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In this paper, we explore the in efficiency of the continuum Bitcoin-USD market in the period ranging from mid to early To deal with, we dynamically analyse the evolution of the self-similarity exponent of Bitcoin-USD daily returns via accurate FD4 approach by a day sliding window with overlapping data.

Further, we define the memory indicator by the difference between the self-similarity exponent of Bitcoin-USD series and the self-similarity index of its shuffled series. We also carry out additional analyses via FD4 approach by sliding windows of sizes equal to 64, , , and days, and also via FD algorithm for values of q equal to 1 and 2 and sliding windows equal to days.

In all the cases, the obtained results were found to be similar to our first analysis. However, this is not due to the presence of significant memory in the series but to its underlying distribution. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results of this study are available in the article and supplemental materials. Competing interests: The authors have declared that no competing interests exist. One of the pioneers in the analysis of market efficiency was Fama [ 3 ], who considered that a market is efficient provided that all the information available is fully reflected in the market and can be used for all the agents.

The market efficiency was classified into three states, namely, weak efficiency, when market prices do reflect all the information contained in the past series of prices, semi-strong efficiency, if prices reflect all the public information, and strong efficiency, as well.

In this way, that last one becomes the most restrictive scenario since it considers that prices reflect all the public and private informations. From a statistical viewpoint, an efficient market follows a random walk, first introduced in finance by the mathematician Bachelier to study the behavior of the French Bond prices [ 4 ].

As such, market prices would be described by independent and identically distributed random variables. The presence of long memory in series of prices or asset returns cannot be accepted in that scenario since it would allow a riskless profitable trading strategy. Since its foundation, EMH has been questioned for being too restrictive and generic.

In this way, several alternatives have been posed. Such a model assumes that prices exhibit long memory, which is not possible under EMH. In a recent contibution, Ponta and Carbone [ 6 ] remark the importance of market heterogeneity as a weak point of EMH, which is based on a homogeneous random process.

They propose a cumulative Market Heterogeneity Index based on previous works c. This new approach takes into account the existence of clusters in financial data. Since Philippatos and Wilson [ 10 ] introduced the concept of entropy in finance, many other researchers have enriched the market finance theory by different entropy concepts to measure risk and describing distributions see Zhou et al. To explore the presence of long memory in stock prices or asset returns have become a discussing topic for market efficiency analysis.

It is worth mentioning that a wide amount of papers do throw some empirical evidence of long memory c. However, a few questions should be still addressed regarding the use of the self-similarity exponent to analyze EMH.

For instance, several authors have recently suggested a relationship between the degree of development of a market and its level of efficiency c. They assume that it may be quantified throughout its self-similarity exponent. As such, they analyze scaling behavior patterns to quantify their level of development. As a consequence, they highlight that mature markets usually display short memory or no memory at all, whereas emerging markets still exhibit long memory properties.

By the other hand, it is also frequent to extract conclusions of a simple analysis of the self- similarity index without addressing some relevant questions, such us, the underlying distribution of data e.

According with this, we consider an interesting issue the analysis of the Hurst exponent of the cryptocurrencies with the objetive of characterizing the degree of development of this new market. The present article is organized as follows. Firstly, Section 1 contains a detailed literature review of the main issues that have been addressed so far regarding the Bitcoin maket behavior.

In Section 2, we provide the basics on FD4 algorithm applied along this paper for self-similarity exponent calculation purposes.

In Section 3, we present the results we obtained concerning the in efficiency of BTC-USD evolution through time, and finally, Section 4 highlights our main conclusions.

Bitcoin was introduced on October 31st, , through a paper, released to a few cryptography enthusiasts [ 28 ]. That email, signed by the pseudonym of Satoshi Nakamoto, explained that it was a cryptocurrency allowing exchange of value tokens between two parts without divulging any transaction details. Since then, Bitcoin has emerged as the most popular and demanded cryptocurrency. Actually, Bitcoin is the cryptocurrency with the highest market capitalization among more than digital currencies existing at present, reaching Since its appearance, its market capitalization has increased from approximately 0.

