Category Archives: Cryptocurrency

As we move into a new decade there will be several changes that mark the transition. New online marketing technologies, the further development of AI and augmented technologies, and of course the way we use money online. Cryptocurrencies have been around since 2008 and since then theyve had a volatile market experience, but now theyre stabilising and are further supported by major financial institutions. These digital currencies are likely to be the future and are worth your investment in 2021.

Photo, Aleksi Ris.

Integrity of Transactions: Conventional transactions, such as direct debits and bank transfers have been around forever, but the more the digital landscape develops the clearer the need for more security and less expense. Internet fraudsters are always finding ways to steal passwords and put your money at risk. Cryptocurrency has the security of the blockchain making online payments fast and super secure. Using crypto-coins, your internet transactions and xrp exchange will have integrity, they wont be at risk and will cost you less overall.

24/7 Payments Tracking: As we move into a new decade and major tamarind nations along with significant independent financial institutions adopt cryptocurrency, the availability of crypto-coin transactions online will be more popular than ever. But thats not the only reason. Conventional transactions between global banks often require periods of days to move the money and secure the transaction. This can be inconvenient and expensive, as fees and charges are often applied. Why bother? Cryptocurrency can be tracked second by second and sent immediately without charges.

Security of Currency and Transactions: The need for secure transactions is only going to rise into the next decade and beyond as the battle between financial security infrastructure and modern digital pirates continues to rage. The blockchain offers an effective solution for the decade ahead, a peer to peer authentication platform that facilitates the immediate and secure transaction of online money without the risk or fees. Although cryptocurrency has been volatile in recent years, it has now stabilised and seems to be the way forward.

Photo, Pascal Bernardon.

You Own It: Unlike conventional currency that needs to be stored in a bank, investment fund, or brokerage house, cryptocurrency belongs to the user and does not have to be kept in safe storage. The process of keeping conventional funds is often expensive, you have to invest in premium services, or pay certain fees; this is not the case with cryptocurrency. Cryptocurrency doesnt have any terms and conditions, it doesnt have fees to pay, and you do not risk losing it. It belongs solely to you until you decide to part with it online.

Available to Everyone: Worldwide there are more than two billion people using the internet and related devices. Cryptocurrencies are available to this many people at least. Cryptocurrencies are easy to buy and store, they are also easy and safe to spend. As the digital markets and infrastructure develops, and cryptocurrency platforms continue to be supported by major financial institutions, more of these people are likely to buy into the digital currency and grow its popularity further. There are no barriers to entry, digital currency is available to anyone willing to invest.

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Why Cryptocurrencies are Sensible in 2021, a Guide to Cryptocurrency - We Heart

Bitcoin has smashed through $US20,000 ($A26,440) for the first time amid increased institutional and corporate interest.

The cryptocurrency hit an all-time peak of $US20,800 and was last up 6.4 per cent at $US20,675.

It has gained more than 170 per cent this year, buoyed by demand from larger investors attracted to its potential for quick gains, purported resistance to inflation and expectations it will become a mainstream payment method.

Its been a wild year for bitcoin, which has soared more than 175% since the end of 2019.

Prices plunged below $4,000 in March as markets around the globe plummeted due to the COVID-19 economic crisis.

But bitcoin has rallied sharply in the past few months as the US dollar weakened.

Crucially, the cryptocurrency has also skyrocketed into the mainstream.

Payments giants Square (SQ) and PayPal (PYPL) both now allow their customers to buy and sell bitcoin.

Money management giant Fidelity is launching a bitcoin fund for wealthy investors.

Bitcoin futures contracts are even trading on the Chicago Mercantile Exchange.

Bitcoin is a (relatively) new currency that was created in 2009 by an unknown person using the alias Satoshi Nakamoto.

Transactions are made with no middle men meaning, no banks.

Bitcoin can be used to book hotels on Expedia, shop for furniture on Overstock and buy Xbox games.

But much of the hype is about getting rich by trading it.

The price of bitcoin skyrocketed into the thousands in 2017.

Bitcoins can be used to buy merchandise anonymously.

In addition, international payments are easy and cheap because bitcoins are not tied to any country or subject to regulation.

Small businesses may like them because there are no credit card fees.

Some people just buy bitcoins as an investment, hoping that theyll go up in value.

Buy on an Exchange

Many marketplaces called bitcoin exchanges allow people to buy or sell bitcoin using different currencies.

Coinbase is a leading exchange, along with Bitstamp and Bitfinex.

But security can be a concern: bitcoins worth tens of millions of dollars were stolen from Bitfinex when it was hacked in 2016.

Transfers

People can send bitcoin to each other using mobile apps or their computers.

Its similar to sending cash digitally.

Mining

People compete to mine bitcoins using computers to solve complex math puzzles.

This is how bitcoins are created.

Currently, a winner is rewarded with 12.5 bitcoins roughly every 10 minutes.

Bitcoin are stored in a digital wallet, which exists either in the cloud or on a users computer.

The wallet is a kind of virtual bank account that allows users to send or receive bitcoin, pay for goods or save their money.

Unlike bank accounts, bitcoin wallets are not insured by the FDIC.

Bitcoin wallet in cloud: Servers have been hacked. Companies have fled with clients bitcoin.

Bitcoin wallet on computer: You can accidentally delete them. Viruses could destroy them.

Though each bitcoin transaction is recorded in a public log, names of buyers and sellers are never revealed only their wallet IDs.

While that keeps bitcoin users transactions private, it also lets them buy or sell anything without easily tracing it back to them.

Thats why it has become the currency of choice for people online buying drugs or other illicit activities.

No one knows what will become of bitcoin.

It is mostly unregulated, but some countries like Japan, China and Australia have begun weighing regulations.

Governments are concerned about taxation and their lack of control over the currency.

- with CNN

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Bitcoin cryptocurrency price is at an all-time high, but what is it exactly? - 7NEWS.com.au

Leading virtual currency bitcoin on Wednesday traded above $20,000 for the first time following a sustained run higher in recent weeks.

