Gradient Boosting and LSTM Based Hybrid Ensemble Learning for Two Step Prediction of Stock Market-Reference-Cited by-同舟云学术

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Gradient Boosting and LSTM Based Hybrid Ensemble Learning for Two Step Prediction of Stock Market

Published:2023 Issue:6 Volume:14 Page:1254-1260
ISSN:1798-2340
Container-title:Journal of Advances in Information Technology
language:
Short-container-title:JAIT

Author:

Mohapatra Pratyush Ranjan,Parida Ajaya Kumar,Swain Santosh Kumar,Basa Santi Swarup

Publisher

Engineering and Technology Publishing

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Information Systems,Software

Link

https://www.jait.us/uploadfile/2023/JAIT-V14N6-1254.pdf

Reference30 articles.

1. [1] E. F. Fama, "Efficient capital markets: A review of theory and empirical work," The Journal of Finance, vol. 25, no. 2, pp. 383-417, May 1970.

2. [2] B. G. Malkiel, "The efficient market hypothesis and its critics," Journal of Economic Perspectives, vol. 17, no. 1, pp. 59-82, 2003.

3. [3] A. Timmermann and C. W. J. Granger, "Efficient market hypothesis and forecasting," International Journal of Forecasting, vol. 20, pp. 15-27, Jan.-Mar. 2004.

4. [4] K. Nti, D. F. Adekoya, and B. A. Weyori, "A systematic review of fundamental and technical analysis of stock market predictions," Artificial Intelligence Review, vol. 53, no. 4, pp. 3007-3057, Aug. 2019.

5. [5] K. O. Nti, A. Adekoya, and B. Weyori, "Random forest based feature selection of macroeconomic variables for stock market prediction," American Journal of Applied Sciences, vol. 16, no. 7, pp. 200-212, Sep. 2019.

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