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Any cryptocurrency worth mining journal if only today i bet my life lyrics

Any cryptocurrency worth mining journal

Nature Sustain. Giraffes in Tanzania gather near a livestock-herding village, a strategy that might fend off predators. Credit: Christian Kiffner. A cosmetics jar unearthed in China contained a 2,year-old moisturizer made, in part, from cave minerals. Credit: B. Han et al. Precisely engineered discs can repeatedly pop high enough into the air to climb a set of low steps. Credit: Y. Kim et al. Standard domesticated rice left, Oryza sativa has shorter stems and larger grains than a wild rice species right, Oryza alta.

Credit: H. Yu et al. Credit: Teague O'Mara. Three cancer cells are engulfed by a macrophage, one of several subtypes of myeloid cell, an important part of the immune system. A layer of mud protects the mummified remains of an ancient Egyptian woman. The explosive growth of ride-sharing has intensified US traffic congestion such as at Los Angeles International Airport in California, shown above. Credit: Patrick T.

A stream of meltwater wends across Greenland, which accounts for some of the trillions of tonnes of ice-sheet loss in the past 30 years. Credit: Ian Joughin. Particles green, artificially coloured of the cold-causing coronavirus HCoVE, whose genome shows evidence of swift evolution of a key protein.

A chemical plant in Qingdao, China. Researchers observed the highest emissions of a newly detected ozone-degrading chemical in eastern China. Credit: Alamy. Credit: Getty. A harvest mouse Micromys minutus and her pups. Young laboratory mice favour the female who has raised them over an unfamiliar mouse. For example, sentiment analysis concerns one such quantitative feature, or the extent to which a document is positive or negative.

We generalised this idea to various other user-defined characteristics. Examples of such characteristics include the extent to which a document is related to finance, immigration, and family issues. In particular, we built a lexicon, i. In this study, we considered a characteristic to be a concept describing a particular phenomenon or object, and defined a concept by constructing a set of keywords, whose meanings were relevant.

Concepts can play an important role in document analysis in diverse fields. That is, one can build useful domain-specific concepts in economics, politics, and social sciences and define the characteristics of documents with respect to these concepts. Here, the concept building process was composed of two steps: 1 the initial construction of a relevant keyword set, followed by its 2 user-interactive expansion.

In order to facilitate the first step, we provided a user with the initial sets of coherent keywords obtained with two different techniques. The first technique we used was topic modelling, which algorithmically computes those representative keywords emerging from a document corpus. The user can then select some of them as an initial word set for their own concepts.

As the other method to provide initial keywords, we computed the representative keywords from the centroid vectors obtained by k-means clustering on word embedding vectors[ 37 ]. Once a user formed an initial, small-sized lexicon for a particular concept, the second step was to interactively expand it by using a recently proposed visual analytics system named ConceptVector. Based on the initial lexicon given as user inputs, ConceptVector recommended potentially relevant keywords to enable users to easily add a subset of them to the lexicon.

As the lexicon expanded, ConceptVector adjusted the recommended keywords that match the semantic meaning of the concept. The topic modelling approach we used to extract representative keywords emerging from a document corpus is non-negative matrix factorisation, where the non-negativity allows users to interpret the value from factor matrices as the relevance score of a word or a document to a particular topic as mentioned above.

In particular, we constructed a document-term matrix A from the 17, forum articles and , user comments collected from the Bitcoin forum See Table 1. We then applied the topic modelling to each so as to extract the different topic sets and their representative keywords across different dates. The mathematical details of this process are as follows. Given a document-term matrix where m is the number of articles and n is the dictionary size, Non-negative Matrix Factorization NMF approximately factorises it into two matrices and , where d represents the number of topics 50 in our study , e.

The columns in the resulting matrix W correspond to different topics and the keywords corresponding to the dimensions of the k largest value in each column function as the representative keywords of the topic. We proposed two types of concepts in the system. A unipolar concept represents exactly one concept such as crude oil and immigration.

A bipolar concept has two polarities that oppose each other, e. In the case of building a concept, the system has positive, negative, and irrelevant word sets. When a user provides a word as an input, the system provides 50 recommended words that are potentially relevant to the seed word. We then automatically sorted the recommended words into five clusters, using the k -means clustering, to gather closely related terms into one group.

Once the lexicon of a concept is created by user interactions, the document rating process utilises the concept built in the process above. Because of the lack of expression resulting from the limited number of words a person could manage, we applied the kernel density estimation KDE in the word rearranging phase. Prior to the KDE, the concept had a limited number of descriptive terms for a characteristic, which resulted in a lack of expression and description.

Therefore, the KDE served for the probabilistic smoothing over every word. This smoothing process is the most important procedure for document analysis since the score rating process cannot consider synonyms or closely related words that also represent a specific concept. Based on the assumption that the input terms describe the concept sufficiently well, we constructed a kernel that exerts influence on the entire vocabulary.

ConceptVector adopts a Gaussian kernel as described below. The conditional probability of a keyword z for a class c can be computed as below: 2 which can also be seen as the relevance score to each class. Since our final goal was to obtain scores by taking all classes into consideration, we rated a concept in view of all classes.

We calculated the bipolar rating as below: 3 4. The Granger causality test is based on the supposition that if a variable X causes Y, then any change in X will methodically happen before any change in Y[ 17 , 22 , 38 ]. As shown in past research, slacked estimations of X display a measurably noteworthy connection with Y[ 17 , 22 , 38 ]. Nevertheless, connection does not imply causation. We test whether the time arrangement of a discussion of conclusions contains any prescient data with respect to vacillations in the Bitcoin transaction and price.