Just 12 years old, bitcoin reached a record-high $20,398.50 before pulling back to $20,145, which was still an intra-day gain of nearly four percent.

It has seen a meteoric rise since March, when it stood at $5,000, spurred by online payments giant PayPal saying it would enable account holders to use cryptocurrency.

A number of central banks have meanwhile responded to the rise of cryptocurrencies and the dwindling global use of cash by announcing plans for bank-backed digital units.

Several central banks including those of China and Sweden -- but also the US Federal Reserve -- are also testing digital applications in response to Facebook's recent moves to produce its own digital unit, Libra.

Unregulated by any central bank, bitcoin emerged as an attractive option for investors with an appetite for the exotic -- although criminals have also seen its under-the-radar appeal.

Debate has meanwhile raged over the status of the digital asset, launched in late 2008, as to whether it should be seen as a form of money, an asset or a commodity.

After the unit surpassed $1,000 for the first time in 2013, it increasingly began to attract the attention of financial institutions and has experienced wild price swings.

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Cryptocurrency Bitcoin Trades Above $20,000 for First Time - News18

Abstract

Code is law is the founding principle of cryptocurrencies. The security, transferability, availability, and other properties of crypto-assets are determined by the code through which they are created. If code is open source, as is customary for cryptocurrencies, this would prevent manipulations and grant transparency to users and traders. However, this approach considers cryptocurrencies as isolated entities, neglecting possible connections between them. Here, we show that 4% of developers contribute to the code of more than one cryptocurrency and that the market reflects these cross-asset dependencies. In particular, we reveal that the first coding event linking two cryptocurrencies through a common developer leads to the synchronization of their returns. Our results identify a clear link between the collaborative development of cryptocurrencies and their market behavior. More broadly, they reveal a so-far overlooked systemic dimension for the transparency of code-based ecosystems that will be of interest for researchers, investors, and regulators.

A cryptocurrency is a digital asset designed to work as a medium of exchange. The underlying Blockchain technology allows transactions to be validated in a decentralized way, without the need for any intermediary (1). Every cryptocurrency is entirely defined and governed by its code, which determines its security, functionality, availability, transferability, and general malleability (2). This code is law architecture immediately puts developers under the spotlight (3). Lack of transparency in the coding process might damage users and other stakeholders of the code (4).

Open code is identified as the antidote to lack of transparency (3). Even if the code is accessible to only a small fraction of users, the reasoning goes, it would protect the asset and stakeholders from manipulations (5). For this reason, the code of the vast majority of cryptocurrencies is stored in public repositories. GitHub alone currently stores the code of more than 1600 cryptocurrencies (6).

Cryptocurrencies are nowadays used both as originally intended, i.e., media of exchange for daily payments and, to a larger extent, for speculation (7, 8). The market value of a cryptocurrency is not based on any tangible asset, resulting in an extremely volatile, and largely unregulated, market (911). However, the cryptocurrency market has attracted private and institutional investors (12, 13). At the moment of writing, more than 3000 cryptocurrencies are traded, capitalizing together more than 200 billion dollars (14, 15).

Here, we challenge the view that open code grants transparency to cryptocurrencies, even accepting that literate users do check it carefully (which is, of course, far from obvious). We do so by analyzing 298 cryptocurrencies (i) whose code is stored in GitHub and (ii) whose daily trading volume has been, on average, larger than 105 U.S. dollars (USD) (16) during their lifetime. We show the following:

1) A substantial fraction of developers (4%) contributes to the code of two or more cryptocurrencies. Hence, cryptocurrencies are not isolated entities but rather form a network of interconnected codes.

2) The temporal evolution of the network of co-coded cryptocurrencies anticipates market behavior. In particular, the first time two independent codes get connected via the activity of one shared developer marks, on average, a period of increased correlation between the returns of the corresponding cryptocurrencies.

Thus, the temporal dynamics of co-coding of cryptocurrencies provides insights on market behaviors that could not be deduced on the basis of the combined knowledge of the code of single currencies and the present state of the market itself. In other words, transparency, i.e., the availability of relevant market information to market participants, is a systemic property. The whole network of cryptocurrencies should be considered both by regulators and by professional investors aiming to maximize portfolio diversification. From this point of view, our work contributes a new dimension to the literature focused on the properties of the cryptocurrency market, which has, so far, adopted approaches ranging from financial (1721) to behavioral (22) and from evolutionary (21, 23, 24) to technological (25, 26) perspectives.

We are interested in the coding and market activity concerning actively traded cryptocurrencies (see Methods). The 298 cryptocurrencies with trading volume larger than 100,000 USD whose code is stored on GitHub (298 projects) include 63 of the top 100 cryptocurrencies, ranked by average market capitalization during October 2019. A total of 6341 developers contributed to these GitHub projects, totaling 879,742 edits (see section S1.2 for more details). The number of developers working on a cryptocurrency project correlates positively with its market capitalization (Spearman correlation coefficient, 0.48, with P <0.0001; see fig. S2A), as previously noted (6).

The activity of the developers is heterogeneous. Twenty-eight percent of developers focused only on the top 10 cryptocurrencies, producing 20% of the edits, while only 15% of the developers worked only on projects with a capitalization lower than the median capitalization of the market, producing only 11% of the developing events. The Ethereum community soars above the others in terms of editing activity (109,527 development events), while Bitcoin has the largest number of developers, 832 (fig. S1). In general, the number of developers and the number of edits for a given project strongly correlate (Spearman correlation coefficient, 0.92, with P <0.0001; see fig. S2B).

We find that 4% of developers contributed to more than one cryptocurrency and are responsible for 10% of all edits. We further investigate their role by representing the GitHub data as a bipartite network, where developers and cryptocurrencies (the nodes) are connected by edit events (the links; Fig. 1A). We then project the bipartite network and obtain the network of connected cryptocurrencies where cryptocurrencies are nodes, and a link exists between them if they share at least one developer (Fig. 1B). We find that this network has 204 links, activated first by 147 different developers, and 123 nonisolated nodes, of which 115 form a giant component. Bitcoin has the largest number of connections, 53, followed by Ethereum with 43. The remaining 175 projects do not share any developer (Fig. 1C). The presence of a small fraction of developers who contributed to more than two cryptocurrencies (22 of 147) makes the network rich in cliques (see section S1.3 for more analyses on the network).

