The attempts to create an adequate model of socio-economic critical events, which, as it has been historically proven, are almost permanent, were, are and will always be made. Actually, it is a supertask, impossible to solve. However, the potentially useful solutions, local in time or other socio-economic logistic coordinates, are possible. In fact, they have to be the object of interest for a real and effective economic science. Econophysics is a young interdisciplinary scientific field, which developed and acquired its name at the end of the last century. Quantum econophysics, a direction distinguished by the use of mathematical apparatus of quantum mechanics as well as its fundamental conceptual ideas and relativistic aspects, developed within its boundaries just a couple of years later, in the first decade of the 21-st century.
It is a very tiring process for people to watch the multiple parallel instant price changes in stock exchanges that are rapidly changing like the crypto money market. As a solution to this, a computer software that can make quick and objective decisions by constant observation can take the place of a person. In this study, an original decision algorithm that evaluates the instantaneous values of price change indicators and obtains relatively high earnings in a short period of time is examined. The Python programming language and Mathlib library have been used to construct this algorithm and to visualize the data, Moving Average Convergence Divergence (MACD) and Bollinger Bands have been used as a basic indicator. The result is an algorithm that requires less processing power and can operate continuously even on ARM-based mini-computers.
Abstract Cryptocurrencies have experienced an exponential growth trend in the past 24 months, followed by a big crash. In the early years of the Internet, inspired entrepreneurs such as Jeffrey Bezos realized that, when something grows exponentially, it becomes ubiquitous within a short time span. Similarly to the Internet in 1994, cryptocurrencies have recently been growing at a dazzling rate, thus one can expect them to be used on a global scale very soon, in spite of the last bubble which has already burst. Alternative currencies are greeted with great enthusiasm, due to their potential to return financial power back to the people, especially in the context of general dissatisfaction and disappointment with the banking sector. They bring about several advantages, such as financial innovations, lower fees as well as increased availability to developing populations. At the same time, their high volatility and lack of supervision might imply that they only serve as complementary financing and not as a substitute of traditional banking. This article discusses the development of cryptocurrencies, including aspects related to Bitcoin, financial technology and the blockchain. Using historical data from Coinmarketcap.com between April 2013 and February 2018, I run a quantitative analysis of the distributions and evolution over time for all listed cryptocurrencies with known market capitalization. I look at the interplay between number of cryptocurrencies and market value, at growth rates, cumulative shares and volatility. I find a phenomenon of exponential growth and violent volatility, which I explain in light of cryptocurrencies’ strengths and weaknesses, as identified in the literature. I emphasize the importance of cryptocurrencies in the context of the global digital economy and I discuss future implications.
Kıvanç Ceyhan, Ekim Kurtulmaz, Onur Can Sert, Tansel Özyer
In the last few years, Bitcoin is one of the most discussed and popular topic in financial system. This article aims to predict Bitcoin movement by using Machine Learning and Text Mining models. Many models have been used to this end, including the most popular models in financial prediction; Artificial Neural Network (ANN), Support Vector Machine (SVM) and Logistic Regression (LR). In addition to this, in order to examine the effect of daily news on Bitcoin movement, the text mining models are involved into the prediction system. This paper focuses on applying Machine Learning models on a integrated dataset, which contains both historical Bitcoin values and features from daily news text. Overall, our model can estimate the direction of Bitcoin with a high success.
One of the most well-known and popular crypto money, that is also a digital currency enabled in 2009, is Bitcoin. Over time, many alternatives to Bitcoin have been developed. The blockchain system, which is a very important technology for crypto money transfer, provides many different possibilities besides providing transaction from one point to another without any intermediary. Block chaining is a distributed, shared form of recording that facilitates the recording of assets and transactions in a network. In this study, after explaining the basic concepts behind distributed architecture and blockchain technology behind crypto money, artificial intelligence algorithms were exploited, and based on last three years values of bitcoin forecasting was performed for Bitcoin which has a huge market share in since nine years.
Dionysios S. Demetis, Michael Mainelli, Matthew Leitch
Cryptocurrencies have the potential to become effective currencies that give a higher level of macroeconomic control, thanks to the information that is available about holdings and transactions, and the potential for automated control mechanisms. However, these cryptocurrencies need to be designed properly and tested before launch. This paper reports the early results of an economic model that simulates a variety of behaviors by economic agents and some simple control mechanisms. An economic simulation model is likely to be a valuable tool in developing effective cryptocurrency systems and interacting with regulators.
