Abstract
The dynamic nature of stock markets makes the task of generalizing stock prediction very challenging. The dependence of stock prices upon many parameters which directly or indirectly affect them; just adds to the challenge. In recent years there is an increased interest in stock prediction using neural networks. Most of the work-related are either based upon global exchange parameters or favor one particular architecture. To the best of our knowledge, a dedicated study of Indian stocks and the prediction of their prices are not reported. This is the main motivation of this work.
In this paper, we describe the implementation of the Long Short-Term Memory (LSTM) network, Gated Recurrent Unit (GRU) networks for stock prediction. We present a comparative study as well as an analysis for four different network architectures. We use the NSEpy 0.8 Python package to extract historic data which is made publicly available by NSE India. We have used three years of training data and one-year of testing data. After an in-depth study of the impact of the parameters on the stock prices, we have chosen ten parameters for training. The metric used is the Loss Function. The prediction is performed for the closing prices of stocks of twenty-five Indian companies. The results indicate that a two-layer GRU outperforms all other networks as far as these twenty-five companies are concerned.
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Acknowledgment
The authors, thank the department of Electronics and Telecommunication at Fr. Conceicao Rodrigues Institute of Technology for extending support and caring in every aspect for carrying out this work. We would also like to thank the anonymous reviewers whose comments were instrumental in improving the content of this paper.
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Salimath, S., Chatterjee, T., Mathai, T., Kamble, P., Kolhekar, M. (2021). Prediction of Stock Price for Indian Stock Market: A Comparative Study Using LSTM and GRU. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1441. Springer, Cham. https://doi.org/10.1007/978-3-030-88244-0_28
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