Abstract
Financial Analysis has become a challenging aspect in today’s world of valuable and better investment. This paper introduces the implementation of Recurrent Neural Network (RNN) along with Long Short-Term Memory Cells (LSTM) for Stock Market Prediction used for Portfolio Management considering the Time Series Historical Stock Data of Stocks in the Portfolio. The comparison of the model with the traditional Machine Learning Algorithms—Regression, Support Vector Machine, Random Forest, Feed Forward Neural Network and Backpropagation have been performed. Various metrics and architectures of LSTM RNN model have been considered and are tested and analysed. There is discussion on how the sentiments of the customer would affect the stocks along with the changes in trends.
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Acknowledgements
This research was partially supported by DSPM International Institute of Information Technology Naya Raipur (IIIT-NR). We thank our colleagues from IIIT-NR who provided insight that greatly helps us in this research. We would like to show our gratitude to Dr. Vivek Tiwari, Asst. Prof. CSE, IIIT-NR for mentoring us and sharing his experience and knowledge with us during this research. We thank every person associated with this research directly or indirectly.
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Pawar, K., Jalem, R.S., Tiwari, V. (2019). Stock Market Price Prediction Using LSTM RNN. In: Rathore, V., Worring, M., Mishra, D., Joshi, A., Maheshwari, S. (eds) Emerging Trends in Expert Applications and Security. Advances in Intelligent Systems and Computing, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-2285-3_58
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DOI: https://doi.org/10.1007/978-981-13-2285-3_58
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