Jethin Abraham, Daniel Higdon, John B. Nelson, Juan G. Ibarra
In this paper, we present a method for predicting changes in Bitcoin and Ethereum prices utilizing Twitter data and Google Trends data. Bitcoin and Ethereum, the two largest cryptocurrencies in terms of market capitalization represent over \$160 billion dollars in combined value. However, both Bitcoin and Ethereum have experienced significant price swings on both daily and long term valuations. Twitter is increasingly used as a news source influencing purchase decisions by informing users of the currency and its increasing popularity. As a result, quickly understanding the impact of tweets on price direction can provide a purchasing and selling advantage to a cryptocurrency user or a trader. By analyzing tweets, we found that tweet volume, rather than tweet sentiment (which is invariably overall positive regardless of price direction), is a predictor of price direction. By utilizing a linear model that takes as input tweets and Google Trends data, we were able to accurately predict the direction of price changes. By utilizing this model, a person is able to make better informed purchase and selling decisions related to Bitcoin and Ethereum.
Bitcoin has recently attracted considerable attention in the fields of economics, cryptography, and computer science due to its inherent nature of combining encryption technology and monetary units. This paper reveals the effect of Bayesian neural networks (BNNs) by analyzing the time series of Bitcoin process. We also select the most relevant features from Blockchain information that is deeply involved in Bitcoin's supply and demand and use them to train models to improve the predictive performance of the latest Bitcoin pricing process. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the recent Bitcoin price.
Zehui Xiong, Yang Zhang, Dusit Niyato, Ping Wang · 5 authors
Blockchain, as the backbone technology of the current popular Bitcoin digital currency, has become a promising decentralized data management framework. Although blockchain has been widely adopted in many applications (e.g., finance, healthcare, and logistics), its application in mobile services is still limited. This is due to the fact that blockchain users need to solve preset proof-of-work puzzles to add new data (i.e., a block) to the blockchain. Solving the proof of work, however, consumes substantial resources in terms of CPU time and energy, which is not suitable for resource-limited mobile devices. To facilitate blockchain applications in future mobile Internet of Things systems, multiple access mobile edge computing appears to be an auspicious solution to solve the proof-of-work puzzles for mobile users. We first introduce a novel concept of edge computing for mobile blockchain. Then we introduce an economic approach for edge computing resource management. Moreover, a prototype of mobile edge computing enabled blockchain systems is presented with experimental results to justify the proposed concept.
Bitcoin time series dataset recording individual transactions denominated in Euro at the COINBASE market between April 23, 2015 and August 15, 2016 is analyzed. Markov switching model is applied to classify the regions of varying volatility represented by three hidden state regimes using univariate autoregressive model and dependent mixture model. Causality extraction and price prediction of daily BTCEUR exchange rates is performed by means of a recurrent neural network using the standard Elman model. Strong correlations is found between the normalized mean squared error of the Elman network (out-of-sample 5-day-ahead prediction) and the realized volatility (sum of minute returns squared throughout the trading day). The present approach is calibrated using simulated regime change in standard econometric models. Our results clearly demonstrate the applicability of recurrent neural networks to causality extraction even in the case of highly volatile cryptocurrency exchange rate time series data.
Using 1-min returns of Bitcoin prices, we investigate statistical properties and multifractality of a Bitcoin time series. We find that the 1-min return distribution is fat-tailed, and kurtosis largely deviates from the Gaussian expectation. Although for large sampling periods, kurtosis is anticipated to approach the Gaussian expectation, we find that convergence to that is very slow. Skewness is found to be negative at time scales shorter than one day and becomes consistent with zero at time scales longer than about one week. We also investigate daily volatility-asymmetry by using GARCH, GJR, and RGARCH models, and find no evidence of it. On exploring multifractality using multifractal detrended fluctuation analysis, we find that the Bitcoin time series exhibits multifractality. The sources of multifractality are investigated, confirming that both temporal correlation and the fat-tailed distribution contribute to it. The influence of "Brexit" on June 23, 2016 to GBP--USD exchange rate and Bitcoin is examined in multifractal properties. We find that, while Brexit influenced the GBP--USD exchange rate, Bitcoin was robust to Brexit.
