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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aggarwal, D.: Do bitcoins follow a random walk model? Res. Econ. 73(1), 15–22 (2019)
Alaoui, M.E., Bouri, E., Roubaud, D.: Bitcoin price-volume: a multifractal cross-correlation approach. Financ. Res. Lett. 31 (2019)
Aslan, A., Sensoy, A.: Intraday efficiency-frequency nexus in the cryptocurrency markets. Financ. Res. Lett. 35, 101298 (2020)
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)
Cabello, N., Naghizade, E., Qi, J., Kulik, L.: Fast and accurate time series classification through supervised interval search, pp. 948–953 (2020)
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)
Chu, J., Zhang, Y., Chan, S.: The adaptive market hypothesis in the high frequency cryptocurrency market. Int. Rev. Financ. Anal. 64, 221–231 (2019)
Cootner, P.H.: The Random Character of Stock Market Prices. Massachusetts Institute of Technology Press, Cambridge (1964)
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)
Deng, H., Runger, G., Tuv, E., Vladimir, M.: A time series forest for classification and feature extraction. Inf. Sci. 239, 142–153 (2013)
Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Financ. 25(2), 383–417 (1970)
Fama, E.F.: Efficient capital markets: II. J. Financ. 46(5), 1575–1617 (1991)
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
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)
Jiang, Y., Nie, H., Ruan, W.: Time-varying long-term memory in bitcoin market. Financ. Res. Lett. 25, 280–284 (2018)
Kaboundan, M.A.: Genetic programming prediction of stock prices. Comput. Econ. 16, 207–236 (2000)
Khuntia, S., Pattanayak, J.: Adaptive market hypothesis and evolving predictability of bitcoin. Econ. Lett. 167, 26–28 (2018)
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)
Nadarajah, S., Chu, J.: On the inefficiency of bitcoin. Econ. Lett. 150, 6–9 (2017)
Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. Decent. Bus. Rev. 21260 (2008)
Palamalai, S., Kumar, K.K., Maity, B.: Testing the random walk hypothesis for leading cryptocurrencies. Borsa Istanbul Rev. 21(3), 256–268 (2021)
Powers, D.: Evaluation: from precision, recall and F-factor to ROC, informedness, markedness & correlation. Mach. Learn. Technol. 2 (2008)
Tiwari, A.K., Jana, R., Das, D., Roubaud, D.: Informational efficiency of bitcoin-an extension. Econ. Lett. 163, 106–109 (2018)
Urquhart, A.: The inefficiency of bitcoin. Econ. Lett. 148, 80–82 (2016)
Wilcoxon, F.: Individual comparisons by ranking methods. Biomet. Bull. 1(6), 80–83 (1945)
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
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
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
DOI: https://doi.org/10.1007/978-3-031-18050-7_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-18049-1
Online ISBN: 978-3-031-18050-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)