Ángel Enrique Chico Frías, Edwin Javier Santamaría Freire
Market forces are not the only influence on currency exchange rates. They can change due to monetary and fiscal policies among other international repercussions. Bitcoin, for its independence from all the central banks worldwide, has a natural shield that will change the direction in the economic policy of the industrialized countries. The research aim is to analyze the influence that indicators and financial assets can have on Bitcoin. The study tries to confirm the reasons why it has begun to be the solution in economies with unstable currencies.The behavior of the different agents appears as the core of the study. It is creating a backward 5-year work horizon. The data are continuous values, and they are the numerical variables for Pearson correlation analysis. The time series in fixed periods are the basis for the study of projections. Besides, the Relative Strength Index or Relative Strength Index called Welles Wilder is useful in the research. Bitcoin does not get influenced by the Dow Jones, gold price, and Gross Domestic Product (GDP). The independence in the creation of this cryptocurrency could in the long term end up turning it into a currency of world use. As a result, the understanding and management of this cryptocurrency could generate new ways of building the future monetary system. The new direction of the economy will be registered in the blockchain and not in a central bank.
Within the decision-making process, investors are interested in finding the most effective solutions that will allow them to obtain short-term benefits. Current economic environment is characterized by the emergence of new financial instruments that can assist investors to diversify their investment portfolio. Crypto-currencies represents a category of financial assets that can be used by investors to reduce risk and achieve significant returns. Therefore, the study intends to analyze the financial behavior of investors in the moment of publishing the financial statements. Financial statements could have a positive or negative influence on the investment portfolio and structure. The issue analyzed by this study is represented by the ability of the cryptocurrency Bitcoin to be considered as an alternative investment asset. The study is divided into two parts. In the first part, the study presents the review of literature about value-relevance, cryptocurrency term and speculative bubble. The second part presents the research methodology and results. The results of the study validate the hypothesis of this study, cryptocurrency Bitcoin being a financial asset that can be used as an alternative investment asset for diversification of investment portfolio.
Aug 1, 2019·CEUR Workshop Proceedings, Vol-2422: Proceedings of the Selected Papers of the 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-EEMLPEED 2019)
Vasily Derbentsev, Наталія Даценко, Olga Stepanenko, Vitalii Bezkorovainyi
This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and the data of the time series. BART combines the classic algorithm classification and regression trees (C&RT) and autoregressive models ARIMA. Using the BART model, we made a short-term forecast (from 5 to 30 days) for the 3 most capitalized cryptocurrencies: Bitcoin, Ethereum and Ripple. We found that the proposed approach was more accurate than the ARIMA-ARFIMA models in forecasting cryptocurrencies time series both in the periods of slow rising (falling) and in the periods of transition dynamics (change of trend).
Abstract This paper provides a comprehensive overview of cryptocurrencies, including the origin of cryptocurrencies, how cryptocurrencies operate, and the current situation of cryptocurrencies. In addition, we also provide the performance comparison of major cryptocurrencies with the performance of the stock market indexes. All the cryptocurrencies exhibit higher average returns and volatility than the stock market indexes, which appeals to risk-taking investors. We then perform additional analysis on the determinants of cryptocurrencies returns. We show that major fundamental variables are less likely to affect the returns of cryptocurrencies except for the S&P 500 index returns and the exchange rates between U.S. dollars and Euros.
This study examines the volatility of certain cryptocurrencies and how they are influenced by the three highest capitalization digital currencies, namely the Bitcoin, the Ethereum and the Ripple. We use daily data for the period 1 January 2018-16 September 2018, which represents the bearish market of cryptocurrencies. The impact of the decline of these three cryptocurrencies on the returns of the other virtual currencies is examined with models of the ARCH and GARCH family, as well as the DCC-GARCH. The main conclusion of the study is that the majority of cryptocurrencies are complementary with Bitcoin, Ethereum and Ripple and that no hedging abilities exist among principal digital currencies in distressed times.
