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A Comparison of Two Hölder Regularity Functions to Forecast Stock Indices by ANN Algorithms

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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 11620))

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Abstract

This study is concerned with local Hölder exponent as a function regularity measure for time series regarding Finance data. The study examines 12 attributes in the Micro, Small and Medium Enterprises (MSME) data for 6 regions (2004–2018). The study has two different approaches. Firstly, (i) multi fractal methods and Brownian motion Hölder regularity functions were used for the identification of the significant and self-similar attributes in the Finance dataset. The steps are: (a) polynomial functions were applied on the Finance dataset and p\(\_\)finance dataset was obtained. (b) exponential functions were applied and e\(\_\)Finance dataset was obtained. Secondly, (ii) Artificial Neural Network (ANN) algorithms ((Feed Forward Back Propagation (FFBP) and Cascade Forward Back Propagation (CFBP)) were applied on both datasets in a and b. Finally, (iii) classification accuracy rates as per the outcomes obtained from the second stage by the ANN algorithms were compared. Our study has been conducted for the first time in the literature since with the ANN algorithms’ application, it revealed how the most significant attributes are identified in the Finance dataset by Hölder functions (polynomial as well as exponential).

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Author information

Authors and Affiliations

  1. University of Massachusetts Medical School, Worcester, MA, 01655, USA

    Yeliz Karaca

  2. Engineering School (DEIM), University of Tuscia, Viterbo, VT, 01100, Italy

    Carlo Cattani

  3. Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Carlo Cattani

Authors
  1. Yeliz Karaca
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  2. Carlo Cattani
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Corresponding author

Correspondence to Yeliz Karaca .

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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Karaca, Y., Cattani, C. (2019). A Comparison of Two Hölder Regularity Functions to Forecast Stock Indices by ANN Algorithms. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11620. Springer, Cham. https://doi.org/10.1007/978-3-030-24296-1_23

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  • DOI: https://doi.org/10.1007/978-3-030-24296-1_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24295-4

  • Online ISBN: 978-3-030-24296-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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