Blockchain Papers

David Neto

No abstract is available for this record.

Çağrı Hamurcu

The main purpose of this study is to reveal whether seasonal/time-oriented/calendar anomalies affect the price and transaction volume of Bitcoin. Day of the week and month of the year anomalies are examined in this context. The data for the years 2013-2021 are handled in 3 different sampling periods, consisting of the whole of this time period and each of its divided parts. The existence of these anomalies is analyzed with EGARCH models created separately. The most important conclusion reached in this study is that the analyzed anomalies differ according to the sampling periods. The common findings reached as a result of the analyzes for all three time intervals are as follows: It has been determined that Monday has positive effects in terms of both Bitcoin return and transaction volume, while Saturday has negative effects only regarding transaction volume. Mondays, Tuesdays, and Wednesdays create volatility-increasing effects concerning returns, Friday, Saturday and Sunday reduce volatility. In terms of trading volume, Monday and Tuesday reduce volatility, while Thursday and Friday increase volatility. Whereas March has a positive effect on return volatility, it has a negative effect on trading volume volatility, and September has only a negative effect on return volatility.

Sana Gaied Chortane, Kamel Naoui

We tracked the impact of the Covid-19 crisis on the liquidity of10 crypto currencies for the period from July 31, 2019 (before the crisis) to December 31, 2020 (in the Covid -19 era). We applied the vector error correction model to each crypto currency. The results show that in the short term, the COVID-19 crisis has no influence on the liquidity of cryptocurrencies except for Cardano. Similarly, in the long term, it has no impact on the liquidity of cryptocurrencies with the exception of Binance coin, Tezos and Cardano. The assumption of having a common liquidity factor implies that, in a shock of liquidity, the entire market will be affected. The cryptocurrency market, however, has proven to be different.

Raquel Quiroga García, Natalia Pariente-Martinez, Mar Arenas‐Parra

This paper analyses the relationship between price clustering and trade volume in the Ether, Ripple and Litecoin cryptocurrencies. We examine at which digits price clustering exists and study the behaviour at different price levels and time frames. By using recent data to provide an updated view of price clustering in the cryptocurrency market, we find a remarkable level of price clustering at round prices: 5.29%, 2.84% and 2.97% for Ether, Ripple and Litecoin for every one minute at open prices, respectively. This paper reaffirms the negotiation hypothesis by finding that price clustering appears at prices at which traded volume is higher.

Satish Kumar

Purpose This study examines the turn-of-the-month (TOM) effect in Bitcoin (BIT), Ethereum (ETH) and Litecoin (LIT) cryptocurrencies from August 2015 to August 2021. Design/methodology/approach Dummy regression model is used to examine the presence of the TOM effect and to test the efficiency of the cryptocurrency market. The characteristics of the returns during TOM days are compared with that of the non-non-TOM trading days. The authors also develop a trading strategy to earn abnormal returns using the TOM effect. Findings The authors show that TOM returns are positive and significantly higher than that of non-TOM returns. Interestingly, the authors empirically show that the TOM effect is not driven by the day-of-the-week (DOW) effect or the January effect. Based on the significant TOM effect, the authors formulate a trading strategy that annually outperforms the buy-and-hold strategy for BIT by 21.77% and for LIT by 47.10%. Finally, the results are robust to using a Generailzed Auto Regressive Conditional Heteroskedasticity (GARCH) (1,1) model and the January 2018 sell-off. Practical implications The results have important implications for both traders and investors. The findings suggest that the investors might be able to earn excess profits by timing their positions in BIT and LIT taking the advantage of the TOM effect. Originality/value First, the authors provide the only study to report the evidence of the TOM effect in three leading cryptocurrencies, viz., BIT, LIT and ETH. Second, the authors control for the DOW effect and the January effect while investigating the TOM effect in cryptocurrency market. Finally, this study develops a trading strategy based on which the investors can time the cryptocurrency markets as indicated by the pattern of the TOM effect during the studied time period.

