Journal Press India^®

Author Details ( * ) denotes Corresponding author

Stock prices prediction is difficult due to various factors, including physical and physiological factors, investors’ sentiments, and market rumors. Machine learning techniques, such as Recurrent Neural Networks (RNNs) and Conventional Neural Network (CNNs) models, have the potential to analyze historical stock price data and uncover patterns to make accurate predictions. The proposed study compared the performance of two deep learning models such as Long Short-Term Memory and Gated Recurrent Unit in predicting stock prices of three IT sector companies listed on National Stock Exchange. The results show that Gated Recurrent Unit is the successful model for stock price prediction as compared to Long Short-Term Memory, and the research suggests suitable values of the epoch for better performance.

Keywords

Long short-term memory; Gated recurrent unit; Stock prices; Recurrent neural networks; Root mean square error.

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