Bitcoin and ethereum have a hidden power structure

Published в How to download bitcoin | Октябрь 2, 2012

bitcoin and ethereum have a hidden power structure

Bitcoin and Ethereum Have a Hidden Power Structure, and It's Just Been Revealed In cryptocurrency circles, calling something "centralized" is. However, with Bitcoin we are trying to build a decentralized currency system, so we will need to combine the state transaction system with a consensus system in. Bill is an award-winning journalist whose articles have appeared in the - Ethereum's Hidden Power Structures | Matt Cutler: Matt Cutler is the. NHL PLAYOFF ODDS BETTING HORSES

The current January overall wealth distribution of Bitcoin is summarized in Table 1. We report that 0. We also report that a single user holds over 0. In Bitcoin, the top addresses contain over Wealth distribution in bitcoin. Overall, there seem to be three distinctive trends in terms of wealth concentration in Bitcoin-like cryptocurrencies: those that tend to on average 9 stay at a higher Gini value than Bitcoin over time Dogecoin and Bitcoin Cash , those that have a higher Gini value than Bitcoin but demonstrate a slightly downwards trend over time Litecoin and ZCash and finally those that have a lower Gini value than Bitcoin but have started to see an increase in their Gini value now Dash.

We will now review the results of other Bitcoin-like currencies based on the structure type. Cryptocurrencies such as Dogecoin do not demonstrate a similar trend towards a fairer wealth distribution despite the increase in adoption. Dogecoin is an interesting example due to its parodic origins. It can also be seen from Supplementary Figure S2 that after the creation of the Dogecoin fork from the Bitcoins network, there is a notable increase in the Gini value. However, unlike Bitcoin, Dogecoin trends towards an increase in the overall wealth concentration.

This growth in wealth concentration can also be observed in the constant decrease in the Nakamoto Index value Supplementary Table S3. We have summarized the overall wealth distribution of Dogecoin in Supplementary Table S4. Here we can see a noteworthy concentration of wealth with 0. We also report that a single user controls over The top addresses, by balance, control over This concentration moves towards violating the honest majority assumption for the secure operation of such crypto-assets potentially posing a security threat.

Like Dogecoin, we observe a similar trend in Litecoin, with an increment in the Gini value after the fork from Bitcoin followed by a consistently high Gini Value compared with Bitcoin. Unlike Dogecoin though, the overall trend for Litecoin is towards a slightly fairer distribution of wealth though at a slower rate than Bitcoin, as is visible in Supplementary Figure S2. This trend can also be observed in the rising trend of the Nakamoto value for Litecoin as observable in Supplementary Table S3.

We also report that the overall wealth distribution in Litecoin is more spread out than Dogecoin, as presented in Table 2 As evident by the Nakamoto index of Litecoin, the wealth is not as concentrated as Dogecoin, with the largest single stakeholder controlling over 2. This demonstrates how, despite having an almost identical structure to Dogecoin, Litecoin has a comparatively better distribution of wealth.

We note that this wealth distribution in both econometric measures is considerably worse than Bitcoin. Litecoin wealth distribution. ZCash has a provision of shielding transactions that make it hard to link the transaction to an account Hopwood et al.

These transactions were excluded from our analysis, thus limiting the accuracy of our results for ZCash. That caveat exposed, in the processed dataset, we observe a trend of an initial spike followed by a sustained fall in the Gini value. In comparison to Litecoin, the wealth distribution is less even in the observed address space.

The overall wealth distribution is also less spread out over the address space than Bitcoin and Litecoin. We report the address based wealth distribution in Supplementary Table S5. The highest wealth accumulator in observed ZCash transactions controls over 2. Like Dogecoin, the top addresses in ZCash contain over The most prominent outlier in the Bitcoin-like cryptocurrencies is Dash.