Our time arrangement at the Bitcoin transaction count and price, indicated by S t , reflects day-to-day change in the Bitcoin transaction count and price. To test whether the idea of gathering feelings in the time arrangement could forecast the change in the vacillation in terms of the Bitcoin transaction and price, we considered the difference clarified by two linear models as in 5 and 6 below.

The first model uses just n slacked estimations of S t for the forecast. We completed the Granger causality test as indicated by the models in 5 and 6. In view of the consequences of the Granger causality test, we can reject the null hypothesis, whereby the time series of a concept of forum opinions does not predict fluctuations in the Bitcoin transaction count and price with a high level of confidence.

The Granger causality test was performed on the Bitcoin transaction count and price for a time lag of 1 to 12 days. Using the gathered data and the analysed and rated comment data, we built a model for predicting the fluctuation in the Bitcoin price and transaction through deep learning. Deep learning is widely used for addressing diverse challenges[ 8 , 39 ]. Despite the quantitative and qualitative increases in Bitcoin-related formal and informal data following the broadening applicability of Bitcoin, deep learning has rarely been used to explore Bitcoin price trends and to address other Bitcoin-related challenges.

We created a setting to apply deep learning to the data spanning a period of 2. As the first step, we standardised the data to improve its applicability to the learning model. An example of applicable input data is provided in Table 2. Subsequently, to use the input data for prediction, we set up a deep learning model. Multiple hidden layers were accumulated for learning to identify deep data structures.

Specifically, 1, 2, 3, and 5 hidden layers were constructed to select the layer structure that returned the best possible prediction result. The number of neurons that were allocated to each hidden layer was 1, As for the input layers, based on the input data provided in Table 2 , 15 input data points were represented as serial vectors to allocate neurons based on the cumulative number of days spent on learning, i.

Fig 3 shows the concept derived from the concept building phase and the words constituting the concept. We focused on a general phenomenal analysis of the meanings of the concept, rather than analysing all the words constituting the concept. Because mining is a means of earning Bitcoin, many users share their opinions about its efficiency. Other than mining, Bitcoin can also be earned by transactions.

Security therefore not only became a popular issue on the Bitcoin forum but also resulted in social problems, leading to the closure of the site. Although the situation was resolved when the site was closed towards the end of , words regarding related exchange markets and companies attracted considerable attention from users.

Since the emergence of Bitcoin, many types of similar cryptocurrencies have been developed and are in use. In view of the after-effects of the Granger causality test, the null hypothesis was rejected. This suggests that the time series of the gathered data failed to forecast the fluctuation in Bitcoin transaction volume and price—i.

Tables 3 and 4 list the test results. In addition, the Pearson Correlation Coefficient between the rating of each concept and Bitcoin price and transaction is shown in Table 5. The foregoing results are partially indicative of the significance of the extracted keyword data.

However, this process was only used for the purpose of verification. The entire data set was used to build the actual deep learning model for prediction. We built and applied the deep learning model based on the gathered and KDE-based rating data to predict the Bitcoin transaction and price. The accuracy rate, the Matthews correlation coefficient MCC , and the F-measure were used to evaluate the performance of the proposed model. Table 6 presents the prediction results.

Table 4 presents the results relative to the layer and learning data structures. Both three or more hidden layers and cumulative learning data for 12 days or longer resulted in negligible differences. Less than two hidden layers and cumulative learning data for less than 7 days proved to be insufficient for learning and compromised the prediction accuracy. Conversely, overfitting could possibly occur with the prediction accuracy failing to significantly improve, if more than five hidden layers and cumulative data for over 12 days were used.

We analysed the user comments posted on a Bitcoin online forum to predict the fluctuation in the Bitcoin price and transaction count. Moreover, online user postings influenced Bitcoin transactions. The causality test result indicated some topics associated with Bitcoin transactions. These findings suggest China exerts a strong influence on the Bitcoin price.

This finding suggests that topics related to the circulation and transaction of other types of cryptocurrencies have an impact on the Bitcoin transaction volume. Hence, the experimental findings revealed some user comments that had the most significant relationship with and effects on the fluctuation in Bitcoin price and transactions. That said, the proposed method has a limitation in terms of its broader applicability due to the fact that the concepts were constructed for a long period of time.

Thus, appropriate subdivision of the sample period would help to obtain a more accurate understanding of the users for topic modelling and to refine the analysis with additional approaches including sentiment analysis. Moreover, the present findings warrant further studies on the analysis of user comments relative to the characteristics of Bitcoin forums. To increase the accuracy of prediction, it is necessary to address a few challenges.

The present work is focused on analysing online forum user comments and adds some formal or structured data to predict the fluctuation in the Bitcoin price and transactions. However, it may add to the reliability of the findings if the search results and relevant content on search engines were quantitatively analysed or if the social network data were analysed as they did in some comparable previous studies[ 21 , 40 ].

Furthermore, it may be an efficient preliminary study to analyse and classify online forum users per se[ 41 — 45 ]. In addition, the postings may be worth filtering more meticulously [ 46 — 50 ] to more accurately corroborate the findings. Information derived from online forum users seems to be well-suited for extensive research on cryptocurrencies as well as Bitcoin. In the same vein, keywords manifested in online forum user comments could be used for further in-depth analysis and understanding of cryptocurrency transactions.