(A) The GitHub dataset can be represented as a bipartite network, where developers (red circles) are linked to the cryptocurrencies (blue circles) that they have edited at least once. (B) Projection of the bipartite network; cryptocurrencies that have at least one common developer are connected. (C) The real network of 123 cryptocurrencies with at least one connection. Node size is proportional to the number of connections, and link width is proportional to the number of common developers between two cryptocurrencies. Bitcoin (BTC) and Ethereum (ETH) play a central role in the graph.

We now consider the temporal evolution of the cryptocurrency network over 5 years of coding activity (from 5 March 2014 to 30 May 2019). A link between two cryptocurrencies is created the first time that a developer of one of the two edits the other (Fig. 2A), referred in the following as the GitHub connection time. What happens to the market behavior of the two cryptocurrencies that have just been linked in the GitHub network?

(A) A developer of cryptocurrency crypto 1 publishes her/his first contribution to crypto 2. If no other developer has worked on both currencies before, then this moment represents the GitHub connection time for the pair composed of crypto 1 and crypto 2. (B) The time series describing the asset returns of the two currencies synchronize after the connection time. (C) The Spearman correlation between the two time series increases when the asset returns synchronize.

We focus on the correlation between asset returns (40, 41). We rescale time so that the connection time corresponds to d = 0 for each pair of GitHub-linked currencies, and we measure the Spearman correlation over a backward rolling window of size s = 4 months [see Figs. 2 (B and C) and Fig. 3A and Methods for definitions; results are robust with respect to variations of this definition; see section S1.4]. To limit the effect of overall changes in market evolution, we standardize the value of the Spearman correlation, for a given pair of linked currencies and at a given time, by subtracting the average correlation across all possible pairs of currencies at that time and dividing by the corresponding SD (see Methods).

(A) Average standardized Spearman coefficients between return time series of linked pairs (red line) and a sample of random pairs of cryptocurrencies (dashed blue line). The size of the random samples is chosen to be the same as the number of existing linked pairs at each time. Its average size in the period reported in (A) is 124. Shaded areas represent 2 SDs of the mean and are determined via bootstrap (see Methods). The gray dot-dashed line corresponds to the average standardized correlation in the 3 months before the connection occurred. Time is shifted such that d = 0 corresponds to the GitHub connection time of each pair. Correlations are measured over a 4-month rolling window. (B) Distributions of the average correlation for linked and random pairs. Averages are computed over periods of 4 months: the 4 months before the connection time and the period between 2.5 and 6.5 months after the connection time. Vertical lines correspond to the average of each distribution. Pairs that synchronized after the connection time shift the distribution toward positive values. All the density distributions are computed using a Gaussian Kernel Density Estimation setting the bandwidth values to 0.39. For raw data histograms, see fig. S12.

Figure 3A shows that the average standardized Spearman correlation between the returns of two linked cryptocurrencies, averaged over the set of 204 linked pairs, increases at the turn of the GitHub connection time, rising from 0.31 0.01, on average (SEM), in the 4 months before the connection time, to 0.66 0.01, in the period included between 2.5 and 6.5 months after the connection time [Fig. 3A, significant under Welch test (42, 43), with P = 0.02). This corresponds to a relative increase of almost 130% after the synchronization occurred (see section S1.9.2 for details about the synchronization period). This result is robust to major perturbations of the network, including the removal of Bitcoin or/and Ethereum from it (fig. S9).

We test that the observed behavior is specific to linked pairs by measuring the synchronization of a random sample of 104 cryptocurrency pairs, selected from the entire market excluding linked pairs. Their connection time is chosen at random from the list of actual GitHub connection times (see section S1.4.1 for different randomization approaches). We find that the standardized correlation of these pairs remains constant across the connection time, ruling out the possibility of ecology effects induced by the specific distribution of connection times (fig. S5). We note also that, on average, the standardized Spearman correlation is higher for linked pairs compared to random pairs.

The increase in correlation observed for linked pairs could (i) be driven by few outliers or (ii) reflect the behavior of the majority of them. Figure 3B shows the distributions of the increase in standardized correlation between the 4 months preceding and the 4 months included between 2.5 and 6.5 months after the connection time. The distribution of linked pairs is centered at positive values of change (i.e., increase in correlation) and shows a significantly higher average synchronization compared to the distribution of random pairs, e.g., under Welch test (for more statistical tests, see section S1.4.1). In particular, approximately 65% of linked couples increased their correlation after GitHub connection time, a percentage significantly higher than random (fig. S13). These observations confirm that the observed change in correlation is not simply driven by outliers, hence supporting hypothesis (ii).

The market behavior of cryptocurrencies is also characterized by other properties. We repeated the analyses reported above to study the correlations between the time series describing daily changes in trading volume and market capitalization. We found no significant effects of the connection time on those measures (see results in section S1.8) under a Welch test at a significance level of 0.05.

We now consider the market properties of GitHub-linked cryptocurrencies across GitHub connection time. First, we focus on the difference in market capitalization and volume among pair constituents. We find that the absolute difference in market capitalization and volume between two linked cryptocurrencies is typically larger than that between randomly selected cryptocurrencies [see Fig. 4 (A and B) and section S1.9.2 for details; note also that the market capitalization and volume of currencies are highly correlated, as expected (fig. S17)].

(A) Probability density function (pdf) of the difference in market capitalization among cryptocurrencies forming linked pairs (continuous line) and random pairs (dashed line). (B) Probability density function of the difference in transaction volume among cryptocurrencies forming linked pairs (continuous line) and random pairs (dashed line). (C) Probability density function of the difference in market age at the connection time among cryptocurrencies forming linked pairs (continuous line) and random pairs (dashed line). All the density distributions are computed using a Gaussian Kernel Density Estimation setting the bandwidth values to 0.36.

