Distinctive Assessment of Neural Network Models in Stock Price Estimation

Shreya Verma; Sushruta Mishra; Vandana Sharma; Manju Nandal; Sayan Garai; Ahmed Alkhayyat

doi:10.4108/eetsis.4643

Authors

Shreya Verma KIIT University
Sushruta Mishra KIIT University
Vandana Sharma Christ University
Manju Nandal Noida Institute of Engineering and Technology
Sayan Garai KIIT University
Ahmed Alkhayyat The Islamic University, Najaf

DOI:

https://doi.org/10.4108/eetsis.4643

Keywords:

Stock, Neural Network, prediction, precision, Machine Learning

Abstract

INTRODUCTION: Due to its potential to produce substantial returns and reduce risks, stock price prediction has garnered a lot of attention in the financial markets.

OBJECTIVES: A comparison of neural network models for stock price prediction is presented in this research report.

METHODS: Through this study, I aim to compare, on the basis of the precision and accuracy, the performance of different neural network models for stock price prediction. LSTM model along with RNN model accuracy in predicting the next day’s stock price i.e., which model can predict closest to the actual value.

RESULTS: It is found that LSTM works better than RNN in predicting a value closer to the actual open price stock value.

CONCLUSION: A comparison between the models shows LSTM is the more accurate model.

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