Moreover, online forums are great sources of abundant informal and formal information, which serves to appreciate cryptocurrencies from diverse perspectives including money laundering, which is closely associated with cryptocurrencies [ 51 — 54 ].

With the increasing circulation of Bitcoin, its acceptability has drawn much attention in many ways [ 2 , 3 , 5 , 14 ]. The present study is noteworthy in that it analysed the topics often mentioned by Bitcoin users and linked their meanings to Bitcoin transactions.

The proposed method for predicting the fluctuation in the Bitcoin price and transactions based on user opinions on online forums is conducive to understanding a range of cryptocurrencies other than Bitcoin and increasing their usability, although it needs to be reinforced. In addition, the present approach to the salience of user comments on online forums is likely to yield more significant results in many other fields.

Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Bitcoin is an online currency that is used worldwide to make online payments. Introduction The advancement of the ubiquitous Internet has resulted in the emergence of unprecedented types of currencies that are distinct from the established currency system. Related work Research on cryptocurrencies, particularly on Bitcoin, has been extensively conducted from diverse perspectives, e.

Methods System overview This section provides an overview of the proposed method. Download: PPT. Data crawling Data crawling was the first step in our analysis. Analysis of user comment data Our intention was to extract significant keywords used in Bitcoin transactions from the aforementioned crawled data. Concept building. Topic modelling for initial lexicon building. Expanding the lexicon via word recommendation. Computation of document relevance to concept.

Prediction modelling Granger causality test. Deep learning model. Results Concept building results Fig 3 shows the concept derived from the concept building phase and the words constituting the concept.

Results of Granger causality test and correlation test In view of the after-effects of the Granger causality test, the null hypothesis was rejected. Table 3. Statistical significance p -values of bivariate Granger causality correlation between Bitcoin price and concepts of forum opinions. Table 4. Statistical significance p -values of bivariate Granger causality correlation between Bitcoin transaction and concept of forum opinions. Prediction results We built and applied the deep learning model based on the gathered and KDE-based rating data to predict the Bitcoin transaction and price.

Table 6. Experimental results of predicted Bitcoin fluctuation. Discussion We analysed the user comments posted on a Bitcoin online forum to predict the fluctuation in the Bitcoin price and transaction count. Conclusion With the increasing circulation of Bitcoin, its acceptability has drawn much attention in many ways [ 2 , 3 , 5 , 14 ].

Supporting information. S1 File. Results of crawling Bitcoin forum. S2 File. Python-based crawler source code for Bitcoin forum data collection. References 1. Nakamoto S. Bitcoin: A peer-to-peer electronic cash system. Bitcoin: Economics, technology, and governance. The Journal of Economic Perspectives.

View Article Google Scholar 3. Grinberg R. Bitcoin: An innovative alternative digital currency. View Article Google Scholar 4.

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Since our final goal was to obtain scores by taking all classes into consideration, we rated a concept in view of all classes. We calculated the bipolar rating as below: 3 4. The Granger causality test is based on the supposition that if a variable X causes Y, then any change in X will methodically happen before any change in Y[ 17 , 22 , 38 ]. As shown in past research, slacked estimations of X display a measurably noteworthy connection with Y[ 17 , 22 , 38 ]. Nevertheless, connection does not imply causation.

We test whether the time arrangement of a discussion of conclusions contains any prescient data with respect to vacillations in the Bitcoin transaction and price. Our time arrangement at the Bitcoin transaction count and price, indicated by S t , reflects day-to-day change in the Bitcoin transaction count and price. To test whether the idea of gathering feelings in the time arrangement could forecast the change in the vacillation in terms of the Bitcoin transaction and price, we considered the difference clarified by two linear models as in 5 and 6 below.

The first model uses just n slacked estimations of S t for the forecast. We completed the Granger causality test as indicated by the models in 5 and 6. In view of the consequences of the Granger causality test, we can reject the null hypothesis, whereby the time series of a concept of forum opinions does not predict fluctuations in the Bitcoin transaction count and price with a high level of confidence.

The Granger causality test was performed on the Bitcoin transaction count and price for a time lag of 1 to 12 days. Using the gathered data and the analysed and rated comment data, we built a model for predicting the fluctuation in the Bitcoin price and transaction through deep learning. Deep learning is widely used for addressing diverse challenges[ 8 , 39 ].

Despite the quantitative and qualitative increases in Bitcoin-related formal and informal data following the broadening applicability of Bitcoin, deep learning has rarely been used to explore Bitcoin price trends and to address other Bitcoin-related challenges.

We created a setting to apply deep learning to the data spanning a period of 2. As the first step, we standardised the data to improve its applicability to the learning model. An example of applicable input data is provided in Table 2. Subsequently, to use the input data for prediction, we set up a deep learning model. Multiple hidden layers were accumulated for learning to identify deep data structures.

Specifically, 1, 2, 3, and 5 hidden layers were constructed to select the layer structure that returned the best possible prediction result. The number of neurons that were allocated to each hidden layer was 1, As for the input layers, based on the input data provided in Table 2 , 15 input data points were represented as serial vectors to allocate neurons based on the cumulative number of days spent on learning, i.

Fig 3 shows the concept derived from the concept building phase and the words constituting the concept. We focused on a general phenomenal analysis of the meanings of the concept, rather than analysing all the words constituting the concept. Because mining is a means of earning Bitcoin, many users share their opinions about its efficiency.

