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Stock Price Forecasting Using Symbiotic Organisms Search Trained Neural Networks

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Computational Science and Its Applications – ICCSA 2019 (ICCSA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11623))

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Abstract

The prediction of stock prices is an important task in economics, investment and financial decision-making. This has for decades, spurred the interest of many researchers through their focused research in designing stock price predictive models. In this paper, the symbiotic organisms search algorithm, a new metaheuristic algorithm is employed as an efficient method for training feedforward neural networks. The training process is used to build a better stock price predictive model. The Straits Times Index, Russell 2,000, NASDAQ Composite and Dow Jones Industrial Average indices are utilized as time series dataset for training and testing the new system. Three evaluation methods namely, Root Mean Squared Error, Mean Absolute Percentage Error and Mean Absolute Deviation are used to compare the results of the implemented model. The results obtained revealed that the hybrid model exhibited outstanding predictive performance compared to the hybrid Particle Swarm Optimization, Genetic Algorithm, and Auto Regressive Integrated Moving Average based models. The new model is a promising predictive technique for solving high dimensional nonlinear time series data that are difficult to capture by traditional models.

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Author information

Authors and Affiliations

  1. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Westville Campus, Private Bag X54001, Durban, 4000, South Africa

    Bradley J. Pillay

  2. School of Computer Science, University of KwaZulu-Natal, King Edward Road, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa

    Absalom E. Ezugwu

Authors
  1. Bradley J. Pillay
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  2. Absalom E. Ezugwu
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Corresponding author

Correspondence to Absalom E. Ezugwu .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg , Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

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Pillay, B.J., Ezugwu, A.E. (2019). Stock Price Forecasting Using Symbiotic Organisms Search Trained Neural Networks. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11623. Springer, Cham. https://doi.org/10.1007/978-3-030-24308-1_53

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  • DOI: https://doi.org/10.1007/978-3-030-24308-1_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24307-4

  • Online ISBN: 978-3-030-24308-1

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