Follow blockchain research across journals, conferences, and preprint repositories.
2,329 results · page 48 of 98
Ahmed Bouteska, Salma MeftehâWali, Trung Thanh Dang
No abstract is available for this record.
Imran Yousaf, Larisa Yarovaya
We examine the static and time-varying herding behavior in three cryptocurrency classes: âconventionalâ cryptocurrencies, non-fungible tokens, and DeFi assets during the most recent cryptocurrency bubble of 2021. While static herding analysis failed to demonstrate any evidence of herding, the time-varying herding has been identified in conventional cryptocurrencies and DeFi assets for the short investment horizons. The herding asymmetry analysis reveals that herding is not evident in conventional cryptocurrencies and NFT during up/down market, high/low volatility days, and high/low trading days. We only find herding in DeFi assets during the low volatility days.
Kwamie Dunbar, Johnson Owusu-Amoako
No abstract is available for this record.
Gil Cohen
Abstract This research makes the first attempt to design, optimize and use average true range (ATR)âbased trading systems for five popular cryptocurrencies. We used particle swarm optimization procedures to optimize systems with multiple objectives that are based on the ATR concept. Our aim was to determine the best configurations for each system that would maximize net profits, the profit factor, and the percentage of profitable trades. We demonstrate that the ATRâbased systems can predict the price trends of the examined cryptocurrencies. Our results also indicate that optimized Keltner Channelâbased systems improve the ability of the standâalone optimized ATR systems to forecast trends, net profits, and the profit factor. Finally, both systems perform better for long trades than for short trades.
David Moreno, Marcos Antoli, David Quintana
No abstract is available for this record.
Ismail O. Fasanya, Oluwatomisin J. Oyewole, Johnson A. Oliyide
No abstract is available for this record.
Jacopo Fior, Luca Cagliero, Paolo Garza
In the last decade, cryptocurrency trading has attracted the attention of private and professional traders and investors. To forecast the financial markets, algorithmic trading systems based on Artificial Intelligence (AI) models are becoming more and more established. However, they suffer from the lack of transparency, thus hindering domain experts from directly monitoring the fundamentals behind market movements. This is particularly critical for cryptocurrency investors, because the study of the main factors influencing cryptocurrency prices, including the characteristics of the blockchain infrastructure, is crucial for driving expertsâ decisions. This paper proposes a new visual analytics tool to support domain experts in the explanation of AI-based cryptocurrency trading systems. To describe the rationale behind AI models, it exploits an established method, namely SHapley Additive exPlanations, which allows experts to identify the most discriminating features and provides them with an interactive and easy-to-use graphical interface. The simulations carried out on 21 cryptocurrencies over a 8-year period demonstrate the usability of the proposed tool.
Kingstone Nyakurukwa, Yudhvir Seetharam
Abstract This study revisits stock market integration in Africa using an informationâtheoretic framework that quantifies the flow of information between exchanges. We use daily return data for seven MSCIâclassified African stock exchanges between 2011 and 2021. As Bitcoin has become an important asset class on the African continent, we also explore whether this cryptocurrency confers any diversification benefits. Our method holds that stock markets are integrated if there is a significant flow of information between exchanges. The results reveal a statistically insignificant flow of information among African stock exchanges, and for the few cases in which information flow is statistically significant, the magnitudes are low. South Africa is the most influential stock market, as it transmits most of the total transfer entropy (informational value) in the system. We also observe that African stock exchanges are weakly integrated with Bitcoin.
Ăzkan HAYKIR, Ä°brahim YaÄlı
This study investigates speculative bubbles in the cryptocurrency market and factors affecting bubbles during the COVID-19 pandemic. Our results indicate that each cryptocurrency covered in the study presented bubbles. Moreover, we found that explosive behavior in one currency leads to explosivity in other cryptocurrencies. During the pandemic, herd behavior was evident among investors; however, this diminishes during bubbles, indicating that bubbles are not explained by herd behavior. Regarding cryptocurrency and market-specific factors, we found that Google Trends and volume are positively associated with predicting speculative bubbles in time-series and panel probit regressions. Hence, investors should exercise caution when investing in cryptocurrencies and follow both crypto currency and market-related factors to estimate bubbles. Alternative liquidity, volatility, and Google Trends measures are used for robustness analysis and yield similar results. Overall, our results suggest that bubble behavior is common in the cryptocurrency market, contradicting the efficient market hypothesis.
