Title: Stock price trend prediction with long short-term memory neural networks

Authors: Varun Gupta; Mujahid Ahmad

Addresses: Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Sector 26, Chandigarh, 160019, India ' Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Sector 26, Chandigarh, 160019, India

Abstract: Stock market is an immensely complex, chaotic and dynamic environment. Thus, the task of predicting changes in such an environment becomes challenging with regards to its accuracy. A number of approaches have been adopted to take on that challenge and machine learning has been at the crux in many of them. There are plenty of examples of algorithms based on machine learning yielding satisfactory results for such type of prediction. This paper presents the usage of long short-term memory (LSTM) networks in this scenario, to predict future trends of stock market prices based on the patterns from price history, paired with technical analysis indicators. To achieve this, a model has been built, and a series of experiments have been conducted through a number of parameters and the results were analysed against predefined metrics to assess if this algorithm presents any improvements in front of other machine learning methods and strategies. Also, a comparative study is presented which analyses popularly used optimisers and error schemes to check which given optimiser yields the best results. The results obtained are promising and presented a reasonably accurate prediction for the rise or fall of a particular stock in the near future.

Keywords: stock market prediction; long short-term memory; LSTM; recurrent neural networks; neural networks; machine learning; deep learning; artificial intelligence.

DOI: 10.1504/IJCISTUDIES.2019.103619

International Journal of Computational Intelligence Studies, 2019 Vol.8 No.4, pp.289 - 298

Received: 02 Mar 2018
Accepted: 08 Oct 2018

Published online: 12 Nov 2019 *

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