Then, we shift our attention to differences in market age, defined as the difference in the amount of time since a currency appeared in the market. We find that the age difference of the two cryptocurrencies in a linked pair, measured at connection time, is significantly higher, on average, than the difference of market age observed for random pairs (Fig. 4C). In particular, we find that the second-edited currency is younger than the first-edited currency in 61% of the cases and has lower market capitalization in 65% of the cases.

Last, we investigate the factors responsible for the observed heterogeneity in synchronization across linked pairs (Fig. 3B). We find that, when a linked pair includes one of the top 10 linked cryptocurrencies in terms of market capitalization (evaluated in the period preceding connection time), the corresponding synchronization of returns following connection is significantly higher than average (fig. S21D). Other factors, including the type of development event (push or pull), the direction of the link (from younger to older or vice-versa), and the connection time, do not explain the observed differences in synchronization across pairs (fig. S21, A, B, E, and F).

We analyzed the relationship between code and market for 298 GitHub-hosted cryptocurrencies whose trading volume was larger than 105 USD for the covered period. We showed that approximately 4% of developers contributed to the code of more than one cryptocurrency and that these developers are more active than the average, contributing together to 10% of all edits. We then defined the network of co-developed cryptocurrencies and showed that, for months after the GitHub connection time, the correlation between the return time series of two GitHub-linked cryptocurrencies increased, on average. We found that other market indicators, and in particular, volume, do not show the same behavior. Last, we showed that developers tend to work on an established currency first and that linked pairs containing at least one top cryptocurrency exhibited a larger correlation of returns following connection.

It is important to delimit the scope of our findings. First, we only considered projects developed on GitHub. While this is, by far, the largest repository of cryptocurrency open-source code (it hosts more than 99% of the project hosted on online repositories), alternatives exist, e.g., GitLab (44). Second, we selected cryptocurrencies on the basis of their average trading volume, possibly neglecting currencies with only a short history of significant trading volume. Third, we focused on the first connecting event and did not investigate the presence and consequence of a possibly increasing pool of shared developers between two cryptocurrencies and/or actions of the developer(s) in that pool. Fourth, we considered pairs of cryptocurrencies, neglecting other possible influences of the network built in the first part of the article. Last, we did not consider the structure of the code or the semantics of the coding that a developer of the first cryptocurrency performs on the second. All these are open directions for future work.

Of course, our analysis cannot identify the mechanisms that drive the observed market synchronization. Speculatively, at least two main dynamics might be at play. The first identifies code as an important fundamental for this market (45, 46). Traders would be aware of and operate (also) based on code and code development. The activity of developers would therefore represent a signal that, perceived by many traders, could result in the observed synchronization. The second dynamics, either complementary or alternative to the previous one, points to a greater role for developers, who could either directly own and trade large amounts of the cryptocurrencies that they edit or be hired by stakeholders who, in their turn, do the trade. At the systemic level, these interlocking directorates of developers/stakeholders would cast a shadow on the transparency of the market and potentially expose it to systemic risks due to hidden structural correlations between cryptocurrency prices.

In this respect, it is worth noting that the lack of incentives for developers is a longstanding issue for cryptocurrencies. Some Bitcoin developers, for example, are paid by companies with an interest in Bitcoin (47); in the case of Ethereum, some are funded by the Ethereum Foundation itself, while bug-bounties, development grants, and visibility remain as other common incentives (48). In this context, our results could suggest that trading on the cryptocurrency market might play the role of incentive for developers to perform certain cross-currency actions. The lack of increase in synchronization for volumes suggests that the observed synchronization of returns is not due to an overall increase in trading interest toward the linked cryptocurrencies. Beyond these two mechanisms, more explanations may exist, and exhausting or testing them, if at all possible, is outside of the scope of this article.

Our results have broad implications. Code has become an important societal regulator that challenges traditional institutions, from national laws to financial markets (5, 49, 50). In particular, whether and how financial markets and technological code development interact is an open and debated question (6, 25, 51, 52). The case of cryptocurrencies is paradigmatic and still largely unexplored. Cryptocurrencies are open-source digital objects traded as financial assets that allow, at least theoretically, everyone to directly shape both an asset structure and its market behavior. Our study, identifying a simple event in the development space that anticipates a corresponding behavior in the market, establishes a first direct link between the realms of coding and trading. In this perspective, we anticipate that our results will be of interest to researchers investigating how code and algorithms may affect the nondigital realm (5355) and spark further research in this direction.

The GitHub dataset. GitHub is a service providing a host for software development using Git version control system (27, 28) largely used in a variety of innovation fields, from science to technological development (29). Previous research on the platform focused on the understanding of collaborative structures and developer behavior, showing the importance of social characteristics in the selection of code modifications (30) and of socialization as a precursor of joining a project (31).

A project is stored on GitHub in a so-called repository, and its production-ready code lives in the master branch of the repository (32) [called by default main branch starting from 1 October 2020 (33)]. Developers can modify the master branch in two ways, depending on their role. So-called collaborators are part of the core development team and can directly edit the code by triggering a push event. In contrast, contributors are anyone who contributed some changes to a project, by submitting their suggestions through a pull request that was later accepted and merged by one of the collaborators. Thus, push and accepted pull requests are the core events in the development of cryptocurrency production-ready code (34).

We retrieved cryptocurrency GitHub repository names from CoinMarketCap (35). We find that 1668 of the 2225 cryptocurrencies listed in CoinMarketCap as of 9 June 2019 shared their source code on GitHub. Then, we queried the GitHub Archive dataset (36), which stores all events on public repositories from 2011, through Google BigQuery (37). This step provided us with all events related to the development of cryptocurrency GitHub projects. Specifically, we queried two types of events: push events and accepted pull request events. Last, we removed all events triggered by GitHub apps (software designed to maintain and update the repositories), and we removed from our dataset GitHub profiles whose name included the term bot to not include noise from users that identified or were reported to be nonhuman.

