A Parameter Based Comparative Study of Deep Learning Algorithms for Stock Price Prediction

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Priyanka Paygude
Anchal Singh
Esha Tripathi
Shruti Priya
Milind Gayakwad
Prashant Chavan
Snehal Chaudhary
Rahul Joshi
Ketan Kotecha


Stock exchanges are places where buyers and sellers meet to trade shares in public companies. Stock exchanges encourage investment. Companies can grow, expand, and generate jobs in the economy by raising cash. These investments play a crucial role in promoting trade, economic expansion, and prosperity. We compare the three well-known deep learning algorithms, LSTM, GRU, and CNN, in this work. Our goal is to provide a thorough study of each algorithm and identify the best strategy when taking into account elements like accuracy, memory utilization, interpretability, and more. To do this, we recommend the usage of hybrid models, which combine the advantages of the various methods while also evaluating the performance of each approach separately. Aim of research is to investigate model with the highest accuracy and the best outcomes with respect to stock price prediction.

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How to Cite
Paygude, P. ., Singh, A. ., Tripathi, E. ., Priya, S. ., Gayakwad, M. ., Chavan, P. ., Chaudhary, S. ., Joshi, R. ., & Kotecha, K. . (2023). A Parameter Based Comparative Study of Deep Learning Algorithms for Stock Price Prediction. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7s), 138–146. https://doi.org/10.17762/ijritcc.v11i7s.6985


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