Shimeng Shi
Abstract This paper presents an empirical analysis of bitcoin futures risk premia. Based on the relevant theories and empirical findings of commodity futures risk premia, we study a battery of predictors, including positionâbased measures, market microstructure factors, and macroeconomic variables. We find that trading activity and extreme sentiment of speculators and retailers present significant predicting power on the subsequent bitcoin futures price changes over different time horizons. We also find evidence that the lower transaction cost, the higher bitcoin futures risk premiums. Regarding macroeconomic variables, financial conditions index, TED spread, US M2 money stock, and funding cost of financial institutions could predict bitcoin futures returns. The return impact of net position changes of hedgers is likely to be affected by extremes in macroeconomic variables. Speculators behave like negative feedback traders, while retailers are positive feedback traders. This detailed analysis of the risk premia of this emerging derivatives market provides critical implications.
Shinji Kakinaka, Ken Umeno
This study investigates the scale-dependent structure of asymmetric volatility effect in six representative cryptocurrencies: Bitcoin, Ethereum, Ripple, Litecoin, Monero, and Dash. By developing the dynamical approach of DFA-based fractal regression analysis, we detect whether the volatility of price changes is positively or negatively related to return shocks at different time scales. We find that the asymmetric volatility phenomenon varies by scale and cryptocurrency, and the structure is time-varying. Contrary to what is typically observed in equity markets, minor currencies show an âinverseâ asymmetric volatility effect at relatively large scales, where positive shocks (good news) have a greater impact on volatility than negative shocks (bad news). The consequences are discussed in the context of who is trading in the market and heterogeneity of the investors.
Dimitrios Koutmos
No abstract is available for this record.
Franklin Allen, Antonio FatĂĄs, Beatrice Weder di Mauro
We investigate whether the market for ICOs in 2017â2018 and 2021 showed signs of contagion from prices of Bitcoin and Ether. During phases of optimism, ICO daily returns display low correlations with those of Bitcoin or Ether. But when the bubble bursts, correlations jump to very high levels, signaling that the ICO market becomes a sideshow of the cryptocurrency dynamics. We demonstrate that this high correlation was not present during the Nasdaq bubble in the 1990s, signaling that the price dynamics of digital tokens seems to be driven by a common factor, much more than in previous bubbles.
Frederick Adjei, Mavis Adjei
In this study, we examine the relationship between changes in cryptocurrency trading volume and contemporaneous cryptocurrency returns. Additionally, we investigate the predictive power of changes in cryptocurrency trading volume for future cryptocurrency returns. We find a direct relationship between change in trading volume and the contemporaneous returns, consistent with Gervars, Kaniel and Mingelgrin for cryptocurrencies tradable on Coinbase exchange. Additionally, we uncover that the changes in trading volume have significant predictive power for future cryptocurrency returns for cryptocurrencies tradable on Coinbase exchange. We, however, do not find a connection between changes in trading volume and returns for cryptocurrencies not tradable on Coinbase exchange. Our findings suggest the presence of a weak-form inefficiency in the cryptocurrency market; cryptocurrency prices do not reflect available information.
Yi Li, Wei Zhang, Andrew Urquhart, Pengfei Wang
No abstract is available for this record.
Rama K. Malladi
Purpose Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models. Design/methodology/approach Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference. Findings The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio. Practical implications One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class. Originality/value This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.
