Abstract Since the emergence of Bitcoin, cryptocurrencies have grown significantly, not only in terms of capitalization but also in number. Consequently, the cryptocurrency market can be a conducive arena for investors, as it offers many opportunities. However, it is difficult to understand. This study aims to describe, summarize, and segment the main trends of the entire cryptocurrency market in 2018, using data analysis tools. Accordingly, we propose a new clustering-based methodology that provides complementary views of the financial behavior of cryptocurrencies, and one that looks for associations between the clustering results, and other factors that are not involved in clustering. Particularly, the methodology involves applying three different partitional clustering algorithms, where each of them use a different representation for cryptocurrencies, namely, yearly mean, and standard deviation of the returns, distribution of returns that have not been applied to financial markets previously, and the time series of returns. Because each representation provides a different outlook of the market, we also examine the integration of the three clustering results, to obtain a fine-grained analysis of the main trends of the market. In conclusion, we analyze the association of the clustering results with other descriptive features of cryptocurrencies, including the age, technological attributes, and financial ratios derived from them. This will help to enhance the profiling of the clusters with additional descriptive insights, and to find associations with other variables. Consequently, this study describes the whole market based on graphical information, and a scalable methodology that can be reproduced by investors who want to understand the main trends in the market quickly, and those that look for cryptocurrencies with different financial performance.In our analysis of the 2018 and 2019 for extended period, we found that the market can be typically segmented in few clusters (five or less), and even considering the intersections, the 6 more populations account for 75% of the market. Regarding the associations between the clusters and descriptive features, we find associations between some clusters with volume, market capitalization, and some financial ratios, which could be explored in future research.
The Bitcoin market exhibits characteristics of a market with pricing bubbles. The price is very volatile, and it inherits the risk of quickly increasing to a peak and decreasing from the peak even faster. In this context, it is vital for investors to close their long positions optimally. In this study, we investigate the performance of the partially observable digital-drift model of Ekström and Lindberg and the corresponding optimal exit strategy on a Bitcoin trade. In order to estimate the unknown intensity of the random drift change time, we refer to Bitcoin halving events, which are considered as pivotal events that push the price up. The out-of-sample performance analysis of the model yields returns values ranging between 9% and 1153%. We conclude that the return of the initiated Bitcoin momentum trades heavily depends on the entry date: the earlier we entered, the higher the expected return at the optimal exit time suggested by the model. Overall, to the extent of our analysis, the model provides a supporting framework for exit decisions, but is by far not the ultimate tool to succeed in every trade.
Bitcoin is currently the most widely used encryption currency in the world, and the Nasdaq Index, as the world's first stock market to use electronic trading, has a certain impact on the price of Bitcoin.Based on the Bitcoin closing price and Nasdaq index data from January 2020 to May 2022, this paper predicts the price of Bitcoin by using ARIMA and ARIMAX models respectively.The linkage was confirmed by the correlation test, and the fitting and prediction effect of the ARIMAX model with the Nasdaq index as the input variable were better than the ARIMA model.
Isfenti Sadalia, Nisrul Irawati, Rico Nur Ilham, Abdul Nasser Hasibuan · 6 authors
This research type is quantitative version with population, particularly 5 cash of cryptocurremcies with the largest market caps in Indonesia, specifically Bitcoin, Ethereum, Ripple, Bitcoin cash, Litecoin in Indonesia. Varians data in this examine is time series facts taken from January 2017 to December 2020 by using conducting a documentation look at carried out at the ebook of monthly digital assets transaction reports, in order that the target population is 240 (four years x 12 months x five cash) monthly report information sampels. The evaluation approach of records on this take a look at makes use of mild Regression analysis (MRA) model regression and information analysis the usage of e-views statistical software. Cryptocurrency is an funding commodity which could generate returns and already has a license to be traded in trade trading through the Indonesian Commodity Futures trading Regulatory organisation. This studies is added a brand new idea of motion crypto asset that we called it pace. on this second we use velocity Token approach that adapted from the monetary Equation of change via economists known as the quantity principle of money, and its model might be large motion with the aid of token costs. in line with this approach , low speed method that crypto assets last longer within the pockets , that means that traders who to begin with attempted to invest on asset purchases speedy are actually turning to holders, or individuals who keep crypto for the long time. Many quite a variety of crypto virtual belongings traded in Indonesia through the trading corporation Indodax. The effects of records analysis within the examine show that coal price efeect has a advantageous however no longer good sized on virtual assets returns in order that it could be justified as a effective hazard evaluation model. pace of cryptocurrency does not moderate the have an impact on of coal rate on digital belongings returns.
