This paper examines the size effect in the cryptocurrency market with a sample of more than 1800 cryptocurrencies over the period from January 2014 to May 2019. We find that cryptocurrencies with small market value tend to perform better in the future, which challenges the Efficient Market Hypothesis. The size effect is stable over the sample period and robust to the sample size. The prior returns and liquidity also have an impact on the size effect. In addition, our findings provide practical implications for cryptocurrency investors.
Valerio Celeste, Shaen Corbet, Constantin Gurdgiev
The substantial volatility and growth in cryptocurrencies valuations between 2009 and the end of 2017 strongly suggest that both long memory and price volatility and return spillovers should be present in these assets’ dynamics. To date, literature on the major cryptocurrencies price processes does not address jointly and comprehensively their fractal properties, long memory and wavelet analysis, that could robustly confirm the presence of fractal dynamics in their prices, and confirm or deny the validity of the Fractal Market Hypothesis as being applicable to the cryptocurrencies. This research shows that Bitcoin prices exhibit long term memory, although its trend has been reducing overtime. In fact, assessing Bitcoin, Ethereum and Ripple across the period between 2016 and 2017, focusing solely on the period prior to the crash of 2018, we can conclude that Bitcoin was better described by a random walk, showing signs of markets maturity emerging, in contrast, other cryptocurrencies such as Ethereum and Ripple present evidence of a growing underlying memory behaviour.
Muhammad Saad, Jinchun Choi, DaeHun Nyang, Joongheon Kim · 5 authors
Recently, the Blockchain-based cryptocurrency market witnessed enormous growth. Bitcoin, the leading cryptocurrency, reached all-time highs many times over the year leading to speculations to explain the trend in its growth. In this article, we study Bitcoin and Ethereum and explore features in their network that explain their price hikes. We gather data and analyze user and network activity that highly impact the price of these cryptocurrencies. We monitor the change in the activities over time and relate them to economic theories. We identify key network features that help us to determine the demand and supply dynamics in a cryptocurrency. Finally, we use machine learning methods to construct models that predict Bitcoin price. Based on our experimental results using two large datasets for validation, we confirm that our approach provides an accuracy of up to 99% for Bitcoin and Ethereum price prediction in both instances.
Giancarlo Giudici, Alistair Milne, Dmitri Vinogradov
The papers in this special issue focus on the emerging phenomenon of cryptocurrencies. Cryptocurrencies are digital financial assets, for which ownership and transfers of ownership are guaranteed by a cryptographic decentralized technology. The rise of cryptocurrencies’ value on the market and the growing popularity around the world open a number of challenges and concerns for business and industrial economics. Using the lenses of both neoclassical and behavioral theories, this introductory article discusses the main trends in the academic research related to cryptocurrencies and highlights the contributions of the selected works to the literature. A particular emphasis is on socio-economic, misconduct and sustainability issues. We posit that cryptocurrencies may perform some useful functions and add economic value, but there are reasons to favor the regulation of the market. While this would go against the original libertarian rationale behind cryptocurrencies, it appears a necessary step to improve social welfare.
Abstract Cryptocurrencies as a new way of transferring assets and securing financial transactions have gained popularity in recent years. Transactions in cryptocurrencies are publicly available, hence, statistical studies on different aspects of these currencies are possible. However, previous statistical analysis on cryptocurrencies transactions have been very limited and mostly devoted to Bitcoin, with no comprehensive comparison between these currencies. In this study, we intend to compare the transaction graph of Bitcoin, Ethereum, Litecoin, Dash, and Z-Cash, with respect to the dynamics of their transaction graphs over time, and discuss their properties. In particular, we observed that the growth rate of the nodes and edges of the transaction graphs, and the density of these graphs, are closely related to the price of these currencies. We also found that the transaction graph of these currencies is non-assortative, i.e. addresses do not tend for transact with a particular type of addresses of higher or lower degree, and the degree sequence of their transaction graph follows the power law distribution.
