Stock Market Performance Analytics Using XGBoost-Reference-Cited by-同舟云学术

Stock Market Performance Analytics Using XGBoost

Published:2023-11-09 Issue: Volume: Page:3-16
ISSN:0302-9743
Container-title:Advances in Computational Intelligence
language:
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Author:

Hussain Nisar^ORCID,Qasim Amna^ORCID,Akhtar Zia-ud-din,Qasim Ayesha,Mehak Gull,del Socorro Espindola Ulibarri Luciana,Kolesnikova Olga,Gelbukh Alexander

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-47765-2_1

Reference10 articles.

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2. Chen, K., Zhou, Y., Dai, F.: A LSTM-based method for stock returns prediction: a case study of China stock market. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 2823–2824: IEEE (2015)

3. Ince, H., Trafalis, T.B.J.E.C., E.C. Studies, and Research, A hybrid forecasting model for stock market prediction, vol. 51, no. 3 (2017)

4. Kim, Y., Jeong, S.R., I. J. I. J. A. S. C. A. Ghani, "Text opinion mining to analyze news for stock market prediction," vol. 6, no. 1, pp. 2074–8523 (2014)

5. Li, X., et al.: Empirical analysis: stock market prediction via extreme learning machine, vol. 27, no. 1, pp. 67–78 (2016)

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