Abstract
A significant challenge to invest in the stock market is to make complex decisions and evaluate different scenarios and trends in financial reports. An alternative capable of attending these needs is the use of algorithms for trading. This strategy may involve the use of artificial intelligence approaches to make effective decisions. Hence, this paper proposes the development of a solution to support decision making in the purchase and sale of stocks from the application of machine learning techniques. For this purpose, the performance of a recurrent neural network architecture, also known as long short-term memory (LSTM), was evaluated concerning its efficiency in the short-term prediction. The generated models were evaluated based on the root-mean-square error and mean absolute percentage error metrics. Results show the LSTM algorithm was capable of predicting stock prices with a low error approximately.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aidemark J, Askenäs L (2018) The creation of users: a learning experience in information system development. In J E-Health Med Commun 9(2):74–88
Arvidsson P, Ånhed T (2017) Sequence-to-sequence learning of financial time series in algorithmic trading
Boccato L (2013) Novas propostas e aplicações de redes neurais com estados de eco. Ph.D. thesis, Universidade Estadual de Campinas, Unicamp, SP
Cervelló-Royo R, Guijarro F, Michniuk K (2015) Stock market trading rule based on pattern recognition and technical analysis: forecasting the DJIA index with intraday data. Expert Syst Appl 42(14):5963–5975
Deorukhkar OS, Lokhande SH, Nayak VR, Chougule AA (2019) Stock price prediction using combination of LSTM neural networks, ARIMA and sentiment analysis. Int Res J Eng Technol 6(3):3497–3503
Eapen J, Bein D, Verma A (2019) Novel deep learning model with CNN and bi-directional LSTM for improved stock market index prediction. In: 2019 IEEE 9th annual computing and communication workshop and conference (CCWC). IEEE, pp 0264–0270
Fischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res 270(2):654–669
Hatcher WG, Yu W (2018) A survey of deep learning: platforms, applications and emerging research trends. IEEE Access 6:24411–24432
Haykin S (2001) Neural networks: principles and practice. Bookman
Hiransha M, Ab Gopalakrishnan E, Menon VK, Soman KP (2018) NSE stock market prediction using deep-learning models. Procedia Comput Sci 132:1351–1362
Kim HY, Won CH (2018) Forecasting the volatility of stock price index: a hybrid model integrating LSTM with multiple GARCH-type models. Expert Syst Appl 103:25–37
Liu S, Liao G, Ding Y (2018) Stock transaction prediction modeling and analysis based on LSTM. In: 13th IEEE conference on industrial electronics and applications (ICIEA), May 31–June 2, Wuhan, China. IEEE, pp 2787–2790
Mitchell TM (1997) Machine learning. McGraw Hill, Burr Ridge, IL
Pang X, Zhou Y, Wang P et al (2018) An innovative neural network approach for stock market prediction. J Supercomput (in press):1–21
Patterson J, Gibson A (2017) Deep learning: a practitioner’s approach. O’Reilly Media, Inc
Pawar K, Jalem RS, Tiwari V (2018) Stock market price prediction using LSTM RNN. Springer, Singapore, pp 493–503
Pawar K, Jalem RS, Tiwari V (2019) Stock market price prediction using LSTM RNN. In: Emerging trends in expert applications and security. Springer, pp 493–503
Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830
Sai Reddy VK (2018) Stock market prediction using machine learning. Int Res J Eng Technol (IRJET) 5(10):1033–1035
Thiele CC, Adami AG (2016) Previsão de séries temporais financeiras: modelo baseado em redes neurais artificiais. Revista Brasileira de Computação Aplicada 8(2):113–128
Zhang K, Zhong G, Dong J, Wang S, Wang Yong (2019) Stock market prediction based on generative adversarial network. Procedia Comput Sci 147:400–406
Acknowledgements
This work has been supported by the Brazilian National Council for Scientific and Technological Development (CNPq) via Grants No. 201155/2015-0 and 309335/2017-5; by the National Funding from the FCT—Fundação para a Ciência e Tecnologia through the UID/EEA/500008/2019 Project; by the International Scientific Partnership Program ISPP at King Saud University through ISPP 0129; and by the Federal Institute of Education, Science and Technology of Ceará (IFCE), via the institutional program of scientific initiation scholarships (PIBIC), Edital No. 1/2019.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
da Silva, A.P., Pereira, S.S.L., Moreira, M.W.L., Rodrigues, J.J.P.C., Rabêlo, R.A.L., Saleem, K. (2020). Performance Evaluation of Recurrent Neural Networks for Short-Term Investment Decision in Stock Market. In: M. Thampi, S., et al. Applied Soft Computing and Communication Networks. ACN 2019. Lecture Notes in Networks and Systems, vol 125. Springer, Singapore. https://doi.org/10.1007/978-981-15-3852-0_16
Download citation
DOI: https://doi.org/10.1007/978-981-15-3852-0_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3851-3
Online ISBN: 978-981-15-3852-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)