Bitcoin exchange volume distribution and half-life
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- WAZIRX CRYPTOCURRENCY EXCHANGE
- 927 People Own Half Of All Bitcoins
- The 100 most traded cryptocurrencies in the last 24 hours as of January 10, 2022
- India may soon see its first Bitcoin and Ethereum futures ETF
- Forecasting and trading cryptocurrencies with machine learning under changing market conditions
- Crypto AM shines its spotlight on Sundaeswap and the Cardano 2022 scaling roadmap
WAZIRX CRYPTOCURRENCY EXCHANGE
Financial Innovation volume 7 , Article number: 3 Cite this article. Metrics details. This study examines the predictability of three major cryptocurrencies—bitcoin, ethereum, and litecoin—and the profitability of trading strategies devised upon machine learning techniques e.
The models are validated in a period characterized by unprecedented turmoil and tested in a period of bear markets, allowing the assessment of whether the predictions are good even when the market direction changes between the validation and test periods. The classification and regression methods use attributes from trading and network activity for the period from August 15, to March 03, , with the test sample beginning on April 13, The trading strategies are built on model assembling.
The ensemble assuming that five models produce identical signals Ensemble 5 achieves the best performance for ethereum and litecoin, with annualized Sharpe ratios of These positive results support the claim that machine learning provides robust techniques for exploring the predictability of cryptocurrencies and for devising profitable trading strategies in these markets, even under adverse market conditions.
Since its inception, coinciding with the international crisis of and the associated lack of confidence in the financial system, bitcoin has gained an important place in the international financial landscape, attracting extensive media coverage, as well as the attention of regulators, government institutions, institutional and individual investors, academia, and the public in general.
The success of bitcoin, measured by its rapid market capitalization growth and price appreciation, led to the emergence of a large number of other cryptocurrencies e. By now, the market of cryptocurrencies has become one of the largest unregulated markets in the world Foley et al.
Although initially designed to be a peer-to-peer electronic medium of payment Nakamoto , bitcoin, and other cryptocurrencies created afterward, rapidly gained the reputation of being pure speculative assets. Their prices are mostly idiosyncratic, as they are mainly driven by behavioral factors and are uncorrelated with the major classes of financial assets; nevertheless, their informational efficiency is still under debate.
Consequently, many hedge funds and asset managers began to include cryptocurrencies in their portfolios, while the academic community spent considerable efforts in researching cryptocurrency trading, with emphasis on machine learning ML algorithms Fang et al. This study examines the predictability and profitability of three major cryptocurrencies—bitcoin, ethereum, and litecoin—using ML techniques; hence, it contributes to this recent stream of literature on cryptocurrencies.
Bitcoin as a peer-to-peer P2P virtual currency was initially successful because it solves the double-spending problem with its cryptography-based technology that removes the need for a trusted third party. Blockchain is the key technology behind bitcoin, which works as a public permissionless digital ledger, where transactions among users are recorded.
Since no central authority exists, this ledger is replicable among participants nodes of the network, who collaboratively maintain it using dedicated software Yaga et al. Litecoin and ethereum were launched on October and August , respectively.
Litecoin has the same protocol as bitcoin, and has a supply capped at 84 million units. It was designed to save on the computing power required for the mining process so as to increase the overall processing speed, and to conduct transactions significantly faster, which is a particularly attractive feature in time-critical situations.
Ethereum is also a P2P network but unlike bitcoin and litecoin, its cryptocurrency token, called Ether in the finance literature this token is usually referred to as ethereum , has no maximum supply.
Additionally, the ethereum protocol provides a platform that enables applications on its public blockchain such that any user can use it as a decentralized ledger. More specifically, it facilitates online contractual agreement applications smart contracts with minimal possibility of downtime, censorship, fraud, or third-party interference.
These characteristics help explain the interest that ethereum has gathered since its inception, making it the second most important cryptocurrency. The main purpose of this study is not to provide a new or improved ML method, compare several competing ML methods, nor study the predictive power of the variables in the input set.