Financial portfolio management is the process of constant redistribution of a\nfund into different financial products. This paper presents a\nfinancial-model-free Reinforcement Learning framework to provide a deep machine\nlearning solution to the portfolio management problem. The framework consists\nof the Ensemble of Identical Independent Evaluators (EIIE) topology, a\nPortfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL)\nscheme, and a fully exploiting and explicit reward function. This framework is\nrealized in three instants in this work with a Convolutional Neural Network\n(CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory\n(LSTM). They are, along with a number of recently reviewed or published\nportfolio-selection strategies, examined in three back-test experiments with a\ntrading period of 30 minutes in a cryptocurrency market. Cryptocurrencies are\nelectronic and decentralized alternatives to government-issued money, with\nBitcoin as the best-known example of a cryptocurrency. All three instances of\nthe framework monopolize the top three positions in all experiments,\noutdistancing other compared trading algorithms. Although with a high\ncommission rate of 0.25% in the backtests, the framework is able to achieve at\nleast 4-fold returns in 50 days.\n
Crptocurrency is a digital or virtual currency that uses cryptography for security, transfer process and storage in ledger.This paper is to validate the correlation between exchange rate changes and trading volume changes.Data selected for this study is hourly data starting from 4 November 2017 until 7 November 2017.Methodology implemented in this study started with normality diagnostics and followed by correlation diagnostic.In this study, Pearson correlation calculation is implemented to evaluate the association between two variables namely exchange rate and trading volume.Pearson's correlation coefficient (r) is a measure of the strength of the association between the two variables.Result shows the coefficient of association is 0.123.Therefore, this study proved that the association between exchange rate changes and trading volume changes is very weak association.This value occurred because there is high volatility in hourly data and existence of outliers.The significant of this finding will help investors to recognize the relationship between trading volume and exchange rate.Therefore, it will help investors to make better decision in developing investment portfolio.
Cryptocurrency is a digital currency designed to work as a medium of exchange using cryptography to secure the transactions, to control the creation of additional units, and to verify the transfer of assets. The objective of this study is to evaluate the volatility condition for cryptocurrency (Bitcoin) exchange rate and return. Volatility calculated as standard deviation of logarithmic returns. This study performed normality test using Shapiro-Wilk method. Then, the high volatility detection performed using box-whisker plot and statistical process control chart. In descriptive statistical analysis, the mean for Bitcoin return is 0.006 and the deviation is 0.04458. The standard error indicates the volatility for Bitcoin is 4.458 %. This value is considered as high value of volatility.High value of volatility indicates the investment in Bitcoin is categorical as high risk investment. The important of this study is to assist investors to develop better investment portfolio in targeting better profit and lowering the loss
Attempts to accurately measure the monetary velocity or related properties of Bitcoin have often attempted to either directly apply definitions from traditional macroeconomic theory or to use specialized metrics relative to the properties of the Blockchain such as bitcoin-days destroyed. In this paper, it is demonstrated that beyond being a useful metric, bitcoin-days destroyed has mathematical properties that allow one to calculate the average dormancy (time since last use in a transaction) of the bitcoins used in transactions over a given time period. In addition, transaction volume and average dormancy are shown to have unexpected significance in helping estimate the average size of the pool of traded bitcoins by virtue of the expression Little's Law, though only under limited conditions.
Programmatically deriving sentiment has been the topic of many a thesis: it’s application in analyzing 140 character sentences, to that of 400-word Hemingway sentences; the methods ranging from naive rule based checks, to deeply layered neural networks. Unsurprisingly, sentiment analysis has been used to gain useful insight across industries, most notably in digital marketing and financial analysis. An advancement seemingly more excitable to the mainstream, Bitcoin, has risen in number of Google searches by three-folds since the beginning of this year alone, not unlike it’s exchange rate. The decentralized cryptocurrency, arguably, by design, a pure free market commodity – and as such, public perception bears the weight in Bitcoins monetary valuation. This thesis looks toward these public perceptions, by analyzing 2.27 million Bitcoin-related tweets for sentiment fluctuations that could indicate a price change in the near future. This is done by a naive method of solely attributing rise or fall based on the severity of aggregated Twitter sentiment change over periods ranging between 5 minutes and 4 hours, and then shifting these predictions forward in time 1, 2, 3 or 4 time periods to indicate the corresponding BTC interval time. The prediction model evaluation showed that aggregating tweet sentiments over a 30 min period with 4 shifts forward, and a sentiment change threshold of 2.2%, yielded a 79% accuracy.