The market dataset. We collected cryptocurrency daily price, exchange volume, and market capitalization from three different web sources: CoinGecko (15), CryptoCompare (14), and CoinMarketCap (35) (the latter was used only until the end of July 2018 because of updates in the website regulations). We processed the data from CryptoCompare and CoinGecko following a standard procedure (38). We preferred the OpenHighLowClose (OHLC) data from the CryptoCompare Application Programming Interface (API). We adopted as a measure of the transaction volume the amount of USD traded for a crypto on the exchanges registered on CryptoCompare. Similarly, we retrieved the market capitalization of a cryptocurrency using the CoinGecko API and processed it to remove the structural biases found in (38), e.g., we shifted by 1 day all data starting from 30 January 2018.

The price of a cryptocurrency represents its exchange rate (with USD or Bitcoin, typically), which is determined by the market supply and demand dynamics. The exchange volume used is the total trading volume across exchange markets, from dollars to one crypto. The market capitalization is calculated as a product of a cryptocurrencys circulating supply (the number of coins available to users) and its price. We retrieved historical data for currently inactive currencies by querying all the 6000 and more cryptocurrencies recorded in the CoinGeko database (39). Our datasets include market indicators from 3 April 2013 (date by which all the webpages started collecting data) until 30 October 2019. Note that to study the effects of GitHub development on market indicators, we collected market data for 6 months longer compared to the GitHub data.

In this work, we focus on cryptocurrencies that can be traded with sufficient ease. We, therefore, consider only cryptocurrencies whose trading volume is larger than 100,000 USD (16). We find that 521 cryptocurrencies meet this condition (see table S1 for full list), of which 298 share their code on GitHub.

We compare various quantities measured for GitHub-linked pairs to the corresponding values measured for random pairs. A random pair is obtained by (i) extracting 2 of the 521 cryptocurrencies that meet the condition of an average daily market volume larger than 100,000 USD and (ii) verifying that the two extracted cryptocurrencies do not form together a GitHub-linked pair. As for the average volume, days with zero transaction volume (days of market inactivity) were discarded and treated as missing values. The resulting set of 521 cryptocurrencies represents 27% of all the cryptocurrencies with a market history on both CryptoCompare and CoinGecko.

A cryptocurrency asset return at time t is defined as R(t)=P(t)P(t1)P(t1), where P(t) is the price (56). The change in market capitalization at t is defined as CM(t)=M(t)M(t1)M(t1), where M(t) is the market capitalization. The change in volume is defined as CV(t)=V(t)V(t1)V(t1), where V(t) is the volume as time t.

Following a standard approach in time series analysis (57, 58), we measure correlation as the Spearman coefficient between two time series. To compare the correlation across pairs of currencies, following, e.g., Schruben (59), we compute the standardized correlation asSCk(t)=Ck(t)C(t)(t),where Ck(t) is the the correlation time series, computed for a pair k by comparing the return time series [Ri(t) and Rj(t)] of paired assets i and j at time t, and C(t) and (t) are the average correlation and corresponding SD across pairs. At each time step t, the set of pairs used to compute the standardized correlation consists of the pairs for which we had price data at time t.

We compute the error associated with the average standardized correlation across pairs using bootstrapping (60). For each value of d, representing the number of days before/after the connection time (such that at the connection time d = 0): (i) We sample Nd pairs of currencies with replacement, where Nd is the number of existing linked pairs at time d; (ii) we compute the average standardized correlation SC(d)=k=1NdSCk(d)/Nd where k is running across the Nd pairs; (iii) we repeat steps (i) and (ii) 104 times; and (iv) we compute the mean and SD across the obtained values of SC(d). These values provide an estimate of the average standardized correlation and associated error for the population of linked pairs d days after the connection. We follow the same procedure for random linked pairs.

A. M. Antonopoulos, Mastering Bitcoin: Unlocking Digital Cryptocurrencies (OReilly Media Inc., 2014).

L. Lessig, Code and Other Laws of Cyberspace (Basic Books, 1999).

L. Lessig, Code: Version 2.0 (Basic Books, 2006).

A. Trockman, R. van Tonder, B. Vasilescu, Proceedings of the 16th International Conference on Mining Software Repositories (IEEE Press, 2019), pp. 181185.

N. Popper, The New York Times, Technology (2017).

P. Vigna, M. J. Casey, The Age of Cryptocurrency: How Bitcoin and the Blockchain are Challenging the Global Economic Order (Macmillan, 2016).

R. van Tonder, A. Trockman, C. L. Goues, Proceedings of the 16th International Conference on Mining Software Repositories (IEEE Press, 2019), pp. 186190.

P. Bell, B. Beer, Introducing GitHub: A Non-Technical Guide (OReilly Media, 2014).

E. Kalliamvakou, G. Gousios, K. Blincoe, L. Singer, D. M. German, D. Damian, Proceedings of the 11th Working Conference on Mining Software Repositories, MSR 2014 (ACM, 2014), pp. 92101.

J. Tsay, L. Dabbish, J. Herbsleb, Proceedings of the 36th International Conference on Software Engineering (ACM, 2014), pp. 356366.

C. Casalnuovo, B. Vasilescu, P. Devanbu, V. Filkov, Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (ACM, 2015), pp. 817828.

L. Dabbish, C. Stuart, J. Tsay, J. Herbsleb, Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work (ACM, 2012), pp. 12771286.

C. Alexander, M. Dakos, Available at SSRN 3382828 (2019).

S. Voshmgir, Token Economy: How Blockchains and Smart Contracts Revolutionize the Economy (BlockchainHub, 2019).

P. D. F. De Filippi, Blockchain and the Law: The Rule of Code (Harvard Univ. Press, 2018).

R. Houben, A. Snyers, EU publications; Directorate-General for Internal Policies of the Union (2018).

W. H. Press, S. A. Teukolsky, B. P. Flannery, W. T. Vetterling, Numerical Recipes in Fortran 77: Volume 1, Volume 1 of Fortran Numerical Recipes: The Art of Scientific Computing (Cambridge Univ. press, 1992).