Instead, the main objective is to see if the profitability of ML-based trading strategies, commonly evidenced in the empirical literature, holds not only for bitcoin but also for ethereum and litecoin, even when market conditions change and within a more realistic framework where trading costs are included and no short selling is allowed.
Other studies have already partly addressed these issues; however, the originality of our paper comes from the combination of all these features, that is, from an overall analysis framework. Additionally, we support our conclusions by conducting a statistical and economic analysis of the trading strategies. Stated differently, changing market conditions means alternating between periods characterized by a strong bullish market, where most returns are in the upper-tail of the distribution, and periods of strong bearish markets, where most returns are in the lower-tail of the distribution see, e.
The remainder of the paper is structured as follows. Early research on bitcoin debated if it was in fact another type of currency or a pure speculative asset, with the majority of the authors supporting this last view on the grounds of its high volatility, extreme short-run returns, and bubble-like price behavior see e. This claim has been shifted to other well-implemented cryptocurrencies such as ethereum, litecoin, and ripple see e.
These determinants have been shown to be highly important even for more traditional markets. For instance, Wen et al. Kristoufek highlights the existence of a high correlation between search queries in Google Trends and Wikipedia and bitcoin prices. Kristoufek reinforces the previous findings and does not find any important correlation with fundamental variables such as the Financial Stress Index and the gold price in Swiss francs.
Polasik et al. Panagiotidis et al. In a more recent article, Panagiotidis et al. Ciaian et al. Zhu et al. Li and Wang find that in early market stages, bitcoin prices were driven by speculative investment and deviated from economic fundamentals. As the market matured, the price dynamics followed more closely the changes in economic factors, such as U.
Dastgir et al. Baur et al. Bouri et al. Pyo and Lee find no relationship between bitcoin prices and announcements on employment rate, Producer Price Index, and CPI in the United States; however, their results suggest that bitcoin reacts to announcements of the Federal Open Market Committee on U.
That bitcoin prices are mainly driven by public recognition, as Li and Wang call it—measured by social media news, Google searches, Wikipedia views, Tweets, or comments in Facebook or specialized forums—was also investigated in the case of other cryptocurrencies. For instance, Kim et al. Phillips and Gorse use hidden Markov models based on online social media indicators to devise successful trading strategies on several cryptocurrencies.
Corbet et al. Sovbetov shows that factors such as market beta, trading volume, volatility, and attractiveness influence the weekly prices of bitcoin, ethereum, dash, litecoin, and monero. Phillips and Gorse investigate if the relationships between online and social media factors and the prices of bitcoin, ethereum, litecoin, and monero depend on the market regime; they find that medium-term positive correlations strengthen significantly during bubble-like regimes, while short-term relationships appear to be caused by particular market events, such as hacks or security breaches.
Accordingly, some researchers, such as Stavroyiannis and Babalos , study the hypothesis of non-rational behavior, such as herding, in the cryptocurrencies market. They highlight that investor sentiment is a good predictor of the price direction of cryptocurrencies and that cryptocurrencies can be used as a hedge during times of uncertainty; but during times of fear, they do not act as a suitable safe haven against equities.
The results indicate the presence of herding biases among investors of crypto assets and suggest that anchoring and recency biases, if present, are non-linear and environment-specific.
In the same line, Chen et al. The authors conclude that during times of market distress e. In another related strand of literature, several authors have directly studied the market efficiency of cryptocurrencies, especially bitcoin. With different methodologies, Urquhart and Bariviera claim that bitcoin is inefficient, while Nadarajah and Chu and Tiwari et al.
However, Urquhart and Bariviera also point out that after an initial transitory phase, as the market started to mature, bitcoin has been moving toward efficiency.