Dash is also an interesting case study for wealth distribution in cryptocurrencies as Dash utilizes a privacy-enhancing technique known as coinjoin mixing Amarasinghe et al. Another important differentiating factor for Dash is its two-tier structure for transaction processing Duffield and Diaz, In the first tier, users can operate computing nodes to participate in a race to include the next block of transactions in the ledger.

This is similar to the approach adopted by Bitcoin and other Bitcoin-like cryptocurrencies. In the second tier, Dash defines a new entity known as Masternode. A masternode is a network participant that has staked 1, dash as collateral for validating all the transactions. This approach is often referred to as a type of hybrid consensus algorithm.

We report that, out of all Bitcoin-like cryptocurrencies, Dash has the lowest Gini value with the current January Gini value of 0. It is worth noting that this Gini value is lower than the lowest observed Gini value for a real-world economy Suisse, Of course this might be due to the comparatively low market capitalization and overall utilization of the Dash ecosystem. The even wealth distribution is also observable in the second-highest Nakamoto index for Dash Supplementary Table S3.

But likewise, the reason for a fairer wealth distribution may be the presence of masternodes in the two-tier operational model. Supplementary Table S6 helps us observe this trend as the total number of active masternodes at the time of this study there are 4, active 11 masternodes with a somewhat uniform distribution of wealth as collateral. We reason that the requirement to have at least 1, Dash as collateral for consensus participation may incentivize users to split their Dash portfolio into multiple accounts, each containing the required 1, Dash.

By doing this, the participants increase their likelihood of receiving a reward from the consensus mechanism. This also incentivizes a more even, if pseudo, distribution of Dash within the ecosystem as it is more profitable to have many accounts with 1, Dash each than a single account with a large Dash portfolio. The address with the highest wealth concentration contains 1. Similarly, the top addresses only have This is closer to the wealth distribution of Bitcoin and notably better than other Bitcoin-like cryptocurrencies.

Another cryptocurrency that close mimics the traits of Bitcoin is Bitcoin Cash. Like other cryptocurrencies, we observe that a fork leads to wealth redistribution before the network attains a more stable increasing trend Dogecoin or decreasing trend Litecoin, Bitcoin and ZCash in the value of Gini. As discussed earlier, Bitcoin Cash provides us with an interesting case study as Bitcoin Cash is a fork of Bitcoin that was subsequently forked to create Bitcoin SV Kwon et al.

The impact of the fork is visible in Supplementary Figure S3. As evident in Supplementary Figure S3 and Supplementary Table S6, Bitcoin Cash has been subjected to a trend of increasing wealth concentration despite the recent nature of the fork. This wealth concentration is also visible in the overall distribution of wealth as documented in Supplementary Table S7 with a vast proportion of the population The highest balance for a single address constitutes 2.

We argue that this is primarily because Bitcoin Cash inherited the Bitcoin Ledger. The top addresses aggregate Ethereum, on the other hand, does not impose a strict limit on the supply of Ethers. This property is also inherited by Ethereum-like cryptocurrencies such as Ethereum Classic.

Thus the figures reported in this subsection will likely change significantly over time, unlike Bitcoin-like currencies in which a large proportion of wealth is already distributed. In this subsection, we review the current January state of wealth distribution for Etheruem and Ethereum Classic. Here we can observe that Ethereun has a higher Gini value than Bitcoin, but it has better wealth distribution than the fork, Ethereum Classic.

Similar to the observations with Bitcoin-like cryptocurrencies, there is a trend of an increase in the value of Gini after the fork; however, Ethereum has since trended towards a more even wealth distribution. Etheruem classic tends to have a higher Gini value with a poor wealth distribution when contrasted with Bitcoin and Ethereum The more concentrated wealth distribution is also observable with our Nakamoto Index calculation results, as manifested in Supplementary Table S8.

Ethereum Classic has a high wealth concentration among the top accounts with The address with the highest wealth concentration in Ethereum classic contains over 6. This trend is also present in Ethereum, however, to a much lower extent. The account with the highest balance in Ethereum contains over 4.