The lack of control from governments and central authorities, the poor and inefficient regulation, and the youth of the cryptocurrencies have made their markets quite speculative and volatile. Despite this, the growing market of these new successful financial instruments and their innovative features attract more and more big and small investors, speculators, policymakers, and academic researchers from around the world. The market of cryptocurrencies is still tiny compared to traditional financial markets.

Therefore, there are only few relevant researches in the literature. It is worth pointing out that EMH has been analysed in cryptocurrency markets since Most of the researchers have been focused on weak efficiency throughout different approaches.

Next, we shall comment the methodologies as well as the results contributed in some papers already appeared in the literature. In [ 30 ], Bartos concentrated efforts to throw some evidence of efficiency in the Bitcoin market and explored the behavior of its price evolution by carrying out an empirical analysis. The results contributed therein suggest that such a cryptocurrency follows EMH and immediately reacts to publicly announced information. It was found that events affect prices of cryptocurrencies.

Also, it was concluded that both demand and supply factors have a crucial impact on the price of that cryptocurrency. That research threw some empirical evidence against EMH regarding the Bitcoin evolution through time. In fact, it was concluded that the inefficiency of Bitcoin market is quite strong. However, it has to be mentioned that the whole time period was splitted into two subperiods, from August 1st, to July 31st, , with the Bitcoin market being efficient only in the second one.

As such, it was stated that it becomes more efficient with time. According to the results provided, there was no evidence against the null hypothesis, except the tests for independence. As such, the authors concluded that the Bitcoin market behaves efficiently.

As a result, they stated that Bitcoin market is not efficient though will behave more efficiently over time. In this way, they suggested that it will be random in the future. They tested the presence of long memory in Bitcoin returns from to by using the Hurst exponent via the Detrended Fluctuation Analysis DFA over a sliding window to measure long range dependence.

They also carried out a multi-scale analysis leading to similar results from the viewpoint of the Hurst exponents. As such, they detected that Hurst exponents changed significantly during the first years of existence of Bitcoin, tending to stabilize in recent times. More specifically, they stated that the Bitcoin series had a persistent behavior a self-similarity exponent greater than 0. Shortly afterward, Bariviera [ 35 ] reexamined the fluctuations of Bitcoin prices. On this occasion, he studied the time varying behavior of long memory for Bitcoin volatility and returns since to Following the results, it was stated that the daily returns exhibit persistent behavior from until , whereas the market became more informational efficient since However, the price volatility exhibits long memory along the whole time period.

Alvarez-Ramirez, Rodriguez, and Ibarra-Valdez [ 36 ], on their part, studied the presence of long-range correlations and informational efficiency of Bitcoin market for the period ranging from June 30th, to June 3rd, , via DFA approach over sliding windows to estimate long-range correlations for Bitcoin price returns. Tiwari, Jana, Das, and Roubaud [ 37 ] also tested the informational efficiency of Bitcoin.

To that end, they used a battery of computationally efficient long-range dependence estimators for a period spanning from July 18th, to June 16th, The conclusions of their study indicated that the market is informational efficient as consistent to recent findings of Urquhart [ 31 ], Nadarajah and Chu [ 32 ], and Bariviera [ 35 ]. The authors emphasized that Bitcoin market is efficient with some exception to the period of April-August, and August-November, Recently, Juang, Nie, and Ruan [ 38 ] investigated the time-varying long-term memory in the Bitcoin market through a rolling window approach and employing a new Efficiency Index [ 39 ], using daily datasets for the period from to They concluded that the generalized Hurst exponents in the Bitcoin market are above 0.