In the last three years, there has been an increasing interest on forecasting and profiting from cryptocurrencies with ML techniques. Table 1 summarizes several of those papers, presented in chronological order since the work of Madan et al. We do not intend to provide a complete list of papers for this strand of literature; instead, our aim is to contextualize our research and to highlight its main contributions.
For a comprehensive survey on cryptocurrency trading and many more references on ML trading, see, for example, Fang et al. In a nutshell, all these papers point out that independent of the period under analysis, data frequency, investment horizon, input set, type classification or regression , and method, ML models present high levels of accuracy and improve the predictability of prices and returns of cryptocurrencies, outperforming competing models such as autoregressive integrated moving averages and Exponential Moving Average.
In the competition between different ML models there is no unambiguous winner; however, the consensual conclusion is that ML-based strategies are better in terms of overall cumulative return, volatility, and Sharpe ratio than the passive strategy. However, most of these studies analyze only bitcoin, cover a period of steady upward price trend, and do not consider trading costs and short-selling restrictions. From the list in Table 1 , studies that are closer to the research conducted here are Ji et al.
The main differences between our research and the first paper are that we consider not only bitcoin but also, ethereum and litecoin, and we also consider trading costs.
Meanwhile, the main differences with the second paper are that we study daily returns and use blockchain features in the input set instead of one-minute returns and technical indicators. The daily data, totaling 1, observations, on three major cryptocurrencies—bitcoin, ethereum, and litecoin—for the period from August 07, to March 03, come from two sources. The sample begins one week after the inception of ethereum, the youngest of the three cryptocurrencies. Exchange trading information—the closing prices the last reported prices before UTC of the next day and the high and low prices during the last 24 h, the daily trading volume, and market capitalization—come from the CoinMarketCap site.
These variables are denominated in U. Arguably these last two trading variables, especially volume, may help the forecasting returns see for instance Balcilar et al. For each cryptocurrency, the dependent variables are the daily log returns, computed using the closing prices or the sign of these log returns. The overall input set is formed by 50 variables, most of them coming from the raw data after some transformation.
The first lag of the other exchange trading information and network information of the corresponding cryptocurrency are included in the input set, except if they fail to reject the null hypothesis of a unit root of the augmented Dickey—Fuller ADF test, in which case we use the lagged first difference of the variable.
This differencing transformation is performed on seven variables. The data set also includes seven deterministic day dummies, as it seems that the price dynamics of cryptocurrencies, especially bitcoin, may depend on the day of the week, Dorfleitner and Lung ; Aharon and Qadan ; Caporale and Plastun Table 2 presents the input set used in our ML experiments.
In this work, we use the three-sub-samples logic that is common in ML applications with a rolling window approach. The performance of the forecasts obtained in these observations is used to choose the set of variables and hyperparameters. This set of observations is not exactly the validation sub-sample used in ML, since most observations are used both for training and for validation purposes e.
Despite not being exactly the validation sub-sample, as usually understood in ML, it is close to it, since the returns in this sub-sample are the ones that are compared to the respective forecast for the purpose of choosing the set of variables and hyperparameters.
However, it is close to it since it is used to assess the quality of the models in new data. The price paths of the three cryptocurrencies are shown in Fig. Although at first glance, looking at Fig. Then, in the first half of the validation sample, the prices show an explosive behavior, followed in the second half by a sudden and sharp decay. In the test sample there is an initial month of an upward movement and then a markedly negative trend. Roughly speaking, at the end of the test sample, the prices are about double the prices in the beginning of the validation sample.
The daily closing volume weighted average prices of bitcoin, ethereum, and litecoin for the period from August 15, to March 3, come from the CoinMarketCap site. Table 3 presents some descriptive statistics of the log returns of the three cryptocurrencies. During the overall sample period, from August 15, to March 03, , the daily mean returns are 0. The median returns are quite different across the three cryptocurrencies and the three subsamples.
As already documented in the literature, these cryptocurrencies are highly volatile. This is evident from the relatively high standard deviations and the range length.