Momentum is one of the most widespread, persistent, and puzzling phenomenon in asset pricing. The prevailing explanation for momentum is that investors under-react to new information, and thus asset prices tend to drift over time. We use a unique feature of cryptocurrency markets: the fact that they are open 24/7, and report returns over the last 24 hours. Thus, the one-day return is subject to predictable fluctuations based on the removal of lagged information. We show that investors respond positively to changes in reported returns that are unrelated to any new release of information, or change in the asset fundamentals. We call this behavioral anomaly "Pure Momentum".
Blockchain is used by different industries like banking, healthcare, law enforcement, IOT, online music, digital transfer, and real estate for transaction security purposes. Blockchain is becoming more sustainable day by day. The objective of this study is to determine the interdependence of major stock market indices and cryptocurrencies, offering investors a potential path for diversification. A quantitative study will investigate the interdependency of cryptocurrencies on different stock market indices. These are selected on the basis of high market capitalization. The research will be based on secondary data collection. Strong correlation between crypto and stocks has been seen in developing or emerging market nations, which have been at the forefront of crypto development and adoption. In 2020–21, for example, the correlation between returns of the MSCI emerging markets index and Bitcoin was 0.34, increased 17-fold from the previous years. Stronger correlation indicates that Bitcoin is becoming a risky investment. Its correlation with stocks has risen above than that with other assets such as gold, investment grade bonds, and major currencies, indicating that risk diversification benefits are limited, contrary to prior beliefs. Increased crypto-stock interconnectedness increases the risks of spillover of investor sentiment spillovers between asset classes. As a result, a severe drop in Bitcoin prices may encourage investor risk aversion, resulting in a drop in stock market investment. Spillovers from the S&P 500 to Bitcoin are on average of equal magnitude, implying that sentiment in one market is passed.
Sowmya Dunnala, Anusha Bandla, Krishna Sai Anjana Sunkara, Ebenezer Jangam
Bitcoin is the most trending cryptocurrency which is used worldwide. Nowadays many general people or investors investing on bitcoin. But it becomes great challenge to analyze or predict the bitcoin price. Because of its fluctuations it is very hard to predict the price of the bitcoin. By this time machine learning came into picture with many models to analyze the behavior of bitcoin price by using time series data. These models will give better insights to the people who wants to invest on the bitcoin and they will able to understand about the volatility of bitcoin. We can use many machine learning models for prediction. But accuracy of the model is the deciding factor. We used ARIMA, LSTM and Facebook Prophet models and after the prediction is over, we have designed an ensemble model which merges the different models. And based upon the error rate we have decided the best model.
Shimon Kogan, Igor Makarov, Marina Niessner, Antoinette Schoar
Trading in cryptocurrencies has grown rapidly over the last decade, primarily dominated by retail investors.Using a dataset of 200,000 retail traders from eToro, we show that they have a different model of the underlying price dynamics in cryptocurrencies relative to other assets.Retail traders in our sample are contrarian in stocks and gold, yet the same traders follow a momentum-like strategy in cryptocurrencies.Individual characteristics do not explain the differences in how people trade cryptocurrencies versus stocks, suggesting that our results are orthogonal to differences in investor composition or clientele effects.Furthermore, our findings are not explained by inattention, differences in fees, or preference for lotterylike stocks.We conjecture that retail investors hold a model of cryptocurrency prices, where price changes imply a change in the likelihood of future widespread adoption, which in turn pushes asset prices further in the same direction.
Bitcoin has had a volatile journey since it was launched in 2009, the current main impressions of Bitcoin are mostly negative, resource-consuming, endangering financial security, and even associated with crimes, such as fraud, money laundering, and so on. However, this paper analyzes the origin of bitcoin and with the creative combination of existing computer technology, the construction of a complete transaction system was founded by Bitcoin, which has caused a huge impact in the fields of finance, technology and the environment. We have to acknowledge its shortcomings and deficiencies in some aspects, On the other hand, realize that Bitcoin has brought great progress and reflection in the fields of finance, technology, environment, etc.