The top accounts in Ethereum constitute over Results from both Bitcoin-like and Ethereum-like cryptocurrencies suggest that the wealth distribution is initially poor likely due to only a select few participants controlling the majority of the wealth. But this concentration often dissipates as more participants join the system, as observed in Bitcoin and Ethereum. However, this trend towards fairer distribution is not universal as some cryptocurrencies have a strong trend towards an increasing Gini value, such as Dogecoin and Ethereum Classic.

Based on our analysis, it seems that some algorithmic interventions such as the one in Dash could assist improve the distribution in the short term; however, it is still unclear if the approach adopted by Dash is sustainable in the long term. We also note that 3 of the observed cryptocurrencies Dogecoin, ZCash, and Ethereum Classic violate the honest majority assumption with less than participants.

This may be an indication of a potential security threat. We have also manifested results from our wealth distribution analysis for all cryptocurrencies in Supplementary Figure S5. We utilize the Gini values of major economies reported by Suisse, This is in line with the results from the cryptocurrency analysis, where the median Gini value of the shortlisted cryptocurrencies is 0.

The highest observed Gini value in real-world economies is 0. This Gini value is considerably higher than the worst-performing crypto asset in our dataset, Dogecoin, with a Gini value of 0. Similarly, the best performing cryptocurrency, Dash, has a Gini value of 0. After extracting and analyzing the Bitcoin Improvement Protocol BIP Repository data, we report that improvement proposals for consensus-based forks seem to relate to a drop in Gini value.

We have visualized this potential association in Supplementary Figure S6. In Supplementary Figure S6, we plot the Bitcoin Improvement Protocol proposals that have demonstrated an impact on the wealth distribution within the Bitcoin ecosystem.

According to the documentation of BIP 16 Bitcoin, , it is considered a controversial soft fork of the Bitcoin blockchain that implemented a change many in the community believed to be unnecessary. This controversial backdrop of BIP 16 might have promoted sell-outs or buy-ins, sequentially impacting the wealth distribution of the cryptocurrency as the total number of participants for Bitcoin was still considerably small in This proposal also defined additional validation rules for newer transactions.

It can be seen from Supplementary Figure S6 that prior to the implementation of BIP 16, there is a drop in the value of Gini, indicating redistribution of Bitcoins before the change. A similar trend is observed with the introduction of BIP34, a change of structure for the representation of transactions. It is worth noting that BIP34 was implemented post the introduction of support for hierarchical deterministic HD wallets in Bitcoin.

HD wallets allow users to generate and manage multiple addresses to enhance the privacy of transactions. We suggest that the fall in the Gini value in late may be due to the growth in the adoption of HD wallet schemes by wallet service provides and users.

The next notable move in the value of Gini for Bitcoin is before the introduction of BIP42, another consensus-based fork, which introduces a supply cap for Bitcoins. This newly induced supply cap for Bitcoin introduced a fundamental change to the economic underpinnings of Bitcoin by suggesting that Bitcoin had an intrinsic value due to limited supply. We reason that this shift in economic policy might have prompted buy-outs or buy-ins, resulting in a change in the wealth distribution.

After this point, based on our dataset, we do not observe a relationship between the Gini Value and the policy changes in the improvement protocol repository. Another notable change in Gini value occurs in late ; as alluded to earlier, this can also be observed in other cryptocurrencies, primarily due to the redistribution of the crypto assets held by the coinbase exchange. It is worth noting the information regarding the policy changes on Bitcoin present in the form of Bitcoin Improvement Proposals is limited in its nature and does not account for the overall sentiment towards Bitcoin during that time period.

However, even in the small dataset related to the implemented changes in Bitcoin obtained from the BIP repository, we suggest that, depending on the implications of the improvement proposal specifically for the consensus aspects of Bitcoin, it can seem to impact the wealth distribution.