927 People Own Half Of All Bitcoins
Have you read these stories? What industry experts want from Budget Updated: Jan 29, , Union Budget will be presented at a time when India's economic recovery from the pandemic blow is firming up. Infrastructure spen
The 100 most traded cryptocurrencies in the last 24 hours as of January 10, 2022
On December 13, cryptocurrency Bitcoin reached 90 per cent of its maximum supply. A research by blockchain. The milestone comes almost 12 years after the first block, which consisted of 50 Bitcoins, was mined on January 9, For the uninitiated, Bitcoin is one of the few cryptocurrencies with limited supply. Bitcoin inventor Satoshi Nakamoto capped the number of Bitcoin at 21 million, to make the cryptocurrency scarce and control inflation that might arise from an unlimited supply. It is a process of adding new Bitcoins into circulation. After performing a set of transactions successfully, the miner is awarded a block of Bitcoins. It should be noted that every four years the reward for mining Bitcoin is halved. So, when Nakamoto created Bitcoin in , the reward for confirming a block of transactions was 50 Bitcoins.
India may soon see its first Bitcoin and Ethereum futures ETF
Australian investors' fervour for digital currencies was put on full display earlier this month as the ASX's first cryptocurrency themed exchange traded fund smashed trading records. More than 2. Instead, it owns a portfolio of 32 companies linked to the crypto economy, from Bitcoin miners to the exchanges where investors buy and sell coins. CRYP is the third fund this year to notch more than half a million trades on the inaugural day, putting the industry on track for the biggest year on record.
Forecasting and trading cryptocurrencies with machine learning under changing market conditions
Tonight, a much-anticipated decentralised finance exchange DEX on Cardano, Sundaeswap , will undertake its beta launch. Fortunately, as a Layer 1 solution, Cardano has capacity to grow. The Sundaeswap team is playing a crucial role in the nascent and effervescent community of development of DeFi applications on Cardano, working closely with Input Output on open source tools for developers to make the process of building on Cardano as easy and efficient as possible. Today marks a major milestone for the Cardano ecosystem with the launch of the Sundaeswap decentralised exchange, which will be the most significant and ambitious launch to date on Cardano. We had always anticipated this in our roadmap, which will see this year focus on performance optimisation and scaling, addressing the classic blockchain trilemma of scalability, security, and decentralisation. All of this comes on a basis of formal development methods and Haskell code — ensuring we have an incredibly secure platform.
Crypto AM shines its spotlight on Sundaeswap and the Cardano 2022 scaling roadmap
This study aims to explore the potential use of the cryptocurrency bitcoin as an investment instrument in Indonesia. The return obtained from bitcoin cryptocurrency is compared to other investment instruments, namely stock returns, gold and the rupiah exchange rate. The research period was carried out based on research data from to This study employee compares means test t test and analysis of variance F test on rate of return of bitcoin investment. The bitcoin return compare to the rate of return form the others investments instruments namely exchange rate, gold and stock. The study collected data of each investments instruments: bitcoin, exchange rate, gold and stock from various of sources during — Then, we calculate the return and risk of individual investment instruments.
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Blockchain promises to solve this problem. The technology behind bitcoin, blockchain is an open, distributed ledger that records transactions safely, permanently, and very efficiently. For instance, while the transfer of a share of stock can now take up to a week, with blockchain it could happen in seconds. Blockchain could slash the cost of transactions and eliminate intermediaries like lawyers and bankers, and that could transform the economy. In this article the authors describe the path that blockchain is likely to follow and explain how firms should think about investments in it. The level of complexity—technological, regulatory, and social—will be unprecedented.
This paper adds to the growing literature of cryptocurrency and behavioral finance. Specifically, we investigate the relationships between the novel investor attention and financial characteristics of Bitcoin, i. Our empirical results show supports in the behavior finance area and argue that investor attention is the granger cause to changes in Bitcoin market both in return and realized volatility.