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Performance Evaluation of Recurrent Neural Networks for Short-Term Investment Decision in Stock Market

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 125)


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.

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  • DOI: 10.1007/978-981-15-3852-0_16
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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.

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Correspondence to Joel J. P. C. Rodrigues .

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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: , et al. Applied Soft Computing and Communication Networks. ACN 2019. Lecture Notes in Networks and Systems, vol 125. Springer, Singapore.

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