It is also worth noting that these consensus-based forks have become considerably less common in recent years. The controversial origin of some of the BIP that were subsequently adopted may be an indicator of governance based centralization Azouvi et al.

However, the economic ecosystem of Ethereum is more complicated due to the possibility of creating newer tokens. This subsection provides an overview of the current January state of wealth distribution in the top 5 tokens on Ethereum. As evident from Supplementary Figure S7, all of the shortlisted tokens currently have a Gini value of close to 1, which denotes an almost perfect inequality in these tokens.

All of the shortlisted tokens start with a fairer distribution followed with the exception of T2 and T3 by a steep trend towards wealth accumulation. This wealth accumulation is also visible in the Nakamoto Index values as well. It is worth noting that T1 and T2 have higher market capitalization than all shortlisted cryptocurrencies except Bitcoin and Ethereum.

However, as evident from both Gini Value and Nakamoto Index, these tokens are more centralized in terms of wealth distribution. We adopt the threat to validity framework utilized by Wohlin et al. Threats to external validity limit our ability to generalize the results from our experiment. This is particularly important in our study as it is primarily restricted to Bitcoin and Ethereum like cryptocurrencies to produce a generic ETL model for further exploration in the field.

This limited focus leads to the omission of other forms of cryptocurrencies such as Ripple Armknecht et al. Having said that, at the time of writing, our analysis captured 6 out of the top 10 crypto assets by market capitalization based on data obtained from CoinMarketCap, Another external validity threat is the comparison between cryptocurrencies and traditional fiat currencies.

It is essential to understand the difference between crypto-economics and real-world economies as these two have a fundamentally different structure. In its current form, using Gini value to compare a real-world economy to a cryptocurrency may be misleading due to the structural and functional differences between the two Chiu and Koeppl, For instance, cryptocurrencies, except for Ethereum-like cryptocurrencies, tend to only serve a single purpose, such as peer-to-peer transactions.

There is no direct equivalent to this in real-world economies as fiat currencies often only serve as a mode of exchange between conventional economies with a complex socio-political make-up Zucman, In cryptocurrencies, thus far, it has been treated as a technical issue Sai et al. In part 4, we restrict our focus to the top 5 smart contracts by market capitalization; this is another external validity threat as it may skew our dataset to only the most widely used tokens.

Another potential issue with the selection of tokens is the ecosystem that these tokens exist in beyond the Ethereum ledger, i. Examining these tokens purely from a transactional focus may result in a bias towards wealth specific to the Ethereum ecosystem. One empirical design issue important in this regard is in unambiguously identifying the owners of wallets.

To do this, this work proposes utilizing established reverse engineering heuristics Ghassemi Toosi et al. One pre-requisite for adopting the wallet clustering is the presence of a tagged dataset 13 for training the classification model. Such a tagged dataset is often generated through the collection of known addresses and their type. For instance, knowing the address of an exchange platform allows our clustering algorithm to extract a transaction pattern that is common amongst all exchange platform in our tagged dataset and then we can utilize this learned pattern to classify yet unknown exchange platforms.

However, as reported by Sai et al. In the following subsection, we first use this tagged training set to assess the implications of clustering on the accuracy of the wealth concentration results. We then attempt to replicate the experimental setup used in Harlev et al. However, the preliminary results from our experiment suggest that a further work on generating appropriate training sets is required for more accurate results.

We utilize advances in de-anonymizing techniques to cluster similar wallets together towards gaining a better view of the macroeconomic wealth-inequality state of Bitcoin. To this end, we adopt the approach used by Harlev et al. Supplementary Table S12 lists the categories derived by Harlev et al. In their clustering analysis Harlev et al.

These tagged entities were then used to train a supervised machine learning model that could predict the type tag of an unknown Bitcoin address. In Harlev et al. We adhere to the method used by Harlev et al. This new dataset with each cluster of wallets represnted as single account is then used for the calculation of the econometric measures.

