Research Article
Distinctive Assessment of Neural Network Models in Stock Price Estimation
@ARTICLE{10.4108/eetsis.4643, author={Shreya Verma and Sushruta Mishra and Vandana Sharma and Manju Nandal and Sayan Garai and Ahmed Alkhayyat}, title={Distinctive Assessment of Neural Network Models in Stock Price Estimation}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={11}, number={4}, publisher={EAI}, journal_a={SIS}, year={2023}, month={12}, keywords={Stock, Neural Network, prediction, precision, Machine Learning}, doi={10.4108/eetsis.4643} }- Shreya Verma
Sushruta Mishra
Vandana Sharma
Manju Nandal
Sayan Garai
Ahmed Alkhayyat
Year: 2023
Distinctive Assessment of Neural Network Models in Stock Price Estimation
SIS
EAI
DOI: 10.4108/eetsis.4643
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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