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3,636 results · page 69 of 152
Édouard Lansiaux, Noé Tchagaspanian, Joachim Forget
No abstract is available for this record.
Çağrı Hamurcu
The main purpose of this study is to examine the effects of Elon Mask's Twitter posts about cryptocurrencies on cryptocurrency markets within the scope of herding behavior bias. For this purpose, the daily price values and transaction volumes of Bitcoin and Dogecoin are analyzed by applying the EGARCH models. The results show that Elon Musk's positive Twitter posts increase dogecoin's volatility more than bitcoin in terms of price and trading volume. In addition, the effect of positive tweets has been found to increase Bitcoin and Dogecoin prices and their market transactions. According to the results, while negative tweet sharing negatively affects bitcoin returns, it manifests itself with an increase in volatility after a certain period of time. Another result is that the Dogecoin return and negative tweet interaction vary according to time intervals, but the presence of the effect on volatility cannot be determined. It is also concluded that after the negative tweet, both bitcoin and dogecoin transaction volumes increased in the first days, but their volatility was not affected. The results are important in terms of showing the effects of an influential person's social media posts on the financial markets by creating a herd behavior effect. Revealing the "influential person effect" as a behavioral finance bias is seen as the originality of the study. It is thought that the findings can be evaluated in terms of pointing out a factor that may pose a potential risk to financial stability in the global sense.
Marcell T. Kurbucz, Péter Pósfay, Antal Jakovác
The goals of this paper are twofold: (1) to present a new method that is able to find linear laws governing the time evolution of Markov chains and (2) to apply this method for anomaly detection in Bitcoin prices. To accomplish these goals, first, the linear laws of Markov chains are derived by using the time embedding of their (categorical) autocorrelation function. Then, a binary series is generated from the first difference of Bitcoin exchange rate (against the United States Dollar). Finally, the minimum number of parameters describing the linear laws of this series is identified through stepped time windows. Based on the results, linear laws typically became more complex (containing an additional third parameter that indicates hidden Markov property) in two periods: before the crash of cryptocurrency markets inducted by the COVID-19 pandemic (12 March 2020), and before the record-breaking surge in the price of Bitcoin (Q4 2020 - Q1 2021). In addition, the locally high values of this third parameter are often related to short-term price peaks, which suggests price manipulation.
Guanghui Yan, Shan Wang, Shikui Li, Binwei Lu
No abstract is available for this record.
Zhiyong Zheng, Yunfan Lu, Junhuan Zhang
No abstract is available for this record.
Riccardo De Blasis, Alexander Webb
Abstract Perpetual futures, first proposed by Shiller (1993), have only seen wide use in cryptocurrency markets. We examine the contract design and market microstructure differences for the behavior of Bitcoin quarterly and perpetual futures prices and assess the implications for market participants and policymakers. We find perpetual futures exhibit multiple “u‐shaped” curves, seasonal effects, and opening effects despite lacking opening and closing hours. There is suggestive evidence of spillover effects between perpetual and quarterly futures contracts. We find quarterly futures offer cash‐and‐carry arbitrage opportunities, but similar to Hattori and Ishida (2021) these opportunities primarily exist during market dislocations.
Chi‐Wei Su, Xi Yuan, Ran Tao, Muhammad Umar
This paper explores how fear sentiment affects the price of Bitcoin by employing the rolling-window Granger causality tests. The analysis reveals negative influences from the volatility index (VIX) to Bitcoin price (BTC), which ascertains that Bitcoin can not be considered a haven in fear sentiment. Due to the liquidity in economic downside risks, BTC may decrease with high VIX to hedge losses, increasing during low VIX periods. The empirical results conflict with the intertemporal capital asset pricing model, which underlines that the increasing VIX can promote the price of Bitcoin. In turn, BTC positively impacts VIX, which shows that Bitcoin price can be treated as the main indicator for a more comprehensive analysis of the fear index. Under severe global uncertainty and changeable fluctuation of market sentiment, investors can optimize investment decisions based on market fear sentiment. The government can also consider VIX to grasp the trend of BTC to participate in cryptocurrency speculation effectively.
Nemo Semret
What happens to mining when the Bitcoin price changes, when there are mining supply shocks, the price of energy changes, or hardware technology evolves? We give precise answers based on the technical forces and incentives in the system. We then build on these dynamics to consider value: what is the cost and purpose of mining, and is it worth it? Does it use too much energy, is it bad for the environment? Finally we extend our analysis to the long term: is mining economically feasible forever? What will the global hash rate be in 40 years? How is mining impacted by the limits of computation and energy? Is it physically sustainable in the long run? From first principles, we derive a fundamental scale-invariant feasibility constraint, which enables us to analyze the interlocking dynamics, find key invariants, and answer these questions mathematically.
