yuanzhu Chen (2014): The effect of news and public mood on stock movements. Information Sciences, 278: 826-840.
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10. Qing Li, Tiejun Wang, Qixu Gong, Yuanzhu Chen, Zhangxi Lin, Sa-kwang Song (2014): Media-aware quantitative trading based on public Web information. Decision Support Systems, 61: 93-105.
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11. Qing Li, Lilin Jiang, Ping Li, Hsinchun Chen (2015): Tensor-Based Learning for Predicting Stock Movements. Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), pp. 1784 -1790.
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