Value Of Bitcoin Research Articles

Bitcoin Price Prediction Using Machine Learning Techniques

This paper discusses, trying to accurately assess the price of Bitcoin by looking at different parameters affects the value of Bitcoin. In our work, we focus on understanding and seeing the evolution of Bitcoin daily market, a1 and gaining intuition in the most relevant aspects surrounding the Bitcoin price. In the meantime, market capitalization of publicly traded cryptocurrencies exceeds $ 230 billion. The most important cryptocurrency, Bitcoin, is used primarily as a digital value store, and its pricing opportunities have been extensively considered. These features are described in more detail in the following paragraph: details of the main Bitcoin, as described in the paper. Bitcoin is the most expensive digital currency in the market. However, Bitcoin prices have been highly volatile, making it difficult to forecast. As a result, the goal of this research is to find the most efficient and accurate model for predicting Bitcoin prices using various machine learning algorithms. Several regression models with scikit-learn and Keras libraries were tested using 1-minute interval trading data from the Bitcoin exchange website bit stamp from January 1. 2012 to January 8, 2018. The best results showed a Mean Squared Error (MSE) as low as 0.00002 and an R- Square (R2) as high as 99.2 percent.

Pornhub and the Value of Bitcoin

Pornhub and the Value of Bitcoin

Risk scenario-based value estimation of Bitcoin

The wild swings in Bitcoin’s valuation keep attracting authorities’ and policy-makers interest. Thus at present, many researchers are focus on analyzing and forecasting. The existing studies on Bitcoin price prediction are mainly in two ways: (1) study how economic factors, market and investor sentiment indicators influence Bitcoin price; (2) apply machine learning and artificial neural networks to predict the value of Bitcoin. This paper aims to implement a scenario analysis method to generate various hypothetical events and then determine their effects on the value of Bitcoin price. Scenario analysis is normally used to measure financial risk. In this paper, we propose a method that combines scenario analysis with historical data. We further aim to find the correlations among scenarios and examine the relationship between the significant shocks and Bitcoin prices. Our findings suggest that what-if analysis is a good way to measure the risk exposure of Bitcoin. The method can also be used for worse-scenario analysis to check how Bitcoin performs during crisis periods.

GenSelfHolding: Fusing Selfish Mining and Block Withholding Attacks on Bitcoin Revisited

Due to the monetary value of Bitcoin, the most influential digital cryptocurrency in the world, Bitcoin has naturally become a valuable target of attacks, resulting in the emergence of many attack strategies on it. Among those attack strategies, selfish mining and block withholding attacks are two typical ones and attackers can obtain higher revenues under certain conditions than with an honest mining strategy. However, the combination of them will be a new type and more serious attack, which has not been analyzed in depth. In this paper, we propose GenSelfHolding, a general combined attack model with one selfish mining pool and random multiple honest pools on Bitcoin. Based on Markov chain, a general state transition graph and a general state distribution probability are presented to describe the internal features of our model. A general principle is then provided to calculate the attacker’s revenue. In addition, we give a detailed proof of the unique stable distribution of state transition probabilities. Such proof is an essential prerequisite for us to further present stable attacker revenue expressions under two specific scenarios, the GenSelfHolding model with two/three honest mining pools. Simulation results validate that the revenues of the attacker in these two specific models can reach up to 40% higher than those of classic selfish attackers in some cases.

Market Efficiency of Exchange Rate of Bitcoin with Dollar and Rupiah of Foreign Exchange Markets: Weak and Semi-Strong Form Test

Bitcoin is one of the cryptocurrencies that had a high rate of return since its appearance in 2009. However, the exchange rate of Bitcoin against any foreign currency is considered to have high volatility making it difficult to determine the real value of Bitcoin. The main purpose of this research is to find the value of Bitcoin, especially US Dollar and Rupiah currencies. The test is carried out using the weak market efficiency hypothesis and the semi-form market coefficient hypothesis. The data processing methods are used the stationary test (ADF, KPSS, and ERS) to test the efficiency of the weak form market and the cointegration test (Johansen Cointegration) with the VECM model to check the efficiency of the semi-strong market. The results show that the Bitcoin exchange rate does not have a unit root so it is inefficient in a weak form and has a negative effect on the USD / IDR exchange rate so that it is not efficient in semi-strong form as well as on the US Dollar and Rupiah exchange rates. This happens because Bitcoin transactions as a medium of exchange in Indonesia are still illegal. So that the Bitcoin exchange rate against the US Dollar and Rupiah exchange rates is biased because it does not reflect the available information, both historical information and public information.
 Keywords—Bitcoin Exchange Rate; Market Efficiency; Unit Root; Cointegration

Ethereum synchronicity, upside volatility and Bitcoin crash risk

The fundamental value of Bitcoin is difficult to evaluate because there is no underlying company or cashflow. Extant literature documented various macro-economic factors and technical factors to price Bitcoin. However, for a highly speculative instrument, investors’ behavior plays an important role in asset pricing. This paper incorporates Bitcoin Ethereum synchronicity conditional on upside volatility of Bitcoin as a proxy for the fear of high Bitcoin prices. Empirical results reveal that when upside volatility is high, Ethereum synchronicity exerts a significant positive influence on Bitcoin crash risk.

Bitcoin and Fiat Currency Interactions: Surprising Results from Asian Giants

This study examines the interaction of Bitcoin with fiat currencies of three developed (euro, pound sterling and yen) and three emerging (yuan, rupee and ruble) market economies. Empirical investigations are executed through symmetric, asymmetric and non-linear causality tests, and Markov regime-switching regression (MRSR) analysis. Results show that Bitcoin has a causal nexus with Chinese yuan and Indian rupee for price and various return components. The MRSR analysis justifies these findings by demonstrating the presence of interaction in contractionary regimes. Accordingly, it can be stated that when markets display a downward trend, appreciation of the Chinese yuan and Indian rupee positively and strongly affects the value of Bitcoin, possibly due to the market timing. The MRSR analysis also exhibits a transition from a tranquil to a crisis regime in March 2020 because of the pandemic. However, a shorter duration spent in the crisis regime in 2020 indicates the limited and relatively less harmful effect of the pandemic on the cryptocurrency market when compared to the turmoil that occurred in 2018.

Application of Error Correction model with monetary function factors in the return of bitcoin

Bitcoin is a kind of virtual currency based on block chain technology. In many scenarios, it shows the function of currency to a certain extent. However, many believe that the value of Bitcoin is hollow, speculative and unable to undertake monetary functions, which prevents it to circulate as a real currency. This paper assumes that Bitcoin can undertake monetary functions, which can verify the relationship between monetary functions and Bitcoin earnings. According to the availability of the data and the previous research results, the monthly value of the indexes to measure the monetary function from 2010 to 2020 are selected to form a regression equation with the monthly return data of Bitcoin in the corresponding period. ADF test shows that the explained variable is a first-order simple integer, and the explanatory variables have both first-order simple integer and stationary sequence, and there is a cointegration relationship between them. The result of error correction model shows that the relationship between Bitcoin rate of return and the relevant indicators to measure monetary function is not significant, which rejects the original assumption that Bitcoin can assume monetary function, indicating that Bitcoin does not have the ability and potential to assume monetary function. This may be due to the speculative nature of Bitcoin and the lack of powerful credit support. Therefore, investment in Bitcoin needs to be cautiously thought, but the blockchain technology used in Bitcoin needs to be vigorously developed, which could lead to a technological revolution; in addition, it is momentous for countries to develop digital currencies of central bank to meet the challenge.

Bitcoinmania: A Ticking Time Bomb Waiting to Explode

At the time of writing this article, the Bitcoin bomb has exploded; after peaking at $64,863 on 14 April 2021, the value of Bitcoin has plunged 45% to $31,276 on May 21, 2021; consequently, nearly $600 billion of value evaporated from Bitcoin’s market cap, i.e. from $1.182 trillion (April 2021) to less than $600 billion (May 2021). Bitcoin’s high price valuations coincides with its halving dates; aftermath of each halving event has led to a bubble formation within one year and a crash in the ensuing few months after a perceived peak has been achieved. The three previous halving events are a testimony to this fact; after the halving #1 on 28 November 2012 (block reward was reduced from 50 BTC to 25 BTC), price of each bitcoin increased from $76 on 9 July 2013 to a peak of $1,153 on 5 December 2013, an increase of 1,417% (but fell to $177 in January 2015). Following the halving #2 on 9 July 2016 (block reward was reduced from 25 BTC to 12.5 BTC), the price of bitcoin charted a remarkable ascent, from $963 on 2 January 2017 to $20,089 on December 17 in the same year (a surge of 1,986%, followed by a nosedive to $3,557 in January 2018). The halving #3 has been most unprecedented by any stretch of imagination, the value of each bitcoin has skyrocketed from $7,194 on 1 January 2020 to $64,863 on 14 April 2021; however in the subsequent month (May 2021), Bitcoin gave back half of the gain (price reduction of 20% or more could be considered as a major correction). It is important to mention that Blockchain is not confined to Bitcoin and its survival as a revolutionary technology does not depend on cryptocurrencies. Bitcoin and some altcoins may become obsolete, but blockchain will continue to forge ahead unabated as many life-changing inventions have done before.

The Asymmetric Effect of Panic Index on Cryptocurrencies

The Covid 19 pandemic is the first major crisis facing cryptocurrencies. Therefore, the reaction of the cryptocurrency markets is important. News about epidemics affects investors' decisions. Panic index (PIndex) is an index created from news about the Covid 19 outbreak. In the study, it is used to measure the impact of decisions on the crypto money market. As cryptocurrencies, Bitcoin (BTC), Etherium (ETH), and Ripple (XRP), which have the highest transaction volume in the crypto money market, are included in the analysis. The relationship between Panic Index and the three major cryptocurrencies with the largest share in the cryptocurrency market was investigated by Ardl and Hatemi-J asymmetric causality test. Traditional causality tests acknowledge that the effects of positive and negative changes are the same. However, there may be asymmetric information and different investor behaviors in financial markets. In the study, Hatemi-J [1] Asymmetric Causality Test was conducted to examine the asymmetric relationship and symmetric relationship between Pindex and cryptocurrencies by separating them into positive and negative shocks. According to the results of the Hatemi-J causality analysis, positive shocks in the panic index are the cause of negative shocks for all cryptocurrencies. In other words, increases in the panic index are caused to fall the value of Bitcoin, Ethereum, and Ripple cryptocurrencies decrease. The results show that cryptocurrencies were not a safe haven for the investor during the Covid 19 period, as they acted similarly to other financial assets.

Study of Swarm Intelligence Algorithms for Optimizing Deep Neural Network for Bitcoin Prediction

Blockchain, a shared digital ledger, operates on a peer-to-peer network which is used for storing the transactions. Cryptocurrencies are used for transactions in blockchain. The most popular breed among cryptocurrency was bitcoin. Predicting the day-to-day value of bitcoin is a challenging task due to nonlinear and market volatility. There are many statistical methods and machine learning algorithms proposed to forecast the cost of bitcoin, but they were lacking to predict the correct result when the input data set is larger and has more noise. To handle large data set, a deep learning technique has been used. The deep learning algorithms, especially LSTM network, also have some drawbacks such as high computational time, inability to generate higher quality prediction result. To avoid these shortcomings and make LSTM a better model for bitcoin prediction, it is necessary to optimize LSTM network. This paper presents a comparative study of numerous optimized deep learning techniques to forecast the price of bitcoin.

Idiosyncrasies of Money: 21st Century Evolution of Money

This paper examines the question of what kind of money will govern the 21st century by examining the developments which characterise this landscape. On the basis of a review of the available literature and evidence, it is clear that certain technological innovations, such as the movement towards electronic money, will undoubtedly change how we operate. However, the conclusion in this paper is less sanguine regarding the prospects of a global currency, regional monetary unions, or states’ exit from or central banks’ control of money. This paper also sees poor prospects for cryptocurrencies at the moment, given their focus on the decentralisation and politicisation of money, because money requires a backstopping force, making it inherently political. Finally, this paper considers how regulators may seek to ensure that money in its digital form is not taken advantage of and applied in malevolent activities. The study used correlation to establish the level of association among variables. A multiple regression analysis was used to draw an econometric model explaining the relationship between the independent and dependent variables. The following variables were used as independent variables: monetary aggregate (M1), harmonised index of consumer prices (HICP), Euro Interbank Offered Rate (EURIBOR), US dollar/euro, and the USD value of Bitcoin. Multiple regression predicted that when inflation rises, the money supply will decrease. M1 includes cash in circulation, current deposits, and other than demand deposits. The study concludes that price increases encourage people to keep their money in longer-term deposits, including in cryptocurrency. Additionally, an increase in EURIBOR and US dollar/euro reduces the supply of money. Otherwise, an increase in the price of bitcoin in the economy would increase the overall money supply.

Generations Y and X Perception Towards Bitcoin in Malaysia

Traditionally, money served as a medium of financial transaction but now the system advances to the introduction of a new electronic payment through Blockchain technology known as cryptocurrency. The phenomenon of this cryptocurrency in Malaysia is very new and it is still in debate among Malaysians especially Bitcoin’s cryptocurrency. This research is to investigate the perception of generation Y and Generation X to Bitcoin. The research is conducted based on both primary data gathered from questionnaire and secondary data collected from valid sources. The results of the research indicate that the value of Bitcoin and the extending network of operations are considered as significant factors towards the perception towards Bitcoin, while security of Bitcoin has an opposite status. In conclusion, Bitcoin does have high potentials in the near future, and it will change the current monetary system, as it is classically known.

Bitcoin boom: What rising prices mean for the network’s energy consumption

Alex de Vries earned his MSc in Economics and Business from the Erasmus University Rotterdam in 2011. In 2014 he founded the blog digiconomist.net . This blog is a platform for research, dedicated to exposing the unintended consequences of digital trends. The blog is best known for featuring the Bitcoin Energy Consumption Index since late 2016, which has played a major role in the global discussion regarding the sustainability of proof-of-work-based blockchains.

THE COINTEGRATION RELATIONSHIP BETWEEN BITCOIN AND THE STOCK INDICES OF TURKEY AND BRICS COUNTRIES

Bitcoin, the most important of all cryptocurrencies, is a currency that is not included in the central monetary system and has a digital format. With the rapid increase in the value of Bitcoin in recent years, it has attracted the attention of investors. Bitcoin, as an alternative to traditional investment tools, has sparked a lot of controversies. In this study, the Bitcoin price relationship between stock market indices and the BRICS countries belonging to Turkey is intended to be detected. In this direction of Bitcoin 01.01.2013-31.12.2019 period to test the relationship between Turkey and the BRICS countries, stock index monthly data are used. After determining the basic statistical properties of the series, cointegration and causality test was performed to determine the financial relationship. ADF (Augmented Dickey-Fuller) unit root tests and stationarity analysis are applied, and then the existence of long-term relationships between stock exchanges is explained with the Johansen cointegration test. The Vector Error Correction Model (VECM) was used to analyze whether the long-term relationship is in equilibrium. Also, short-term relationships were determined by Granger causality analysis. The analysis performed between variables were identified as a result of a long-term relationship, Russia (MOEX) and Turkey (BIST100) was found to be the cause of the stock market index of Bitcoin. It has been determined that Bitcoin is the cause of China (SHANGAI) exchange. In these exchanges, it has been observed that the change in Bitcoin prices in the short term affects investment decisions.

The Value of Bitcoin in the Year 2141 (and Beyond!)

The emergence of bitcoin poses an important question for monetary theorists: can bitcoin compete with or even replace existing fiat monies? To answer this question, one must be able to determine what gives intrinsically useless monies their value, what determines the coexistence of alternative monies, and under what conditions economic agents would prefer to hold one money relative to another. We attempt to answer these questions in light of the emergence of bitcoin. In particular, we outline a theoretical model in which an intrinsically useless money is essential.

Bitcoin Financial Forecasting

Bitcoin is one of the crypto currencies and is most unpredictable currencies. In the world of crypto currencies, the value of a coin can unpredictably upgrade or degrade. In this study, the model has been trained to predict the value of Bitcoin in USD at any given time stamp. For this prediction, three algorithms of machine learning - Linear Regression (LR), Support Vector Regression (SVR) and Neural Network Regression (NNR) have been used. The model is trained using the collected dataset. After the collection of data set, we first applied SVR algorithm, then we used LR and then NNR to calculate the error compared to the actual value. Root mean squared error (RMSE) is used as the predictive measure. Out of the three algorithms, LR was found out to be more accurate for predicting the value of bitcoin.

Fiat Money, Cryptocurrencies, and the Pure Theory of Money

This paper attempts to account for the rising value of cryptocurrencies using basic concepts of monetary theory. A positive value of fiat money is itself problematic inasmuch as that value apparently depends entirely on its expected resale value. A current value entirely dependent on expected future resale value seems inconsistent with backward induction. While fiat money can avoid the backward-induction problem if it is made acceptable in payment of taxes, acceptability for tax payments is unavailable to cryptocurrencies. Is the rising value of bitcoin and other cryptocurrencies a bubble? The paper argues that network effects may be an alternative mechanism for avoiding the logic of backward induction. Because users of any good subject to substantial network effects incur costs by switching to an incompatible alternative to the good currently used, users of a bitcoin for certain transactions may be locked into continued use of bitcoin despite an expectation that its future value will eventually go to zero. Thus, even if bitcoin and other cryptocurrencies are bubble phenomena, network effects may lock existing users of bitcoin into continued use of bitcoin for those transactions for which bitcoins provide superior transactional services to those provided by conventional currencies. Nevertheless, the prospects for bitcoin’s expansion beyond its current niche uses are dim, because its architecture implies that a significant expansion in the demand for its transactional services would lead to rapid appreciation that is incompatible with service as a medium of exchange.

Rynek kryptowalut w Polsce i jego instytucjonalne uwarunkowania

Rynek kryptowalut już od czasu swojego powstania budził wiele kontrowersji oraz obaw związanych z zagrożeniami, które stwarza obrót walutą wirtualną. Gwałtowny wzrost wartości Bitcoina w 2017 roku, miał wpływ na znaczny wzrost zainteresowania inwestorów, instytucji oraz podmiotów związanych z rynkami finansowymi, które nie dotyczyło jedynie chęci wzbogacenia się. Technologia blockchain oraz bazujący na niej rynek kryptowalut, umożliwiają rozwój technologii, szybki i bezpieczny przepływ kapitału oraz mają wiele innych zastosowań, które w przyszłości mogą doprowadzić do rewolucyjnych zmian i odkryć. Jednakże należy również pamiętać, że rynek waluty wirtualnej stwarza warunki dla szeroko rozumianej działalności przestępczej i terrorystycznej, będących zagrożeniem dla funkcjonowania całego systemu polityczno-gospodarczego. Kluczowym elementem dla zapewnienia pełnej wydajności rynkowej i wykorzystania całego zakresu możliwości rozwoju, przy zachowaniu bezpieczeństwa istniejących na nim podmiotów, jest kreowanie budujących rynek właściwych uwarunkowań instytucjonalnych. Umożliwią one zachowanie równowagi między poziomem ingerencji w system finansowy związanej z wprowadzeniem regulacji dotyczących waluty wirtualnej a swobodnym rozwojem rynku i możliwościami, które za sobą niesie.

Intertemporal asset pricing with bitcoin

This paper develops and tests an intertemporal regime-switching asset pricing model characterized by heterogeneous agents that have different expectations about the persistence and volatility of bitcoin prices. The model is estimated using daily bitcoin price data from 2013 until 2020 whereby three types of agents are considered: mean–variance optimizers, speculators and fundamentalists, respectively. While mean–variance optimizers trade on the basis of conditional first and second moments of the return distribution, speculators engage in trend chasing and buy when prices are rising and sell when prices are declining. Fundamentalists trade on the basis of fundamental factors that can impact the value of bitcoin. The fractions of agents engaging in one strategy over another shows statistically substantial variation during high and low bitcoin price volatility regimes. Estimation results reveal the following. First, unlike in traditional asset classes, there is evidence of mean–variance optimizers. Second, there is evidence of speculators who engage in ‘bandwagon behavior’ and buy bitcoins during price appreciations and sell bitcoins during price declines. Finally, there is evidence of fundamentalists who trade bitcoins when fundamental factors deviate from their long-run trends. Remarkably, these fundamentalists exhibit contrarian-type behaviors during low price volatility regimes while behaving more like fundamental traders during high price volatility regimes.