Skip to main content
SpringerLink
Log in

Assessing the Efficient Market Hypothesis for Cryptocurrencies with High-Frequency Data Using Time Series Classification

  • Conference paper
  • First Online:
17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) (SOCO 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 531))

Abstract

This work analyzes the performance of several state-of-the-art Time Series Classification (TSC) techniques in the cryptocurrency returns modeling field. The data used in this study comprehends the close price of 6 of the principal cryptocurrencies, collected with a frequency of 5 minutes from January 1st to September 21th of 2021. The aim of this work is twofold: 1) to study the weak form of the Efficient Market Hypothesis (EMH) and 2) to examine the veracity behind the theory of the Random Walk Model (RWM). For this, two datasets are built. The first uses autoregressive values, whereas the second dataset is constructed by introducing randomized past values from the time series. Then, a comparison of the performances achieved by the different TSC techniques is carried out. Results obtained show a pronounced difference in terms of performance obtained by all the TSC models when applied to the original dataset against the randomized one. The results achieved by the models applied to the original dataset are significantly better in terms of Area Under ROC Curve (AUC) and Recall. Therefore, the EMH is refused in its weak form, and indisputable evidence against the RWM in a high-frequency scope is provided.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

Notes

  1. 1.

    https://www.kaggle.com/competitions/g-research-crypto-forecasting/data.

References

  1. Aggarwal, D.: Do bitcoins follow a random walk model? Res. Econ. 73(1), 15–22 (2019)

    Article  Google Scholar 

  2. Alaoui, M.E., Bouri, E., Roubaud, D.: Bitcoin price-volume: a multifractal cross-correlation approach. Financ. Res. Lett. 31 (2019)

    Google Scholar 

  3. Aslan, A., Sensoy, A.: Intraday efficiency-frequency nexus in the cryptocurrency markets. Financ. Res. Lett. 35, 101298 (2020)

    Article  Google Scholar 

  4. Bradley, A.P.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)

    Article  Google Scholar 

  5. Cabello, N., Naghizade, E., Qi, J., Kulik, L.: Fast and accurate time series classification through supervised interval search, pp. 948–953 (2020)

    Google Scholar 

  6. Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: Addressing imbalance in multilabel classification: measures and random resampling algorithms. Neurocomputing 163, 3–16 (2015)

    Article  Google Scholar 

  7. Chu, J., Zhang, Y., Chan, S.: The adaptive market hypothesis in the high frequency cryptocurrency market. Int. Rev. Financ. Anal. 64, 221–231 (2019)

    Article  Google Scholar 

  8. Cootner, P.H.: The Random Character of Stock Market Prices. Massachusetts Institute of Technology Press, Cambridge (1964)

    Google Scholar 

  9. Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Min. Knowl. Disc. 34, 1454–1495 (2020)

    Article  MathSciNet  Google Scholar 

  10. Deng, H., Runger, G., Tuv, E., Vladimir, M.: A time series forest for classification and feature extraction. Inf. Sci. 239, 142–153 (2013)

    Article  MathSciNet  Google Scholar 

  11. Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Financ. 25(2), 383–417 (1970)

    Article  Google Scholar 

  12. Fama, E.F.: Efficient capital markets: II. J. Financ. 46(5), 1575–1617 (1991)

    Article  Google Scholar 

  13. Fawaz, H.I., et al.: InceptionTime: finding AlexNet for time series classification. Data Min. Knowl. Disc. 34, 1936–1962 (2020). https://doi.org/10.1007/s10618-020-00710-y

    Article  MathSciNet  Google Scholar 

  14. Guijo-Rubio, D., Gutiérrez, P.A., Bagnall, A., Hervás-Martínez, C.: Time series ordinal classification via ShapeLets. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2020)

    Google Scholar 

  15. Jiang, Y., Nie, H., Ruan, W.: Time-varying long-term memory in bitcoin market. Financ. Res. Lett. 25, 280–284 (2018)

    Article  Google Scholar 

  16. Kaboundan, M.A.: Genetic programming prediction of stock prices. Comput. Econ. 16, 207–236 (2000)

    Article  Google Scholar 

  17. Khuntia, S., Pattanayak, J.: Adaptive market hypothesis and evolving predictability of bitcoin. Econ. Lett. 167, 26–28 (2018)

    Article  Google Scholar 

  18. Latif, S., Mohd, M., Amin, M., Mohamad, A.: Testing the weak form of efficient market in cryptocurrency. J. Eng. Appl. Sci. 12, 2285–2288 (2017)

    Google Scholar 

  19. Nadarajah, S., Chu, J.: On the inefficiency of bitcoin. Econ. Lett. 150, 6–9 (2017)

    Article  Google Scholar 

  20. Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. Decent. Bus. Rev. 21260 (2008)

    Google Scholar 

  21. Palamalai, S., Kumar, K.K., Maity, B.: Testing the random walk hypothesis for leading cryptocurrencies. Borsa Istanbul Rev. 21(3), 256–268 (2021)

    Article  Google Scholar 

  22. Powers, D.: Evaluation: from precision, recall and F-factor to ROC, informedness, markedness & correlation. Mach. Learn. Technol. 2 (2008)

    Google Scholar 

  23. Tiwari, A.K., Jana, R., Das, D., Roubaud, D.: Informational efficiency of bitcoin-an extension. Econ. Lett. 163, 106–109 (2018)

    Article  Google Scholar 

  24. Urquhart, A.: The inefficiency of bitcoin. Econ. Lett. 148, 80–82 (2016)

    Article  Google Scholar 

  25. Wilcoxon, F.: Individual comparisons by ranking methods. Biomet. Bull. 1(6), 80–83 (1945)

    Article  Google Scholar 

Download references

Acknowledgements

This work has been supported by “Agencia Española de Investigación (España)” (grant reference: PID2020-115454GB-C22/AEI/10.13039/501100011033); the “Consejería de Salud y Familia (Junta de Andalucía)” (grant reference: PS-2020-780); and the “Consejería de Transformación Económica, Industria, Conocimiento y Universidades (Junta de Andalucía) y Programa Operativo FEDER 2014-2020” (grant references: UCO-1261651 and PY20_00074). David Guijo-Rubio’s research has been subsidized by the University of Córdoba through grants to Public Universities for the requalification of the Spanish university system of the Ministry of Universities, financed by the European Union - NextGenerationEU (grant reference: UCOR01MS).

Author information

Authors and Affiliations

  1. Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain

    Rafael Ayllón-Gavilán, David Guijo-Rubio, Pedro A. Gutiérrez & César Hervás-Martínez

Authors
  1. Rafael Ayllón-Gavilán
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Guijo-Rubio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pedro A. Gutiérrez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. César Hervás-Martínez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rafael Ayllón-Gavilán .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. Mechanical Engineering Department, University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martinez-de-Pison

  4. Inteligencia Artificial, University of Oviedo, A Coruña, La Coruña, Spain

    José Ramón Villar Flecha

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

    Alicia Troncoso Lora

  6. University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  8. School of engineering, Pablo Olavide University, 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. Department of Computing Science, University of Salamanca, Salamanca, Spain

    Emilio S. Corchado Rodriguez

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ayllón-Gavilán, R., Guijo-Rubio, D., Gutiérrez, P.A., Hervás-Martínez, C. (2023). Assessing the Efficient Market Hypothesis for Cryptocurrencies with High-Frequency Data Using Time Series Classification. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_14

Download citation

Publish with us

Policies and ethics

Search

Navigation