New cryptocurrency 2019
Ryan Haar is a former personal finance reporter for NextAdvisor. She previously wrote for Bloomberg News, The…. While either can be a good choice for crypto beginners, determining which is right for you may require a closer look at your own goals. Bitcoin BTC vs. Beyond the technical differences in the two cryptos , Bitcoin and Ethereum offer two completely different value propositions for investors, which could be the deciding factor for you. Bitcoin was the first cryptocurrency, and is known as digital gold.
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New cryptocurrency 2019
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Cooperation View all 6 Articles. In this paper, we analyze Twitter signals as a medium for user sentiment to predict the price fluctuations of a small-cap alternative cryptocurrency called ZClassic. We extracted tweets on an hourly basis for a period of 3. We then compiled these tweets into an hourly sentiment index, creating an unweighted and weighted index, with the latter giving larger weight to retweets.
Price predictions produced from this model were compared to historical price data, with the resulting predictions having a 0. A cryptocurrency or crypto currency is a digital asset designed to work as a medium of exchange that uses cryptography to secure its transactions, control the creation of additional cryptocurrencies, and verify the secure transfer of assets [ 1 ].
Cryptocurrencies can be classified as types of digital or alternative currencies, distinct from traditional currencies in that they are founded on the principle of decentralized control, compared to the central banking systems that typical currencies rely on [ 2 ].
The inception of cryptocurrencies dates back to , when an unknown entity under the pseudonym Satoshi Nakamoto publicly released a paper titled Bitcoin: A Peer-to-Peer Electronic Cash System [ 3 ]. In January , Nakamoto implemented the bitcoin software as open source code, releasing it to the public on SourceForge [ 4 ]. Nakamoto's contributions galvanized a wave of public attention, spurring others to create alternative cryptocurrencies that relied on the same fundamental technology but were specialized in purpose [ 5 ].
This wave of new cryptocurrencies has received much attention by the media and investors alike due to the assets' innovative features, potential capability as transactional tools, and tremendous price fluctuations.
This exponential growth is the result of both increased investor speculation and the introduction of various new cryptocurrencies, with current estimates of the total number of cryptocurrencies topping 1, different coins [ 7 ]. Thus, analyzing evolutionary dynamics of the cryptocurrency market is a topic of current interest and can provide useful insight about the market share of cryptocurrencies [ 5 , 8 , 9 ]. Moreover, longitudinal datasets of Bitcoin transactions have been used to identify the socio-economic drivers in cryptocurrency adoption [ 10 ].
The speculation behind these digital assets has increased to such magnitudes that even cryptocurrencies with no functionality have surpassed the market value of established companies whose stocks are publicly traded in the equity markets. This rapid and exponential increase in cryptocurrency prices suggests that price fluctuations are driven primarily by retail investor speculation, and that this market exhibiting signs of a financial bubble [ 11 ].
In light of this, a recent study quantifies the inefficiency of the Bitcoin market by studying the long-range dependence of Bitcoin return and volatility from until [ 12 ]. Such dramatic volatility of the cryptocurrency market may be partly due to the inevitable fragility of decentralized systems based on blockchain technology [ 13 ]. Noteworthy, there has been increasing attention paid to improving our understanding of cryptocurrency market behavior, for example, by means of field experiments of peer influence exerted by bots on human trading decisions [ 14 ] and probabilistic modeling of buy and sell orders [ 15 ].
Given that the alternative cryptocurrency market is dominated by retail investors, with few large institutional investors, sentiment on social media platforms and online forums may present a viable medium to capture total investor sentiment [ 16 ]. More recently, it has been shown that social media data such as Twitter can be used to track investor sentiment, and price changes in the Bitcoin market and other predominant cryptocurrencies [ 17 — 20 ].
In Garcia and Schweitzer [ 18 ], the authors demonstrate that Twitter sentiment, alongside economic signals of volume, price of exchange for USD, adoption of the Bitcoin technology, overall trading volume could be used to predict price fluctuations. As a consequence, investors may have adopted a similar strategy within the Bitcoin market, thereby weakening the correlation between Twitter sentiment and Bitcoin prices.
Moreover, the daily trading volume of cryptocurrencies has increased such that conditions are now suitable for high-frequency trading firms to exploit this correlation [ 21 ]. Therefore, we aim to analyze and build a machine learning pricing model for this highly speculative market through gauging investor sentiment via Twitter, a pervasive social network that has been strongly suggested to serve as a powerful social signal for Bitcoin prices [ 18 ]. We began by researching different alternative cryptocurrencies to ultimately decide which would be best suited within the confines of our analysis.
Ultimately, we decided to choose ZClassic ZCL , a private, decentralized, fast, open-source community driven virtual currency, as the primary target of our academic focus given its unique technological dynamics and suitability of trading volume within the confines of our computational capacity. First off, the technological nature of the ZClassic cryptocurrency lends itself to a high level of predictability via tweet analysis. A hardfork is a major change to blockchain protocol which makes previously invalid blocks or transactions valid [ 22 ].
