2022

Journal

Applied Artificial Intelligence, 36:1, 2111134

The stock market is viewed as an unpredictable, volatile, and
competitive market. The prediction of stock prices has been
a challenging task for many years. In fact, many analysts are
highly interested in the research area of stock price prediction.
Various forecasting methods can be categorized into linear and
non-linear algorithms. In this paper, we offer an overview of the
use of deep learning networks for the Indian National Stock
Exchange time series analysis and prediction. The networks
used are Recurrent Neural Network, Long Short-Term Memory
Network, and Convolutional Neural Network to predict future
trends of NIFTY 50 stock prices. Comparative analysis is done
using different evaluation metrics. These analysis led us to
identify the impact of feature selection process and hyperparameter optimization on prediction quality and metrics used in the prediction of stock market performance and prices. The performance of the models was quantified using MSE metric.
These errors in the LSTM model are found to be lower compared
to RNN and CNN models.

@article{fathali2022stock,
  title={Stock market prediction of Nifty 50 index applying machine learning techniques},
  author={Fathali, Zahra and Kodia, Zahra and Ben Said, Lamjed},
  journal={Applied Artificial Intelligence},
  volume={36},
  number={1},
  pages={2111134},
  year={2022},
  publisher={Taylor \& Francis}
}