Skip to main content
SpringerLink
Log in

Time Series Forecasting Using Artificial Neural Networks

A Model for the IBEX 35 Index

  • Conference paper
  • First Online:
Hybrid Artificial Intelligent Systems (HAIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13469))

Included in the following conference series:

  • 708 Accesses

Abstract

The amount of data generated daily in the financial markets is diverse and extensive; hence, creating systems that facilitate decision-making is crucial. In this paper, different intelligent systems are proposed and tested to predict the closing price of the IBEX 35 using ten years of historical data with four different neural networks architectures. The first was a multi-layer perceptron (MLP) with two different activation functions (AF) to continue with a simple recurrent neural network (RNN), a long-short-term memory (LSTM) network and a gated recurrent unit (GRU) network. The analytical results of these models have shown a strong, predictable power. Furthermore, by comparing the errors of predicted outcomes between the models, the LSTM presents the lowest error with the highest computational time in the training phase. Finally, the empirical results revealed that these models could efficiently predict financial data for trading purposes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/. Software available from tensorflow.org

  2. Adebiyi, A.A., Adewumi, A.O., Ayo, C.K.: Comparison of ARIMA and artificial neural networks models for stock price prediction. J. Appl. Math. 2014, 1–7 (2014). https://doi.org/10.1155/2014/614342

    Article  MathSciNet  Google Scholar 

  3. Alom, M.Z., et al.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3), 292 (2019). https://doi.org/10.3390/electronics8030292

    Article  Google Scholar 

  4. Baur, D.: Financial contagion and the real economy. J. Banking Financ. 36(10), 2680–2692 (2012)

    Article  Google Scholar 

  5. Bengio, Y.: Probabilistic neural network models for sequential data. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium (2000). https://doi.org/10.1109/ijcnn.2000.861438

  6. Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994). https://doi.org/10.1109/72.279181

    Article  Google Scholar 

  7. Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8(1), 1–12 (2018). https://doi.org/10.1038/s41598-018-24271-9

    Article  Google Scholar 

  8. Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014). https://doi.org/10.3115/v1/d14-1179

  9. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling (2014)

    Google Scholar 

  10. Enke, D., Thawornwong, S.: The use of data mining and neural networks for forecasting stock market returns. Expert Syst. Appl. 29(4), 927–940 (2005). https://doi.org/10.1016/j.eswa.2005.06.024

    Article  Google Scholar 

  11. Gocken, M., Ozcalici, M., Boru, A., Dosdogru, A.T.: Integrating metaheuristics and artificial neural networks for improved stock price prediction. Expert Syst. Appl. 44, 320–331 (2016). https://doi.org/10.1016/j.eswa.2015.09.029

    Article  Google Scholar 

  12. Guresen, E., Kayakutlu, G., Daim, T.U.: Using artificial neural network models in stock market index prediction. Expert Syst. Appl. 38(8), 10389–10397 (2011). https://doi.org/10.1016/j.eswa.2011.02.068

    Article  Google Scholar 

  13. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  14. Kara, Y., Boyacioglu, M.A., Baykan, Ö.K.: Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul stock exchange. Expert Syst. Appl. 38(5), 5311–5319 (2011). https://doi.org/10.1016/j.eswa.2010.10.027

    Article  Google Scholar 

  15. Kim, H.Y., Won, C.H.: Forecasting the volatility of stock price index: a hybrid model integrating LSTM with multiple GARCH-type models. Expert Syst. Appl. 103, 25–37 (2018). https://doi.org/10.1016/j.eswa.2018.03.002

    Article  Google Scholar 

  16. Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  17. Lee, J., Suh, T., Roy, D., Baucus, M.: Emerging technology and business model innovation: the case of artificial intelligence. MDPI (2019). https://www.mdpi.com/2199-8531/5/3/44

  18. Lo, A.W., MacKinlay, A.C.: Stock market prices do not follow random walks: evidence from a simple specification test. Rev. Financ. Stud. 1(1), 41–66 (1988)