Jan Arvid Berg, Robin Fritsch, Lioba Heimbach, Roger Wattenhofer

Decentralized exchanges are revolutionizing finance. With their ever-growing increase in popularity, a natural question that begs to be asked is: how efficient are these new markets? We find that nearly 30% of analyzed trades are executed at an unfavorable rate. Additionally, we observe that, especially during the DeFi summer in 2020, price inaccuracies across the market plagued DEXes. Uniswap and SushiSwap, however, quickly adapt to their increased volumes. We see an increase in market efficiency with time during the observation period. Nonetheless, the DEXes still struggle to track the reference market when cryptocurrency prices are highly volatile. During such periods of high volatility, we observe the market becoming less efficient - manifested by an increased prevalence in cyclic arbitrage opportunities.

Subhan Ullah, Rexford Attah‐Boakye, Kweku Adams, Ghasem Zaefarian

The global market capitalisation of bitcoin has exponentially increased in recent years and there are concerns that the current prices of bitcoin do not reflect the true and fair underlying value of this particular type of digital asset. Applying Cue utilisation theory and signalling theory, and using a panel data on bitcoin prices from Bloomberg between 1st November 2019 and 31st May 2021, we examine the association between celebrity and government endorsements and volatility in bitcoin prices. We find that positive celebrity tweets and positive government sentiments towards bitcoin are significantly positively associated with positive changes in its prices. Our findings imply that although celebrity endorsements may cause a temporary ‘exponential rise’ in bitcoin prices, investors need to carefully diversify their portfolio to maximise their risk–return relationship.

Natividad Blasco, Pilar Corredor, Nerea Satrústegui

This paper analyses the herding behaviour among exchanges around the expiration of bitcoin futures traded on the Chicago Mercantile Exchange (CME). The database extends from December 2017 to October 2020, taking as a reference the main exchanges that trade bitcoin (Binance, Bitfinex, Bitstamp, Coinbase, itBit, Kraken, and Gemini) and using hourly closing prices and trading volumes in bitcoin and US dollars. Adapting the proposal of Chang, Cheng and Khorana (2000) (CCK) to test conditional herding, we obtain results that indicate that the herding effect is significant during the week before expiration. After expiration, the herding effect lasts for a few hours and disappears. Information overload originating, among other causes, from sophisticated investors' strategies may generate this mimetic behaviour. The results show the relevance of intraday data applied to specific events such as expiration since the unconditional analysis shows, in general, anti-herding behaviour throughout the period of study.

Tetiana Zatonatska, Volodymyr Suslenko, Oleksandr Dluhopolskyi, Vasyl Brych · 5 authors

Purpose. Significant capital inflows in the cryptocurrency market and record-breaking prices on cryptocurrency assets have led to the creation of alternative investment options on cryptocurrency markets, including a new field of decentralized investing, known as decentralized finance, operating on smart contracts. The objective of this study is to review investment options in the industry sector available to investors on cryptocurrency markets and decentralized protocols. Methodology. The model of decentralized cryptocurrency exchanges was used in the article. It is based on providing liquidity into the liquidity pool. Findings. The results of this study demonstrate that new industrial cryptocurrency investors have a wide range of investment options that can outperform strategies like passive holding of cryptocurrency or investing in the stock. Given the liquidity mining model attracts early investors, they need to look at assets such as governance tokens of different platforms. The Sharpe ratio of COMP and UNI tokens is higher than S&P500. In addition, these tokens are mined via a liquidity mining model. Originality. The crypto market has been growing rapidly since the beginning of the pandemic. The calculations for crypto assets might be influenced by the bull run on the crypto market because the last time such high Sharpe ratio for BTC and ETH was observed during the 20172018 cryptocurrency bubble. Investing in the crypto market is riskier than investing in the stock market due to high operational risks. Crypto market investors might prefer to mine or buy UNI or COMP tokens to diversify their portfolios. Practical value. According to the analysis results of the received information, a Sharpe ratio of investments in protocols for loanable funds is lower compared to investment options on the stock market or CeFi lending. It is also potentially riskier due to volatile interest rates and high operational risks.

Chuanhai Zhang, Haicui Chen, Zhe Peng

No abstract is available for this record.

Yongkil Ahn

No abstract is available for this record.

Bingbing Dong, Lei Jiang, Jinyu Liu, Yifeng Zhu

No abstract is available for this record.

David Vidal-Tomás

No abstract is available for this record.