Cryptocurrencies have experienced recent surges in interest and price. It has been discovered that there are time intervals where cryptocurrency prices and certain online and social media factors appear related. In addition it has been noted that cryptocurrencies are prone to experience intervals of bubble-like price growth. The hypothesis investigated here is that relationships between online factors and price are dependent on market regime. In this paper, wavelet coherence is used to study co-movement between a cryptocurrency price and its related factors, for a number of examples. This is used alongside a well-known test for financial asset bubbles to explore whether relationships change dependent on regime. The primary finding of this work is that medium-term positive correlations between online factors and price strengthen significantly during bubble-like regimes of the price series; this explains why these relationships have previously been seen to appear and disappear over time. A secondary finding is that short-term relationships between the chosen factors and price appear to be caused by particular market events (such as hacks / security breaches), and are not consistent from one time interval to another in the effect of the factor upon the price. In addition, for the first time, wavelet coherence is used to explore the relationships between different cryptocurrencies.
In this paper, by using econometric techniques we provide evidence that bitcoin exhibited the formation of speculative bubble in 2017. To conceptually rationalize the results, we delve into the extant theoretical approaches developed by Kindleberger's (1978) speculative bubbles and Minsky's (1992) financial instability hypothesis. Certainly, bitcoin has spurred a revolution in payment technology that, if treated cautiously can facilitate financial intermediation and inclusion. Ultimately, whether or not bitcoin constitutes a bubble is a decision for investors as the road to hell is paved with good promises.
Stanisław Drożdż, Robert Gȩbarowski, Ludovico Minati, Paweł Oświȩcimka · 5 authors
Based on 1-minute price changes recorded since year 2012, the fluctuation properties of the rapidly-emerging Bitcoin (BTC) market are assessed over chosen sub-periods, in terms of return distributions, volatility autocorrelation, Hurst exponents and multiscaling effects. The findings are compared to the stylized facts of mature world markets. While early trading was affected by system-specific irregularities, it is found that over the months preceding Apr 2018 all these statistical indicators approach the features hallmarking maturity. This can be taken as an indication that the Bitcoin market, and possibly other cryptocurrencies, carry concrete potential of imminently becoming a regular market, alternative to the foreign exchange (Forex). Since high-frequency price data are available since the beginning of trading, the Bitcoin offers a unique window into the statistical characteristics of a market maturation trajectory.
In 2017, the Blockchain-based crypto currency market witnessed enormous growth. Bitcoin, the leading crypto currency, reached all-time highs many times over the year leading to speculations to explain the trend in its growth. In this paper, we study Bitcoin and explore features in its network that explain its price hikes. We gather data and analyze user and network activity that highly impact Bitcoin price. We monitor the change in the activities over time and relate them to economic theories. We identify key network features that determine the demand and supply dynamics of a crypto currency. Finally, we use machine learning methods to construct models that predict Bitcoin price. Our regression model predicts Bitcoin price with 99.4% accuracy and 0.0113 root mean squared error (RMSE).
The evolution of the economic processes is reflected on the way the currency work. The recent development of new methods of payment based on the computer systems – and, in particular, the electronic-based systems used to register the debit\credit position, the operationalization of the market – have elicited the growth of the phenomenon of cryptocurrency, and bitcoin is nowadays the most common. There is still no precise definition of cryptocurrency at the moment, due to the complexity in matching the cryptocurrency with the proper related case in issue. That said, it is crucial as in the face of a growing interest in bitcoins, the predisposition of an adequate control mechanism, still missing, is assuming a more and more importance; and in such a critical context, this lack treats the potential traders in this new segment. The awareness of the effective consistency and diffusion of the phenomenon should encourage the authorities in taking actions against the potential risks, especially for those inexperienced operators that are not able to identify and evaluate them, attracted by the promise of high profits with low investments. One of the most critical aspect in subiecta materia is the fiscal treatment of those bitcoin operations with particular regard to money laundering and terrorism financing. The growing phenomenon of crypto currencies – in addition to introduce potential danger (with evident damages for those who use them improperly) – emphasizes the need to move forward new forms of regulation of such complex matter, so that it can be redefined under the competence of the public authority.