Other than mining, Bitcoin can also be earned by transactions. Security therefore not only became a popular issue on the Bitcoin forum but also resulted in social problems, leading to the closure of the site. Although the situation was resolved when the site was closed towards the end of , words regarding related exchange markets and companies attracted considerable attention from users.

Since the emergence of Bitcoin, many types of similar cryptocurrencies have been developed and are in use. In view of the after-effects of the Granger causality test, the null hypothesis was rejected. This suggests that the time series of the gathered data failed to forecast the fluctuation in Bitcoin transaction volume and price—i. Tables 3 and 4 list the test results.

In addition, the Pearson Correlation Coefficient between the rating of each concept and Bitcoin price and transaction is shown in Table 5. The foregoing results are partially indicative of the significance of the extracted keyword data. However, this process was only used for the purpose of verification. The entire data set was used to build the actual deep learning model for prediction.

We built and applied the deep learning model based on the gathered and KDE-based rating data to predict the Bitcoin transaction and price. The accuracy rate, the Matthews correlation coefficient MCC , and the F-measure were used to evaluate the performance of the proposed model. Table 6 presents the prediction results. Table 4 presents the results relative to the layer and learning data structures. Both three or more hidden layers and cumulative learning data for 12 days or longer resulted in negligible differences.

Less than two hidden layers and cumulative learning data for less than 7 days proved to be insufficient for learning and compromised the prediction accuracy. Conversely, overfitting could possibly occur with the prediction accuracy failing to significantly improve, if more than five hidden layers and cumulative data for over 12 days were used.

We analysed the user comments posted on a Bitcoin online forum to predict the fluctuation in the Bitcoin price and transaction count. Moreover, online user postings influenced Bitcoin transactions. The causality test result indicated some topics associated with Bitcoin transactions.

These findings suggest China exerts a strong influence on the Bitcoin price. This finding suggests that topics related to the circulation and transaction of other types of cryptocurrencies have an impact on the Bitcoin transaction volume. Hence, the experimental findings revealed some user comments that had the most significant relationship with and effects on the fluctuation in Bitcoin price and transactions.

That said, the proposed method has a limitation in terms of its broader applicability due to the fact that the concepts were constructed for a long period of time. Thus, appropriate subdivision of the sample period would help to obtain a more accurate understanding of the users for topic modelling and to refine the analysis with additional approaches including sentiment analysis. Moreover, the present findings warrant further studies on the analysis of user comments relative to the characteristics of Bitcoin forums.

To increase the accuracy of prediction, it is necessary to address a few challenges. The present work is focused on analysing online forum user comments and adds some formal or structured data to predict the fluctuation in the Bitcoin price and transactions. However, it may add to the reliability of the findings if the search results and relevant content on search engines were quantitatively analysed or if the social network data were analysed as they did in some comparable previous studies[ 21 , 40 ].

Furthermore, it may be an efficient preliminary study to analyse and classify online forum users per se[ 41 — 45 ]. In addition, the postings may be worth filtering more meticulously [ 46 — 50 ] to more accurately corroborate the findings. Information derived from online forum users seems to be well-suited for extensive research on cryptocurrencies as well as Bitcoin. In the same vein, keywords manifested in online forum user comments could be used for further in-depth analysis and understanding of cryptocurrency transactions.

Moreover, online forums are great sources of abundant informal and formal information, which serves to appreciate cryptocurrencies from diverse perspectives including money laundering, which is closely associated with cryptocurrencies [ 51 — 54 ]. With the increasing circulation of Bitcoin, its acceptability has drawn much attention in many ways [ 2 , 3 , 5 , 14 ].

The present study is noteworthy in that it analysed the topics often mentioned by Bitcoin users and linked their meanings to Bitcoin transactions. The proposed method for predicting the fluctuation in the Bitcoin price and transactions based on user opinions on online forums is conducive to understanding a range of cryptocurrencies other than Bitcoin and increasing their usability, although it needs to be reinforced. In addition, the present approach to the salience of user comments on online forums is likely to yield more significant results in many other fields.

Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Bitcoin is an online currency that is used worldwide to make online payments. Introduction The advancement of the ubiquitous Internet has resulted in the emergence of unprecedented types of currencies that are distinct from the established currency system.

Related work Research on cryptocurrencies, particularly on Bitcoin, has been extensively conducted from diverse perspectives, e. Methods System overview This section provides an overview of the proposed method. Download: PPT. Data crawling Data crawling was the first step in our analysis. Analysis of user comment data Our intention was to extract significant keywords used in Bitcoin transactions from the aforementioned crawled data. Concept building.

Topic modelling for initial lexicon building. Expanding the lexicon via word recommendation. Computation of document relevance to concept. Prediction modelling Granger causality test. Deep learning model. Results Concept building results Fig 3 shows the concept derived from the concept building phase and the words constituting the concept. Results of Granger causality test and correlation test In view of the after-effects of the Granger causality test, the null hypothesis was rejected.

Table 3. Statistical significance p -values of bivariate Granger causality correlation between Bitcoin price and concepts of forum opinions. Table 4. Statistical significance p -values of bivariate Granger causality correlation between Bitcoin transaction and concept of forum opinions. Prediction results We built and applied the deep learning model based on the gathered and KDE-based rating data to predict the Bitcoin transaction and price.

Table 6. Experimental results of predicted Bitcoin fluctuation. Discussion We analysed the user comments posted on a Bitcoin online forum to predict the fluctuation in the Bitcoin price and transaction count. Conclusion With the increasing circulation of Bitcoin, its acceptability has drawn much attention in many ways [ 2 , 3 , 5 , 14 ].

