Cryptocurrency is a cutting-edge Fintech innovation and currently a worldwide hotspot. However, the high-speed evolution of it has already caused a series of public security related events all around the world. Cryptocurrency was built initially as a possible implementation of digital currency, then various derivatives were created in a variety of fields such as financial transactions, capital management, and even nonmonetary applications. This paper aims to offer analytical insights to help understand cryptocurrency by treating it as a financial asset. We position cryptocurrency by comparing its dynamic characteristics with two traditional and massively adopted financial assets: foreign exchange and stock. Based on the daily close prices about four years, we first construct the correlation matrices and asset trees of all three markets, then conduct comparisons on five properties: volatility, centrality, clustering structure, robustness, and risk. Our investigation suggests that the dynamics of cryptocurrency are more similar to stock. As to the robustness and clustering structure, our analysis shows cryptocurrency market is more fragile than stock market, thus it is currently a high-risk financial market. Our work is the first to study cryptocurrency with the help of well-understood financial assets and may shed some light on investment decisions, regulation, and legislation.
We compare the ability of two measures of uncertainty, a newspaper-based measure and an internet search-based measure, to predict Bitcoin returns. Using monthly data from July 2010 to May 2019 and a predictive regression model characterized by a heteroskedastic error structure and, we show that Bitcoin is a hedge against both measures. However, the predictive content of the internet-derived uncertainty related queries measure is statistically stronger than the measure of uncertainty based on newspapers for predicting Bitcoin returns, which is possibly due to the fact that the measure of uncertainty is now directly obtained from individual investors via internet searches.
Public information arrivals and their immediate incorporation in asset price is a key component of semi-strong form of the Efficient Market Hypothesis. In this study, we explore the impact of public information arrivals on cryptocurrency market via Twitter posts. The empirical analysis was conducted through various methods including Kapetanios unit root test, Maki cointegration analysis and Markov regime switching regression analysis. Results indicate that while in bull market positive public information arrivals have a positive influence on Ripple’s value; in bear market, however, even if the company releases good news, it does not divert out the Ripple from downward trend.
Asset pricing models and investment styles have been researched intensively in equities, bonds, FX, and commodities. However, a new asset class has emerged since the end of 2008, namely, cryptocurrencies such as Bitcoin and Ethereum, among others. The author uses an extensive data set of over 1,500 cryptocurrencies and shows that almost none of the traditional investment styles such as momentum or defensive appear to be successful in this young asset class. Cryptocurrencies are also independent from the macroeconomic environment and cannot be explained by a standard asset pricing model. A cryptocurrency specific model yields clearly better results. In addition, the whole cryptocurrency space is dominated by only a few individual digital coins. Equally weighted mean monthly returns appear to be random with low or even no correlation with traditional asset classes such as US equities and global FX. <b>TOPICS:</b>Currency, portfolio construction, risk management, performance measurement
Predicting the trends in Bitcoin market prices is a very challenging task due to the many uncertainties and variables influencing the market value. The market is susceptible to quick changes, causing seemingly random fluctuations in the Bitcoin price. Due to the chaotic and highly volatile nature of Bitcoin behavior, investments come with high risk. To minimize the risk involved, knowledge of the Bitcoin price movement in the future is desirable. Different studies have shown that Machine Learning algorithms can predict, to varying degrees, the price fluctuations of Bitcoin. However, most researches do not explore the relationship between the price and other features outside the transaction network, such as market capitalization, Bitcoin mining speed, or entity behavior. Also, most of the features are extracted from the network level, which means obtaining the number of transactions, users, Bitcoins mined, etc. In this research, we focus on additional features, such as features outside the transaction network and node-based features inside the transaction network, which could improve the price prediction of Bitcoin. The investigated features are the “fairness and goodness” measure and the “1-ARW-betweenness cen- trality” measure. Fairness and goodness are entity behavior measures. The goodness of a Bitcoin address captures how much this address is liked/trusted by other addresses, while the fairness of a Bitcoin address captures how fair the address is in rating other addresses’ likeability or trust level. The 1-ARW-betweenness centrality is a feature based on absorbing random walks. The feature captures the extent to which a Bitcoin address has control over the money flow between different addresses. A benchmark, based on the machine learning algorithm Random Forest with commonly used features, is used to test the impact of the additional features. The Random Forest tries to predict the sign (up-down movement) of the price per day, using data from the two previous days. Comparing this benchmark with a similar model, but then including the additional features, will gain more information about how these additional features influence the Bitcoin price.
