Uncertainty Optimization Based Feature Selection Model for Stock Marketing
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DOI: 10.1007/s10614-022-10344-5
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- Mojtaba Nabipour & Pooyan Nayyeri & Hamed Jabani & Amir Mosavi, 2020. "Deep learning for Stock Market Prediction," Papers 2004.01497, arXiv.org.
- V. Vismayaa & K. R. Pooja & A. Alekhya & C. N. Malavika & Binoy B. Nair & P. N. Kumar, 2020. "Classifier Based Stock Trading Recommender Systems for Indian stocks: An Empirical Evaluation," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 901-923, March.
- Juvenal José Duarte & Sahudy Montenegro González & José César Cruz, 2021. "Predicting Stock Price Falls Using News Data: Evidence from the Brazilian Market," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 311-340, January.
- Niladri Das & J K Pattanayak, 2013. "The Effect of Fundamental Factors on Indian Stock Market: A Case Study of Sensex and Nifty," The IUP Journal of Applied Finance, IUP Publications, vol. 19(2), pages 84-99, April.
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Keywords
Stock market; Uncertainty optimization; Rough set; Feature selection; Optimization algorithm;All these keywords.
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