M. R. Chernick, Bootstrap Methods: A Guide for Practitioners and Researchers (John Wiley & Sons, 2011), vol. 619.

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From code to market: Network of developers and correlated returns of cryptocurrencies - Science Advances

SAN JOSE, Calif., Dec. 16, 2020 /PRNewswire/ -- Today FICO, a global analytics leader, and Crystal Blockchain of Bitfury Group ("Crystal"), a leading digital currency analytics company, announced a partnership to provide cryptocurrency risk management and monitoring services. With an increasing number of financial service providers looking to expand services into the crypto market, the joint offering will help to protect new business models and effectively connect the worlds of virtual and fiat currency for the benefit of their customers.

Banks have been hesitant to engage in crypto business due to the complexity of tracing transactions and measuring risk on the blockchain in order to combat money laundering activities. Combining FICO's industry-leading financial crime solutions with Crystal's blockchain analysis, the joint offering will help banks assess the risk of their clients' crypto business at the onboarding stage, as well as monitor that risk on all active accounts. This unique combination will enable banks to fully understand and actively manage the risk-exposure from customers individuals and corporations alike - that engage in virtual currency transactions. With rigorous KYC and AML controls in place, banks can expand their service portfolio into the fast growing market of virtual assets, while managing crypto-related risks.

"Cryptocurrency services are an under-utilized market for many large banks, due to the crypto-related risks and lack of transactional intelligence available," said Dr. Sebastian Hetzler, vice president of financial crimes product management, at FICO. "This partnership integrates FICO's AI-powered financial crimes detection with Crystal Blockchain by Bitfury Group's extensive blockchain analysis, providing financial institutions with an in-depth crypto-risk assessment of client activities and relationships."

The partnership will feature an integration between FICO's anti-financial crime solutions and Bitfury Group's Crystal analysis. At the onboarding stage, banks will gather a potential client's information including their virtual assets and wallets. FICO's KYC solution will cross reference against the Crystal Blockchain analytics platform to obtain Crystal's risk score, which is calculated based on the client's transaction history with anonymous and deanonymized sources and links, and the bank specific risk model. Traditional KYC risk factors and the Crystal risk score become part of the initial risk assessment and may inform further due diligence.

Story continues

Banks will also be able to apply the blockchain analysis to existing clients for ongoing monitoring in real-time. For instance, if the Crystal risk score changes due to nefarious crypto transactions or activities on the darknet, the bank will be alerted and can immediately start investigations. In FICO's Alert and Case Manager (ACM), further details from Crystal's blockchain analysis will be visualized in an interactive and powerful UI to support investigators' decisions.

"At Crystal, we are committed to providing security and transparency in the cryptocurrency markets through top-class blockchain transaction analytics for AML and CFT. We provide high-quality risk-scoring mechanisms via predictive analytics and data science, along with entity monitoring to understand the provenance of funds," said Marina Khaustova, CEO of Crystal Blockchain. "The crypto industry is relatively young and as the tech develops it has unique compliance requirements. So when it comes to anti-fraud operations it's vital to combine the best practice of more mature financial industries with the knowledge amassed by crypto market experts. We look forward to working with FICO in our shared mission to make the global financial markets be it real or virtual more secure and trusted for banks, financial institutions, and their clients, by improving fraud identification and suspicious activity tracking on the blockchain."

To learn more about the FICO Financial Crimes Solutions, visit https://www.fico.com/en/latest-thinking/product-sheet/fico-anti-money-laundering-solutions.

About Crystal BlockchainCrystal Blockchain of Bitfury Group is the all-in-one analytics and monitoring tool for crypto AML compliance for VASPs. The Crystal Blockchain team of world-class blockchain analysts, award-winning mathematicians and professional software developers, are united by the idea of leveraging blockchain technology to build a better, safer future, by designing best-in-class blockchain solutions for virtual asset service providers and regulatory agencies. Crystal's anti-money-laundering tools enable crypto businesses and financial institutions to operate in a safe and secure environment and allows compliance officers to do their job more efficiently. Crystal was developed by the Bitfury Group's software team and is distributed worldwide by Crystal Blockchain BV, headquartered in Amsterdam. Learn more at https://crystalblockchain.com.

Follow us on Twitter at https://twitter.com/CrystalPlatform

About FICO FICO (NYSE: FICO) powers decisions that help people and businesses around the world prosper. Founded in 1956 and based in Silicon Valley, the company is a pioneer in the use of predictive analytics and data science to improve operational decisions. FICO holds more than 195 U.S. and foreign patents on technologies that increase profitability, customer satisfaction and growth for businesses in financial services, telecommunications, health care, retail and many other industries. Using FICO solutions, businesses in more than 100 countries do everything from protecting 2.6 billion payment cards from fraud, to helping people get credit, to ensuring that millions of airplanes and rental cars are in the right place at the right time. Learn more at https://www.fico.com.

Join the conversation at https://twitter.com/fico & https://www.fico.com/en/blogs/

FICO is a registered trademark of Fair Isaac Corporation in the U.S. and other countries.

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SOURCE FICO

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FICO and Crystal Blockchain of Bitfury Group Announce Partnership To Deliver Real-Time Cryptocurrency Risk Management - Yahoo Finance

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Stories that bitcoin is difficult to withdraw and have nothing to spend have long been out of date. Today, digital coins have become a common payment instrument, and people of different professions around the world receive rewards in cryptocurrency for their work.

In the legislation of each country, certain requirements are prescribed for the minimum wage and the forms of its payment. For example:

In countries that are more loyal to the digital industry, not everything is simple with payments in cryptocurrency either.

Those companies that are not afraid of high volatility and problems with regulators can use digital coins to get rid of high fees for remittances and simplify the reward system for freelancers and remote workers.

Basically, companies that work in the digital industry and receive most of their income in bitcoins or altcoins are switching to cryptocurrencies, but there are exceptions.

Salary is calculated based on the price of the cryptocurrency for a predetermined date and time. Simplifying the math: if a cryptocurrency costs ten thousand dollars, and an employee wants to transfer a thousand to cryptocurrency, then he will receive 0.1.