The paper focuses on the study of the effect of long memory and the analysis of the multifractal properties of the time series of the most capitalized cryptocurrencies for the period from 2010 to 2018. To do this, the Hurst exponent is calculated by both R/S analysis and the Detrended Fluctuation Analysis being more stable in the case of non-stationary time series. Our results show that time series of cryptocurrencies to be persistent during almost the whole study period that do not allow accepting the hypothesis concerning the efficiency of the cryptocurrency market. We also found that (i) time series became anti-persistent during the periods of market crisis phenomena and turbulence; (ii) the Hurst exponents showed significant fluctuations about the value of 0.5. In addition, we conduct a multifractal analysis of cryptocurrency time series that allows us to assess the state and stability of the market.The calculated spectrum of multifractality shows that the cryptocurrency market comes out of a crisis state, since the width of the multifractality spectrum has the maximum value for all cryptocurrencies.
How do cryptocurrency prices evolve? Is there any interdependence among cryptocurrency returns and/or volatilities? Are there any return spillovers and volatility spillovers between the cryptocurrency market and other financial markets? To answer these questions, we use GARCH-in-mean models to examine the relationship between volatility and returns of leading cryptocurrencies, to investigate spillovers within the cryptocurrency market, and also from the cryptocurrency market to other financial markets. Overall, we find statistically significant transmission of shocks and volatilities among the leading cryptocurrencies. We also find statistically significant spillover effects from the cryptocurrency market to other financial markets in the United States, as well as in other leading economies (Germany, the United Kingdom, and Japan).
We study the time varying co-movement patterns of the crypto-currency prices with the help of wavelet-based methods; employing daily bilateral exchange rate of four major crypto-currencies namely Bitcoin, Ethereum, Lite and Dashcoin. First, we identify Bitcoin as potential market leader using Wavelet multiple correlation and Cross correlation. Further, Wavelet Local Multiple Correlation for the given crypto-currency prices are estimated across different time-scales. From the results, it is found that that the correlation follows an aperiodic cyclical nature, and the crypto-currency prices are driven by Bitcoin price movements. Based on the results obtained, we suggest that constructing a portfolio based on crypto-currencies may be risky at this point of time as the other crypto-currency prices are mainly driven by Bitcoin prices, and any shocks in the latter is immediately transformed to the former.
The rapid development of cryptocurrencies has drawn attention to this particular market, with investors trying to understand its behaviour and researchers trying to explain it. The evolution of cryptocurrencies’ prices showed a kind of bubble and a crash at the end of 2017. Based on this event, and on the fact that Bitcoin is the most recognized cryptocurrency, we propose to evaluate the contagion effect between Bitcoin and other major cryptocurrencies. Using the Detrended Cross-Correlation Analysis correlation coefficient (ΔρDCCA) and comparing the period after and before the crash, we found evidence of a contagion effect, with this particular market being more integrated now than in the past—something that should be taken into account by current and potential investors.
Alexandre Bovet, Carlo Campajola, Francesco Mottes, Valerio Restocchi · 7 authors
Cryptocurrencies (the most paradigmatic blockchain-based systems) are distributed systems that allow to exchange tokens among participants.These cryptocurrencies can also be acquired in exchange markets.The availability of the historical bookkeeping of cryptocurrency transfers in a public ledger opens up the possibility of understanding the relationship between aggregate users' behaviour and the cryptocurrency pricing in exchange markets.This paper analyses the properties of the transaction network of Bitcoin.We consider different representations over a period of nine years since its creation and involving 16 million users and 283 million transactions.Importantly, these transactions do not include orders filled in exchange markets, which are settled outside of the blockchain, and ultimately determine Bitcoin price.By analysing these networks, we show the existence of Granger causal relationships between Bitcoin price movements and changes of its transaction network topology.Our results reveal the interplay between structural quantities, indicative of the collective behaviour of Bitcoin users, and price movements, showing that, during price drops, the system is characterised by a larger heterogeneity of users' activity.
