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- Title
Distinctive Assessment of Neural Network Models in Stock Price Estimation.
- Authors
Verma, Shreya; Mishra, Sushruta; Sharma, Vandana; Nandal, Manju; Garai, Sayan; Alkhayyat, Ahmed
- 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.
- Subjects
ARTIFICIAL neural networks; VALUE (Economics); FINANCIAL markets
- Publication
EAI Endorsed Transactions on Scalable Information Systems, 2024, Vol 11, Issue 4, p1
- ISSN
2032-9407
- Publication type
Article
- DOI
10.4108/eetsis.4643