From their point of view, long-term memory exists in Bitcoin market. They also observed a high degree of inefficiency in such a market and stated that it does not become more efficient over time. At the beginning of , Demir, Gozgor, Lau, and Vigne [ 40 ] published a paper which aimed to analyze the prediction power of economic policy uncertainty EPU index on the daily Bitcoin returns for the period from July 18th, to November 15th, , via the Bayesian Graphical Structural Vector Autoregressive model, the ordinary Least Squares, and the Quantile-on-Quantile Regression estimations.

The authors deduced that Bitcoin returns are negatively associated with changes in the EPU, but they also pointed out that the effect is positive and significant at lower and higher quantiles of Bitcoin returns and EPU.

Extending the Bitcoin market investigation, Brauneis and Mestel [ 41 ] linked efficiency to measures of liquidity. In their opinion, there is evidence of market inefficiency since these markets exhibit persistence. In particular, they insist that there are positive correlations between their past and future values which change over time. From their point of view, the cryptocurrency market is still inefficient, but it is becoming less so, especially, in the case of Litecoin market, where the Hurst exponent dropped considerably over time.

In addition, Cheap, Mishra, and Zhang [ 43 ] proposed a new mechanism to understand dynamic interdependence of Bitcoin prices in a cross-market context. They modeled cross-market Bitcoin prices as long-memory processes and studied dynamic interdependence in a fractionally cointegrated VAR framework. As a result, long-memory was found in both, the individual markets and the system of markets depicting non-homogeneous informational inefficiency.

Kristoufek [ 44 ] recently published a paper on the study of efficiency of Bitcoin market with respect to both USD and Chinese yuan currencies, and their evolution over time. He used the Efficiency Index [ 23 ], for testing them for different types of in efficiency measures. As regards the USD market, he commented that there are only two longer periods of time when the market can be considered as efficient—from the middle of to the middle of , and between March and November of Aside from them, there is no efficiency in the Bitcoin market.

It is worth mentioning that the results of the analyses carried out regarding the CNY market are not so strong since the examination period misses some very important bubble-like dynamics before Therefore, he insists there is strong evidence that both Bitcoin markets remain mostly inefficient between and , except several periods directly connected with cooling down after the bubble-like price surges.

Khuntia and Pattanayak [ 45 ] evaluated the adaptive market hypothesis AMH as well as the evolving return predictability in Bitcoin market, using two robust methods in a rolling-window framework to capture time-varying linear and nonlinear dependence in Bitcoin returns. The conclusions of their study are that efficiency of Bitcoin market evolves with time and the evidence of its dynamics adheres to the AMH. According to the authors, some crucial events coincide with episodes of in efficiency, so creation of events and behavioral bias may change its efficiency.

Gox markets by applying the methodology of the event study. They wanted to check how digital currencies respond to monetary policy and Bitcoin events. The authors observed that Bitcoin has become more efficient over time in relation to its own events, but at the same time, they concluded that the cryptocurrency is not affected by monetary policy news, highlighting the absence of any kind of control on Bitcoin. Nevertheless, the dynamic behavior of the cryptocurrencies has not been practically explored and the studies that have been carried out on these issues are still scarce.

This is one of the reasons for the choice of analysis of the memory in the Bitcoin market as a subject of our enquiry.

After a stellar year for Bitcoin and record crypto prices, what to expect in 2022

We use cookies and other tracking technologies to improve your browsing experience on our site, show personalized content and targeted ads, analyze site traffic, and understand where our audiences come from. To learn more or opt-out, read our Cookie Policy. Adams officially received his first paycheck yesterday, which was converted to Bitcoin and Ethereum through cryptocurrency exchange Coinbase. The move came out of a Twitter exchange between Adams and Miami Mayor Francis Suarez, who said he would receive his next paycheck in Bitcoin.

As Elon Musk tweets go, so goes the crypto market. The billionaire and Tesla CEO has been tweeting about crypto a lot, too, sending the.

What Makes Cryptocurrency Go Up or Down?