We have reported the top 18 wallet clusters sorted by the number of addresses in each group in Supplementary Table S These results are in-line with Wang et al. We recalculated the present Gini value for Bitcoin while considering all individual clusters as a single unit; the results from this clustering analysis suggest that the current Gini value increments by 0. Likewise, this only has a small impact on the Nakamoto Index.

The index value changes from 4, to 4,, indicating that the majority of these cluster addresses with high wealth were also included in the Nakamoto Index calculation. Supplementary Table S13, coupled with the wider analysis performed here allows us to observe how exchange platforms denominate the wealth distribution in Bitcoin.

The second-largest group of known wealth accumulators is the merchant services, followed by mining pools. Harlev et al. While we demonstrate here how utilizing a machine learning based clustering approach could potentially improve the accuracy of the econometric analysis for cryptocurrencies, it is important to note that the degree to which this improvement impacts is quite small, and that the effect is probably consistent over time, meaning that the trends we report on in this paper are probably accurate.

We utilize the type schema suggested by Harlev et al. However, we were only able to retrieve tagged addresses belonging to the class Exchange and Mining Pool. This training dataset is considerably lower than the tagged addresses in the Harlev et al. This likely has an adverse impact on the accuracy of our classification model. Due to the lack of publically available data, it may be speculative to assess the accuracy of this classification model.

We suggest that further work is required to generate appropriate training data for Ethereum and other cryptocurrencies in our dataset before this approach can be applied when assessing wealth centralization. However, this is beyond the scope of our study, and we leave this as a potential future work avenue.

To establish if policy changes impact wealth distribution, we only examine the improvement proposal repository. A majority of bitcoin and other cryptocurrency-related discussions take place in forums of these cryptocurrencies. Thus this limiting focus may omit potentially insightful qualitative data. The selection of BIPs is selective, primed by the changes in the Gini data. We acknowledge that a fuller investigation in the future is required.

We also recognize that this study proposes relationships between the Gini behavior and market validation, the presence of masternodes and BIPs. However, as this is the first study in this field, additional work needs to be performed to probe the hypotheses derived from this study further. The conclusion section discusses the implications of our findings, pointing out the core contributions and potential avenues for future work.

We report that most shortlisted cryptocurrencies have a wealth distribution that is in-line with real world economies; for example, the current Gini value for Bitcoin, of 0. On the other hand, Dogecoin results in the highest observed value of Gini in our dataset, with the current Gini value of 0.

Unlike Dogecoin, Dash, the best performing cryptocurrency in terms of Gini value, has a current Gini value of 0. However, as indicated earlier, this low value of Dash may reflect the two-tier operational structure that requires select participants known as masternodes with a considerable proportion of Dash coins in their balance. This may lead to the incentivization for a more even wealth distribution i. Cryptocurrencies analyzed in our study do not seem to have an apparent influential factor that impacts wealth distribution.

However, we were able to identify some policy change incidents and their correlational impacts on wealth distribution. This is also evident in the case of Ethereum and Ethereum Classic which, despite having identical functionality and structure, tend to have differing wealth distribution. One factor that we can attribute to this disparity in the wealth distribution among otherwise similar cryptocurrencies is the market capitalization.

Cryptocurrencies with higher market capitalization Bitcoin and Ethereum tended to have a fairer distribution of wealth Our study specifically provides the researcher with a generic mechanism for data analysis and processing that can be employed to conduct econometric analysis. We also discuss the impact of policy on the state of wealth distribution; this aligns well with the argument presented in Sai et al.

Objectively, cryptocurrencies are not necessary because government-backed currencies function adequately. For most adopters, the advantages of cryptocurrencies are theoretical. Therefore, mainstream adoption will only come when there is a significant tangible benefit of using a cryptocurrency. So what are the advantages to using them?

Pseudonymity Near Anonymity Buying goods and services with cryptocurrencies takes place online and does not require disclosure of identities. However, a common misconception about cryptocurrencies is that they guarantee completely anonymous transactions.