Those employees who have agreed in advance to sell the cryptocurrency immediately after the accrual will receive its value in regular currency. But if an employee wants to save bitcoins, then over time he can benefit from the growth of the rate.

Employees who keep cryptocurrency with them whether for a day, a month, or longer can lose money. It all depends on changes in rate: a thousand dollars can turn into five thousand, or maybe nothing.

That is why some experts believe that paying salaries in cryptocurrency is akin to gambling.

Godex is a very prosperous, fast and easy exchange service on a market today. You can exchange almost every cryptocurrency here. If you get salary in LTC for example, we can help. You can exchange 1ltc to btc or to any money equivalent.

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Salary in cryptocurrency - News Anyway

December11, 20205 min read

Opinions expressed by Entrepreneur contributors are their own.

Ivan Caballero is a Catalan technological serial entrepreneur who , in his fourth company, received a call from his main client, an airline, saying that they were about to close and that all the debt they had with them could not be paid, which implied firing his team of 17 people.

An hour later Ivan receives the news that he was going to be a father, that moment of shock going from business success to failure, to the feeling of absolute creation by his future fatherhood, prompted him to seek to leave a better world.

So in 2013, as a social experiment in Barcelona, he decided to validate the hypothesis that a disinterested action of help, by generating a chain effect, can transform an entire society.

So he decided to create Social Coin , a virtual currency in which, by helping someone, the person receiving this support had the commitment to help someone else, generating a chain of favors. In one year they achieved more than 500,000 positive actions with more than 20,000 social coins distributed in more than 70 countries. This social experiment became a global movement allowing you to understand what motivates people to help others.

"To want to help people, they need to have a context of what are the social needs around them, generosity can improve our life, but also that of others", says Ivan.

After this learning, at the end of 2015 they were winners of a grant of one million euros by the European Commission with Citibeats , a project to detect through social data the real-time needs of citizens to be able to make informed decisions using intelligence artificial ethics.

They were subsequently recognized by the United Nations World Summit Award as the most innovative project in the global inclusion and empowerment section, in addition to being selected as one of the Gifted Citizens Projects in Ciudad de las Ideas , Puebla.

During 2017 they were investing the capital received by the European Commission to develop their technology in conjunction with the Spanish Artificial Intelligence Institute , launching in 2018 the first version of Citibeats.

By creating an analytics platform augmented with Machine Learning and Natural Language Processing algorithms, you can process large volumes of text data in any language and text format. The objective is to help multilateral organizations and Development Banks to react more quickly to social problems, allowing the creation of initiatives that help to solve them in a better way.

Citibeats team / Image: Courtesy Citibeats

During COVID-19 , Citibeats has collaborated with the Inter-American Development Bank and the United Nations Development Program to detect the needs of the population, mainly in Latin America and the Caribbean.

Helping to identify needs that may be hidden in the population, for example, on April 15 the New York Times reported famine problems in Venezuela and Colombia, but the Caballero algorithm had detected it 14 days earlier.

We have learned that one of the pillars to promote citizen participation is to understand the needs of society that is why we have created Citibeats using artificial intelligence for the massive analysis of social data in text format, but this would not make sense if we did not use this type of technologies following three pillars that we consider ethical ".

1. User privacy, avoiding collecting personal data and, if for any reason we have to do so, we immediately delete them once the analysis is completed, without generating traceability of the users who have generated this information.

2. Reduce bias, around 70% of the content generated on social networks in Latin America is by men, which skews and misrepresents the voice of women. The same occurs with certain ages and races that are not so represented in the total analysis of social networks, so we have developed our own technology to draw balanced conclusions.

3. Use cases with a positive impact on society. Many of the Artificial Intelligence tools are used to sell more, however, at Citibeats we are very clear about the importance of using these technologies for projects in which there is a positive impact on society, directly impacting people's well-being , says Ivan .

According to the director of the Stanford Artificial Intelligence Laboratory, Andrew Ng , "artificial intelligence is the new electricity and all industries will be impacted by it" so that every day it will play a fundamental role in our lives, being key that Directors of public and private organizations understand the possibilities that are presented to them when using this type of technology and specifically in the public sector to empower citizens to help detect the main problems and promote participation in the creation of solutions that benefit to his community and with this to the world.

As entrepreneurs, access to technologies such as artificial intelligence is democratized every day, which gives us great power to help solve the world's great problems, but every great power implies great responsibility, which is why it is very important to detonate talks about artificial intelligence. ethics .

See the article here:
This Entrepreneur Developed a Cryptocurrency and Artificial Intelligence to Help People - Entrepreneur

Elrond's eGold (EGLD) cryptocurrency, will debut in the eToro ecosystem on Wednesday the 23 December, when it will become available on the eToroX cryptocurrency exchange.

eToro is a rapidly growing global USD2.5 billion unicorn that offers digital asset management services to 15 million users trading stocks, commodities, forex, and cryptocurrency markets. eToroX is eToro's professional crypto exchange, conceived and produced for the corporate and institutional-grade crypto trading community. eToroX is a highly effective, trusted and secure crypto trading solution.

Elrond had earlier announced the launch of Maiar, its digital wallet and global payments app, scheduled for 31 January. With these milestones, and a series of strategic moves, Elrond is positioned on a hyper-growth path to make a powerful leap towards mainstream adoption.

Through Maiar, Elrond is positioned for accelerated growth, aiming to gain widespread mainstream adoption, as in addition to crypto, the app aims to gradually provide the same features as PayPal, Venmo and Google Pay but without collecting any personal information and at much lower fixed fees.

Elrond is a public blockchain that has delivered the first truly scalable sharding architecture solution, which is able to deliver more than 15,000 transactions per second (TPS) and can scale even further, having achieved 263,000 TPS in public tests. Compared to the top cryptos today, Elrond is broadband to the early days of slow internet connections.

Beniamin Mincu, CEO & Founder of Elrond Network, says: "Bitcoin has proven that cryptocurrencies are secure and can serve an important purpose in the internet economy. Elrond opens up a new frontier offering internet-scale blockchain performance, and an intuitive application called Maiar, through which we aim to bring the next billion people to the blockchain space."