Lennart Ante, Ingo Fiedler, Marc von Meduna, Fred Steinmetz

Cryptocurrencies, such as Bitcoin, are a highly volatile asset class where very high returns are offset by large losses. This study examines the financial success of individual investments in cryptocurrencies and analyzes whether it relates to similar explanatory factors as for investments in other asset classes. For this purpose, a nationally representative survey data set of 3,864 German citizens is used, of which 354 (9.2%) reported owning cryptocurrencies in March 2019. We analyze the subpopulation of 225 cryptocurrency owners who classify as investors. 56% of them experienced positive returns, while 29% had negative results. The remaining respondents broke even. The average investment was €1,773 in a portfolio of two cryptocurrencies. At the time of the survey, the average portfolio value had risen to €7094 — an average gain of 300%. While nearly half of the investors (44%) outperformed Bitcoin market returns, not a single one of the early investors (2009–2012) did. We find that net income, the degree of cryptocurrency knowledge and the degree of ideological motivation for owning cryptocurrency have positive effects on returns. This first scientific analysis of individual investment in cryptocurrencies provides a basis for future research and for regulatory decision-making.

Aditya Vikram Singh, Jyoti Shaw, Varsha Mishra, Ankita Singh

Cryptocurrencies are considered digital asset that is saved on a computerized database in a public ledger format using strong cryptography methods or blockchain to secure the transaction record. In recent times it has attracted a large number of holders. Cryptocurrencies are based on entirely new technology, whose prospects are still undiscovered and unknown, which in turn attracted considerable academic studies directed towards creating theoretical models of cryptocurrencies and their possible prospects. In this study, we have considered the topmost cryptocurrencies namely Bitcoin, Ethereum, Ripple, and Litecoin as they have a major part in the cryptocurrency market capitalization which is around 83.4% of the total market. The objective of the study is to understand whether there is a significant association between 4 cryptocurrency prices namely Bitcoin, Ethereum, Litecoin, Ripple, and understand whether there is a significant relationship between the Bitcoin cryptocurrency pricing concerning Ethereum, Litecoin Ripple cryptocurrency prices. Secondary and primary data from different websites and questionnaires have been used for the study. It has emerged from the study that there is a high correlation between these cryptocurrencies' prices. It has also been found that Bitcoin prices increases, if Ethereum and Litecoin prices increases. Bitcoin prices decrease, only if Ripple prices increase.

Rebekka Buse, Konstantin Görgen, Melanie Schienle

We study the prediction of Value at Risk (VaR) for cryptocurrencies. In contrast to classic assets, returns of cryptocurrencies are often highly volatile and characterized by large fluctuations around single events. Analyzing a comprehensive set of 105 major cryptocurrencies, we show that Generalized Random Forests (GRF) (Athey, Tibshirani & Wager, 2019) adapted to quantile prediction have superior performance over other established methods such as quantile regression, GARCH-type and CAViaR models. This advantage is especially pronounced in unstable times and for classes of highly-volatile cryptocurrencies. Furthermore, we identify important predictors during such times and show their influence on forecasting over time. Moreover, a comprehensive simulation study also indicates that the GRF methodology is at least on par with existing methods in VaR predictions for standard types of financial returns and clearly superior in the cryptocurrency setup.

Megan Czasonis, Mark Kritzman, Baykan Pamir, David Turkington

The role of cryptocurrencies as a vehicle for speculation has been well established. However, it is less clear if cryptocurrencies can also serve to manage risk. The authors seek to determine the diversification potential of cryptocurrencies both for short and long horizons. For short horizons, they estimate correlations that consider the direction and magnitude of returns for relevant asset classes, rather than focusing on full-sample correlations, as is customary. For long horizons, they compute “single period correlations” that capture the extent to which cryptocurrencies move synchronously with, or drift apart from, other assets over an investor’s horizon. They also identify utility-maximizing allocations to cryptocurrencies directly from historical return samples that account for all features of the data as well as more nuanced preferences than are typically assumed.

