The Predictability of High-Frequency Returns in the Cryptocurrency Markets and the Adaptive Market Hypothesis

Karasiński Jacek ()
Additional contact information
Karasiński Jacek: University of Warsaw, Faculty of Management, Szturmowa 1/3, 02-678 Warsaw, Poland

Central European Economic Journal, 2025, vol. 12, issue 59, 34-48

Abstract: The objective of this study was to examine the level and behaviour of the weak-form efficiency of the 16 most capitalised cryptocurrencies using intraday data. The study employed martingale difference hypothesis tests utilising the rolling window method. The predictability of high frequency returns varied over time. For most of the time, the cryptocurrencies were unpredictable. Nevertheless, their weak-form efficiency appeared to decrease along with an increase in frequency. In general, most cryptocurrencies were marked by high levels of unpredictability. However, there were some significant differences between the most and least efficient ones. To exploit market inefficiencies, investors should focus on higher frequencies. Higher frequencies should also be a concern to regulators when it comes to ensuring market efficiency.

Keywords: cryptocurrency markets; adaptive market hypothesis; efficient market hypothesis [EMH]; predictability of returns; intraday returns (search for similar items in EconPapers)
JEL-codes: G14 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/ceej-2025-0003 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:ceuecj:v:12:y:2025:i:59:p:34-48:n:1003

DOI: 10.2478/ceej-2025-0003

Access Statistics for this article

Central European Economic Journal is currently edited by Anna Matysiak

More articles in Central European Economic Journal from Sciendo
Bibliographic data for series maintained by Peter Golla ().