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Elrond eGold to list on eToroX - HedgeWeek

The Tokyo-based financial services company Monex Group released the results of its Global Retail Investor Survey, which shows that the number of investors in Japan with active cryptocurrency investments reached a record high of 16.2%.

According to the survey findings, the US reported a record increase in the retail crypto investors as 21% of the respondents said that they already invested in cryptocurrencies. The firm conducted the survey across Japan, China (Hong Kong) and the US from 19 November to 30 November.

The firm surveyed investors in Japan through Monex and in the US through TradeStation securities. Additionally, the Group surveyed investors in China (Hong Kong) through MONEX Boom Securities. The number of investors in digital assets decreased slightly in China (Hong Kong) from 14.5% in 2019 to 14.1% in 2020.

FTX Selects Capitalise.ai to Provide New Standard of Trading ExperienceGo to article >>

We asked retail investors about much-discussed cryptocurrency such as Bitcoin, with respect to their experience with it and their willingness to invest in it. The percentage of retail investors replying that they had already invested in cryptocurrency was 16.2% in Japan and 21.0% in the U.S., the highest level in each country since this question was introduced in June 2017. The percentage of investors in China (Hong Kong) who had invested in cryptocurrency fell slightly from the previous survey, Monex mentioned in the official report.

The Group surveyed other trends in the global retail trading industry. According to the survey, retail investors in China (Hong Kong) were the most optimistic about the performance of global stock markets while investors in the US and Japan remained cautious. The report indicated optimism among investors regarding the US stock market due to a potential rebound in economic activities.

The total number of cryptocurrency investors has increased across the world as more people are planning to diversify their investment portfolio with the inclusion of crypto assets. The recent numbers from Japan and the US indicate the mass adoption of digital assets.

Read more here:
Cryptocurrency Investors in US and Japan Reaches New High - Finance Magnates

Cryptocurrencymarket has been analyzed by utilizing the best combination of secondary sources and in-house methodology along with a unique blend of primary insights. The real-time assessment of the Cryptocurrency market is an integral part of our market sizing and forecasting methodology, wherein our industry experts and team of primary participants helped in compiling the best quality with realistic parametric estimations.

In4Researchs latest market research report on theCryptocurrency market, with the help of a complete viewpoint, provides readers with an estimation of the global market landscape. This report on the Cryptocurrency market analyzes the scenario for the period of 2020 to 2026, wherein, 2019 is the base year. This report enables readers to make important decisions regarding their business, with the help of a variety of information enclosed in the study.

This report on the Cryptocurrency market also provides data on the developments made by important key companies and stakeholders in the market, along with competitive intelligence. The report also covers an understanding of strengths, weaknesses, threats, and opportunities, along with the market trends and restraints in the landscape.

Questions Answered in Cryptocurrency Market Report:

Request for a sample copy of the report to get extensive insights into Cryptocurrency market @https://www.in4research.com/sample-request/10232 Based on Product type, Cryptocurrency market can be segmented as:

Based on Application,Cryptocurrency market can be segmented:

The Cryptocurrency industry study concludes with a list of leading companies/suppliers operating in this industry at different stages of the value chain.

List of key players profiled in the report:

If you are planning to invest into new products or trying to understand this growing market, this report is your starting point.

Ask for more details or request custom reports from our industry experts @https://www.in4research.com/customization/10232

Regional Overview & Analysis of Cryptocurrency Market:

Analysis of COVID-19 Impact & Post Pandemic Opportunities in Cryptocurrency Market:The outbreak of COVID-19 has brought along a global recession, which has impacted several industries. Along with this impact COVID Pandemic has also generated few new business opportunities for Cryptocurrency market. Overall competitive landscape and market dynamics of Cryptocurrency has been disrupted due to this pandemic. All these disruptions and impacts has been analysed quantifiably in this report, which is backed by market trends, events and revenue shift analysis. COVID impact analysis also covers strategic adjustments for Tier 1, 2 and 3 players of Cryptocurrency market.

Table of Content: Global Cryptocurrency Market

Chapter 1. Research Objective1.1 Objective, Definition & Scope1.2 Methodology1.2.1 Primary Research1.2.2 Secondary Research1.2.3 Market Forecast Estimation & Approach1.2.4 Assumptions & Assessments1.3 Insights and Growth Relevancy Mapping1.3.1 FABRIC Platform1.4 Data mining & efficiency

Chapter 2. Executive Summary2.1 Cryptocurrency Market Overview2.2 Interconnectivity & Related markets2.3 Ecosystem Map2.4 Cryptocurrency Market Business Segmentation2.5 Cryptocurrency Market Geographic Segmentation2.6 Competition Outlook2.7 Key Statistics

Chapter 3. Strategic Analysis3.1 Cryptocurrency Market Revenue Opportunities3.2 Cost Optimization3.3 Covid19 aftermath Analyst view3.4 Cryptocurrency Market Digital Transformation

Chapter 4. Market Dynamics4.1 DROC4.1.1 Drivers4.1.2 Restraints4.1.3 Opportunities4.1.4 Challenges4.2 PEST Analysis4.2.1 Political4.2.2 Economic4.2.3 Social4.2.4 Technological4.3 Market Impacting Trends4.3.1 Positive Impact Trends4.3.2 Adverse Impact Trends4.4 Porters 5-force Analysis4.5 Market News By Segments4.5.1 Organic News4.5.2 Inorganic News

Chapter 5. Segmentation & Statistics5.1 Segmentation Overview5.2 Demand Forecast & Market Sizing

Any Questions/Queries or need help? Speak with our analyst: https://www.in4research.com/speak-to-analyst/10232

FOR ALL YOUR RESEARCH NEEDS, REACH OUT TO US AT:Contact Name: Rohan S.Email:[emailprotected]

Phone:+1 (407) 768-2028

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Global Cryptocurrency Market Overview, Cost Structure Analysis, Growth Opportunities and Forecast to 2026 - The Haitian-Caribbean News Network