Stock Price Prediction Using RNN and LSTM

Authors

  • Janki Patel Student, Information Technology, G. H. Patel Collage of Engineering, India
  • Miral Patel Professor, Information Technology, G. H. Patel Collage of Engineering, India
  • Mittal Darji Professor, Information Technology, G. H. Patel Collage of Engineering, India

DOI:

https://doi.org/10.37591/joosd.v5i3.1942

Keywords:

Prediction, Deep Learning, LSTM, RNN

Abstract

Prediction of stock market has been an attractive topic to the stockbrokers. In stock market the decision on when buying or selling stock is important in order to achieve profit. There are number of techniques that can be used to help investors in order to make a decision for financial gain. In this research work we have proposed a prediction algorithm that will give the relation between the dependent factor like price and independent factors like opening price, closing price, high value of stock, low value of stock and volume of stocks bought. In this research, we have explained development of stock price prediction with the use of deep learning algorithm. In this work, we are going to use different deep learning architecture for the price prediction of BSE listed company and compares their performance. Here we had used LSTM (Long short-term memory) and RNN (Recurrent neural network) algorithms. We had shown comparative study of this two deep learning algorithm. Study shows that RNN gives better performance than LSTM. Accuracy of LSTM is 87% and accuracy of RNN is 89%.

Cite this Article

Janki Patel, Miral Patel, Mittal Darji. Stock Price Prediction Using RNN and LSTM. Journal of Open Source Developments. 2018; 5(3): 26–34p.

References

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Published

2019-03-02

Issue

Vol. 5 No. 3 (2018)

Section

Research Articles

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