Olubunmi Adewole Ogunode, A. T. Iwala, O. A. Awoniyi, B. O. Amusa · 7 authors
This paper examined cryptocurrency and its global practices with particular reference to salient lessons for the Nigerian economy. The desk review methodology anchored on content analysis was used for the study. The paper identified distrust in political systems, weak domestic currency and high inflation rates as key factors fueling the growth of cryptocurrency usage in Nigeria thus motivating individuals to resort to cryptocurrencies as a tool for wealth preservation and inflation hedge. The study also found that the existence of trust deficit and challenges associated with privacy concerns, system uptime and stringent onboarding requirements were capable of derailing the success of the newly launched digital currency(âe-nairaâ) issued by government to curtail cryptocurrency usage in Nigeria. The study concluded that cryptocurrencies and central bank issued digital currencies (CBDCs) are now part and parcel of the new economic order and represents the future of finance. It therefore recommended that nation states should work assiduously to develop uniformly agreed regulatory framework and global standards for the usage of cryptocurrencies.
Jang Ho Kim
No abstract is available for this record.
Arianna Agosto, Paola Cerchiello, Paolo Pagnottoni
No abstract is available for this record.
Giorgos Felekis, Jesper Kristensen
One of the most exciting recent developments in Decentralized Finance (DeFi) has been the development of decentralized exchanges, called Automated Market Mak-ers (AMMs). In this work, we study the most prominent special class of them, the Constant Function Market Makers (CFMMs). We introduce a generalized formula for CFMMs, called λCFMMs, which encapsulates the idea of combining the advantages of constant sum and constant mean CFMMs by blending their functions where$\lambda$is the degree of mixture. Our experiments demonstrate the behaviour of this generalized formula for various token pools with different properties and price differences, and evaluate its performance regarding slippage and imper-manent loss for different degrees of mixture during a trading period. We further show that given the nature of the pool and an optimization objective, different levels of mixture lead to optimal non-trivial functions, which as we show, outperform some of the most popular AMMs such as Uniswap. The novelty of$\lambda$CFMMs is both the mixing method that helps us target more efficient AMM functions and also the fact that motivates the idea of dynamic AMM functions that given certain features can self-adjust their parameters in order to produce mutual profits for both the traders and the liquidity providers.
Jangyoun Lee, Taehee Oh
No abstract is available for this record.
Carol Alexander, Jun Deng, Bin Zou
Bitcoin derivatives positions are maintained with a self-selected margin, which is often too low to avoid automatic liquidation by the exchange, without notice, especially during periods of excessive volatility. Indeed, according to CryptoQuant, almost $80 billion of positions on centralised exchanges were liquidated during 2021, that is an average of over $200 million per day. So hedgers of bitcoin price risk should account for the possibility of automatic liquidation when taking positions on bitcoin futures. We derive a semi-closed form for an optimal hedging strategy with dual objectives â to minimize both the variance of the hedged portfolio and the probability of liquidation due to insufficient collateral. The solution depends on the statistical characteristics of the spot and futures extreme returns, and other parameters that characterize the hedger by choice of leverage, loss aversion and collateral management. An empirical analysis based on minute-level data compares the performance of the major direct and inverse bitcoin hedging instruments traded on five major exchanges.
Chi-Wei He, Yung-Jang Wang
Bitcoin has attracted significant attention from investors over recent years. Due to infrequent jumps in Bitcoin prices, this paper employs the ARJI model of Chan and Maheu (2002) to describe jump risks of Bitcoin prices, and to examine the possible influencing factors of jump risks. Empirical results find that the jump component is the most important driving force of the volatility of Bitcoin returns, and that two investor sentiment indicators (the Bitcoin trading volumes and the number of Bitcoin unique addresses) are positive related to the jump risk of Bitcoin returns. These findings provide an important insight into the investment risk of Bitcoin prices.
Nathan Davies, Simon Ferris
Public health harms from gambling are now well recognised. Public Health England estimates societal gambling-related harm in England exceeds £1·27 billion. Australian research estimates that the years lost to disability from gambling exceeds that of diabetes.1 Policy makers have begun to take action; gambling advertising and sponsorship is banned in Italy, and English football clubs and the UK government are currently considering sponsorship bans for the Premier League.2