IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v10y2017i1p6-d89445.html
   My bibliography  Save this article

Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model

Author

Listed:
  • Adriano Beluco

    (Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Sul (IFRS), Campus Viamão, Av Sen Salgado Filho, 7000, Bairro Sáo Lucas, 94440-000, Viamão, RS, Brazil)

  • Denise L. Bandeira

    (Universidade Federal do Rio Grande do Sul (UFRGS), Escola de Administração, Rua Washington Luiz, 855, Centro Histórico, 90010-460, Porto Alegre, RS, Brazil)

  • Alexandre Beluco

    (Universidade Federal do Rio Grande do Sul (UFRGS), Instituto de Pesquisas Hidráulicas (IPH), Av Bento Gonçalves, 9500, Bairro Agronomia, 91501-970, Porto Alegre, RS, Brazil)

Abstract

Neural networks are well suited to predict future results of time series for various data types. This paper proposes a hybrid neural network model to describe the results of the database of the New York Stock Exchange (NYSE). This hybrid model brings together a self organizing map (SOM) with a multilayer perceptron with back propagation algorithm (MLP-BP). The SOM aims to segment the database into different clusters, where the differences between them are highlighted. The MLP-BP is used to construct a descriptive mathematical model that describes the relationship between the indicators and the closing value of each cluster. The model was developed from a database consisting of the NYSE Composite US 100 Index over the period of 2 April 2004 to 31 December 2015. As input variables for neural networks, ten technical financial indicators were used. The model results were fairly accurate, with a mean absolute percentage error varying between 0.16% and 0.38%.

Suggested Citation

  • Adriano Beluco & Denise L. Bandeira & Alexandre Beluco, 2017. "Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model," JRFM, MDPI, vol. 10(1), pages 1-13, February.
    • Handle: RePEc:gam:jjrfmx:v:10:y:2017:i:1:p:6-:d:89445
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/10/1/6/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/10/1/6/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Leonidas Sandoval Junior & Asher Mullokandov & Dror Y. Kenett, 2015. "Dependency Relations among International Stock Market Indices," JRFM, MDPI, vol. 8(2), pages 1-39, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhao, Xiaojun & Shang, Pengjian & Lin, Aijing, 2017. "Transfer mutual information: A new method for measuring information transfer to the interactions of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 517-526.
    2. Arfaoui, Nadia & Naeem, Muhammad Abubakr & Boubaker, Sabri & Mirza, Nawazish & Karim, Sitara, 2023. "Interdependence of clean energy and green markets with cryptocurrencies," Energy Economics, Elsevier, vol. 120(C).
    3. Arnab Bhattacharjee & Sudipto Roy, 2019. "Abnormal Returns or Mismeasured Risk? Network Effects and Risk Spillover in Stock Returns," JRFM, MDPI, vol. 12(2), pages 1-13, March.
    4. Xue Guo & Hu Zhang & Tianhai Tian, 2019. "Multi-Likelihood Methods for Developing Stock Relationship Networks Using Financial Big Data," Papers 1906.08088, arXiv.org.
    5. Nie, Chun-Xiao, 2020. "Correlation dynamics in the cryptocurrency market based on dimensionality reduction analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    6. Rehman, Mobeen Ur & Shahzad, Syed Jawad Hussain & Ahmad, Nasir & Vo, Xuan Vinh, 2021. "Dependence among metals and mining companies of the US and Europe during normal and crises periods," Resources Policy, Elsevier, vol. 73(C).
    7. Chuang, Hongwei, 2016. "Brokers’ financial network and stock return," The North American Journal of Economics and Finance, Elsevier, vol. 36(C), pages 172-183.
    8. Simona Moagăr-Poladian & Dorina Clichici & Cristian-Valeriu Stanciu, 2019. "The Comovement of Exchange Rates and Stock Markets in Central and Eastern Europe," Sustainability, MDPI, vol. 11(14), pages 1-22, July.
    9. Ömer Akgüller & Mehmet Ali Balcı & Larissa M. Batrancea & Lucian Gaban, 2023. "Path-Based Visibility Graph Kernel and Application for the Borsa Istanbul Stock Network," Mathematics, MDPI, vol. 11(6), pages 1-25, March.
    10. Rehman, Mobeen Ur & Shahzad, Syed Jawad Hussain & Ahmad, Nasir & Vo, Xuan Vinh, 2022. "Dependence dynamics of US REITs," International Review of Financial Analysis, Elsevier, vol. 81(C).
    11. Chong, Oiping & Bany- Ariffin, A.N. & Matemilola, Bolaji Tunde & McGowan, C.B., 2020. "Can China’s cross-sectional dispersion of stock returns influence the herding behaviour of traders in other local markets and China’s trading partners?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    12. Xiurong Chen & Yixiang Tian & Rubo Zhao, 2017. "Study of the cross-market effects of Brexit based on the improved symbolic transfer entropy GARCH model—An empirical analysis of stock–bond correlations," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-14, August.
    13. Volta, Vittoria & Aste, Tomaso, 2022. "Causal coupling between European and UK markets triggered by announcements of monetary policy decisions," LSE Research Online Documents on Economics 114947, London School of Economics and Political Science, LSE Library.
    14. Shahzad, Syed Jawad Hussain & Naifar, Nader, 2022. "Dependence dynamics of Islamic and conventional equity sectors: What do we learn from the decoupling hypothesis and COVID-19 pandemic?," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    15. Pang, Raymond Ka-Kay & Granados, Oscar M. & Chhajer, Harsh & Legara, Erika Fille T., 2021. "An analysis of network filtering methods to sovereign bond yields during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    16. He, Chengying & Huang, Ke & Lin, Jianwu & Wang, Tianqi & Zhang, Zuominyang, 2023. "Explain systemic risk of commodity futures market by dynamic network," International Review of Financial Analysis, Elsevier, vol. 88(C).
    17. Guo, Xue & Li, Weibo & Zhang, Hu & Tian, Tianhai, 2022. "Multi-likelihood methods for developing relationship networks using stock market data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    18. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa & Stosic, Tatijana, 2016. "Correlations of multiscale entropy in the FX market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 52-61.
    19. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    20. Rehman, Mobeen Ur & Ahmad, Nasir & Shahzad, Syed Jawad Hussain & Vo, Xuan Vinh, 2022. "Dependence dynamics of stock markets during COVID-19," Emerging Markets Review, Elsevier, vol. 51(PB).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:10:y:2017:i:1:p:6-:d:89445. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.