Price delay and market frictions in cryptocurrency markets

doi:10.1016/j.econlet.2018.10.025

Economics Letters

Volume 174, January 2019, Pages 39-41

https://doi.org/10.1016/j.econlet.2018.10.025 Get rights and content

Highlights

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We investigate the reaction time to unexpected relevant information of 75 cryptocurrencies.
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We measure reaction time using three price delay measures.
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The average price delay significantly decreases during the last three years.
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Price delay is highly correlated to market capitalization and liquidity.

Abstract

We study the efficiency of cryptocurrencies by measuring the price’s reaction time to unexpected relevant information. We find the average price delay to significantly decrease during the last three years. For the cross-section of 75 cryptocurrencies we find delays to be highly correlated with liquidity.

Introduction

The efficiency of cryptocurrencies and especially of Bitcoin has recently gained academic interest. In an efficient market as defined by Fama (1970) (EMH), prices should quickly incorporate new information without delay. Due to market frictions and lack of liquidity prices can react with a significant delay to new information making markets less efficient. The weak-form efficiency of Bitcoin is subject of many studies (Urquhart, 2016, Nadarajah and Chu, 2017, Vidal-Tomás and Ibañez, 2018, Kristoufek, 2018, Jiang et al., 2018, Bariviera, 2017, Tiwari et al., 2018, Khuntia and Pattanayak, 2018, Alvarez-Ramirez et al., 2018). Bitcoin is mainly found to be inefficient but to gain weak-form efficiency over time. For the cross-section of cryptocurrencies, the weak-form is investigated by Brauneis and Mestel (2018) showing liquidity and market cap to affect the efficiency. Wei (2018) studies the return predictability of 456 cryptocurrencies finding that there is a strong relationship with liquidity.

This study extends the efficiency debate of cryptocurrencies by investigating the average price delay of the market to new information. Using three delay measures as given in Hou and Moskowitz (2005) we show news to be faster incorporated in prices during the last three years. We further establish a connection between liquidity and price delay in the cross-section and find a strong relationship between estimated bid–ask spreads and price delay when not distinguishing between shorter and longer lags.

Section snippets

Data and delay measures

We obtain daily cryptocurrency prices, dollar volume, and market capitalization from coinmarketcap.com. Due to the dependence on other blockchains we do not include so-called “crypto tokens”. Our sample covers the period from 31/08/2015, the starting month of Ethereum trading, to 31/08/2018. We use only cryptocurrencies with a complete time series and a market capitalization of at least USD 1 million at the end of August 2018 leaving a set consisting of 75 cryptocurrencies. Cf. Brauneis and

Results

The results for the average price delay using the three measures $\bar{D 1}$ , $\bar{D 2}$ and $\bar{D 3}$ are given in Fig. 1. The delay measure $\bar{D 1}$ is between 0.35 and 0.45 at the beginning of our observation period and gradually declines to about 0.1. A value of one means that return variation is explained by lagged market returns only and a value of zero means that the lagged returns have no explanatory power for the variation of single cryptocurrency returns.

Thus, $\bar{D 1}$ implies that cryptocurrencies significantly

Concluding remarks

Adding to the recent literature of weak-form market efficiency we study the price delay in the cryptocurrency market. We use three delay measures and 75 cryptocurrencies to calculate the average price delay. Our findings show that price delay significantly decreases during the last three years giving further insights into the efficiency of the cryptocurrency market. Looking at the cross-section, we show that price delay is strongly related to liquidity and size. When not distinguishing between

References (16)

Alvarez-RamirezJ. et al.
Long-range correlations and asymmetry in the bitcoin market
Physica A
(2018)
AmihudY.
Illiquidity and stock returns: cross-section and time-series effects
J. Financ. Markets
(2002)
BarivieraA.F.
The inefficiency of Bitcoin revisited: A dynamic approach
Econ. Lett.
(2017)
BrauneisA. et al.
Price discovery of cryptocurrencies: Bitcoin and beyond
Econ. Lett.
(2018)
JiangY. et al.
Time-varying long-term memory in Bitcoin market
Financ. Res. Lett.
(2018)
KhuntiaS. et al.
Adaptive market hypothesis and evolving predictability of Bitcoin
Econ. Lett.
(2018)
KristoufekL.
On Bitcoin markets (in)efficiency and its evolution
Physica A
(2018)
NadarajahS. et al.
On the inefficiency of Bitcoin
Econ. Lett.
(2017)

There are more references available in the full text version of this article.

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^☆: Declarations of interest: none.

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Price delay and market frictions in cryptocurrency markets☆

Highlights

Abstract

Introduction

Section snippets

Data and delay measures

Results

Concluding remarks

Physica A

J. Financ. Markets

Econ. Lett.

Econ. Lett.

Financ. Res. Lett.

Econ. Lett.

Physica A

Econ. Lett.