The cryptocurrency is a decentralized digital money. Bitcoin is a digital asset designed to work as a medium of exchange using cryptography to secure the transactions, to control the creation of additional units, and to verify the transfer of assets. The objective of this study is to forecast Bitcoin exchange rate in high volatility environment. Methodology implemented in this study is forecasting using autoregressive integrated moving average (ARIMA). This study performed autocorrelation function (ACF) and partial autocorrelation function (PACF) analysis in determining the parameter of ARIMA model. Result shows the first difference of Bitcoin exchange rate is a stationary data series. The forecast model implemented in this study is ARIMA (2, This model shows the value of Rsquared is 0.444432. This value indicates the model explains 44.44% from all the variability of the response data around its mean. The Akaike information criterion is 13.7805. This model is considered a model with good fitness. The error analysis between forecasting value and actual data was performed and mean absolute percentage error for ex-post forecasting is 5.36%. The findings of this study are important to predict the Bitcoin exchange rate in high volatility environment. This information will help investors to predict the future exchange rate of Bitcoin and in the same time volatility need to be monitor closely. This action will help investors to gain better profit and reduce loss in investment decision.
The emergence of financial technology in the last 10 years has created a new type of asset that is Cryptocurrency. Cryptocurreny offers a small transaction fee without involving a third party in its transaction and the ability to make its users anonymous. It became one of its main selling points and was quickly accepted widely in the financial world. Cryptocurrency price movements become volatile. For examples, Bitcoin issued in 2009, the value is not more than USD 10, but in early June 2017, Bitcoin is worth about USD 3000 (Bloomberg, July 5th, 2017). Many investors are interested to invest in Cryptocurrency, especially investors with high risk tolerance. This study aims to find the effects of Cryptocurrency on well-formed portfolios. The assets we use are Foreign Currency, Commodity, Stock, and ETF. The Cryptocurrency we will use is Bitcoin, Ripple and Litecoin. Using the Modern Portfolio Theory approach, we can create an investment portfolio. The results show that the portfolio with Cryptocurrency indeed increases the effectiveness of the portfolio in two ways. The first is to minimize the standard deviation and the second is to create more allocation options for investors to choose from. The optimum allocation of Cryptocurrency is from 5% to 20% depending on the risk tolerance of the investor.
Portfolio management is the decision-making process of allocating an amount of fund into different financial investment products. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. This paper presents a model-less convolutional neural network with historic prices of a set of financial assets as its input, outputting portfolio weights of the set. The network is trained with 0.7 years' price data from a cryptocurrency exchange. The training is done in a reinforcement manner, maximizing the accumulative return, which is regarded as the reward function of the network. Back test trading experiments with trading period of 30 minutes is conducted in the same market, achieving 10-fold returns in 1.8 month's periods. Some recently published portfolio selection strategies are also used to perform the same back tests, whose results are compared with the neural network. The network is not limited to cryptocurrency, but can be applied to any other financial markets.
In dieser Thesis wird ein Marktindex konstruiert, wobei neu entwickelte Methoden für solch eine Aufgabe verwendet werden. Die Entscheidung über die Anzahl der Indexteilnehmer wird mithilfe des AIC und BIC Kriteriums getroffen und die Liquiditätsregel wird auf Grundlage der BIS Umfrage ermittelt. Dieser neu entwickelte Index, CRIX, wird dann benutzt, um den Kryptowährungsmarkt gegen Bitcoins und andere Märkte zu vergleichen. Es wurde herausgefunden, dass dieser Markt wesentlich risikoreicher ist als andere Märkte. Es wird außerdem ein Minimum Varianz CRIX und ein optimales Vorhersagemodel für den Index entwickelt, wobei Daten aus sozialen Netzwerken verwendet werden.
Augur is a trustless, decentralized platform for prediction markets. It is an extension of Bitcoin Core's source code which preserves as much of Bitcoin's proven code and security as possible. Each feature required for prediction markets is constructed from Bitcoin's input/output-style transactions.
Ifigeneia Georgoula, Demitrios E. Pournarakis, Christos Bilanakos, Dionisios N. Sotiropoulos · 5 authors
This paper uses time-series analysis to study the relationship between Bitcoin prices and fundamental economic variables, technological factors and measurements of collective mood derived from Twitter feeds. Sentiment analysis has been performed on a daily basis through the utilization of a state-of-the-art machine learning algorithm, namely Support Vector Machines (SVMs). A series of short-run regressions shows that the Twitter sentiment ratio is positively correlated with Bitcoin prices. The short-run analysis also reveals that the number of Wikipedia search queries (showing the degree of public interest in Bitcoins) and the hash rate (measuring the mining difficulty) have a positive effect on the price of Bitcoins. On the contrary, the value of Bitcoins is negatively affected by the exchange rate between the USD and the euro (which represents the general level of prices). A vector error-correction model is used to investigate the existence of long-term relationships between cointegrated variables. This kind of long-run analysis reveals that the Bitcoin price is positively associated with the number of Bitcoins in circulation (representing the total stock of money supply) and negatively associated with the Standard and Poor's 500 stock market index (which indicates the general state of the global economy).
