A dynamic scenario‐driven technique for stock price prediction and trading
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DOI: 10.1002/for.2848
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References listed on IDEAS
- O. B. Sezer & M. Ozbayoglu & E. Dogdu, 2017. "An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework," Papers 1712.09592, arXiv.org.
- Junyi Li & Xitong Wang & Yaoyang Lin & Arunesh Sinha & Micheal P. Wellman, 2020. "Generating Realistic Stock Market Order Streams," Papers 2006.04212, arXiv.org.
- Guosheng Hu & Yuxin Hu & Kai Yang & Zehao Yu & Flood Sung & Zhihong Zhang & Fei Xie & Jianguo Liu & Neil Robertson & Timothy Hospedales & Qiangwei Miemie, 2017. "Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions," Papers 1709.03803, arXiv.org, revised Feb 2018.
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