The inefficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average

doi:10.1016/j.physa.2018.07.032

Physica A: Statistical Mechanics and its Applications

Volume 510, 15 November 2018, Pages 658-670

https://doi.org/10.1016/j.physa.2018.07.032 Get rights and content

Highlights

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We explore the (in)efficiency of cryptocurrency market.
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Bitcoin, Ripple, Ethereum, NEM, Stellar, Litecoin, Dash, Monero and Verge are inefficient markets.
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We construct a value-weighted Cryptocurrency Composite Index (CCI).
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CCI and DJIA are persistently cross-correlated.
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MF-DFA and MF-DCCA are employed.

Abstract

As a form of cryptocurrency, the issue of informational efficiency of Bitcoin has received much attention recently. We add to the literature by investigating nine forms of cryptocurrencies, i.e., Bitcoin, Ripple, Ethereum, NEM, Stellar, Litecoin, Dash, Monero and Verge, with a battery of efficiency tests and the empirical results indicate that all these cryptocurrencies are inefficient markets. What is more, we further construct a value-weighted Cryptocurrency Composite Index (CCI) and show that CCI and Dow Jones Industrial Average are persistently cross-correlated.

Introduction

The rise in cryptocurrency prices using blockchain technology has been very rapid, which has attracted the attention of governments, investors, the media and the public. At the same time, price increases and declines are accompanied by very sharp fluctuations. Bitcoin has fallen by more than 60% in April 2018 from the previous high of 20,000 U.S. dollars. Some people think that crazy Bitcoin is a product of speculation and it is in the bubble phase. In order to increase the price discovery function of Bitcoin in the financial market and meet the demand of investors to invest in cryptocurrencies indirectly, Chicago Mercantile Exchange (CME) and Chicago Board Options Exchange (CBOE) have launched bitcoin futures. This raises the question of whether Bitcoin is effective.

However, Bitcoin only captures less than 35% of cryptocurrencies capitalization (see Fig. 1). The hash algorithms of cryptocurrencies are different. For example, the algorithm for Bitcoin is the SHA-256-based, while those for Ethereum, Ripple and Litecoin are Ethash-based, Non proof-of-work and Scrypt-based, respectively. We therefore believe that only Bitcoin cannot reflect the full picture of cryptocurrency and we should consider more cryptocurrencies. Besides, the Efficient Market Hypothesis developed by Fama [1] is considered as one crucial cornerstone of finance. The efficiency tests for other forms of cryptocurrency have so far been unexplored. Therefore, in this paper, we fill this gap by examining the efficiency of nine forms of cryptocurrencies with a battery of efficiency tests, including tests used by Urquhart [2], Nadarajah and Chu [3], Bariviera [4] and Tiwari [5]. And we further provide evidence for the cross-correlation relationship between cryptocurrency and Dow Jones Industrial Average by employing the Multifractal Detrended Cross-correlation Analysis (MF-DCCA).

The rest of this paper is organized as follows. Next section briefly reviews the literature on efficiency, Bitcoin, MF-DFA and MF-DCCA. Section 3 describes the cryptocurrency and the Dow Jones Industrial Average data. Section 4 presents a brief description of methodology. The main empirical results are discussed in Sections 5 Empirical results, 6 Conclusions concludes.

Section snippets

The efficiency for financial assets

The efficient market hypothesis (EMH) proposed by Fama is an important concept in the field of finance. A weak-form efficiency means that the stock price cannot be predicted because the current stock price already reflects the information of the past stock price [1]. The issue on market efficiency has been explored in stock market, futures market, foreign exchange market and other markets [[6], [7], [8], [9]]. From the perspective of measuring efficiency, the existing literature can be divided

Data description

There are two data sources, i.e., trading data for Dow Jones Industrial Average (DJIA) and cryptocurrencies. We download the DJIA data directly from the China Stock Market & Accounting Research Database (CSMAR). As for cryptocurrency, we collect data from https://coinmarketcap.com, which provides closing prices and market capitalization of various cryptocurrencies from 28 April 2013 (as the earliest date available)¹

Methodology

In an efficient market, asset prices are not predictable and the variations are random due to the unpredictable nature of the unexpected news and therefore prices follow a random walk. We examine the efficient market hypothesis of cryptocurrency by using a battery of tests, including the Ljung–Box test, Runs test, Bartels Rank test, Variance Ratio test in Urquhart [2], Automatic Portmanteau test in Nadarajah and Chu [3], long range dependence test in Bariviera [4] and Tiwari et al. [5], and

The inefficiency of cryptocurrency

Table 4 summarizes the results of various efficiency tests. In each test, we report the corresponding p-values, except the MF-DFA where we report the Hurst exponent values. There are many inefficiency measures based on multifractal and Hurst exponents [79]. We use the following definition: $H (2)$ values greater than 0.5 is the evidence of persistence and $H (2)$ values lower than 0.5 is the evidence of anti-persistence, which are the evidences of inefficiencies. Bariviera et al. state that Hurst

Conclusions

The issue of information efficiency of Bitcoin has been a matter of interest since Urquhart [2]. After that, Nadarajah and Chu [3], Bariviera [4] and Tiwari et al. [5] also focus on Bitcoin with computationally efficient long-range dependence estimation and dynamic approach. We add to the literature by investigating nine forms of cryptocurrencies with a battery of efficiency tests, rolling windows analysis and inefficiency index analysis, and the results indicate that all these cryptocurrencies

Acknowledgments

This work is supported by the National Natural Science Foundation of China (71790594 and 71701150), Young Elite Scientists Sponsorship Program by Tianjin (TJSQNTJ-2017-09) and Fundamental Research Funds for the Central Universities (63182064).

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The inefficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average

Highlights

Abstract

Introduction

Section snippets

The efficiency for financial assets

Data description

Methodology

The inefficiency of cryptocurrency

Conclusions

Acknowledgments

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