What they actually offer is pseudonymity , which is a near-anonymous state. They allow consumers to complete purchases without providing personal information to merchants. However, from a law enforcement perspective, a transaction can be traced back to a person or entity. Still, amid rising concerns of identity theft and privacy, cryptocurrencies can offer advantages to users.

Peer-to-Peer Purchasing One of the biggest benefits of cryptocurrencies is that they do not involve financial institution intermediaries. With cryptocurrencies, even if a portion were compromised, the remaining portions would continue to be able to confirm transactions. Still, cryptocurrencies are not completely immune from security threats.

Fortunately, most of the funds were restored. Cryptocurrencies could also include fractional ownership interests in physical assets such as art or real estate. Blockchain Technology Explained Blockchain technology underlies Bitcoin and many other cryptocurrencies. It relies on a public, continuously updating ledger to record all transactions that take place. Blockchain is groundbreaking because it allows transactions to be processed without a central authority—such as a bank, the government, or a payments company.

The buyer and seller interact directly with each other, removing the need for verification by a trusted third-party intermediary. It thus cuts out costly middlemen and allows businesses and services to be decentralized. Another distinguishing feature of blockchain technology is its accessibility for involved parties. With blockchain, you and your friend would view the same ledger of transactions.

The ledger is not controlled by either of you, but it operates on consensus, so both of you need to approve and verify the transaction for it to be added to the chain. The chain is also secured with cryptography , and significantly, no one can change the chain after the fact.

From a technical perspective, the blockchain utilizes consensus algorithms , and transactions are recorded in multiple nodes instead of on one server. A node is a computer connected to the blockchain network, which automatically downloads a copy of the blockchain upon joining the network.

For a transaction to be valid, all nodes need to be in agreement. Though blockchain technology was conceived as part of Bitcoin in , there may be many other applications. Technology consulting firm CB Insights has identified 27 ways it can fundamentally change processes as diverse as banking, cybersecurity, voting, and academics.

The Swedish government, for example, is testing the use of blockchain technology to record land transactions , which are currently recorded on paper and transmitted through physical mail. Effective mining requires both powerful hardware and software. To address this, miners often join pools to increase collective computing power, allocating miner profits to participants.

Groups of miners compete to verify pending transactions and reap the profits, leveraging specialized hardware and cheap electricity. This competition helps to ensure the integrity of transactions. Cryptocurrency Exchanges Cryptocurrency exchanges are websites where individuals can buy, sell, or exchange cryptocurrencies for other digital currency or traditional currency.

The exchanges can convert cryptocurrencies into major government-backed currencies, and can convert cryptocurrencies into other cryptocurrencies. Almost every exchange is subject to government anti-money laundering regulations, and customers are required to provide proof of identity when opening an account.

Instead of exchanges, people sometimes use peer-to-peer transactions via sites like LocalBitcoins , which allow traders to avoid disclosing personal information. In a peer-to-peer transaction, participants trade cryptocurrencies in transactions via software without the involvement of any other intermediary.

Cryptocurrency Wallets Cryptocurrency wallets are necessary for users to send and receive digital currency and monitor their balance. Wallets can be either hardware or software, though hardware wallets are considered more secure. While the transactions and balances for a bitcoin account is recorded on the blockchain itself, the private key used to sign new transactions is saved inside the Ledger wallet. When you try to create a new transaction, your computer asks the wallet to sign it and then broadcasts it to the blockchain.

Since the private key never leaves the hardware wallet, your bitcoins are safe, even if your computer is hacked. In contrast, a software wallet such as the Coinbase wallet is virtual. Coinbase introduced its Vault service to increase the security of its wallet.

Bitcoin Released in by someone under the alias Satoshi Nakamoto, Bitcoin is the most well known of all cryptocurrencies. Despite the complicated technology behind it, payment via Bitcoin is simple. In a transaction, the buyer and seller utilize mobile wallets to send and receive payments. The list of merchants accepting Bitcoin continues to expand, including merchants as diverse as Microsoft, Expedia, and Subway, the sandwich chain.