In this paper, we analyse the long memory process in the cryptocurrencies Bitcoin (BTC), Cardano (ADA), Binance Coin (BNB), Dogecoin (DOGE), Ethereum (ETH) and Ripple (XRP) from January 1st, 2018, to November 10th, 2022, which includes the 2020 and 2022 events. The results demonstrate that the daily returns are leptokurtic, and the distributions are non-Gaussian. We also observe non-linearity, implying autocorrelation or conditional heteroscedasticity in digital currencies. The DFA exponents reveal that throughout the Tranquil period, digital currencies with current values higher than 0.5 exhibited long memory in their returns. The BNB digital currency has an exponent of 0.5, indicating that the series were unpredictable throughout this period. As can be shown, all cryptocurrencies offer values of the DFA exponent greater than 0.5 in the Stress subperiod, implying that the higher the DFA exponent and closer to 1, the higher the persistence, as well as the autocorrelation between observations and stronger predictive ability. The findings support the evidence examined by the BDS test, namely that price movements are not i.i.d. (independent and identically distributed) and that investors have a high possibility of achieving above-average returns through arbitrage.
The purpose of this study is to explore the co-movement between COVID-19 cases and eight cryptocurrencies. Cryptocurrencies (Bitcoin, Ethereum, Tether, Binance Coin, Dogecoin, Ripple, USD Coin and Bitcoin Cash) are selected based on their market capitalisations. Daily data is considered from 30 January 2020 to 19 May 2021. The continuous wavelet transform (wavelet coherence) is used to determine the time-varying co-movement between COVID-19 instances and cryptocurrencies in this research. COVID-19 and cryptocurrency prices are interlinked, as found using the wavelet method. Similar results were discovered for Tether, Binance Coin, and Ripple. Although this seems to be the case, Dogecoin appears to be an alternative investment during COVID-19. The research is unique and adds to the existing body of knowledge, even though some of the results address the function of cryptocurrencies in times of crisis. The research findings indicate that investors and crypto enthusiasts should keep an eye out in the scenario of COVID-19 scenarios when making investments in cryptocurrency marketplaces.
When compared to traditional financial markets, cryptocurrencies were seen as assets with minimal correlations. However, because this continually expanding financial market is marked by substantial volatility and strong price movements over a short period, developing an accurate and reliable forecasting model is deemed crucial for portfolio management and optimization. Given the relevance of cryptocurrencies in the global economy, it is important to determine if Bitcoin (BTC) becomes more predictable as investors adopt more aggressive trading positions. We examine BTC over the period from May 15th, 2021, to April 14th, 2022 (8676-time data), using intraday (hourly) time scales. The results reveal that the random walk hypothesis is rejected at lags of 3 to 16 days, while we see that the BTC market tends toward efficiency (see the evolution between lags of 16 and 2). These findings reveal that, given the uncertainty in the global economy in 2022, namely the Russian invasion of Ukraine, the BTC market shows values of the variance ratios close to unity, implying that it is, apparently, not predictable and that the residuals are not autocorrelated in time. In addition, the results of the Detrended Fluctuation Analysis (DFA) exponent show that this market does not exhibit characteristics of (in) efficiency in its weak form. In other words, this market does not have persistent and mean-reverting properties, thus validating the results of Wright’s Rankings and Signs variance test.
The growth of Cryptocurrency has been considered as a future legitimate tender of currency with great possibilities, and it has contributed to lots of different fronts like investments and forms of trading, on the contrary, has caused several troubles.As virtual currencies are developing rapidly, people should comprehend basic concepts and their global influences of them.Our research paper has included the histories and functions with rules and regulations comprehensively.Our goal is to make sure that the audience understands cryptocurrency by the details and examples given and explore further diversification of critical thinking on the topic.We have retrieved lots of resources from articles, websites, and statistical data and discussed insightful analysis to make sure the accuracy is guaranteed.Our study would be beneficial to people with zero understanding of the concept of cryptocurrency.
This article examines the impact of technological changes to cryptocurrency—known as “forking” that triggers blockchain splits—on market conditions. Despite the explicit distinction in log return distributions between the two splitting blockchains, adopting new technology does not result in a disparity in market conditions: no significant difference exists in market efficiency and long‐term market equilibrium between the two splitting blockchains. Technological changes accompanying market separation do not impede the underlying uniformity in market conditions. The findings suggest that mutual information flows linked to market liquidity explain the results between the new and old forks.
Cryptocurrency has become a popular asset in global financial markets, meaning that not only individual investors but also asset management companies around the world are considering this new investment class. The main contribution of this research is to address an intra-day forecasting problem with hourly granularity by comparing deep network architectures, including ones with and attention mechanisms for the Ethereum intrinsic cryptocurrency (ETH). The results showed that the TCN outperformed other architectures considered for a short-term forecast period in terms of processing time and it is amongst the most accurate models using an ARIMA model as a baseline.
