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Stock-Pred: The LSTM Prophet of the Stock Market

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Optimization, Learning Algorithms and Applications (OL2A 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1754))

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Abstract

This paper presents the development process of a LSTM prophet which predicts stock market prices, based on historical data and machine learning to identify trends. Predicting prices in the stock market is a challenging task, surrounded by possible turbulent events that impact the final result. In this scenario, for the method proposed here, it is presented (i) the steps to obtain and process the used data for the applied LSTM network; (ii) the motivations for this implementation; (iii) the details regarding methodology and architecture; (iv) the neural architecture search technique used to determine the hyperparameters by tuning them through the iRace algorithm, which is responsible for structuring the network topology in a robust way; and (v) a discussion about quality measures used to compare network settings. Computational experiments in order to analyze the developed tool considered stock tickers from USA and Brazilian financial markets, in addition to the foreign exchange market involving BRL–USD. The obtained results were satisfactory in general, which achieved a high model accuracy, in relation to price tendency, and a low mean absolute percentage error, according to price values, with the analyzed test dataset.

Supported by organization the Brazilian agencies CAPES, CNPq and FAPEMIG.

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Authors and Affiliations

  1. Departamento de Computação, Centro Federal de Educação Tecnológica de Minas Gerais, Belo Horizonte, MG, 30510 -000, Brazil

    Thiago Figueiredo Costa & André Rodrigues da Cruz

Authors
  1. Thiago Figueiredo Costa
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  2. André Rodrigues da Cruz
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Corresponding author

Correspondence to Thiago Figueiredo Costa .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. University of Ljubljana, Ljubljana, Slovenia

    Andrej Košir

  3. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  5. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Teixeira

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    Rui P. Lopes

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Costa, T.F., da Cruz, A.R. (2022). Stock-Pred: The LSTM Prophet of the Stock Market. In: Pereira, A.I., Košir, A., Fernandes, F.P., Pacheco, M.F., Teixeira, J.P., Lopes, R.P. (eds) Optimization, Learning Algorithms and Applications. OL2A 2022. Communications in Computer and Information Science, vol 1754. Springer, Cham. https://doi.org/10.1007/978-3-031-23236-7_21

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  • DOI: https://doi.org/10.1007/978-3-031-23236-7_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23235-0

  • Online ISBN: 978-3-031-23236-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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