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A network designed to give users back control and ownership of their data, while supporting new privacy-first applications and use-cases. The Foundation intends to guide the long-term development of the network and coordinate community initiatives. Community members will be allowed to submit input or improvement proposals that will eventually be voted on by Oasis validators as part of the network's upcoming on-chain governance system. This on-chain system will enable validators and delegators to vote on network changes, with voting power based proportionally on stake weight. The Oasis network will use meritocratic, consensus-based community decision-making.

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• Join the new era of collecting
• A novel approach to solve a mining work centralization problem in blockchain technologies
• Emerald Crypto
• Ascending Triangle
• Travel + Leisure Home
• SC orders demolition of twin 40-storey towers of Supertech
• 5 Different Types Of Cryptocurrencies And Their Importance
• DeFi is Coming to Oasis!

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Cryptocurrencies have become increasingly popular since the introduction of bitcoin in But this observation obfuscates the notion that cryptocurrencies, unlike fiat currencies, are technologies entailing a true innovation potential. By using, for the first time, a unique measure of innovation potential, we find that the latter is in fact the most important factor associated with increases in cryptocurrency returns.

By contrast, we find that the buzz surrounding cryptocurrencies is negatively associated with returns after controlling for a variety of factors, such as supply growth and liquidity. Finally, we find that an increase in supply is positively associated with weekly returns.

Taken together, our findings show that cryptocurrencies do not behave like traditional currencies or commodities—unlike what most prior research has assumed—and depict an industry that is much more mature, and much less speculative, than has been implied by previous accounts.

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.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Since the introduction of bitcoin in [ 1 ], cryptocurrencies have become increasingly popular. Cryptocurrencies are digital tokens that can be exchanged online, using cryptographic hashing and digital signatures to verify transactions and avoid double-spending of the same token.

Thanks to these technical features, cryptocurrencies have introduced the notion of scarcity to the digital world by preventing users from copying the bytes that represent the token [ 2 , 3 ]. Because the scarcity of cryptocurrencies is protected by the cryptography embedded in their open-source code typically auditable by anybody , cryptocurrencies can potentially become valuable.

But what explains fluctuations in their market value? Typically, a cryptocurrency supply is predetermined by the underlying code, so market actors can anticipate most of the future variations in supply. For instance, 25 new bitcoin are generated every 10 minutes on average to reward those who lend computer power to verify transactions; this reward is halved every four years, until the supply of bitcoin reaches its programmed maximum of 21 million.

Shifting patterns in mining activity e. For instance, bursts of media visibility [ 8 ] can attract waves of new users, and this movement can be partly anticipated by various market actors, such as cryptocurrency traders, thereby leading to price bubbles.

Thus, cryptocurrencies are not only scarce but also potentially useful, which is likely to drive up their demand independently of short-term media cycles [ 10 — 11 ]. As explained by Ben Bernanke, then Chairman of the U. But because prior research treated cryptocurrencies mostly as money rather than as technology, researchers never tested the relationship between innovation potential and cryptocurrency prices.

Our findings show that the innovation potential embedded in technological upgrades is the most important factor associated positively with cryptocurrency returns. By contrast, we find that, after controlling for a variety of factors, such as supply growth and liquidity, the buzz surrounding cryptocurrencies is negatively associated with weekly returns. Finally, we find that upward variations in supply are positively related to returns. This result warrants a detailed discussion at the end of this paper since it appears to be at odds with the Quantity Theory of Money [ 12 ], according to which, an increased supply should, ceteris paribus, lead to lower prices—and lower returns.

This observation potentially implies that cryptocurrencies, at the microeconomic level of supply and demand, do not behave like traditional currencies, in contrast to the assumptions of most of prior research. Taken together, our findings emphasize the crucial role played by the technological upgrades underpinning innovation potential in the cryptocurrency ecosystem, and depict an industry that is much more mature, and much less speculative, than has been implied by previous accounts.

Since the launch of the first cryptocurrency, bitcoin, in , dozens of other cryptocurrencies have been created. Most of them, though, do not represent serious attempts at establishing a foothold in the market.

Cryptocurrencies belonging to the latter group, and whose existence is typically short-lived, are not to be confused with the serious attempts at introducing value-creating innovations. For the purpose of this study, we focus on five cryptocurrencies whose major innovations were widely recognized by the community, and whose code has been audited and verified by multiple independent third parties.

We decided to include bitcoin BTC since it represents the benchmark against which the value of other cryptocurrencies can be assessed. We then picked one cryptocurrency from each major wave of cryptocurrency creation. From the second wave of cryptocurrencies, which began in , we chose to include Litecoin—the second cryptocurrency ever introduced.

The fourth wave of cryptocurrencies, heralded in , sought to create value outside the realm of peer-to-peer payments. Ripple XRP represents an interesting case in point, with a team of developers managed by a for-profit organization called Ripple Labs, and a verification process that does not rely on mining to achieve consensus. The fifth wave, which started in , consisted of cryptocurrencies seeking to combine advantages introduced in previous waves e.

