Analysis of Gradient Descent Optimization Techniques with Gated Recurrent Unit for Stock Price Prediction: A Case Study on Banking Sector of Nepal Stock Exchange

  • Arjun Singh Saud Central Department of Computer Science & Information Technology, Tribhuvan University, Kirtipur
  • Subarna Shakya Institute of Engineering, Pulchowk Campus, Tribhuvan University, Pulchowk, Lalitpur
Keywords: Stock prediction, GRU, Momentum, RMSProp, Adam

Abstract

The stock price is the cost of purchasing a security or stock in a stock exchange. The stock price prediction has been the aim of investors since the beginning of the stock market. It is the act of forecasting the future price of a company's stock. Nowadays, deep learning techniques are widely used for identifying the stock trends from large amounts of past data. This research has experimented two big and robust commercial banks listed in the Nepal Stock Exchange (NEPSE) and compared stock price prediction performance of GRU with three widely used gradient descent optimization techniques: Momentum, RMSProp, and Adam. GRU with Adam is more accurate and consistent approach for predicting stock prices from the present study.

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Published
2019-12-31
How to Cite
Saud, A., & Shakya, S. (2019). Analysis of Gradient Descent Optimization Techniques with Gated Recurrent Unit for Stock Price Prediction: A Case Study on Banking Sector of Nepal Stock Exchange. Journal of Institute of Science and Technology, 24(2), 17-21. https://doi.org/10.3126/jist.v24i2.27247
Section
Research Article