Wang, J.J., He, J.J., Feng, C.C., Feng, L. and Li, Y. (2021) Stock Index Prediction and Uncertainty Analysis Using Multi-Scale Nonlinear Ensemble Paradigm of Optimal Feature Extraction, Two-Stage Deep Learning and Gaussian Process Regression. Applied Soft Computing, 113, Article ID: 107898.
https://doi.org/10.1016/j.asoc.2021.107898

has been cited by the following article:

• TITLE: Stock Price Forecasting with Artificial Neural Networks Long Short-Term Memory: A Bibliometric Analysis and Systematic Literature Review

  AUTHORS: Cristiane Orquisa Fantin, Eli Hadad

  KEYWORDS: Stock Price Forecasting, Long-Term Memory, Backpropagation, Bibliometric Analysis, Systematic Review

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.12, December 26, 2022

  ABSTRACT: This study maps the academic literature on Stock Price Forecasting with Long-Term Memory Artificial Neural Networks—RNA LSTM. The objective is to know if it is suitable for time series studies, especially for stock price projection. Through bibliometric analysis and systematic literature review, it is observed that 333 authors wrote on the topic between 2018 and March 2022, and the journals Expert Systems with Applications, IEEE Access, Big Data Journal and Neural Computing and Applications, published the most relevant articles. Of the 99 articles published in this period, 43 are associated with Chinese institutions, the most cited being that of Kim and Won, who studies the volatility of returns and the market capitalization of South Korean stocks. The basis of 65% of the studies is the comparison between the RNN LSTM and other artificial neural networks. The daily closing price of shares is the most analyzed type of data, and the American (21%) and Chinese (20%) stock exchanges are the most studied. 57% of the studies include improvements to existing neural network models and 42% new projection models.