STOCK PRICE PREDICTION USING GRID HYPER PARAMETER TUNING IN GATED RECURRENT UNIT

Authors

  • Bhavsar Shachi Research Scholar, Department of Mathematics, Gujarat University, Ahmedabad-380009
  • Ravi Gor Department of Mathematics, Gujarat University, Ahmedabad-380009

DOI:

https://doi.org/10.29121/ijoest.v6.i3.2022.345

Keywords:

Machine Learning, Gated Recurrent Unit, Back Propagation, Neural Networks, Hyper Parameter Tuning

Abstract

Nowadays people are using social media to show their talent, to voice their viewpoint to society, etc. The use of social media has drastically grown during and after pandemic. Since, the power of social media is known to us, it would be beneficial to invest in such trending companies. But, understanding market pattern will be required to get maximum benefit from stock market, otherwise it may lead to losses. Machine learning is an essential tool for predicting such tasks. Here deep learning based Gated Recurrent Unit neural network is used for prediction. To develop optimized model, grid search algorithm is used for Gated Recurrent Unit hyper parameter tuning. Also, the hyper parameter values obtained by the model was used to verify and predict stock prices for other companies.

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References

Assaf, R. & Kolasani, S. (2020). Predicting Stock Movement Using Sentiment Analysis of Twitter Feed with Neural Networks. Journal of Data Analysis and Information Processing, 8, 309-319. https://doi.org/10.4236/jdaip.2020.84018

Baheti, R. Shirkande, G. Bodake, S. Deokar, J. & Archana, K. (2021). Stock Market Analysis from Social Media and News using Machine Learning Techniques. International, Journal on Data Science and Machine Learning with Applications, 1(1), 59-67. https://engg.dypvp.edu.in/ijdsmla/downloads/VI/DSMLA-12-2021.pdf

Bhavsar, S. & Gor, R. (2022). Comparison of Back propagation algorithms: Bidirectional GRU and Genetic Deep Neural Network for Churn Customer (In press). International Organization of Scientific Research Journal of Computer Engineering (IOSR-JCE).

Bhavsar, S. & Gor, R. (2022). Predicting Restaurant Ratings using Back Propagation Algorithm. International Organization of Scientific Research journal of Applied Mathematics (IOSR-JM), 18(2), 5-9. https://iosrjournals.org/iosr-jm/papers/Vol18-issue2/Ser-2/C1802021014.pdf

Cho, K. Merrienboer, B. V. Bahdanau, D. & Bengio , Y. (2014). On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. https://arxiv.org/abs/1409.1259

Das , S. Mishraa, D. & Rout, M. (2019). Stock market prediction using Firefly algorithm with evolutionary framework optimized feature reduction for OSELM method. Expert Systems with Applications, 1-24. https://doi.org/10.1016/j.eswax.2019.100016

Dozat, T. (2015). Incorporating Nesterov Momentum into Adam. https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ

Fama, E. F. (1970). Efficient capital markets: a review of theory and empirical work. 5e Journal of Finance, 25(2), 383-417. https://doi.org/10.2307/2325486

Gao, Y. Wang , R. & Zhou, E. (2021). Stock Prediction Based on Optimized LSTM and GRU Models. Hindawi Scientific Programming, 8. https://doi.org/10.1155/2021/4055281

Ghosh, M. & Gor, R. (2022). Ad-Campaign Analysis and Sales prediction using K-means Clustering and Random Forest Regressor. International Organization of Scientific Research Journal of Applied Mathematics, 18(2), 10-14. https://iosrjournals.org/iosr-jm/papers/Vol18-issue2/Ser-2/B1802020509.pdf

Guan, C. Liu , W. & Cheng, J. C. (2021). Using Social Media to Predict the Stock Market Crash and Rebound amid the Pandemic: The Digital 'Haves' and 'Have mores'. Annals of Data Science, 9(1), 5-31. https://doi.org/10.1007/s40745-021-00353-w

Kingma, D. & Ba, J. (2015). Adam: A Method for Stochastic Optimization. 3rd International Conference for Learning Representations, San Diego. https://arxiv.org/abs/1412.6980

Mehta , P. Pandya , S. & Kotecha, K. (2021). Harvesting social media sentiment analysis to enhance stock market prediction using deep learning. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.476

Pengfei, Y. & Yan, X. ( 2019). Stock price predictions based on deep neural networks, Neural computing and applications. 1609-1628. https://doi.org/10.1007/s00521-019-04212-x

Polamuri, S. Srinivas , K. & A. , K. (2019). Stock Market Prices Prediction using Random Forest and Extra Tree Regression. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 1224-1228. https://doi.org/10.35940/ijrte.C4314.098319

Rajpurohit, V. Bhavsar , S. & Gor, R. (2021). A comparision of GRU-based ETH price prediction. Proceeding of International Conference on Mathemaitcal Modelling and Simulation in Physical Sciences (MMSPS-2021), 424-431.

Raut, A. & Sethia , P. (2018). Application of LSTM, GRU and ICA for Stock Price Prediction. Proceedings of ICTIS, 2, 1-10. https://doi.org/10.1007/978-981-13-1747-7_46

Selvamuthu, D. Vineet, K. & Mishra, A. (2019). Indian Stock Market prediction using Artificial Neural Networks on tick data. Financial Innovation, 16(5), 1-12. https://doi.org/10.1186/s40854-019-0131-7

Shahi, T. Shrestha , A. Neupan, A. & Guo, W. (2020). Stock Price Forecasting with Deep Learning: A Comparative Study. Mdpi journal, 8, 1-15. https://doi.org/10.3390/math8091441

Shena, G. Tana, Q. Zhanga, H. & Ze, P. (2018). Deep Learning with Gated Recurrent Unit Networks for Financial Sequence Predictions. 8th International Congress of Information and Communication Technology, 895-903. https://doi.org/10.1016/j.procs.2018.04.298

Soni, P. Tewari , Y. & Krishnan, D. (2022). Machine Learning Approaches in Stock Price, Prediction: A Systematic Review. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/2161/1/012065

Srivinay, Manujakshi, B. C. Kabadi, M. G. & Naik, N. (2022). A hybrid stock price prediction model based on PRE and DeepNeural Network. MDPI. https://doi.org/10.3390/data7050051

Touzani, Y. & Douzi, K. (2021). An LSTM and GRU based trading strategy adapted to the Moroccan market, Journal of Big data, 1-16. https://doi.org/10.1186/s40537-021-00512-z

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Published

2022-06-24

How to Cite

Bhavsar, S., & Gor, R. (2022). STOCK PRICE PREDICTION USING GRID HYPER PARAMETER TUNING IN GATED RECURRENT UNIT. International Journal of Engineering Science Technologies, 6(3), 63–73. https://doi.org/10.29121/ijoest.v6.i3.2022.345

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