The bitcoin perpetual swap, the most liquid and traded futures instrument, is a contract that allows traders to speculate on the bitcoin price with leverage. When the contract price of a perpetual futures contract a futures contract that never expires is above the spot market bitcoin price, the perpetual futures funding rate will be positive, meaning longs pay shorts a percentage of their notional position size. The opposite is also true. Typically, a bullish bias is present in futures markets. Throughout much of , perpetual futures contracts were persistently leading spot markets by a wide margin, indicating a strong bullish bias from speculators. Recently, funding has flipped negative, showing that perpetual futures are trading below spot, and this isn't a result of cascading liquidations driving price, but rather a flip in sentiment and market expectation. The bitcoin price weighted by the hourly perpetual funding rate. Over the last 24 hours, perpetual futures funding has been negative 8.

Bitcoin plummets as much as 15% just days after hitting record high

btc coin market

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When Elon Musk tweets, crypto prices move

The cryptocurrency was invented in by an unknown person or group of people using the name Satoshi Nakamoto. Bitcoins are created as a reward for a process known as mining. They can be exchanged for other currencies, products, and services. Bitcoin has been criticized for its use in illegal transactions, the large amount of electricity and thus carbon footprint used by mining, price volatility , and thefts from exchanges. Some investors and economists have characterized it as a speculative bubble at various times.

Bitcoin value tumbles almost 50% since record November

T he Shiba Inu token dives into the Metaverse sphere by announcing the launch of 'Shiberse' in Shiberse would be the first release of the year for its ever-growing and expanding ecosystem. SHIB lead developer Shytoshi Kusama had been teasing investors about the development for almost a week, and the team finally made it official. Shiberse graphics look extremely rich and professional with detail in every aspect of the image. From the lush green leaves to the trees and rocks, the image is nothing short of a work of art. This announcement has excited the Shiba Inu community and brought a ray of hope amidst the market downturn. Social media is flooding with posts about the latest SHIB update and how the upcoming Metaverse will change the fortunes of the tokens.

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Bitcoin profit measures suggest we’re in a prolonged bear market

The overall market also bounced back slightly, though the price rise slowed down considerably on Thursday. You can follow all the latest news, analysis and expert price predictions in our live blog below. However, the leading cryptocurrency is still down by over 7 per cent compared to its value a week earlier. Solana has grown by nearly 4 per cent in the last 24 hours, although it is still down by over 25 per cent compared to its value 7 days ago.

Bitcoin's Derivative Market Bulls Have Vanished


We use cookies and other tracking technologies to improve your browsing experience on our site, show personalized content and targeted ads, analyze site traffic, and understand where our audiences come from. To learn more or opt-out, read our Cookie Policy. The cryptocurrency market fell to multimonth lows. Does anyone know why?

The total market value of a cryptocurrency's circulating supply. It is analogous to the free-float capitalization in the stock market.


We use cookies and other tracking technologies to improve your browsing experience on our site, show personalized content and targeted ads, analyze site traffic, and understand where our audiences come from. To learn more or opt-out, read our Cookie Policy. As Elon Musk tweets go, so goes the crypto market. The billionaire and Tesla CEO has been tweeting about crypto a lot, too, sending the price of bitcoin — as well as dogecoin — up and down with fewer than characters. They also raise questions about the solidity of a market that can be so easily swayed, especially as retail investors increasingly flock to cryptocurrencies. In April, the cryptocurrency exchange platform Coinbase became the first major cryptocurrency company to go public in the US, signifying the mainstreaming of blockchain-based currencies like bitcoin, ethereum, and dogecoin. The current price roller coaster got started back in May.

Bitcoin and other cryptos have slumped after record highs. Is market manipulation the reason why?

Here's What Investors Should Know. Ethereum Just Hit a 6-Month Low. Upgrade Bitcoin Rewards Card: 1. There Are Thousands of Different Altcoins.

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  1. Bramley

    It seems to me it is very good idea. Completely with you I will agree.

  2. Faegan

    not easy choice for you

  3. Anwealda