Supporting information. S1 File. Results of crawling Bitcoin forum. S2 File. Python-based crawler source code for Bitcoin forum data collection. References 1. Nakamoto S. Bitcoin: A peer-to-peer electronic cash system. Bitcoin: Economics, technology, and governance. The Journal of Economic Perspectives. View Article Google Scholar 3. Grinberg R. Bitcoin: An innovative alternative digital currency.

View Article Google Scholar 4. Bitter to better—how to make bitcoin a better currency. View Article Google Scholar 5. Reid F, Harrigan M. An analysis of anonymity in the bitcoin system. Security and privacy in social networks. View Article Google Scholar 6.

Tensorflow: Large-scale machine learning on heterogeneous distributed systems. Deep learning. UMAP Workshops; Kaminski J. Nowcasting the Bitcoin Market with Twitter Signals. Kristoufek L. Scientific reports. View Article Google Scholar What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis. PloS one. Yelowitz A, Wilson M.

Characteristics of Bitcoin users: an analysis of Google search data. Applied Economics Letters. Bitcoin pricing, adoption, and usage: Theory and evidence. Why would online gamers share their innovation-conducive knowledge in the online game user community? Integrating individual motivations and social capital perspectives. Computers in Human Behavior. Virtual world currency value fluctuation prediction system based on user sentiment analysis.

Patterns and dynamics of users' behavior and interaction: Network analysis of an online community. An analysis of interaction and participation patterns in online community. Bitcoin transaction graph analysis. Mood and the market: can press reports of investors' mood predict stock prices?

Twitter mood predicts the stock market. Journal of Computational Science. Latent dirichlet allocation. Journal of machine Learning research. Learning the parts of objects by non-negative matrix factorization. Wang C, Blei DM. For some data, we have to make estimates. Specifically, the installed mining hardware base is unknown over time but an important cost to actors.

We therefore develop a method to estimate this installed base. The way of estimating is an important contribution of this paper. Finally, we analyze the results for sustainability. Concerning data collection, a significant amount of publicly available data is an advantage of the bitcoin system. In particular, we use data retrieved from blockchain. For the analysis of sustainability, we first look at the expenses and revenues of miners and the resulting value flows from these.

We start by inferring which mining hardware is in use during which specific period. This is necessary as the hardware investment represents a large cash outflow for the miners. Third, the computing performance of specific hardware directly determines the expected number of bitcoins mined by that hardware.

Formally, we solve an equation that models the total bitcoin hash rate on each day as a function of the hardware in operation. From the hardware in operation we can deduce the hardware spending and the electricity costs.

Other expenses pool expenses, bank costs and exchange fees follow from the total production of bitcoins. Starting from the observed total bitcoin hash rate, TH t on day t , it must be the case that. As long as no better type is available, the machines stay in operation to produce the total hash rate that we observe in the data. At a first increase in the hash rate, the number of machines increases to reach the total hash rate. At a decrease in the hash rate, we assume that new machines are throttled back or old machines are turned off.

Footnote 5. Once a new machine becomes available, we assume that buyers choose between hardware types by picking the machine with the lowest estimated payback time. This way of calculating the attractiveness of an investment is common practice Berk and DeMarzo and the simplicity of the technique fits the dynamism and fast-changing nature of the bitcoin miners.

For each machine on the market, the payback time is computed using the day moving average of the bitcoin price:. Existing machines stay in operation as long as the marginal profit is positive, i. If that is not the case, we assume that they are switched off on that day. They can come online again if they become profitable again, for example, when the bitcoin price increases. The combination of machines in operation on any given day is then simply equal to the number in operation on the previous day, minus machines that have become unprofitable, plus new machines of the type that have the lowest payback time.

Then, we have that. Although the hash rate is increasingly almost continuously in our sample period, there are a few instances where the hash rate declines. We allocate those decreases to the most recent machines that we assume are throttled back proportionally. Footnote 6 Since declines in the hash rate are rare and small see Fig. We now turn to the data that is fed into Eqs. Figure 4 shows the hash rate and difficulty of the bitcoin network increasing by a factor of more than , from to There are two reasons why this happens.

First, faster hardware is added to replace slower running hardware for which electricity expenses outnumber mining and transaction revenues. Second, new hardware is added to increase production, as bitcoin mining becomes increasingly popular. In both cases, we attribute the increase in computing power in the bitcoin network to new hardware. Regarding the purchasing of mining hardware, we assume that miners behave rationally and therefore buy the hardware with the lowest payback time.

During the year the payback time of the cost-efficient hardware is shorter than that of energy-efficient hardware. Payback time for most energy-efficient en. During the first 6 months of , the payback time is so high, it would take decennia to earn back the hardware. At the beginning of our analysis period, we assume that the AMD is installed, which was the best available hardware at that time.

Regarding the operation of mining hardware, we assume that mining hardware remains in operation until the daily electricity expenses related to that hardware is equal or higher than the expected revenues for that day, namely the value of the mined bitcoins and the transaction fees.

In other words: after initial investment, the only incentive for miners to turn their hardware off is that the marginal expenses for mining electricity outweigh the marginal revenues. The energy cost for a particular type of hardware is known. Therefore, in order to calculate the payback period, we must know the expected revenue.