In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and Litecoin. We apply detrended fluctuation analysis (the regular one and with a sliding windows approach) and detrended cross-correlation analysis and the respective correlation coefficient. We find that Bitcoin and Ripple seem to behave as efficient financial assets, while Ethereum and Litecoin present some evidence of persistence. When correlating Bitcoin with the other cryptocurrencies under analysis, we find that for short time scales, all the cryptocurrencies have statistically significant correlations with Bitcoin, although Ripple has the highest correlations. For higher time scales, Ripple is the only cryptocurrency with significant correlation.
The uncertainties in future Bitcoin price make it difficult to accurately\npredict the price of Bitcoin. Accurately predicting the price for Bitcoin is\ntherefore important for decision-making process of investors and market players\nin the cryptocurrency market. Using historical data from 01/01/2012 to\n16/08/2019, machine learning techniques (Generalized linear model via penalized\nmaximum likelihood, random forest, support vector regression with linear\nkernel, and stacking ensemble) were used to forecast the price of Bitcoin. The\nprediction models employed key and high dimensional technical indicators as the\npredictors. The performance of these techniques were evaluated using mean\nabsolute percentage error (MAPE), root mean square error (RMSE), mean absolute\nerror (MAE), and coefficient of determination (R-squared). The performance\nmetrics revealed that the stacking ensemble model with two base learner (random\nforest and generalized linear model via penalized maximum likelihood) and\nsupport vector regression with linear kernel as meta-learner was the optimal\nmodel for forecasting Bitcoin price. The MAPE, RMSE, MAE, and R-squared values\nfor the stacking ensemble model were 0.0191%, 15.5331 USD, 124.5508 USD, and\n0.9967 respectively. These values show a high degree of reliability in\npredicting the price of Bitcoin using the stacking ensemble model. Accurately\npredicting the future price of Bitcoin will yield significant returns for\ninvestors and market players in the cryptocurrency market.\n
The paper describes the linear model for Bitcoin price which includes regression features based on Bitcoin currency statistics, mining processes, Google search trends and Wikipedia pages visits. The pattern of deviation of regression model prediction from real prices is simpler comparing to price time series. It is assumed that this pattern can be predicted by an experienced expert. In such a way, using the combination of the regression model and expert correction, one can receive better results than with either regression model or expert opinion only. It is shown that Bayesian approach makes it possible to utilize the probabilistic approach using distributions with fat tails and take into account the outliers in Bitcoin price time series.
Abstract Background: Cryptocurrencies represent a specific technological innovation in financial markets that keeps getting more and more popular among investors around the world. Given the specific characteristics of the cryptocurrencies, this paper examines the possibility of their use as a diversification instrument. Objectives: This paper examines the direction and strength of the relationship between the selected cryptocurrencies and important financial indicators on the European Union market. Since cryptocurrencies are a novelty in the financial system, the empirical literature in this area is rather scarce. Methods/Approach: In order to assess diversification properties of cryptocurrencies for European traders, a comprehensive econometric analysis was carried out. The first part of the analysis refers to the estimation of the multivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, whereas the second part focuses on wavelet transforms. Results: Bitcoin and Ripple proved as a possible diversification instrument on most of the observed European markets since corresponding coefficients of unconditional correlation are negative. Conclusions: The relationship between the value of the cryptocurrencies and selected indices is generally very weak and slightly negative, indicating that some cryptocurrencies can serve as a means of diversification. However, investors need to take into account the extreme volatility, exhibited in all existing cryptocurrencies.
As Cryptocurrencies are emerging as a new class of investment assets, understanding their price and volatility dynamics has begun to gather momentum, especially the volatility can influence investment decisions. Most of previous literature concentrates primarily on several aspects of Bitcoin and endeavoring to generalize them for the whole cryptocurrency markets. In this study, we attempted to examine the return and volatility spillover effects across a wide range of cryptocurrency markets, i.e. eight major cryptocurrencies (determined by market capitalization) using a Vector Error Correction approach and Diagonal BEKK Multivariate GARCH model. We found the evidence of interdependencies and volatility co-movements among the various pairs of cryptocurrency markets. However, the study suggests that there exists a limited window of opportunity for the short-term portfolio diversification benefits from the selected large-cap cryptocurrency markets.