    Article  Google Scholar 

  19. Menkhoff, L.: The use of technical analysis by fund managers international evidence. J. Banking Financ. 34(11), 2573–2586 (2010). https://doi.org/10.1016/j.jbankfin.2010.04.014

    Article  Google Scholar 

  20. Moghaddam, A.H., Moghaddam, M.H., Esfandyari, M.: Stock market index prediction using artificial neural network. J. Econ. Financ. Adm. Sci. 21(41), 89–93 (2016). https://doi.org/10.1016/j.jefas.2016.07.002

    Article  Google Scholar 

  21. Pei, S., Shen, T., Wang, X., Gu, C., Ning, Z., Ye, X., Xiong, N.: 3DACN: 3D augmented convolutional network for time series data. Inf. Sci. 513, 17–29 (2020). https://doi.org/10.1016/j.ins.2019.11.040

    Article  Google Scholar 

  22. Pyo, S., Lee, J., Cha, M., Jang, H.: Predictability of machine learning techniques to forecast the trends of market index prices: hypothesis testing for the Korean stock markets. PLoS One 12, e0188107 (2017). https://doi.org/10.1371/journal.pone.0188107

    Article  Google Scholar 

  23. Qiu, M., Song, Y., Akagi, F.: Application of artificial neural network for the prediction of stock market returns: the case of the Japanese stock market. Chaos Solitons Fractals 85, 1–7 (2016). https://doi.org/10.1016/j.chaos.2016.01.004

    Article  MathSciNet  Google Scholar 

  24. Sagir, A., Sathasivan, S.: The use of artificial neural network and multiple linear regressions for stock market forecasting. Matematika 33, 1–10 (2017)

    Article  MathSciNet  Google Scholar 

  25. Sezer, O.B., Gudelek, M.U., Ozbayoglu, A.M.: Financial time series forecasting with deep learning : a systematic literature review: 2005–2019. Appl. Soft Comput. 90, 106181 (2020). https://doi.org/10.1016/j.asoc.2020.106181

    Article  Google Scholar 

  26. Tkáč, M., Verner, R.: Artificial neural networks in business: two decades of research. Appl. Soft Computi. 38, 788–804 (2016). https://doi.org/10.1016/j.asoc.2015.09.040

    Article  Google Scholar 

  27. Wang, J., Zhang, Y., Yu, L.C., Zhang, X.: Contextual sentiment embeddings via bi-directional GRU language model. Knowl.-Based Syst. 235, 107663 (2022). https://doi.org/10.1016/j.knosys.2021.107663

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. NEOMA Business School, rue du Maréchal Juin, 76825, Mont Saint Aignan Cedex, France

    Daniel González-Cortés & Jian Wu

  2. Faculty of Engineering, University of Deusto, 48007, Bilbao, Spain

    Enrique Onieva & Iker Pastor

Authors
  1. Daniel González-Cortés
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Enrique Onieva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Iker Pastor
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel González-Cortés .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  4. University of Oviedo, Oviedo, Spain

    José Ramón Villar Flecha

  5. Data Science and Big Data Analytics Lab, Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso Lora

  6. Department of Computer Science, University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Applied Computational Intelligence, University of Burgos, Burgos, Burgos, Spain

    Álvaro Herrero

  8. Universidad Pablo de Olavide, Seville, Spain

    Francisco Martínez Álvarez

  9. DIGIP, University of Bergamo, Dalmine, Bergamo, Italy

    Giuseppe Psaila

  10. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  11. University of Salamanca, Salamanca, Spain

    Emilio Corchado

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

González-Cortés, D., Onieva, E., Pastor, I., Wu, J. (2022). Time Series Forecasting Using Artificial Neural Networks. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2022. Lecture Notes in Computer Science(), vol 13469. Springer, Cham. https://doi.org/10.1007/978-3-031-15471-3_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-15471-3_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15470-6

  • Online ISBN: 978-3-031-15471-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation