Bitcoin Exchange Rate Research Articles

Построение и анализ модели машинного обучения для краткосрочного прогнозирования рынка биткоина на основе рекуррентных нейронных сетей

In this article, the construction and analysis of machine learning models were performed for short-term forecasting in the cryptocurrency market on the example of bitcoin – one of the most popular cryptocurrencies in the world. The initial data for the study leads to the conclusion that over the long period of its existence, bitcoin has shown a high degree of volatility, especially evident in comparison with traditional financial instruments. The article substantiates that this market is influenced by a multitude of factors. No one can say for sure what makes up the value of a particular cryptocurrency, as it involves a range of reasons, which cannot be fully taken into account. To overcome this problem, we have considered the principle of recurrent neural network. It is described why networks with memory are better at making predictions on the time series than conventional autoregressive model and standard forward propagation networks. The initial data processing algorithm and transformation methods are defined. The sample was reduced in order to increase the speed of the network, by reducing the number of recalculations of weights. The algorithm of the family of recurrent neural networks was built and trained to test the hypothesis about their better adaptivity due to short-term and long-term memory. The model is evaluated on the test data representing the bitcoin exchange rate for 2021–2022, since this period is characterized by high volatility. It is concluded that it is reasonable to use a similar type of models for short-term forecasting of cryptocurrency rates.

Analysis of the effect of e-currencies on financial performance based on information technology

E-currency is a form of digital currency that employs encryption to safeguard transactions, limit the manufacture of new units, and verify asset transfers. Bitcoin exchange rates and returns are the primary subjects of this study. In order to measure volatility, the standard deviation of logarithmic returns is determined. This study used a special test to determine whether or not the data were normal. Findings of high volatility were also made using a plot, a statistical process control chart, and other methods. Normality test (casual test) has been investigated accordingly to approve and validate the results. The F-test has been considered as the main indicator for the validity of the results. It has been based on the F-value of 9.3. As well as the financial performance has been done using the time and currency with upper and lower limits. The maximum limit is 34 with a G-value of 0.34. Furthermore, market return-based e-currency has been investigated and analyzed using free and fixed limits for both main variables time and currency. According to these data, the greatest value is 23 in fixed limit circumstances, while it is 18.4 in broad trend cases. The financial performance-based ANP method has been examined using the ANP approach with return values for the currency. The upper limit reached 544 with 0.43 as a G-value. An increasing number of people are valuing volatility. Because of the present high level of volatility, investing in Bitcoin is seen as a high-risk endeavor. The purpose of this study is to assist investors in developing a strategy that maximizes returns while minimizing risk

Technical trading rules' profitability and dynamic risk premiums of cryptocurrency exchange rates

PurposeThe study considers time-varying risk premium in investigating the capability of technical analysis (TA) to predict and outperform a buy–hold strategy in Bitcoin exchange rate returns.Design/methodology/approachThe study tests the technical trading rule of fixed moving average (FMA) on daily actual and equilibrium returns of Bitcoin exchange rates. The equilibrium returns are computed using dynamic CAPM in conjunction with a VAR-MGARCH (1, 1) system. The empirical evaluation of the study uses a case study of four Bitcoin exchange rates (BTC/AUD, BTC/EUR, BTC/JPY and BTC/ZAR) for the period 19 June 2010 to 30 October 2020.FindingsThe findings are consistent with related studies in conventional foreign exchange markets that find TA to be profitable, especially in emerging markets. Nevertheless, the consideration of risk premium has the effect of reducing the abnormal returns. Also, further robust tests reveal that Bitcoin returns possess a momentum effect which prompts further study in efficient market hypothesis research.Practical implicationsThe empirical findings of this study should benefit portfolio managers and active investors on the strength of TA to predict returns in a speculative market like the Bitcoin exchange rate market.Originality/valueThe study takes cognisance that cryptocurrency trading is speculative in nature which renders it a good candidate for TA methods. While there are studies that have explored the value of TA in Bitcoin exchange rates, these studies fail to incorporate the effects of time-varying risk premiums, the strength and focus of the current paper.

On fitting and forecasting the log-returns of cryptocurrency exchange rates using a new logistic model and machine learning algorithms

<abstract><p>Cryptocurrency is a digital currency and also exists in the form of coins. It has turned out as a leading method for peer-to-peer online cash systems. Due to the importance and increasing influence of Bitcoin on business and other related sectors, it is very crucial to model or predict its behavior. Therefore, in recent, numerous researchers have attempted to understand and model the behaviors of cryptocurrency exchange rates. In the practice of actuarial and financial studies, heavy-tailed distributions play a fruitful role in modeling and describing the log returns of financial phenomena. In this paper, we propose a new family of distributions that possess heavy-tailed characteristics. Based on the proposed approach, a modified version of the logistic distribution, namely, a new modified exponential-logistic distribution is introduced. To illustrate the new modified exponential-logistic model, two financial data sets are analyzed. The first data set represents the log-returns of the Bitcoin exchange rates. Whereas, the second data set represents the log-returns of the Ethereum exchange rates. Furthermore, to forecast the high volatile behavior of the same datasets, we apply dual machine learning algorithms, namely Artificial neural network and support vector regression. The effectiveness of these models is evaluated against self exciting threshold autoregressive model.</p></abstract>

A Review of the Fractal Market Hypothesis for Trading and Market Price Prediction

This paper provides a review of the Fractal Market Hypothesis (FMH) focusing on financial times series analysis. In order to put the FMH into a broader perspective, the Random Walk and Efficient Market Hypotheses are considered together with the basic principles of fractal geometry. After exploring the historical developments associated with different financial hypotheses, an overview of the basic mathematical modelling is provided. The principal goal of this paper is to consider the intrinsic scaling properties that are characteristic for each hypothesis. In regard to the FMH, it is explained why a financial time series can be taken to be characterised by a 1/t1−1/γ scaling law, where γ>0 is the Lévy index, which is able to quantify the likelihood of extreme changes in price differences occurring (or otherwise). In this context, the paper explores how the Lévy index, coupled with other metrics, such as the Lyapunov Exponent and the Volatility, can be combined to provide long-term forecasts. Using these forecasts as a quantification for risk assessment, short-term price predictions are considered using a machine learning approach to evolve a nonlinear formula that simulates price values. A short case study is presented which reports on the use of this approach to forecast Bitcoin exchange rate values.

اقتصاديات العملات الافتراضية المشفرة وماقد تفرضه من تحديات إقتصادية (دراسة حالة للبتکوين )

Summary: Since 2009, virtual currencies have been based on the “Block chain” technology, making these virtual currencies a reality that raises many controversies about whether or not it is a currency, as well as the challenges it may pose at the local and international levels, creating a space for disagreement. And what may result from the spread of the world and more than it is now, and the study aimed to answer “what the economies of Bitcoin and the mechanism of circulation? What are the most important economic challenges posed by virtual cryptocurrencies, whether they are real or not?” Using the inductive descriptive approach to present the theoretical rooting of virtual currencies, their 2 characteristics and trading mechanism, characterize the developments of the Bitcoin exchange rate as an example and a case study representative of virtual currencies, and the volume of global trade in them from 2009 to 2018, which is based on extrapolating the most important challenges that these virtual currencies may pose. The study concluded that Bitcoin and then virtual currencies are not a currency, as well as the existence of many local and international economic challenges that may be posed by these virtual currencies now and in the future.

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

An Equilibrium Model of the Market for Bitcoin Mining

We propose a model that uses the exchange rate of Bitcoin against the US dollar to predict the computing power of Bitcoin’s network. We show that free entry places an upper bound on mining revenues and explain how it can be identified. Calibrating the model’s parameters allows us to accurately forecast the evolution of the network computing power over time. We find that a significant share of mining rewards was invested in mining equipment and that the seigniorage income of miners was limited.

Relationship between Bitcoin Exchange Rate and Other Financial Indexes in Time Series

The Bitcoin exchange rate (BER) is influenced by many variables such as human speculation and policies and, thus, is dependent on the financial system. The fluctuation of BER submitted has been extensively investigated. However, the correlation analysis of the short- and long-term effects by indicators of online sentiment is unexplored. Therefore, this study establishes a VAR model for BER which provides a framework to the Google search volume index (SVI), the investor fear gauge (VIX), and the S&P500 Index. The findings of the analysis suggest that BER and Google SVI have a Granger causality feedback relationship in both the short- and long-term co-integration equilibrium, and the VIX is significantly related to BER in the long-term co-integration.

Bitcoin i kriptovalutne klauzule

Nowadays, it is almost impossible to imagine an effective legal system that is not somehow inspired by nominalistic ideas. However, the principle of monetary nominalism is not necessary in correlation with other higher principles, such as the principle of fairness, for example. Thus, legislators build and implement corrective instruments in legal acts, most of the time allowing legal subjects to choose and adapt those instruments to best fit their economic interests. In that context, (foreign) currency clauses are probably the most frequently used instrument. Those norms, when implemented in contract, prevent the negative effects of domestic currency depreciation through the denomination of the amount of debt in foreign currency. Whether we regard them as currency or not, cryptocurrencies are increasingly becoming an important part of our digitalized economic world. So, unless the legislature strictly limits or abolishes the freedom of will (the principle of party autonomy) in contract law by banning cryptocurrencies, contracting parties can hedge against domestic currency depreciation by pegging the amount of debt to the exchange rate of one of thousands of existing cryptocurrencies. If parties choose to make such an agreement, it is most likely that they will peg the amount of debt to the Bitcoin exchange rate. If parties choose to make such an agreement, it is most likely that they will peg the amount of debt to the Bitcoin exchange rate. In this paper, the author analyzes (crypto)currency clauses nominated in Bitcoin and their effects on contract relations in the legal system of the Republic of Serbia. This research heavy relies on the advantages of the normative and the comparative method, and various techniques of the analytical method.

АНАЛІЗ ВЗАЄМОЗВ’ЯЗКУ БІТКОЇНА З ФОНДОВИМИ ІНДЕКСАМИ, ТОВАРАМИ ТА ЦІННИМИ ПАПЕРАМИ

The article analyzes the impact of 44 different factors on the change in the price of bitcoin, such as: yields on US one-year bonds, prices for futures on palladium, platinum, gold, silver, copper, Chinese stock indices, German stock index DAX, French index CAC 40, stock indices of South Africa, India, Mauritius, England, Switzerland, Sweden, South Korea, etc. The most influential factors were selected, with the help of which two regression models were built. The presence of insignificant factors in the first model led to the need to consider the second one, which is much better. The second model lacks multicollinearity, but there are autocorrelation of residues and heteroscedasticity. There was an attempt to eliminate autocorrelation of residues, but it led to a significant reduction in the coefficient of determination to 22%. An attempt to explain the causality of the Bitcoin exchange rate to the KOSPRI index has been made. South Korea is a country, which actively cooperates with two main leaders in Bitcoin mining, namely China, where 65 % of new bitcoins are created, and the United States – 23 % of new bitcoins. Modern economic leaders actively influence not only the economy of their country, but also other economies. Korean companies are actively penetrating both the Chinese economy and the US national economy, which determines their impact on Bitcoin mining, as a large amount of necessary electronics is produced by South Korean companies such as SK hynix Inc. As for the palladium futures rate, palladium is a metal which is actively used in production of the electronics needed to mine bitcoin. Prospects for further research depend on trends in the price of bitcoin. It is possible that the price of the cryptocurrency Bitcoin will gradually rise, as some corporations begin to recognize Bitcoin as a means of payment for goods and services. This will increase demand and, as a result, prices. The confidence of large companies in Bitcoin will indicate the reliability of the cryptocurrency, which in turn will give confidence to minority investors in the need to add cryptocurrency to investment portfolios. The rise in prices will indicate the need to study the impact of various factors on changes in the price of Bitcoin. If the popularity of cryptocurrency begins to wane, it will lead to a rapid loss of its price. This development option will indicate the inexpediency of continuing to study the relationship of this cryptocurrency with these factors and the need to find other ones.

To Mine or to Trade? An Empirical Study of Bitcoin Exchange and Mining Markets

Problem Definition: The recent upsurge in Bitcoin prices has raised interest in the investment of Bitcoin. However, faced with the investment options of mining and trading, the behavior of investors is not clear and requires a closer scrutiny of both the Bitcoin exchange market and the mining market. Specifically, little is known about the pattern of the interaction of the two markets. As mining significantly determines the reliability and security of the operation of the Bitcoin system, it’s important to understand whether the trading market can influence the investment in mining. Academic/Practical Relevance: Our research bridges the gap between operation management, finance and information technology, and makes contribution to studies in the operations and economics of cryptocurrency systems. Methodology: Using a variety of datasets at a large scale, our empirical study uncovers the causal impact of the exchange market on the mining market. Specifically, we formulate an empirical framework that encompasses key dimensions of the Bitcoin exchange market, exchange rate and liquidity, and also jointly models the entry and exit of the mining market characterized by supply and demand of Bitcoin mining rigs. The framework also accounts for a holistic set of demand and supply shifters that exogenously alter the investment behavior in the Bitcoin mining market. Results: We empirically show people tend to associate a higher valuation of Bitcoin with a higher Bitcoin exchange rate, and such exchange rate has significant impacts on both the supply and demand of the mining rigs. Furthermore, we demonstrate that the Bitcoin exchange market serves as a substitute for the Bitcoin mining market for investment. This substitution effect is particularly pronounced to those investors as potential entrants to the Bitcoin mining market. Managerial Implications: Our study sheds light on the impact of the Bitcoin exchanges on the Bitcoin mining activities which contribute to the operation of the Bitcoin system.

Review on Bitcoin Price Prediction Using Machine Learning and Statistical Methods

Bitcoin is invented in 2009 by the pseudonymous Satoshi Nakamoto. Bitcoin is a decentralized digital currency system [1]. Bitcoin is the most acknowledged cryptocurrency in the world, which provide it interesting for financier. The cryptocurrency market capitalization on date 22nd July 2020 value represents roughly USD 277 billion of dollars, bitcoin representing 62% of it. However, a disadvantage for investors is the difficulty of predicting the price of bitcoin due to the high volatility of the bitcoin exchange rate. Measurement, estimation, and modeling of currency exchange rate volatility compose a significant research area. For this reason, a lot of studies done about bitcoin price prediction both Machine Learning (ML) and Statistical Methods. In comparison studies, ML methods perform better in general. This review is a comprehensive study on how we can better predict bitcoin prices by grouping previously done studies. The presentation of Bitcoin price prediction studies in groups reveals, the difference from other review studies. These are statistical methods, ML and statistical methods, ML-ML, frequency effect of selected time, effect of social media and web search engine, causality, optimization of hyperparameters methods.

The Impact of Derivatives on Cash Markets: Evidence From the Introduction of Bitcoin Futures Contracts

Cryptocurrencies provide a unique opportunity to identify how derivatives impact cash markets. They are fully fungible across multiple trading venues and futures contracts were selectively introduced on bitcoin (BTC) exchange rates against the USD in December 2017. Following the futures introduction, we find a significantly greater increase in cross-exchange price synchronicity for BTC-USD relative to other exchange rate pairs, as demonstrated by an increase in price correlations and a reduction in arbitrage opportunities. We also find support for an increase in price efficiency, market quality, and liquidity. Overall, our analysis supports the view that the introduction of BTC-USD futures was beneficial to the bitcoin cash market by making the underlying prices more informative.

Pricing of illicit drugs on darknet markets: a conceptual exploration

Purpose Trading illicit drugs on cryptomarkets differs in many ways from material retail markets. This paper aims to contribute to existing studies on pricing by studying the relationship between price changes in relation to changes in nominal value of the cryptocurrency. To this, the authors qualitatively study product descriptions and images to expand the knowledge on price formation. Design/methodology/approach The authors analysed 15 samples based on visual and textual scrapes from two major drug markets – for Dream Market between January 2014 and July 2015 and for Tochka between January 2015 and July 2015. This longitudinal study relates changes in process to variations in the Bitcoin exchange rate and selling strategies. The analysis of the marketing of drugs online also addressed the development of the vendor profile and product offers. Findings Product prices change in relation to variations in the Bitcoin exchange rate. This points to the application of mechanisms for automatic price adaptations on the market level. Real prices of the drug offers constantly increase. The authors assert that there is a bidirectional relationship. Vendors structure price and discounts to encourage feedback. And feedback in combination with signals of commitment and authenticity inform pricing. Product descriptions are an important feature in the successful marketization of goods, whereas product images are predominantly used as an aspect of recognisability and feature of the vendor’s identity. Research limitations/implications Findings suggest that there is great potential for further qualitative research into the relationship between the online and offline identity of drug vendors, as well as price setting when entering the market and subsequent changes for offered products. Practical implications Findings also suggest that further investigation into the constitution and management of vendor’s identity on the cryptomarkets would allow a better understanding of vendors and their interactions on cryptomarkets. Social implications A better understanding of drug trading on cryptomarkets helps to more effectively address potentials for harm in the online drug trade. Also targetting crime would benefit from a better understanding of vendor idenities and pricing. Originality/value The findings represent a valuable contribution to existing knowledge on drug trading on cryptomarkets, particularly in view of pricing and vending strategies.

Leveraging the Users Graph and Trustful Transactions for the Analysis of Bitcoin Price

Cryptocurrencies are notorious for their exchange rate high volatility, and are often tools of wild speculation rather than decentralised value exchange. This is especially true for Bitcoin, still, nowadays, the most popular cryptocurrency. This paper presents an analysis to detect the influence of a set of topological properties of the Bitcoin Users Graph on Bitcoin's exchange rate. 1 1. In the rest of this paper we will use the terms “Bitcoin price” and “Bitcoin's exchange rate” interchangeably to represent the amount of fiat currency (USD) needed to buy one Bitcoin at a given time. We consider, besides classical properties, a novel notion of Trustful Transaction Graph introduced to describe partial Users Graphs derived by chains of 0-confirmation transactions. We present a temporal analysis of the evolution of a set of features with a single day granularity. Afterwards, we applied autoregressive distributed-lag linear regression to assess whether and with which strength and duration a change in the considered features is likely to influence the exchange rate up to a prespecified number of days (fifteen) in the future. The results show that some of the considered features significantly influence the exchange rate up to several days, and that such relationships are likely not to be spurious, since we found that those features contribute significantly to decrease the error in predicting the exchange rate.

Impact of Twitter Sentiment Related to Bitcoin on Stock Price Returns

Twitter is becoming an increasingly popular platform used by financial analysts to monitor and forecast financial markets. In this paper we investigate the impact of the sentiments expressed in Twitter on the subsequent market movement, specifically the bitcoin exchange rate. This study is divided into two phases, the first phase is sentiment analysis, and the second phase is correlation and regression. We analyzed tweets associated with the Bitcoin in order to determine if the user’s sentiment contained within those tweets reflects the exchange rate of the currency. The sentiment of users over a 2-month period is classified as having a positive or negative sentiment of the digital currency using the proposed CNN-LSTM deep learning model. By applying Pearson's correlation, we found that the sentiment of the day (d) had a positive effect on the future Bitcoin returns on the next day (d+1). The prediction accuracy of the linear regression model for the next day's revenue was 78%.

Does Bitcoin behave as a currency?: A standard monetary model approach

We derive the Bitcoin exchange rate dynamics by solving the exchange rate equation of the standard flexible-price monetary model to investigate any characteristics of Bitcoin like a currency. The dynamics is driven by an asymmetric mean-reverting fundamental shock which can be attributed to a money demand shock. A crash occurs when the exchange rate with a weakened mean-reverting force breaches a lower boundary where a smooth-pasting condition is imposed. The empirical results show the exchange rate dynamics can be calibrated according to the model, in which the mean reversion of the dynamics is positively co-integrated with the Bitcoin transaction volume indicating demand for Bitcoin; and with the risk reversals of the commodity currencies (Australian dollar and Canadian dollar) in currency option markets. The analysis shows that the Bitcoin exchange rate shares some characteristics of commodity currencies with crash risk. This suggests that Bitcoin behaves as a currency between fiat money and a crypto-commodity used for trading and investment purposes.

Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants

In recent years, Bitcoin exchange rate prediction has attracted the interest of researchers and investors. Some studies have used traditional statistical and econometric methods to understand the economic and technology determinants of Bitcoin, few have considered the development of predictive models using these determinants. In this study, we developed a two-stage approach for exploring whether the information hidden in economic and technology determinants can accurately predict the Bitcoin exchange rate. In the first stage, two nonlinear feature selection methods comprising an artificial neural network and random forest are used to reduce the subset of potential predictors by measuring the importance of economic and technology factors. In the second stage, the potential predictors are integrated into long short-term memory (LSTM) to predict the Bitcoin exchange rate regardless of the previous exchange rate. Our results showed that by using the economic and technology determinants, LSTM could achieve better predictive performance than the autoregressive integrated moving average, support vector regression, adaptive network fuzzy inference system, and LSTM methods, which all use the previous exchange rate. Thus, information obtained from economic and technology determinants is more important for predicting the Bitcoin exchange rate than the previous exchange rate.

Policy uncertainty and Bitcoin returns

Bitcoin is the digital currency of the digital economy. This article is an attempt to reveal the effects of policy uncertainty on Bitcoin returns with economic policy uncertainty (EPU) in the US, the UK, Japan, China, and Hong Kong. Furthermore, we also present the results of monetary policy uncertainty (MPU) on the Bitcoin market. The robust estimations from the quantile regression and Markov regime-switching model show that Bitcoin returns are affected by EPU. One of the essential findings is that Bitcoin returns are more responsive to EPU in the US, China, and Japan. In the US and Japan, uncertainty has a negative effect on the Bitcoin market whereas in China it has a positive effect. Global MPU uncertainty is also significant in explaining Bitcoin exchange rates. Moreover, the Bitcoin market is negatively affected by uncertainty in Federal Open Market Committee (FOMC), the gross domestic product, and other macroeconomic data. Uncertainty in the equity market and Bitcoin returns are negatively associated.