Although Bitcoin is widely recognized as pioneering, it is not without limitations. For example, it can only process seven transactions a second. By contrast, Visa handles thousands of transactions per second. The time it takes to confirm transactions has also risen.

Not only is Bitcoin slower than some of its alternatives, but its functionality is also limited. Other currencies like Bitcoin include Litecoin , Zcash and Dash , which claim to provide greater anonymity. Ether and Ethereum Ether and currencies based on the Ethereum blockchain have become increasingly popular. However, issues with Ethereum technology have since caused declines in value.

Ethereum has seen its share of volatility. Put simply, smart contracts are computer programs that can automatically execute the terms of a contract. With traditional operations, numerous contracts would be involved just to manufacture a single console, with each party retaining their own paper copies.

However, combined with blockchain, smart contracts provide automated accountability. Smart contracts can be leveraged in a few ways: When a truck picks up the manufactured consoles from the factory, the shipping company scans the boxes. Beyond payments, a given worker in production could scan their ID card, which is then verified by third-party sources to ensure that they do not violate labor policies.

Other Popular Cryptocurrencies Litecoin: Launched in , Litecoin functions similarly to Bitcoin in that is also open sourced, decentralized, and backed by cryptography. Zcash: Released in October , Zcash is a relative newcomer in the space. However, there are claims that it is the first truly anonymous cryptocurrency in existence due to its employment of zero knowledge SNARKS, which involves no transaction records whatsoever. The technology ensures that, despite all the information being encrypted, it is still correct and that double spending is impossible.

Monero: Monero possesses unique privacy properties. Ripple: Released in , Ripple offers instant and low-cost international payments. It thus requires less computing power. Investing in Cryptocurrencies As mentioned previously, cryptocurrency has no intrinsic value—so why all the fuss? People invest in cryptocurrencies for a couple primary reasons. Apart from pure speculation, many invest in cryptocurrencies as a geopolitical hedge.

During times of political uncertainty, the price of Bitcoin tends to increase. The supply of Bitcoin is limited by code in the Bitcoin blockchain. The rate of increase of the supply of Bitcoin decreases until the number of Bitcoin reaches 21 million, which is expected to take place in the year As Bitcoin adoption increases, the slowing growth in the number of Bitcoin all but assures that the price of Bitcoin will continue to grow. Bitcoin is not the only cryptocurrency with limits on issuance.

The supply of Litecoin will be capped at 84 million units. The purpose of the limit is to provide increased transparency in the money supply, in contrast to government-backed currencies. With the major currencies being created on open source codes, any given individual can determine the supply of the currency and make a judgment about its value accordingly. Applications of the Cryptocurrency. Cryptocurrencies require a use case to have any value. The same dynamic applies to cryptocurrencies.

Bitcoin has value as a means of exchange; alternate cryptocurrencies can either improve on the Bitcoin model, or have another usage that creates value, such as Ether. As uses for cryptocurrencies increase, corresponding demand and value also increase. Regulatory Changes.

Because the regulation of cryptocurrencies has yet to be determined, value is strongly influenced by expectations of future regulation. In an extreme case, for example, the United States government could prohibit citizens from holding cryptocurrencies, much as the ownership of gold in the US was outlawed in the s.

Technology Changes. Unlike physical commodities, changes in technology affect cryptocurrency prices. July and August saw the price of Bitcoin negatively impacted by controversy about altering the underlying technology to improve transaction times. Conversely, news reports of hacking often lead to price decreases. Still, given the volatility of this emerging phenomenon, there is a risk of a crash.

Many experts have noted that in the event of a cryptocurrency market collapse, that retail investors would suffer the most. ICOs help firms raise cash for the development of new blockchain and cryptocurrency technologies.

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