This assumes that miners possess no superior timing ability, which seems sensible. Given the assumptions on purchasing and operations we can estimate the hardware in use over time. As the market of mining hardware is not transparent, the archived pages Footnote 8 of a public wiki page Footnote 9 are used to select the most cost-effective hardware over the period to This data was cross-referenced with discussions on the public forum bitcointalk.

The results are in Table 1. Since the performance of the bitcoin network is known, we can calculate the upfront hardware investment, if we assume all hardware was the AMD at that time. Then, for each subsequent day we can infer the hardware purchases using the increase in hash rate and available hardware on that day. With the assumption of positive marginal revenues, we also can calculate when new hardware is added or retired.

Note that, because the hardware is tailored to bitcoin mining, we consider the residual value of hardware zero as it cannot be used economically for other tasks. Now that we know which specific kind of hardware is into operation during which specific period, we can also calculate the electricity consumption of that hardware, and related to that, the electricity expenses.

We assume that mining is always running during the period of operation. Figure 7 shows the rapidly increasing energy usage of the bitcoin network from to This seems sensible, given the hash rate ultimo of 2 bln. It does question the earlier estimate of O'Dwyer and Malone , who find a number that is close to the electricity use 3GW of Ireland in Their estimates, however, are based on a theoretical estimate of the hash rate instead of the real rate, and is a mid-point estimate of a wide range of possibilities.

Figure 8 gives a graphical representation of our estimates of when certain hardware was in use. The sudden drops of profitability during periods like the fourth quarter of and the second quarter of , suggest the predicted gradual linear and exponential profit declines of online mining calculators are an unreliable tool for net cash flow prediction. Assuming that all mined bitcoins and earned transaction fees are immediately exchanged for dollars, exchange and bank expenses directly relate to the amount of bitcoins transferred and mined each day.

The expenses are summarized in Table 3 , by hardware type. Table 4 summarizes the expenses and revenues, and calculates per hardware the estimated generated net cash flow. As can be seen from the table, the first part of our analysis period shows a positive net cash flow for miners.

The numbers of the flows in Table 4 correspond to the numbered value transfers in Fig. However, the last two periods have a loss. At the end of the measurement period, only the Antminer S9 was still running on a profitable basis, so the losses might be compensated in the later periods.

Table 4 also shows that in some time periods the investments in hardware have been very profitable, such as with the Avalon 1 in Most of the income stems from the generated bitcoins, while most of the costs are due to the hardware investments. The hardware expenses are by far the biggest expense to bitcoin miners. This upfront investment in hardware, combined with a high daily energy cost leads to considerable losses in the later years. Figure 9 shows the day moving average of total revenues and expenses.

As can be seen, the expenses related to bitcoin mining approach the revenues, which is also predicted by economic theory: under full competition, marginal revenue approaches marginal costs. This holds for normal goods as well as for virtual goods and currencies as bitcoin. Figure 10 shows the marginal expenses not counting the upfront hardware purchases compared to marginal revenues.

During and these lines approach each other, leading to very little profits. This makes it very difficult to have a return on investment on the acquired hardware. The sudden drop in revenue and expenses in mid is likely a result of the blockchain halving, where the bitcoin reward was halved from 25 to Marginal daily expenses and revenues on a logarithmic scale of Figure 11 shows the cumulative net cash flow that resulted from Fig.

Positive flows are followed by periods where money is invested in new hardware, leading to temporarily negative net cash flows. By mid, the high revenues of and are countered by high expenses, leading to a negative net cash flow from that moment on. It can be seen that this results in a positive net cash flow, but due to necessary new investments, the total net cash flow drops with each innovation.

Energy prices determine the profitability of mining hardware, so it could be argued that these prices heavily influence the resulting profits. It is therefore meaningful to do a sensitivity analysis with respect the energy prices. A question we can ask is what the exchange rate of the bitcoin should have been in order to reach the break-even point for the modes. The estimates in Table 6 should be interpreted with care.

It is likely to expect that a change in the exchange rate would influence other parameters too, e. Since our analysis is based on factual data of the bitcoin network, we cannot compensate for these effects. To do so, a proper simulation model of the bitcoin network should be developed to include the market dynamics.

An important question is how reliable our estimates are. Our calculation relies on the one hand on publicly available data which are factual e. Understanding of the installed base is important, because the kind of hardware installed determines the expenses by miners, namely the initial hardware investment and the expenses for energy.

A recent other study by De Vries also aims to estimate the total energy consumption for the bitcoin, although a different analysis period is used Feb 10th — present, see the Bitcoin Energy Consumption Index BECI Footnote 13 , which displays the results of their installed base estimate model.

In our calculation, at June 20 , the electricity power consumption was The BECI estimates for February 10th the first date of analysis the yearly energy consumption as 9. The difference of The BECI uses a fairly straightforward model: it assumes that hardware remains in production by miners until it reaches its minimum sales price. Our model supposes a rational behaving miner in the sense that 1 at each point of time, the miner buys the hardware that has the shortest payback time, and 2 the miner takes hardware out of production and replaces it by newer hardware if the marginal expenses for mining electricity outweigh the marginal revenues.

Given the purpose of this paper, namely to argue that the bitcoin network is not sustainable on the long term, our estimate of the installed base is conservative; using the hardware estimation method of the BECI would result in higher energy costs and therefore in increased losses for the miner.

This is what economic theory predicts for a market with profit-maximizing companies under full competition. A comparison could be drawn with the value of the Somali shilling between and Luther documents how, in the absence of a central monetary authority, Somali clans produced currency themselves or imported it from foreign producers of paper money.