The erratic price behavior and inefficiency in the crypto markets offer possibility to examine the behavioral aspects in cryptocurrency prices. Further, the cryptocurrency market is dominated by the retail investor providing an interesting platform to examine the impact of attention-driven trading in this particular asset class. Thus, the authors investigate the influence of investor attention in the cryptocurrency prices using the quantile causality approach. The results indicate that investors pay attention to the frequent news-making and ranked cryptos (Bitcoin and Ethereum). For newer cryptocurrencies like Ripple, investor attention influences their prices only during superior performance. The study provides evidence of attention-induced price pressure hypothesis in the prices of cryptocurrencies during expansionary phases and fear selling during poor market performance.
Zhengyang Wang, Li Xingzhou, Ruan Jinjin, Kou Jiaqing
Nowadays, encrypted digital currency offers a new way of secure trading and exchanging and has become increasingly important in our financial system. However, the temporal dynamics of cryptocurrencies is highly complex, and predictions are still challenging. In this study, we establish two prevailing machine learning models, fully-connected Artificial Neural Network (ANN) and the Long-Short-Term-Memory (LSTM), to predictively model the price of several popular cryptocurrencies, including Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Stellar Lumens (XLM), Litecoin (LTC), and Monero (XMR). We evaluate model performance and conduct sensitivity analysis to further understand our model behaviors. We find that although LSTM seems more appropriate for time sequence prediction task, ANN, in general, outrivals LSTM in our experiments. Using price information from other different cryptocurrencies for joint training and prediction could largely facilitate the prediction of BTC. Finally, the model predictive error is highly sensitive to the time scale of interest.
The Bitcoin market becomes the focus of the economic market since its birth, and it has attracted wide attention from both academia and industry. Due to the absence of regulations in the Bitcoin market, it may be easier to bring some kinds of illegal behaviors. Thus, it raises an interesting question: Is there abnormity or illegal behavior in Bitcoin platforms? To answer this question, we investigate the abnormity in five leading Bitcoin platforms. By analyzing the financial index, i.e. the normalized logarithmic price return, we find that the properties of price return in bitFlyer are completely different from others. To find the possible reasons, we find that the abnormal ask price and bid price appear simultaneously in bitFlyer, which may be potentially linked to either price manipulation or money laundering. It verifies our conjecture that there may be abnormity or price manipulation in Bitcoin platforms. Furthermore, our findings in price return could also provide an innovative and effective method to detect the abnormity in Bitcoin platforms.
Stanisław Drożdż, Ludovico Minati, Paweł Oświȩcimka, Marek Stanuszek · 5 authors
Based on the high-frequency recordings from Kraken, a cryptocurrency exchange and professional trading platform that aims to bring Bitcoin and other cryptocurrencies into the mainstream, the multiscale cross-correlations involving the Bitcoin (BTC), Ethereum (ETH), Euro (EUR) and US dollar (USD) are studied over the period between 1 July 2016 and 31 December 2018. It is shown that the multiscaling characteristics of the exchange rate fluctuations related to the cryptocurrency market approach those of the Forex. This, in particular, applies to the BTC/ETH exchange rate, whose Hurst exponent by the end of 2018 started approaching the value of 0.5, which is characteristic of the mature world markets. Furthermore, the BTC/ETH direct exchange rate has already developed multifractality, which manifests itself via broad singularity spectra. A particularly significant result is that the measures applied for detecting cross-correlations between the dynamics of the BTC/ETH and EUR/USD exchange rates do not show any noticeable relationships. This could be taken as an indication that the cryptocurrency market has begun decoupling itself from the Forex.
The development of financial technology arrived at a new form, namely cryptocurrency. The development of cryptocurrency with an increasingly widespread network and its autonomous nature cannot be controlled by the state, which makes China implement policies to block all domestic cryptocurrency activities. The policy becomes unnatural when China occupies the top position in the developing global digital currency market. This anomaly is interesting to study further to find reasons that motivate China to take firm policy when in a safe position as a center of production and global crypto currency transactions. To explain this reason, national interest theory and rational choice are used as the main tools in analyzing this problem. The national interest theory and rational choice explain that the policies taken by China are motivated by economic and security interests, as well as rational calculations of the advantages and disadvantages of those policies
This paper investigates if real investors other than rational investors could add value to their investment portfolios considering their mentality and psychology. The universe of assets constitutes 21 cryptocurrencies (37 international equities) and covers, respectively, the period from August 1, 2016, to March 31, 2018 (January 7, 2002, to March 23, 2018). The cumulative prospect theory and variant specifications were utilized to validate and compare the classification and selection of assets driven by some decision theories. The results of optimization analysis of all the formulated portfolios constituting assets from both markets showed that portfolios constituting assets with lower cumulative prospect theory values outperformed their counterpart with higher cumulative prospect theory values. The superiority of the cumulative prospect theory was established as an empirically corroborated theory of decision-making with rich psychological content. The findings of this paper are crucial for finance practitioners as they showcase an intuitive and coherent manner to guide fund managers, investors and other economic agents in their investment practices.