As a result, the single cryptocurrency ZClassic preceding the hard fork will be split into two, ZClassic and Bitcoin Private [ 22 ]. Previous hardforks include Bitcoin Cash and Bitcoin Gold, and the history of each suggests that ZClassic's price fluctuations will be largely based off speculation regarding the future success and accessibility of Bitcoin Private.
For example, any news release that is seen by investors as indicative of the possibility that Bitcoin Private will be traded on a major exchange or that the fork will be supported by a certain exchange will exert upwards price pressure on the cryptocurrency's price.
As such, real-time tweet analysis serves as a suitable means to gauge investor sentiment following these news releases, and pinpoint spontaneous news releases themselves. Secondly, the relatively lower trading volume of ZCL compared to that of alternative cryptocurrencies suggests that it may be more susceptible to sentiment-based price movement.
To collect the tweets, we decided to base our program in RStudio, given its motley of free Twitter-analysis packages and foundations within data analysis and statistical computing.
We then merged all data sets, and eliminated any duplicate tweets given that a single tweet could contain all three of these terms and therefore be accounted for thrice in the final data set. In the end, we garnered a final data set of , unique tweets. We then created an algorithm to classify each tweet as positive, negative, or neutral sentiment using natural language processing.
If the polarity value is zero, then the tweet receives a sentiment value of 0. Another important aspect to note regarding the character of each tweet is the chained network effect that each retweet creates. Thus, we believe cryptocurrency investors will be more likely to react to retweets than to single tweets.
Both the values of our weighted and unweighted sentiment indices were then calculated on an hourly basis by summing the weights of all coinciding tweets, which allowed us to directly compare this index to available ZCL price data. For model selection, we employed fold cross validation on data points to choose an optimal model framework among linear regression, logistic regression, polynomial regression, exponential regression, tree model, and support vector machine regression.
A tree model called the Extreme Gradient Boosting Regression also known as XGBoost [ 24 ] , exhibited the smallest loss, or inaccuracy, and was thus chosen to train the model on our data.
The XGBoost model, as well as other tree-based models, is particularly suited for applications on our data for the following reasons:. Tree models are not sensitive to the arithmetic range of the data and features. Thus, we do not need to normalize the data and possibly prevent loss due to normalization. Tree models are by far the most scalable machine learning model due to their construction processes—simply adding more children nodes to the pre-existing tree nodes will update the tree and allow our strategy to continue to accurately predict price as our collection of price and tweet data increases into the future.
It also makes the model adaptable for currencies with larger daily tweet volumes. On the abstract level, the tree model is a rule-based learning method which, unlike a traditional regression learning method, has more potential to unveil insightful relationships between features. XGBoost is a tree ensemble model, which outputs a weighted sum of the predictions of multiple regression trees, by weighing mislabeled examples more heavily.
Our goal is to minimize the objective function L , defined below:. The definition of this regularization follows the above equation where w is the coefficient at each node and T is the number of leaves in the tree. To minimize the above objective function, we employed a greedy Algorithm 1 to create our regression tree forest F as originally implemented in Chen and Guestrin [ 24 ].
Algorithm 1: Exact greedy algorithm for split finding [ 24 ] used in our price prediction model. One-third of the data points is separated as the testing data, and the remainder is used as the training set as we built our Extreme Gradient Boosting Regression model.
The model also tests different lead-lag on the range of [0, 1, 2, 3, 4, 5h] since we do not know how quickly the public would react to the market update or the social media sentiment. Based on the testing result, we decided that there is a 3-h lag effect between social media information and price effects. To begin, our natural language processing classification algorithm showed significant accuracy in identifying the sentiment of each tweet see Table 1.
Examples of tweets that received positive, neutral, and negative sentiment values are shown in Table 2. Table 1. Validation analysis of algorithm sentiment prediction by manual inspection. Table 2. Examples of Tweets with positive, neutral and negative sentiment classifications in our dataset. Upon reviewing our data set of tweets, one major concern we had was the flood of computer-generated bot tweets, which often promote contests and giveaways.
In practice, retail investors often ignore these tweets, given their obvious usage as means of commercial promotions. These are often written using positive language; however, the vast majority of these were properly characterized as neutral. To further gauge the accuracy of our algorithm, we manually classified a sample of random tweets, comparing them to our algorithm's classifications to measure false classification rates.
Table 1 shows the general distinctions between our algorithm's classifications and manual classifications. These six features proved to be varied enough to train the model effectively on a variety of different trading points and resulted in the best and most accurate overall correlation with the testing data as shown in Table 3.