Feroz Ahmad Ahmad, Prashant Kumar, Gulshan Shrivastava, Med Salim Bouhlel
ON 12 JANUARY 2009 a pseudonymous entity signed a transaction that instructed a distributed network to transfer a small amount of digital currency to Hal Finney, one ofthe key figures of the cypherpunk movement. After a few minutes, the transaction was recorded on a distributed public ledger, permanently updating the balance ofbothparties. This transaction— the first Bitcoin transaction—marked the beginning of a new era of decentralized payment systems, ushering in a variety of financial Services that do not depend on any centralized clearinghouse or other financial middleman. Bitcoin is regarded by many as a powerful technological innovation that could disrupt many sectors, in the realm of finance and beyond. But the underlying technology on which the network operates, the Bitcoin blockchain can do much more than that. Just as the internet did in the early-1990s, blockchain technology carries with it a whole new range of promises concerning how decentralization can support and promote individual freedoms and autonomy. Blockchain proponents believe that Bitcoin and other cryptocurrency platforms will revolutionize mechanisms of value exchange in the same way that the internet transformed information sharing, by providing a platform for people to exchange digital resources, in a secure and decentralized manner without the need to rely on any intermediary or trusted authority. But this revolutionary potential also carries with it serious implications for censorship, intellectual property, and the regulated flow of information. A blockchain is a decentralized database of transactions maintained by a distributed network of computers, which all contribute to the verification and the validation of transactions. Once accepted, these transactions are recorded inside a “block” of transactions, which incorporates a reference to previous blocks. This creates a long chain of blocks—a “blockchain”—that stores the history of all transactions in a chronological order. Every block contains information about a particular set of transactions, a reference to the preceding block in the blockchain, and the answer to a complex mathematical puzzle that is used to validate the data associated with that block. A copy of the blockchain is stored on every computer in the network, making it virtually impossible for anyone unilaterally to modify the data stored on this decentralized database: if anyone tries to modify any transaction the fraud will be immediately detected by all other network participants.
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Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Dániel Kondor, István Csabai, János Szüle, Márton Pósfai · 5 authors
A main focus in economics research is understanding the time series of prices of goods and assets. While statistical models using only the properties of the time series itself have been successful in many aspects, we expect to gain a better understanding of the phenomena involved if we can model the underlying system of interacting agents. In this article, we consider the history of Bitcoin, a novel digital currency system, for which the complete list of transactions is available for analysis. Using this dataset, we reconstruct the transaction network between users and analyze changes in the structure of the subgraph induced by the most active users. Our approach is based on the unsupervised identification of important features of the time variation of the network. Applying the widely used method of Principal Component Analysis to the matrix constructed from snapshots of the network at different times, we are able to show how structural changes in the network accompany significant changes in the exchange price of bitcoins.
In this paper, we discuss the method of Bayesian regression and its efficacy for predicting price variation of Bitcoin, a recently popularized virtual, cryptographic currency. Bayesian regression refers to utilizing empirical data as proxy to perform Bayesian inference. We utilize Bayesian regression for the so-called "latent source model". The Bayesian regression for "latent source model" was introduced and discussed by Chen, Nikolov and Shah (2013) and Bresler, Chen and Shah (2014) for the purpose of binary classification. They established theoretical as well as empirical efficacy of the method for the setting of binary classification. In this paper, instead we utilize it for predicting real-valued quantity, the price of Bitcoin. Based on this price prediction method, we devise a simple strategy for trading Bitcoin. The strategy is able to nearly double the investment in less than 60 day period when run against real data trace.
This paper analyzes correlations and causalities between Bitcoin market indicators and Twitter posts containing emotional signals on Bitcoin. Within a timeframe of 104 days (November 23rd 2013 - March 7th 2014), about 160,000 Twitter posts containing "bitcoin" and a positive, negative or uncertainty related term were collected and further analyzed. For instance, the terms "happy", "love", "fun", "good", "bad", "sad" and "unhappy" represent positive and negative emotional signals, while "hope", "fear" and "worry" are considered as indicators of uncertainty. The static (daily) Pearson correlation results show a significant positive correlation between emotional tweets and the close price, trading volume and intraday price spread of Bitcoin. However, a dynamic Granger causality analysis does not confirm a statistically significant effect of emotional Tweets on Bitcoin market values. To the contrary, the analyzed data shows that a higher Bitcoin trading volume Granger causes more signals of uncertainty within a 24 to 72-hour timeframe. This result leads to the interpretation that emotional sentiments rather mirror the market than that they make it predictable. Finally, the conclusion of this paper is that the microblogging platform Twitter is Bitcoin's virtual trading floor, emotionally reflecting its trading dynamics.