Alex Plastun, Ludmila Khomutenko, Serhii Bashlai

This paper explores price effects caused by the expiration of derivatives in the cryptocurrency market. Applying different statistical tests (ANOVA, Mann–Whitney, and t-tests) and econometric methods (the modified cumulative abnormal return approach, regression analysis with dummy variables, and the trading simulation approach) to daily and weekly Bitcoin data over the period 2018–2021, the following hypotheses are tested: (H1) Expiration days create patterns in price behavior in the cryptocurrency market; and (H2) Price patterns can be exploited to generate abnormal profits from trading. The results suggest that expiration effects are only nominally present in the cryptocurrency market. There are differences in returns between expiration-related periods and average returns, but these differences are statistically insignificant. The only case in which an anomaly was detected was related to abnormally high returns during the week of expiration: returns during such weeks were positive in 65% of cases, and were on average 5 times higher than during usual weeks. Trading strategies based on this fact were able to generate results different from those of random trading, with a Sharpe ratio above 1. This is evidence in favor of the existence of a real price anomaly, which contradicts the efficient market hypothesis, and this could be implemented in the practice of traders and investors by creating trading strategies based on detected price effects or special technical analysis indicators to generate trading signals. For academics, these results might provide an opportunity to improve time series forecasting analysis in the case of Bitcoin.

Bernhard K. Meister, Henry C. W. Price

In this chapter structures that generate yield in cryptofinance will be analyzed and related to leverage. While the majority of crypto-assets do not have intrinsic yields in and of themselves, similar to cash holdings of fiat currency, revolutionary innovation based on smart contracts, which enable decentralised finance, does generate return. Examples include lending or providing liquidity to an automated market maker on a decentralised exchange, as well as performing block formation in a proof of stake blockchain. On centralised exchanges, perpetual and finite duration futures can trade at a premium or discount to the spot market for extended periods with one side of the transaction earning a yield. Disparities in yield exist between products and venues as a result of market segmentation and risk profile differences. Cryptofinance was initially shunned by legacy finance and developed independently. This led to curious and imaginative adaptions, reminiscent of Darwin's finches, including stable coins for dollar transfers, perpetuals for leverage, and a new class of exchanges for trading and investment.

Elie Bouri, Ladislav Krištoufek, Tanveer Ahmad, Syed Jawad Hussain Shahzad

No abstract is available for this record.

Emrah Öget

In recent years, cryptocurrencies have become a new topic for financial studies. In this study, the effects of positive and negative events related to cryptocurrencies on the prices of related cryptocurrencies were researched using the event study. These events include major listing, delisting and airdrop announcements, and SEC enforcements. As a result of the analysis, 22 significant abnormal return values related to negative events and eight significant abnormal return values related to positive events were determined at 1% significance level within the event window (-5, +10). Therefore, it has been determined that negative events have more effect on cryptocurrencies than positive events. The number of significant cumulative abnormal return values obtained (13 for negative events, three for positive events) also supports these results. The results of the study have crucial implications for investors, centralized cryptocurrency exchanges, and cryptocurrency CEOs. Even after the negative events were announced publicly, pull out of the market will prevent investors from making more losses. In addition, it is recommended that investors sell for profits in case of a rapid high return on the day of the listing announcement. Because it was determined that the prices returned to the equilibrium prices at the closing.

Dhanar Prayoga

Robo-advisor is one of the most prominent innovation in the wealth management industry, and its success in Indonesia has been evident in the case of Bibit. Therefore, wealth management companies need to employ Robo-Advisor to overcome their competition. This research aims to give recommendation on asset allocation method and asset class selection for Robo-Advisors in Indonesia using Sharpe Ratio Analysis. Then, the author will analyze the robo-advisor’s performance during equity market downturn. Finally, The Robo-Advisor’s actual performance will be tested in 2018, 2019, and 2020. The Sharpe ratio analysis result showed that Robo-Advisors seeking higher risk-adjusted return should choose mean-variance optimization over risk parity for asset allocation method, and the inclusion of gold and bitcoin in a portfolio of stock mutual fund and bond mutual fund increases the risk-adjusted return of the portfolio. The proposed robo-advisor’s portfolio protected investors from equity market downturn in 2011-2010 in 83,3% of the case. Finally, the proposed robo-advisor’s portfolio generated better return for the conservative, moderate and aggressive investor during 2018, 2019, and 2020 when compared to LQ45.

Zhuocheng Wang, Huang Zhiwei, Rongkuan He, Yixin Feng

In the financial market, investors often do not consider the market environment, but only invest based on the investment behavior of others. This is commonly known as Herd behavior. The herd behavior not only takes part in traditional financial markets but also in cryptocurrency markets. This paper aims to build a model which is applicable in cryptocurrency markets. To research the herd behavior in cryptocurrency markets and the dynamic relationship between the investors and the value of cryptocurrency with the model.

Fei Su, Xinyi Wang, Yulin Yuan

No abstract is available for this record.