The adaptive market hypothesis in the high frequency cryptocurrency market

By Jeffrey Chu, Yuanyuan Zhang, Stephen Chan. International Review of Financial Analysis, 2019

121 citations recorded by the source corpus.

Abstract This paper investigates the adaptive market hypothesis (AMH) with respect to the high frequency markets of the two largest cryptocurrencies — Bitcoin and Ethereum, versus the Euro and US Dollar. Our findings are consistent with the AMH and show that the efficiency of the markets varies over time. We also discuss possible news and events which coincide with significant changes in the market efficiency. Furthermore, we analyse the effect of the sentiment of these news and other factors (events) on the market efficiency in the high frequency setting, and provide a simple event analysis to investigate whether specific factors affect the market efficiency/inefficiency. The results show that the sentiment and types of news and events may not be significant factor in determining the efficiency of cryptocurrency markets.

Subfields: Economics and Econometrics, Finance, Information Systems

View Paper: https://www.semanticscholar.org/paper/ee045e3c8fc71e9cd4fd0d23dd20f9586abe2527