Blockchain Papers

Nizan Geslevich Packin, Sean Stein Smith

No abstract is available for this record.

Joshua T. White, Sean Wilkoff, Serhat Yildiz

No abstract is available for this record.

Danqi Hu, Sarit Markovich, Valerie Zhang

Using data from one of the first and most popular decentralized lending protocols, MakerDao, we study whether computer-language-based information lends itself for the efficient use of information in a market that features real-time transparency. We first find that there is persistent cross-sectional difference in performance, where persistence increases with investors sophistication. We then study how different types of processing costs affect the extent to which investors use past loan performance to mimic experts in real time (i.e., efficient mimicking). Our results show that awareness costs, proxied by loan activity level, hinder efficient mimicking. More importantly, acquisition and integration costs associated with translating code-based information into useful trading signals impedes investors’ ability to take advantage of information embedded in smart contracts. Our paper has important implications for regulators and practitioners aiming at more efficient use of information in smart contracts and on blockchain.

Conghui Chen, Lanlan Liu, Ningru Zhao

This paper studies the impact of fear sentiment caused by the coronavirus pandemic on Bitcoin price dynamics. We construct a new proxy for coronavirus fear sentiment using hourly Google search queries on coronavirus-related words. The results show that market volatility has been exacerbated by fear sentiment as the result of an increase in search interest in coronavirus. Moreover, we find that negative Bitcoin returns and high trading volume can be explained by fear sentiment regarding the coronavirus. Our results also show that Bitcoin fails to act as a safe haven during the pandemic.

Adnan Qayyum, Junaid Qadir, Muhammad Umar Janjua, Falak Sher

In recent years, `fake news' has become a global issue that raises unprecedented challenges for human society and democracy. This problem has arisen due to the emergence of various concomitant phenomena such as (1) the digitization of human life and the ease of disseminating news through social networking applications (such as Facebook and WhatsApp); (2) the availability of `big data' that allows customization of news feeds and the creation of polarized so-called `filter-bubbles'; and (3) the rapid progress made by generative machine learning (ML) and deep learning (DL) algorithms in creating realistic-looking yet fake digital content (such as text, images, and videos). There is a crucial need to combat the rampant rise of fake news and disinformation. In this paper, we propose a high-level overview of a blockchain-based framework for fake news prevention and highlight the various design issues and consideration of such a blockchain-based framework for tackling fake news.

Shuyu Zhang, Xuanyu Zhou, Huifeng Pan, Junyi Jia

Abstract We investigate whether Chinese cryptocurrency investors show confirmatory bias when processing authority‐related news. Authority‐related news is defined as news that is related to government authority (including central bank) policies or talk. By using data from the largest cryptocurrency exchange in China, we find that investors’ response to authority‐related news is negative and significant in general. Moreover, we find that the abnormal trading volume and standard deviation of abnormal trading volume are significantly higher for authority‐related news with higher readability, suggesting investors respond to the more readable authority‐related news with more trading behaviour.

Dennis K. P. Ng, Paul Griffin

No abstract is available for this record.

Sheila Ogochukwu Nnabuife, Yosra Jarrar

Given the economic hardship in Nigeria in the past few years, Nigerians resorted to finding alternative ways as a survival strategy. Bit-coin and a good number of other crypto currencies formed the long sought economic alternative to such extent that it is gradually getting public acceptance as a means of transaction. Subsequent upon the collapse of MMM, the Nigerian government dismissed the use of crypto as legal means of exchange in Nigeria. This brought about reaction from bit-coin participants, experts and government officials. This study therefore is an examination of online media coverage aimed at ascertaining the slant of coverage, the dominant media source and type and how detailed the reports issued in the media concerning Bit-coin are. The researcher used the qualitative and quantitative content analysis research method to examine the manifest contents of the selected online media while framing and social responsibility theory formed the benchmark for the study. The study spanned for three months covering December 2017 through February 2018. Findings revealed that Nigerian online media gave negative slant to their coverage of Bit-coin crypto currency. It was also found that while government source dominates news source, straight news reports dominated the types of media used in the coverage of Bit-coin crypto-currency. The researcher concluded that the media had played their social responsibility role to the public by providing detailed reports on bit-coin and recommended that the mainstream media should also join hands in delivering detailed messages on salient issues.

Ross C. Phillips, Denise Gorse

Financial price bubbles have previously been linked with the epidemic-like spread of an investment idea; such bubbles are commonly seen in cryptocurrency prices. This paper aims to predict such bubbles for a number of cryptocurrencies using a hidden Markov model previously utilised to detect influenza epidemic outbreaks, based in this case on the behaviour of novel online social media indicators. To validate the methodology further, a trading strategy is built and tested on historical data. The resulting trading strategy outperforms a buy and hold strategy. The work demonstrates both the broader utility of epidemic-detecting hidden Markov models in the identification of bubble-like behaviour in time series, and that social media can provide valuable predictive information pertaining to cryptocurrency price movements.

Barnaby Craggs, Awais Rashid

Studies have demonstrated that news reporting (as information) is critical to the adoption and pricing of Bitcoin. This early stage work represents the first look into how this information is being used as part of the speculation decision making process and how this might be compatible with a trust model. The outputs of this work will build a trust model for Bitcoin speculators’ use of news reporting as an information<br/>source. The work will further demonstrate if, and how, this trust model might be usurped by something as simple as a confirmation bias thus confirming a more psychological approach to speculative behaviours than that portrayed in a rational economics approach

Ladislav Krištoufek

Digital currencies have emerged as a new fascinating phenomenon in the financial markets. Recent events on the most popular of the digital currencies--BitCoin--have risen crucial questions about behavior of its exchange rates and they offer a field to study dynamics of the market which consists practically only of speculative traders with no fundamentalists as there is no fundamental value to the currency. In the paper, we connect two phenomena of the latest years--digital currencies, namely BitCoin, and search queries on Google Trends and Wikipedia--and study their relationship. We show that not only are the search queries and the prices connected but there also exists a pronounced asymmetry between the effect of an increased interest in the currency while being above or below its trend value.