The emergency of cryptocurrency has caused a shift in the financial markets. Although it was created as a currency for exchange, cryptocurrency has been shown to be an asset, with investors seeking to profit from it rather than using it as a medium of exchange. Despite being a financial asset, cryptocurrency has distinct, stylised facts like any other asset. Studying these stylised facts allows the creation of better-suited models to assist investors in making better data-driven decisions. The data used in this thesis was of three leading cryptocurrencies: Bitcoin, Ethereum, and Dogecoin and the Johannesburg Stock Exchange (JSE) data as a guide for comparison. The sample period was from 18 September 2017 to 27 May 2021. The goal was to research the stylised facts of cryptocurrencies and then create models that capture these stylised facts. The study developed risk-quantifying models for cryptocurrencies. The main findings were that cryptocurrency exhibits stylised facts that are well-known in financial data. However, the magnitude and frequency of these stylised facts tend to differ. For example, cryptocurrency is more volatile than stock returns. The volatility also tends to be more persistent than in stocks. The study also finds that cryptocurrency has a reverse leverage effect as opposed to the normal one, where past negative returns increase volatility more than past positive returns. The study also developed a hybrid GARCH model using the extreme value theorem for quantifying cryptocurrency risk. The results showed that the GJR-GARCH with GDP innovations could be used as an alternative model to calculate the VaR. The volatile nature of cryptocurrency was also compared with that of the JSE while accounting for structural breaks and while not accounting for them. The results showed that the cryptocurrencies’ volatility patterns are similar but differ from those of the JSE. The cryptocurrency was also found to be an inefficient market. This finding means that some investors can take advantage of this inefficiency. The study also revealed that structural breaks affect volatility persistence. However, this persistence measure differs depending on the model used. Markov switching GARCH models were used to strengthen the structural break findings. The results showed that two-regime models outperform single-regime models. The VAR and DCC-GARCH models were also used to test the spillovers amongst the assets used. The results showed short-run spillovers from Bitcoin to Ethereum and long-run spillovers based on the DCC-GARCH. Lastly, factors affecting cryptocurrency adoption were discussed. The main reasons affecting mass adoption are the complexity that comes with the use of cryptocurrency and its high volatility. This study was critical as it gives investors an understanding of the nature and behaviour of cryptocurrency so that they know when and how to invest. It also helps policymakers and financial institutions decide how to treat or use cryptocurrency within the economy.
<abstract><p>The Bitcoin futures market is growing and, as such, becoming more sophisticated. A small change in price may therefore have a large impact on the market. This paper investigates the propensity of 18 different competing GARCH family models and error distributions to model and forecast the volatility of Bitcoin futures returns. The study employs two different time periods (from January 2, 2018 to June 14, 2021; and March 11, 2020 to June 14, 2021). From the results, iGARCH(1, 1)-Students't-distribution (STD) is selected as the best performing model among the constructed models for the first period. By fitting the best three models from the first period to the second period, the iGARCH(1, 1)-STD is again selected as the optimal model. However, the iGARCH(1, 1)-normal inverse Gaussian (NIG) provides a significant variance forecast when used for in-sample and out-of-sample forecasts before the financial crisis and during the financial crisis, respectively. Our results indicate the impacts of past squared shocks on squared returns of Bitcoin futures and the ability of iGARCH(1, 1)-STD to capture such innovations and the propensity of iGARCH(1, 1)-NIG to optimally forecast the variance of Bitcoin futures returns.</p></abstract>
The great economic crisis has shown that the global financial system primarily protects those who are ,,too big to fail". In order to provide the common man at least a partial liberation from the hegemony of this bureaucratized and undemocratic system, Bitcoin was created, the first cryptocurrency that functions in a decentralized monetary system based on the blockchain. The emergence of cryptocurrencies, which are beyond the control of the traditional political and economic structures, has raised hopes that the world monetary system can be democratized and freed from the influence of inefficient regulatory institutions. This paper analyzes how realistic the scenario is that in the foreseeable future cryptocurrencies will prevail over traditional currencies, starting from the basic characteristics of cryptocurrencies, regulation of their accounting and tax status, mutual influence of monetary policy and cryptocurrency system, potential benefits that cryptocurrencies can offer to developing countries, as well as a summary of the advantages and disadvantages of cryptocurrencies and recommendations for their improvement.