Analysis of the hedging effect of financial futures based on improved machine learning algorithms-Reference-Cited by-同舟云学术

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Analysis of the hedging effect of financial futures based on improved machine learning algorithms

Published:2021-03-15 Issue: Volume: Page:
ISSN:1868-5137
Container-title:Journal of Ambient Intelligence and Humanized Computing
language:en
Short-container-title:J Ambient Intell Human Comput

Author:

Guan Bowen,Wu He,Liang Biming

Funder

Phased results of Jilin Province Enterprise Finance and Accounting Research Center

2019 Research Fund of Jilin University of Finance and Economics

The New Doctoral Research Fund of Jilin Univer-sity of Finance and Economics

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Link

http://link.springer.com/content/pdf/10.1007/s12652-021-03047-6.pdf

Reference21 articles.

1. Ansari M, Vakili V, Bahrak B (2019) Evaluation of big data frameworks for analysis of smart grids. J Big Data 6(109):1–14

2. Bikmetov R, Raja M, Sane T (2017) Infrastructure and applications of Internet of Things in smart grids: a survey. In: Proceedings of the 2017 North American power symposium (NAPS). IEEE, Morgantown, pp 1–6

3. Bouteraa Y, Abdallah IB (2017) A gesture-based telemanipulation control for a robotic arm with biofeedback-based grasp. Ind Robot 44(5):575–587

4. Chen G, Song Y, Guan Y (2018) Terminal sliding mode-based consensus tracking control for networked uncertain mechanical systems on digraphs. IEEE Trans Neural Netw Learn Syst 29(3):749–756

5. Delavari H (2017) A novel fractional adaptive active sliding mode controller for synchronization of non-identical chaotic systems with disturbance and uncertainty. Int J Dyn Control 5(1):102–114

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