Imtiaz Sifat, Azhar Mohamad, Mohammad Syazwan Bin Mohamed Shariff
This paper investigates lead-lag relationship between heavyweight cryptocurrencies Bitcoin and Ethereum. Traditional studies of information flow between markets preponderate on cash vs. futures, whereby researchers are interested in the stabilizing impact of futures on spot markets. While interest in the same relationship in the nascent cryptocurrency sphere is emerging, little is known regarding price leadership between these assets. In this paper, we employ a battery of statistical tests—VECM, Granger Causality , ARMA, ARDL and Wavelet Coherence—to identify price leadership between the two crypto heavyweights Bitcoin and Ethereum. Based on one year hourly and daily data from August 2017 through to September 2018, our tests yield varied results but largely suggest bi-directional causality between the two assets. Moreover, the results indicate that intraday crypto traders can barely exploit Bitcoin-Ethereum hourly or daily price discovery process to their advantage.
Information transfer between time series is calculated using the asymmetric information-theoretic measure known as transfer entropy. Geweke’s autoregressive formulation of Granger causality is used to compute linear transfer entropy, and Schreiber’s general, non-parametric, information-theoretic formulation is used to quantify nonlinear transfer entropy. We first validate these measures against synthetic data. Then we apply these measures to detect statistical causality between social sentiment changes and cryptocurrency returns. We validate results by performing permutation tests by shuffling the time series, and calculate the Z -score. We also investigate different approaches for partitioning in non-parametric density estimation which can improve the significance. Using these techniques on sentiment and price data over a 48-month period to August 2018, for four major cryptocurrencies, namely bitcoin (BTC), ripple (XRP), litecoin (LTC) and ethereum (ETH), we detect significant information transfer, on hourly timescales, with greater net information transfer from sentiment to price for XRP and LTC, and instead from price to sentiment for BTC and ETH. We report the scale of nonlinear statistical causality to be an order of magnitude larger than the linear case.
Ten years have passed since the emergence of Bitcoin and with it cryptocur- rencies as a new class of assets. Now, cryptocurrencies are not uncommon tool of investment and subject of academic research. This thesis focuses on investigating possible presence of weekly and monthly seasonal patterns in cryptocurrencies, namely Bitcoin, Litecoin, Ripple, Monero, Dash, Stellar and partly Ethereum, which are selected as representative sample. Insuffi- cient evidence is found for the day-of-the-week effect, the January effect is however revealed as significant by different methods in the whole sample, with cryptocurrencies generally exhibiting higher returns towards the end of the year and lowest from January to March. Examining probable causes of revealed seasonality, it is found that these are not likely to be caused by peculiar price development in 2017 and 2018, as well as the Chinese New Year or brought to the market by proposed price drivers of Bitcoin. How- ever, significant evidence for correlation of patterns followed by Bitcoin and other examined cryptocurrencies is found.
This paper investigates the effects of the launch of Bitcoin futures on the intraday volatility of Bitcoin. Based on one-minute price data collected from four cryptocurrency exchanges, we first examine the change in realized volatility after the introduction of Bitcoin futures to investigate their aggregate effects on the intraday volatility of Bitcoin. We then analyze the effects in more detail utilizing the discrete Fourier transform. We show that although the Bitcoin market became more volatile immediately after the introduction of Bitcoin futures, over time it has become more stable than it was before the introduction.
We develop a model of stable assets, including non-custodial stablecoins backed by cryptocurrencies. Such stablecoins are popular methods for bootstrapping price stability within public blockchain settings. We derive fundamental results about dynamics and liquidity in stablecoin markets, demonstrate that these markets face deleveraging feedback effects that cause illiquidity during crises and exacerbate collateral drawdown, and characterize stable dynamics of the system under particular conditions. The possibility of such `deleveraging spirals' was first predicted in the initial release of our paper in 2019 and later directly observed during the `Black Thursday' crisis in Dai in 2020. From these insights, we suggest design improvements that aim to improve long-term stability. We also introduce new attacks that exploit arbitrage-like opportunities around stablecoin liquidations. Using our model, we demonstrate that these can be profitable. These attacks may induce volatility in the `stable' asset and cause perverse incentives for miners, posing risks to blockchain consensus. A variant of such attacks also later occurred during Black Thursday, taking the form of mempool manipulation to clear Dai liquidation auctions at near zero prices, costing $8m.