The detailed co-plots of the different features vs. Table 3. Figure 1. Shown are the price fluctuations vs. Volume, B Price vs. Unweighted Index, C Price vs. Weighted Index, D Price vs. Sentiment, E Price vs. Sentiment, and F Price vs. Neutral Sentiment. These six features proved to be varied enough to train the model effectively on a variety of different trading points and resulted in the best and most accurate overall correlation with the testing data, as summarized in Table 3.
In testing our model, we were able to produce price data that strongly reflected the actual fluctuations see Figure 2. In particular, it is significant that our model achieved a Pearson correlation of 0. As such, our model provides a viable method to predict price fluctuations, and also serves as a proof of concept that statistical analyses using Twitter sentiment can also be used to analyze price fluctuations in additional cryptocurrencies.
One possible explanation to this gap is the discrepancies between the training and testing data as summarized in Table 4. First, it is important to note that the model was trained on data that primarily exhibited a negative trend see Table 4.
As such, it is possible that the model became more desensitized to positive stimuli, and more sensitive to negative stimuli. As such, it is possible that the model reacted to the change in these factors by exhibiting a slightly lower price expectation than what the actual market reflected. However, the overall directionality and correlation within the model remained strong, suggesting that if the model were also trained on data that exhibited positive trends, a more accurate set of predictions would have resulted.
Figure 2. Comparison of model prediction and actual price data. A plots the fitted price curve obtained from the training price data and the predicted price curve with respect to the testing data. B details the model prediction price data as compared to the testing real price data.
By incorporating Twitter sentiment and trading volume, the Extreme Gradient Boosting Regression Tree Model provides a viable means of predicting price fluctuations within the ZClassic cryptocurrency market. Moreover, it serves as a proof of concept that statistical analyses using Twitter sentiment can also be used to analyze price fluctuations in other cryptocurrencies of interest.
In conclusion, our results suggest that by analyzing Twitter sentiment and trading volume, an Extreme Gradient Boosting Regression Tree Model serves as a viable means of predicting price fluctuations within the ZClassic cryptocurrency market. As such, given the complete lack of research within this academic sphere, our model serves as a proof of concept that social media platforms such as twitter can be used to capture investor sentiment, and that this sentiment is an early signal to future price fluctuations in alternative cryptocurrencies.
Of particular interest is seeing whether this approach produces similarly strong results when applied to other alternative cryptocurrencies such as ZCash and Bitcoin Private.
However, this discovery sheds light to the possibility of arbitrage opportunities that utilize social media platform sentiment to predict future cryptocurrency prices.
Cryptocurrency List 2019 *
The IRS has not released significant guidance on virtual currency transactions in over five years. In March , the IRS issued Notice the Notice , stating that cryptocurrency was to be treated as property, rather than currency for US federal income tax purposes. The IRS also stated that taxpayers must "in computing gross income, include the fair market value of the virtual currency, measured in US dollars, as of the date the virtual currency was received. However, the Notice left many unanswered questions. For example, many people raised concerns about the taxability of events resulting from a change to the cryptocurrency itself, without any action on the part of the taxpayer. In the new guidance released by the IRS, the IRS attempts to address two such situations — "hard forks" and "air drops. A hard fork occurs when a cryptocurrency on a distributed ledger undergoes a protocol change that may result in a permanent diversion from the legacy distributed ledger and in some instances, may create a new cryptocurrency. You can think of a "hard fork" as something similar to receiving a new credit card if your old one was compromised. If your card was stolen by a thief, and you report it, you will receive a new card with a different number.
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Bitcoin or Ethereum: What New Crypto Investors Should Know About Both Before They Buy
Much time has been spent lauding blockchain and cryptocurrencies in this series. As such, it is important to identify and to understand the drawbacks and obstacles that may refrain mainstream adoption of these technologies. Probably the biggest concerns with cryptocurrencies are the problems with scaling that are posed. While the number of digital coins and adoption is increasing rapidly, it is still dwarfed by the number of transactions that payment giant, VISA, processes each day. Additionally, the speed of a transaction is another important metric that cryptocurrencies cannot compete with on the same level as players like VISA and Mastercard until the infrastructure delivering these technologies is massively scaled. Such an evolution is complex and difficult to do seamlessly.
Toward a New Economy: Cryptocurrency and International Development
List of cryptocurrencies
Cooperation View all 6 Articles. In this paper, we analyze Twitter signals as a medium for user sentiment to predict the price fluctuations of a small-cap alternative cryptocurrency called ZClassic. We extracted tweets on an hourly basis for a period of 3.
Cryptocurrencies like Bitcoin and Ethereum have indeed proven resilient. Investor interest, both retail and institutional, in digital currencies has risen dramatically in recent months. Many early investors who were eager to make gains from the "cryptocurrency craze" have since moved on to other ventures, leaving a smaller group of stalwart HODL -ers behind. But there are still reasons to believe that the cryptocurrency industry has some fight in it left. Investors are again asking: how high digital coins could fly? Although trade figures for individual investors are down in many cases, institutions are climbing on board in a significant way for the first time.
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