Iman Abu Hashish, Fabio Forni, Gianluca Andreotti, Tullio Facchinetti · 5 authors
With the recent advances in the Blockchain technology, and due to its decentralized nature, it has been a much considered approach for solving issues in the Internet of Things (IoT) sector, in particular, for IoT payment platforms. As Machine-to-Machine (M2M) payments are fundamental in the IoT economy, the development of Blockchain-based payment platforms, using cryptocurrency, is continuously increasing as it enables a pure M2M, secure and private financial transactions. Unlike traditional assets, cryptocurrencies have a higher index of volatility, which makes it essential to understand the movement of their prices, as a first step to optimize Blockchain-based M2M payment transactions. In this paper, we propose a novel hybrid model that deals with this challenge from a descriptive, as well as predictive points of view. We use Hidden Markov Models to describe cryptocurrencies historical movements to predict future movements with Long Short Term Memory networks. To evaluate the proposed hybrid model, we have chosen 2-minute frequency Bitcoin data from Coinbase exchange market. Our proposed model proved its effectiveness compared to traditional time-series forecasting models, ARIMA, as well as a conventional LSTM.
This paper investigates the dynamic price movements of cryptocurrency market in Korea by employing asymmetric DCC multivariate GARCH and risk decomposition model to reflect the time-varying integration process. We find that the law of one price does not hold between Korean and developed markets like U.S. and Japan, implying that emerging cryptocurrency market can be exploited as a scapegoat of arbitragers. Specifically, the price spreads of 20 to 30 percent between BTC-KRW and BTC-USD persist, exhibiting a sign of economic speculative bubble in Korean cryptocurrency market. Additionally, while there are significant price and volatility spillover effects between cryptocurrency markets of U.S. and Japan, the feedback effects do not exist in the case of Korean market. Our analyses also indicate that the pricing in Korea is mostly based on domestic factors rather than global factors. Finally, we show that this arbitrage opportunity in Korean market has disappeared after a government regulation, which includes banning foreigners and minors from opening new cryptocurrency accounts and prohibiting initial coin offerings (ICOs). The results suggest that a suitable regulation is important to eliminate bubbles.
Abstract This paper carries out a comprehensive examination of technical trading rules in cryptocurrency markets, using data from two Bitcoin markets and three other popular cryptocurrencies. We employ almost 15,000 technical trading rules from the main five classes of technical trading rules and find significant predictability and profitability for each class of technical trading rule in each cryptocurrency. We find that the breakeven transaction costs are substantially higher than those typically found in cryptocurrency markets. To safeguard against data-snooping, we implement a number of multiple hypothesis procedures which confirms our findings that technical trading rules do offer significant predictive power and profitability to investors. We also show that the technical trading rules offer substantially higher risk-adjusted returns than the simple buy-and-hold strategy, showing protection against lengthy and severe drawdowns associated with cryptocurrency markets. However there is no predictability for Bitcoin in the out-of-sample period, although predictability remains in other cryptocurrency markets.
This paper first evaluates the volatility modeling in the Bitcoin market in terms of its realized volatility, which is considered to be a reliable proxy of its true volatility. Based on the 5-minute return of Bitcoin, the proxy of its true volatility is computed as the sum of the squared intraday returns. To evaluate the performance of volatility modeling, this paper relies on MSE and QLIKE, which are the measures for making the forecast accuracy robust to noise in the imperfect volatility proxy, while different measures are also used for the robustness check. The empirically findings summarized as (1) the asymmetric volatility models such as EGARCH and APARCH have a higher predictability, and (2) the volatility model with normal distribution performs better than the fat-tailed distribution such as skewed t distribution.
This paper investigates the prediction power of Economic Policy Uncertainty on three aspects of Bitcoin, particularly the return, volume, and volatility. We employed the Transfer Entropy model with two different regimes: (i) stationary and (ii) non-stationary assumption. We constructed different algorithm calculations for returns, volume, and volatility to test how this proxy impacts. We find that the Global Economic Policy Uncertain negatively causes Bitcoin volumes and volatilities. Therefore, under uncertain regimes, investors are risk-averse to trade, which makes the market less volatile. Our findings confirm the existence of pessimistic risk premium and the theory of deteriorating liquidity under uncertainties in the Bitcoin market.