Trending Papers in cryptocurrency

We report the results of regressing the Sharpe ratios of 72 cryptocurrencies on first, second and third co-moments of their returns. Our general aim is to examine the risk-return trade-off characteristics of cryptocurrencies. In other words, to determine whether the returns of cryptocurrencies justify their huge volatility especially with regard to the higher moment components of their systemic risk? We find that adjusted Sharpe ratios of the cryptocurrencies and traditional indexes do not differ significantly in this respect.

Controlling for the polarity and subjectivity of social media data based on the development of the COVID-19 outbreak, we analyse the relationships bet…

This paper examines the research hotspots and evolution trends of cryptocurrency-related papers published between 2009 and 2019, and identify the important journals, scholars and research findings by bibliometric analysis. The empirical results show that research priorities of cryptocurrency shift from underlying technology to economic applications currently. In addition, research institutions of cryptocurrency are mainly distributed in Europe and China with complementary research fields. In the future, advanced methods of computer science and econometrics may be applied to the research of cryptocurrency, and some specific application schemes of cryptocurrency will become new research focus.

We analyze the liquidity of four cryptocurrencies on four large trading venues over a four-year period. We estimate the Abdi-Ranaldo spread estimator from hourly transactions data and compare liquidity across cryptocurrencies and exchanges. In order to identify the drivers of cryptocurrency liquidity we analyze a broad set of explanatory variables from general financial markets, global cryptocurrency markets as well as variables specific to each exchange-currency pair. Volatility of cryptocurrency returns, the dollar trading volume and the number of transactions are the most important determinants of liquidity while general financial market variables have no explanatory power.

As the indispensable trading platforms of the ecosystem, hundreds of cryptocurrency exchanges are emerging to facilitate the trading of digital assets. While, it also attracts the attention of attackers. A number of scam attacks were reported targeting cryptocurrency exchanges, leading to a huge amount of financial loss. However, no previous work in the research community has systematically studied this problem. This paper makes the first effort to identify and characterize the cryptocurrency exchange scams. First, over 1500 scam domains and over 300 fake apps are identified, by collecting existing reports and using typosquatting generation techniques. Then, by investigating the relationship between the scam domains and fake apps, this paper identifies 94 scam domain families and 30 fake app families. By further characterizing the impacts of such scams, it is revealed that these scams have incurred financial loss of 520k US dollars at least. It is further observed that the fake apps have been sneaked to major app markets (including Google Play) to infect unsuspicious users. The findings in this paper demonstrate the urgency to identify and prevent cryptocurrency exchange scams. To facilitate future research, all the identified scam domains and fake apps have been publicly released to the research community.

The extreme price swings and complexity in cryptocurrency markets drives multifarious research into co-movements, in both time and frequency, among cryptocurrencies. In this paper, we investigate the dynamics of multiscale interdependencies among five leading and liquid cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash) using wavelet-based analyses that account for the heterogeneous behaviour of crypto-traders and crypto-investors. The results provide evidence of high levels of dependency from 2016 to 2018 at daily frequency scales. The cross wavelet transforms demonstrate Ripple and Ethereum to be trivial origins of market contagion. The results of wavelet coherence confirm the short-run and long-run market integration among some cryptocurrency pairs. However, the coherence is found to fluctuate at higher frequencies and be significantly stable at lower frequencies. Furthermore, the switch in the lead and lag relations of cryptocurrency returns suggests alternating time and frequency interdependencies. Our findings are useful to scale-conscious traders and multi-prospect (various investment horizon) investors and portfolio managers.

We examine the price discovery contributions of cryptocurrency exchanges in the presence of market microstructure noise. Cryptocurrency markets exhibit a decisively higher level of microstructure noise compared with the NYSE or NASDAQ. Therefore, traditional measures of price discovery could be biased. To overcome this concern, we draw on the information leadership share proposed by Putniņš (2013); by leveraging this information, we find that Bitfinex is the leader in the price discovery process. These results highlight the importance of accounting for different levels of noise when evaluating price discovery contributions.

The Fourth industrial revolution has seen many innovative technologies that are now challenging traditional economies. The innovative and technological financial instruments are inspiring individuals and expert investors to investigate the broader investment spectrum, and consequently diversify their portfolios. Going beyond the conventional portfolios and developing state-of-the-art strategies that comply with the ever-changing financial and technological advancements are the keys to long term sustainability. Therefore, to cater to the needs of all segments of the society, the investment strategies during the fourth industrial revolution demand exposure to technological and digital financial innovations. This study investigates the impact of diversification with the addition of five cryptocurrencies from November 2015 to November 2019 on four traditional asset portfolios. The results show that the diversification increased the returns in most of the cases, and reduced the portfolio volatility in all portfolios, and also provided higher returns as compared to the traditional portfolios for the same level of risk. This study also revealed that the results might improve when short sales are allowed. Moreover, we can conclude that the addition of multiple cryptocurrencies in a portfolio provides enhanced results for diversification, and Ethereum provides a better diversification opportunity as compared to Bitcoin.

This paper investigates how idiosyncratic volatility is priced in the cross-section of cryptocurrency returns. By conducting both portfolio-level analysis and Fama-MacBeth regression analysis, we demonstrate that idiosyncratic volatility is positively related to the expected returns of cryptocurrencies. This finding is not subsumed by effects of size, momentum, liquidity, volume, and price and is robust to different weighting schemes, holding periods, and sample sizes. Besides, we find no evidence of temporal relation between idiosyncratic volatility and returns in cryptocurrency markets.