V. Dimitrova, M. Fernández–Martínez, M.A. Sánchez-Granero, Juan Evangelista Trinidad Segovia
In this paper, we explore the (in)efficiency of the continuum Bitcoin-USD market in the period ranging from mid 2010 to early 2019. To deal with, we dynamically analyse the evolution of the self-similarity exponent of Bitcoin-USD daily returns via accurate FD4 approach by a 512 day sliding window with overlapping data. Further, we define the memory indicator by the difference between the self-similarity exponent of Bitcoin-USD series and the self-similarity index of its shuffled series. We also carry out additional analyses via FD4 approach by sliding windows of sizes equal to 64, 128, 256, and 1024 days, and also via FD algorithm for values of q equal to 1 and 2 (and sliding windows equal to 512 days). Moreover, we explored the evolution of the self-similarity exponent of actual S&P500 series via FD4 algorithm by sliding windows of sizes equal to 256 and 512 days. In all the cases, the obtained results were found to be similar to our first analysis. We conclude that the self-similarity exponent of the BTC-USD (resp., S&P500) series stands above 0.5. However, this is not due to the presence of significant memory in the series but to its underlying distribution. In fact, it holds that the self-similarity exponent of BTC-USD (resp., S&P500) series is similar or lower than the self-similarity index of a random series with the same distribution. As such, several periods with significant antipersistent memory in BTC-USD (resp., S&P500) series are distinguished.
Despite the current growing interest in Bitcoins-and cryptocurrencies in general-financial instruments, as well as studies related to them, are quite underdeveloped. Therefore, this article aims to provide a suitable pricing model for options written on this peculiar underlying. This is done through an artificial neural network approach, where classical pricing models-namely the trinomial tree, Monte Carlo simulation, and explicit finite difference method-are used as input layers. Results show that options written on Bitcoin turn out to be systematically overpriced when considering classical methods, whereas a noticeable improvement in price predictions is achieved by means of the proposed neural network model.
Sanal ve kripto para niteliğinde olan Bitcoin, dijital formata sahip, teknik olarak blok zinciri olarak ifade edilen işlemleri kapsayan ve merkezi para sistemine dahil olmayan bir para birimidir. Çalışmada, kripto para Bitcoin ile döviz kurları arasındaki ilişkiyi ortaya çıkarmak amaçlanmıştır. ABD Doları bazında Bitcoin kuru ile Euro, Japon Yeni, İngiliz Sterlini, Avustralya Doları, Kanada Doları, İsviçre Frankı, Yuan Renminbisi ve İsveç Kronu döviz kurları arasındaki ilişki, 3.02.2012-04.10.2017 dönemindeki günlük kur değerleri esas alınarak, yapısal kırılmalı Gregory ve Hansen eşbütünleşme ve Granger nedensellik analizleri ile incelenmiştir. Analiz sonucunda, BTC/USD döviz kurunda yapısal kırılmaların, 2013 yılı Nisan ve Aralık aylarında gerçekleştiği belirlenmiştir. Ayrıca çalışmada, döviz kurlarına ilişkin zaman serileri arasında uzun dönemli eşbütünleşme ilişkisi tespit edilirken, CNY/USD döviz kuru ile BTC/USD döviz kuru arasında tek yönlü pozitif nedensellik ilişkisi tespit edilmiştir.
Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics. For example, forecasting stock market prices and influenza outbreaks. Recently, social data has been explored to forecast price fluctuations of cryptocurrencies, which are a novel disruptive technology with significant political and economic implications. In this paper we leverage and contrast the predictive power of social signals, specifically user behavior and communication patterns, from multiple social platforms GitHub and Reddit to forecast prices for three cyptocurrencies with high developer and community interest - Bitcoin, Ethereum, and Monero. We evaluate the performance of neural network models that rely on long short-term memory units (LSTMs) trained on historical price data and social data against price only LSTMs and baseline autoregressive integrated moving average (ARIMA) models, commonly used to predict stock prices. Our results not only demonstrate that social signals reduce error when forecasting daily coin price, but also show that the language used in comments within the official communities on Reddit (r/Bitcoin, r/Ethereum, and r/Monero) are the best predictors overall. We observe that models are more accurate in forecasting price one day ahead for Bitcoin (4% root mean squared percent error) compared to Ethereum (7%) and Monero (8%).
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.
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 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
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.