Similarly, the pattern in Fig. Given that bitcoins can be mined by everyone and everywhere, this is a direct result of the competitive pressure on mining bitcoins. Once hardware has been purchased, it becomes a sunk cost and only the marginal costs need to be covered. At the same time, the operators of mining hardware need to make an average profit over the lifetime of the hardware, taking into account the wildly fluctuating prices of bitcoin.

This makes them reluctant to switch off the hardware, even at very low rates of profitability. Actual loss-making operations are of course irrational, but could reflect the fact that a sizeable fraction of miners in the bitcoin industry are not financially literate and might underestimate the electricity costs that they are incurring, for example. There are a number of ways how the bitcoin can be made economically sustainable. Unfortunately, none of these possibilities are very realistic.

First, the energy price could drop significantly world-wide, for example to 0. Then there would a slight profit for the miners. But even in Inner Mongolia, which is considered to have one of the lowest energy prices 0. Additionally, reducing energy consumption use could be achieved by introducing predefined and trustful parties to operate the consensus mechanism and the release of additional coins , which can be done in a far more energy-efficient way.

Although this contradicts the design philosophy of the bitcoin somewhat, i. Finally, a more efficient consensus mechanism could be used, including proof-of-stake consensus should only be reached by parties who own the most bitcoins, since they have the most interests in trust in the currency Narayanan , Byzantine fault tolerance a voting mechanism in distributed systems, e.

Bitcoin-NG Eyal et al. Sieve, as used in Hyperledger Cachin ; Cachin et al. However, other limitations and hurdles to the acceptance of bitcoin as an efficient payment mechanism will remain. For example, it is not clear whether any distributed ledger mechanism could rule out multiple equilibria, Biais et al.

Also, some consensus mechanism e. Byzantine fault tolerance do not scale to millions of users. Second, the bitcoin price may increase substantially, which happened in , which however outside our analysis period. The recent history however has shown that the bitcoin exchange rate is very volatile. Actually, bitcoin is nowadays used as a very high risk speculation instrument, rather than a payment instrument.

Therefore, speculating on the increase of the bitcoin exchange rate is very risky, and therefore not reliable enough to justify long-term economic sustainability. Third, another solution might be to increase the transaction fees that miners get if they include transactions in the blockchain. However, if we take the numbers of for example, the transaction should be increased dramatically: the earnings from transactions fees were In other words, the income for transaction clearing is neglectable compared to mining.

Moreover, a substantial raise of the transaction fees would change the business model of the bitcoin significantly: from neglectable transaction costs to high transaction costs. Finally, it can be doubted whether the bitcoin is a significant and desirable payment solution at all, compared to traditional payments as offered as banks.

Take for example the transaction volume of VISA Footnote 14 alone, which is billion transactions in In that same year, the bitcoin platform processed about 83 million transactions. Footnote 15 This implies that the bitcoin is neglectable as it comes to the world wide transaction volume. This paper analyzed the long term financial sustainability of proof-of-work mining for the bitcoin network.

We have considered the profitability of the miner, expressed by a sustainable net positive cash flow, as the key factor in judging bitcoin sustainability. By reverse-engineering the type and number of computers that have been mining bitcoin, we found a negative net cash flow for most of the measurement period. This answers research question 2: on the long term, miners can not be sustainable.

Since the miners are crucial for the correct functioning of the bitcoin network, this endangers the sustainability of the bitcoin network itself research question 1. In terms of future research, an important question is how to build a payment service that is 1 economically sustainable, and 2 can scale up to transaction volumes handled by the traditional banks, and 3 fully decentralized, that is, without any intermediate party such as banks.

A key component of the answer is a consensus mechanism that is very scalable and economically sustainable. Clearly, Proof-of-work is not economically sustainable, as argued in this paper. Finding such a consensus mechanism is ongoing work, although important steps are taken. PoET claims to be highly scalable and energy friendly. We leave out the costs of internet connectivity, since mining is a very low bandwidth activity.

Therefore, internet service providers are not included in the model. The total network hashrate can fluctuate on a daily basis, but in general it increased by an average of 1. In reality, a decrease in the hash rate could be due to start-up problems of new machines due to overclocking, decommissioning of older hardware, negative price shocks in the value of bitcoin, or alternative use of hardware, for example, to mine other cryptocurrencies.

Alt, R. The rise of customer-oriented banking-electronic markets are paving the way for change in the financial industry. Electronic Markets, 22 4 , — Barber, S. Bitter to better - how to make bitcoin a better currency. Keromytis Ed. Berlin: Springer. Google Scholar.

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Davies, S. Financial Times, January 20, Decker, C. A fast and scalable payment network with bitcoin duplex micropayment channels. In: Symposium on Self-Stabilizing Systems pp. Springer International Publishing. Edgar Fernandes, N. Ember, S. Jitters after bitcoin exchange suspends services. European Central Bank Virtual currency schemes — a further analysis. Eyal, I. Bitcoin-ng: A scalable blockchain protocol. Forte, P. Gervais, A. On the privacy provisions of bloom filters in lightweight bitcoin clients.

Goldman S. All about bitcoin. Top of Mind. Gordijn, J. Value-based requirements engineering: Exploring innovative e-commerce ideas. Requirements Engineering, 8 2 , — Grinberg, R. Bitcoin: An innovative alternative digital currency. Law Journal, 4 , Higgins, S. Holbrook, M. Consumer value: A framework for analysis and research. New York: Routledge. Kroll, J. The economics of Bitcoin mining, or Bitcoin in the presence of adversaries.

