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

Research areas:

• Uncategorized

Year:

2022

Type of Publication:

In Proceedings

Authors:

• Ayllón-Gavilán, Rafael
• Guijo-Rubio, David
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César

Volume:

531

Book title:

Proceedings of the 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022)

Series:

Lecture Notes in Networks and Systems

Pages:

146-155

Organization:

Salamanca, Spain

Month:

September

ISBN:

978-3-031-18049-1

BibTex:

@conference{AyllonGavilan2022, author = "Rafael Ayll{\'o}n-Gavil{\'a}n and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", 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 $1$st to September $21$th 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.", booktitle = "Proceedings of the 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022)", doi = "10.1007/978-3-031-18050-7_14", isbn = "978-3-031-18049-1", month = "September", organization = "Salamanca, Spain", pages = "146--155", series = "Lecture Notes in Networks and Systems", title = "{A}ssessing the {E}fficient {M}arket {H}ypothesis for {C}ryptocurrencies with {H}igh-{F}requency {D}ata using {T}ime {S}eries {C}lassification", url = "doi.org/10.1007/978-3-031-18050-7_14", volume = "531", year = "2022", }

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 $1$st to September $21$th 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.