Stellar STR , created in August , illustrates this endeavor well. Our analyses of the five cryptocurrencies began in September , shortly after the introduction of Stellar on popular online exchanges, and ended one year later, in August Since all these cryptocurrencies were still in existence in August , we thus obtained balanced panel data, which allowed us to control for unobserved characteristics of each cryptocurrency and minimize the noise created by cross-panel heterogeneity.

Data for some of the key variables were available only weekly, so we decided to aggregate all other variables at the week level to obtain a rich explanation of the drivers of cryptocurrency returns, and mitigate the noise created by relying on daily observations [ 14 ]. Our dataset includes observations, but due to some variables being lagged in our models, we ran all of our analyses on observations i. We decided to predict weekly cryptocurrency returns instead of price because finance theory rationalizes asset return as a reward for investors.

An equally important reason for modelling return is its desirable statistical property, i. In contrast, the price time series may not be stationary, which may result in spurious correlations [ 16 , 17 ]. Indeed, a joint Im—Pesaran—Shin IPS test for panel data led us to conclude that we cannot reject the non-stationarity hypothesis for price time series this result holds even after removing the time trend.

Table 2 compares the stationarity test results for price and returns. IPS is the preferred test here because of sample size, and because it allows the time dimension dynamics of each panel, which drives non-stationarity, to vary. In the next section, we model weekly returns as a linear combination of various supply- and demand-side variables.

Our models control for market liquidity and unexpected supply growth , for time-invariant unobserved heterogeneity e. Details on data, measures, and estimation method follow in the next section. To enhance causal inference, we lagged all our predictors except liquidity , which by design should have a contemporaneous effect on demand and returns.

The basic model is specified as follows:. We acquired data from CoinGecko. CoinGecko systematically collects data on various cryptocurrencies, including information on trading volume, price, market capitalization, and quantity in circulation.

CoinGecko founders also developed and validated four longitudinal, multidimensional indicators to capture liquidity, developer activity, community support, and public interest [ 18 ].

For instance, the CoinGecko web application connects to the official application program interfaces APIs from Reddit, Facebook, Twitter, Github, and Bitbucket to continuously update the values taken by its indicators over time.

For price and volume data, the API of a third-party price data provider is used. Market capitalization data were obtained from Coinmarketcap. Finally, we used the Factiva database to collect media coverage data on each cryptocurrency. All our data were aggregated at the week level and were collected for an entire year starting in September Fig 1 below plots the distribution of the dependent variable. Overall, the distribution of weekly returns was close to a normal distribution over our study period.

The latter is often assumed to decrease cryptocurrency prices by deterring future user adoption, leading to investor exit [ 19 , 20 ]. To capture negative publicity , we hired a graduate research assistant to count how many media articles were published each week that associated the name of a given cryptocurrency with some form of suspicious or fraudulent activity, using appropriate keyword searches in the Factiva database i. For instance, using the latter search query, 36 unique articles were identified for the period 3—9 January, For Ripple and Stellar, we used a slightly more constraining search query to avoid capturing articles that have nothing to do with the two cryptocurrencies i.

We logged the number of article counts plus one before inclusion in our models. We measure public interest using the CoinGecko indicator computed as a weighted average of both the number of web search results obtained on Bing when searching a given cryptocurrency e. Thus, public interest increases when more people look for information about the cryptocurrency e. CoinGecko calibrates public interest and other indicators by normalizing the raw value using the benchmark bitcoin value both logged.

For example,. Serious cryptocurrency projects such as those tracked in our study vary in the extent to which their technology is improved, and how sustained that effort is over time—two dimensions thoroughly captured by our measure. In addition, more weight is given to indicators that would be more difficult to manipulate. Due to a confidentiality agreement with CoinGecko, we are unable to reveal the exact weightings, which they consider to be proprietary information.

As mentioned earlier, the evolution of supply for each cryptocurrency comprises a large predictable component, which can easily be anticipated by market participants and thus should not affect price or returns.

These adjustments go in hand in hand with temporary deviations from the average block validation time, which cause unexpected variations in supply in the short term. We measured cryptocurrency liquidity using the CoinGecko score based on the trading volume for each cryptocurrency, as obtained from all the major online exchanges.

Results remain the same. The more liquid a cryptocurrency, the easier for a participant to find a counterparty to trade with. Finally, we controlled for time-invariant unobserved heterogeneity using cryptocurrency fixed effects, and for time-varying unobserved variables using a week time trend. Both random-effects RE and fixed-effects FE estimators rely on ordinary least-squares assumptions e.

When these conditions are met, theory states that FE estimation is unbiased and consistent. RE estimation requires an additional assumption: the group-level effect and the regressors must be independent to avoid omitted variable bias [ 22 ]. When this assumption is met, RE estimation is unbiased, consistent, and, because it utilized both the within- and between-group variation, efficient. Under this assumption, FE estimation is not efficient because it only utilizes the within-group variation.

So, in our context, if the cryptocurrency-specific fixed effect is exogenous to other predictors, then we should opt for the RE estimator, and if not, for the FE estimator.