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Here are a few suggestions for managing your coin mining vulnerabilities:. Transfer your coins regularly from your online storage if using a cloud mining service into your detachable wallet so they do not accumulate online. Also develop a personal habit of backing up your wallet every two days and keep your password written down in a safe place. Some electricity providers will allow you to lock in your per-kilowatt-hour fee for a year or two. If you can do so at 14 cents or less per kWh, then do it.

Another question with no correct and fixed answer. Look at it this way: once a new coin comes into the market, it fairly unknown and can be mined easily as there are not a lot of miners interested in it. As it starts getting some traction and recognition in the community, people start turning their attention and rigs towards it making it more difficult to mine with every new rig that enters its network.

So, the best way to find coins that are easy to mine is to sift through forums and crypto groups and picking out coins that sound promising but still lack stronger name presence in the community. Mine and accumulate the new coins as much as you can and hope the price will rocket some time later once it hits bigger exchanges and broader community gets to know it. Top 3 coins for huge ROI in ? Experts believe this will happen again in , the only question is which coin do you bet on?

My friend and cryptocurrency expert Dirk is personally betting on 3 under-the-radar cryptocurrencies for huge ROI in Click here to learn what these coins are watch till the end of the presentation. One of the reasons Ravencoin has gained popularity so quickly is the X16R algorithm it uses for proof-of-work mining. At one point, it was easiest cryptocurrency to mine. The X16R algorithm is actually 16 different algorithms, which are used randomly during mining and the order depends on the hash of the previous block.

And even if someone tried to make an ASIC for the algorithm the developers could simply change the algorithms being used in X16R. There are several ways to estimate your mining profitability with Ravencoin. The bot is pretty accurate. Grin is the latest darling of cryptocurrency world, a new privacy focused coin with unlimited supply has surprisingly seen support among traditionally altcoin-hostile bitcoin maximalists as well.

It is also one of the best cryptocurrencies to mine these days. Read our updated guide on top staking cryptocurrencies. Secure nodes also do not require locking your tokens so if you decide you want to sell one day you are free to move your cryptocurrencies and do so.

It is also always in the top 5 or so on WTM in my experience. Metaverse is coin coming from China that has a goal to facilitate a low-cost transfer of digital assets, properties, and identities. The project itself has slim chances of succeeding in its stipulated aims but if you quickly convert your ETP gains into other cryptocurrencies, you should safely preserve your mining profits.

The main features of Quarkchain are its reshardable two-layered blockchain, collaborative mining, horizontal scalability, cross-shard transactions, and streamlined account management. Coin can be traded on Binance, Gate. It is fork of Bitcoin that was created to kick out ASICs and make it possible to mine it with GPUs and also belongs to the best crypto to mine group of cryptos. Monero is regularly recognized as the most advanced privacy coin out there but also as a most profitable cryptocurrency to mine.

It is based on a proof-of-work hashing algorithm known as CryptoNight , which is designed with certain specifications that make it difficult for Monero mining using ASICs to work well. In turn, it is actually relatively easy to mine Monero on your PC. To mine with just your CPU, all you need to do is download Monero mining software and install it. However, you can increase your earnings by purchasing a graphics card that will increase your computing power so that you can mine even more Monero.

AMD graphic cards are best suited for this task although Nvidia cards work also. You can store your Monero on the official Monero desktop wallet. Unironically Dogecoin is another very popular cryptocurrency that can be mined using a PC. It is not the best coin to mine but it can earn you some change. Programmer Billy Markus based a fully functional internet cryptocurrency capable of storing value and being used for transactions… on a meme image of a rather perplexed looking shiba inu.

Obviously the internet loved the idea and Dogecoin is currently holding an impressive 34 th place on coinmarketcap. Vertcoin is also one of the easiest cryptocurrencies to mine, it is a coin that uses a Lyra2RE proof-of-work algorithm to verify transactions. Instead it uses a Vertcoin team issued one-click miner. It has two pools based on your computing power. If you have less than two graphics cards then you should pick Network 2; if you have more you should pick Network 1.

Ultimately, there are many, many more coins that can be mined. Usually you can find these coins listed on websites like Coinwarz , Minergate or Whattomine. These websites compare various cryptocurrencies mining profitability to Bitcoin to determine if a cryptocurrency is more profitable to mine than Bitcoin. The cryptocurrency profitability information displayed is based on a statistical calculation using the hash rate values entered.

Next, you need to download the mining software specific to your hardware set up. Then, you are ready to go to start mining ZEC. Grin is an ASIC-resistant, CPU-minable proof-of-work digital currency, which is why it immediately became popular among small-scale crypto miners upon its launch. Next, you set up your wallet so that you can receive mining rewards.

While mining altcoins at home remains feasible if you have the right hardware and software, there are still some guidelines you should follow in order to maximize mining revenues. Laptops are not suitable for mining as they are likely to overheat. In addition, it is advisable to purchase extra fans if you want to run several graphics cards during the mining process. Moreover, be sure to stay current with the updates offered by the software you use. Mining software developers, digital currency wallets, and even mining pools will offer updates occasionally.

This will help protect you from any vulnerabilities as well as potentially increase your efficiency as a miner. Should you buy? Sign up for our daily newsletter. Conclusion While mining altcoins at home remains feasible if you have the right hardware and software, there are still some guidelines you should follow in order to maximize mining revenues.

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