Mohsen Asgari, Seyed Hossein Khasteh
Deep Reinforcement Learning solutions have been applied to different control problems with outperforming and promising results. In this research work we have applied Proximal Policy Optimization, Soft Actor-Critic and Generative Adversarial Imitation Learning to strategy design problem of three cryptocurrency markets. Our input data includes price data and technical indicators. We have implemented a Gym environment based on cryptocurrency markets to be used with the algorithms. Our test results on unseen data shows a great potential for this approach in helping investors with an expert system to exploit the market and gain profit. Our highest gain for an unseen 66 day span is 4850 US dollars per 10000 US dollars investment. We also discuss on how a specific hyperparameter in the environment design can be used to adjust risk in the generated strategies.
Syed Jawad Hussain Shahzad, Muhammad Anas, Elie Bouri
No abstract is available for this record.
Toan Luu Duc Huynh
We present a textual analysis that explains how Elon Musk's sentiments in his Twitter content correlates with price and volatility in the Bitcoin market using the dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity model, allowing less sensitive to window size than traditional models. After examining 10,850 tweets containing 157,378 words posted from December 2017 to May 2021 and rigorously controlling other determinants, we found that the tone of the world's wealthiest person can drive the Bitcoin market, having a Granger causal relation with returns. In addition, Musk is likely to use positive words in his tweets, and reversal effects exist in the relationship between Bitcoin prices and the optimism presented by Tesla's CEO. However, we did not find evidence to support linkage between Musk's sentiments and Bitcoin volatility. Our results are also robust when using a different cryptocurrency, i.e., Ether this paper extends the existing literature about the mechanisms of social media content generated by influential accounts on the Bitcoin market.
Luis Lorenzo, Javier Arroyo
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.
Berna Aydoğan, Gülin Vardar, Caner Taçoğlu
Purpose The existence of long memory and persistent volatility characteristics of cryptocurrencies justifies the investigation of return and volatility/shock spillovers between traditional financial market asset classes and cryptocurrencies. The purpose of this paper is to investigate the dynamic relationship between the cryptocurrencies, namely Bitcoin and Ethereum, and stock market indices of G7 and E7 countries to analyze the return and volatility spillover patterns among these markets by means of multivariate (MGARCH) approach. Design/methodology/approach Applying the newly developed VAR-GARCH-in mean framework with the BEKK representation, the empirical results reveal that there exists an evidence of mean and volatility spillover effects among Bitcoin and Ethereum as the proxies for the cryptocurrencies, and stock markets reviewed. Findings Interestingly, the direction of the return and volatility spillover effects is unidirectional in most E7 countries, but bidirectional relationship was found in most G7 countries. This can be explained as the presence of a strong return and volatility interaction among G7 stock markets and crypto market. Originality/value Overall, the results of this study are of particular interest for portfolio management since it provides insights for financial market participants to make better portfolio allocation decisions. It is also increasingly important to understand the volatility transmission mechanism across these markets to provide policymakers and regulatory bodies with guidance to eliminate the negative impact of cryptocurrency's volatility on the stability of financial markets.
Zehra Eksi, Daniel Schreitl
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.
Sunitha Kumaran
The appealing features of cryptocurrency in the digital money sector have put them into the category of investable assets. Investment professionals have begun to consider their investability and diversification benefits. It is vital for investors to understand the return-risk behaviour among investable assets to reap the benefits of diversification. This paper considers a proxy of cryptos, specifically Bitcoin, Litecoin, Ethereum, Ripple & Neo, and the Middle East stock market indices, to examine the dynamic relationship among them using the vector error correction model. This study found evidence to suggest that cryptos exhibit a co-integrated relationship while there is no evidence of significant cointegrated movements occurring between the cryptos and the market indices. The latter finding implies that cryptos are decoupled from the market indices and can serve as a diversification option for investors. The mean-variance approach confirms that cryptocurrencies fit into an optimal portfolio and involve an enhanced return-risk reward for investors.
Viviane Y. Naïmy, Omar Haddad, Rim El Khoury
No abstract is available for this record.
Valerio Potì, Rocco Caferra, Andrea Morone
No abstract is available for this record.
Ruixin Hu, Xuecheng Wang
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.
Zihan Zhang, Yifei Ma, Yan Kong
No abstract is available for this record.
Cesare Fracassi, Shimon Kogan
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".
Prabhjot Kaur, Mukesh Kumar Meena
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.
Ahmet Faruk Aysan, Kayani, Farrukh Nawaz
No abstract is available for this record.
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.