In our context, the cryptocurrency effect c i captures unobservable properties such as the inherent managerial skills of cryptocurrency founders in nurturing a community, which could be correlated with past levels of negative publicity or technological development , and make the RE estimator biased. In line with best practice, we used a Hausman test to assess which estimator is more suitable in our context [ 22 ].

Since the variance of the error terms may differ across cryptocurrencies, we resorted to the Sargan-Hansen SH statistic, which is robust to heteroscedasticity. As explained below, we estimate our fixed-effects panel least-squares regressions using a variety of standard errors, and our results remained stable across specifications. If the dependent variable and a given regressor are unrelated but are both non-stationary, the regression analysis tends to produce a statistically significant relationship, i.

Therefore, regression results can be interpreted confidently as long as these variables are included simultaneously in the models. We explicitly model the main effects of our primary predictors public interest , negative publicity , and technological development as linear relationships. We made this choice for three reasons.

First, we have no theoretical reason to believe that a curvilinear relationship would be at work. This could have been the case, for instance, if a major exogenous shock had happened over our period of study, opening up a new era wherein the influence of one of our predictors would suddenly become much greater. Besides, scholars find that going beyond the linear case does not necessarily enhance the replication power of studies that predict hedge fund performance. Rather, selecting factors with a straightforward economic interpretation allows for a substantial out-of-sample performance improvement in replication quality, whatever the underlying form of the factor model [ 23 ].

A novel approach to solve a mining work centralization problem in blockchain technologies

Try out PMC Labs and tell us what you think. Learn More. All relevant data are within the manuscript and its Supporting Information files. In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world.

Emerald Crypto

Horst Treiblmaier on ResearchGate. His research interests include implications of Blockchain and Distributed Ledger Technology, gamification, as well as epistemological and methodological issues. Publication date: Blockchain-based technologies are predicted as major disruptors for numerous business applications and processes, which bears huge implications for e-commerce. Blockchain has the potential to shake the foundation of ecommerce by enabling exchange relations that are trustless and operate without dedicated intermediaries or even central authorities in the case of permissionless blockchains. Furthermore, the exchange of information and value between companies and consumers might change considerably by enabling unified access to immutable data along the entire supply chain. In this paper, a framework and 19 high-level research questions are developed to inspire researchers to closely investigate the potential impact of blockchain on e-commerce. The main categories include a technological, b legal and c organizational and quality issues as well as d consumer issues. This paper illustrates how blockchain potentially impacts different elements of e-commerce in these respective areas. Horst Treiblmaier.

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A blockchain , originally block chain , is a growing list of records, called blocks , that are linked using cryptography. Each block contains a cryptographic hash of the previous block, a timestamp, and transaction data generally represented as a Merkle tree. There is a consistent issue in solutions today. In most cases, they are not scalable and cannot be adopted by the industry in their current format. Teams developing solutions need financial backing and support, and when the backing stops, the chain disappears even if technically it was a great solution.

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SC orders demolition of twin 40-storey towers of Supertech

Buying the ingredients required to make a bottle costs 64 coin. The token tracker page also shows the analytics and historical data. Harvesting []. Connect your wallet and. If that cryptocurrency coin ranks 1st in the ranking, it shows how many times the current price. The latest Tweets from Grape Protocol grapeprotocol. Early Sunday morning, Jan. There are 0 coins in circulation.

5 Different Types Of Cryptocurrencies And Their Importance

But how much truth is there in the stories? Even intrepid tech and business columnist Ashlee Vance had a hard time pinning Errol down, both as a source and as the patriarch of the Musk family, when researching for his book Elon Musk: Tesla, SpaceX, and the Quest for a Fantastic Future. However, while piecing together reporting and various interviews may not offer up a holistic picture of this contentious father figure or the short family life the Musks experienced in South Africa, it does shed light on some of the lore regurgitated on Twitter. Musk in recent years.

DeFi is Coming to Oasis!

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Cookie Clicker is a seemingly simple game that conceals a surprising amount of depth. Jia Xuan has 6 jobs listed on their profile. Some of the cool things the mod adds are: A low tech miner that uses normal fuel. This course helps you seamlessly upload your code to GitHub and introduces you to exciting next steps to elevate your project. Copy PIP instructions.

Cryptocurrencies have become increasingly popular since the introduction of bitcoin in But this observation obfuscates the notion that cryptocurrencies, unlike fiat currencies, are technologies entailing a true innovation potential. By using, for the first time, a unique measure of innovation potential, we find that the latter is in fact the most important factor associated with increases in cryptocurrency returns. By contrast, we find that the buzz surrounding cryptocurrencies is negatively associated with returns after controlling for a variety of factors, such as supply growth and liquidity. Finally, we find that an increase in supply is positively associated with weekly returns. Taken together, our findings show that cryptocurrencies do not behave like traditional currencies or commodities—unlike what most prior research has assumed—and depict an industry that is much more mature, and much less speculative, than has been implied by previous accounts. 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.

This paper aims to solve a mining work centralization problem using a gamification-based approach. The authors have developed a simple blockchain application that incorporates a gamification concept into the mining work. Then, they asked some participants in an experiment to use the application for a week and gathered some insights from the responses on questionnaires.