This paper aims to identify the likely determinants for cryptocurrency value formation, including for that of bitcoin. Due to Bitcoinâs growing popular appeal and merchant acceptance, it has become increasingly important to try to understand the factors that influence its value formation. Presently, the value of all Bitcoins in existence represent approximately $7 billion, and more than $60 million of notional value changes hands each day. Having grown rapidly over the past few years, there is now a developing but vibrant marketplace for bitcoin, and a recognition of digital currencies as an emerging asset class. Not only is there a listed and over-the-counter market for bitcoin and other digital currencies, but also an emergent derivatives market. As such, the ability to value bitcoin and related cryptocurrencies is becoming critical to its establishment as a legitimate financial asset.Using cross-sectional empirical data examining 66 of the most widely used cryptocurrencies, a regression model was estimated that points to three main drivers of cryptocurrency value: the level of competition in the network of producers, the rate of unit production, and the difficulty of algorithm used to âmineâ for the cryptocurrency. These amount to relative differences in the cost of production of one digital currency over another at the margin, pointing to differences in relative cost of production â electricity goes in, cryptocurrency comes out. Using that as a starting point, a no-arbitrage situation is established for Bitcoin-like cryptocurrencies followed by the formalization of a cost of production model to determine the fair value of a bitcoin.
Martina Matta, Maria Ilaria Lunesu, Michele Marchesi
In the last decade, Web 2.0 services such as blogs, tweets, forums, chats, email etc. have been widely used as communication media, with very good results. Sharing knowledge is an important part of learning and enhancing skills. Furthermore, emotions may affect decisionmaking and individual behavior. Bitcoin, a decentralized electronic currency system, represents a radical change in financial systems, attracting a large number of users and a lot of media attention. In this work, we investigated if the spread of the Bitcoinâs price is related to the volumes of tweets or Web Search media results. We compared trends of price with Google Trends data, volume of tweets and particularly with those that express a positive sentiment. We found significant cross correlation values, especially between Bitcoin price and Google Trends data, arguing our initial idea based on studies about trends in stock and goods market.
This paper aims to identify the likely source(s) of value that cryptocurrencies exhibit in the marketplace using cross sectional empirical data examining 66 of the most used such 'coins'. A regression model was estimated that points to three main drivers of cryptocurrency value: the aggregate computational power employed in mining for units of the cryptocurrency; the rate of unit production; and the cryptologic algorithm used for the protocol. Bitcoin-denominated relative prices were used, avoiding much of the price volatility associated with the dollar price of Bitcoin. The resulting model can be used so better understand the drivers of value observed in cryptocurrencies. These findings may also have implications in understanding other assets such as commodity forms of money.
Crashes have fascinated and baffled many canny observers of financial markets. In the strict orthodoxy of the efficient market theory, crashes must be due to sudden changes of the fundamental valuation of assets. However, detailed empirical studies suggest that large price jumps cannot be explained by news and are the result of endogenous feedback loops. Although plausible, a clear-cut empirical evidence for such a scenario is still lacking. Here we show how crashes are conditioned by the market liquidity, for which we propose a new measure inspired by recent theories of market impact and based on readily available, public information. Our results open the possibility of a dynamical evaluation of liquidity risk and early warning signs of market instabilities, and could lead to a quantitative description of the mechanisms leading to market crashes.
As bitcoin becomes more important as a worldwide financial phenomenon, it also becomes important to understand its sources of value formation. There are three ways to obtain bitcoins: buy them outright, accept them in exchange, or else produce them by 'mining'. Mining employs computational effort which requires electrical consumption for operation. The cost of electricity per kWh, the efficiency of mining as measured by watts per unit of mining effort, the market price of bitcoin, and the difficulty of mining all matter in making the decision to produce. Bitcoin production seems to resemble a competitive market, so in theory miners will produce until their marginal costs equal their marginal product. Break-even points are modeled for market price, energy cost, efficiency and difficulty to produce. The cost of production price may represent a theoretical value around which market prices tend to gravitate. As the average efficiency increases over time due to competition driving technological progress â as inefficient capital becomes obsolete it is removed while new capital replaces them â the break-even production cost of bitcoins denominated in dollars will fall. Increased efficiency, although necessary to maintain competitive advantage over other miners could serve to drive the value of bitcoin down, however adjustments in the mining difficulty and the regular halving of the block reward throughout time will tend to counteract a decreasing tendency in cost of production.
We propose a high level network architecture for an economic system that integrates money, governance and reputation. We introduce a method for issuing, and redeeming a digital coin using a mechanism to create a sustainable global economy and a free market. To maintain a currency's value over time, and therefore be money proper, we claim it must be issued by the buyer and backed for value by the seller, exchanging the products of labour, in a free market. We also claim that a free market and sustainable economy cannot be maintained using economically arbitrary creation and allocation of money. Nakamoto, with Bitcoin, introduced a new technology called the cryptographic blockchain to operate a decentralised and distributed accounts ledger without the need for an untrusted third party. This blockchain technology creates and allocates new digital currency as a reward for "proof-of-work", to secure the network. However, no currency, digital or otherwise, has solved how to create and allocate money in an economically non-arbitrary way, or how to govern and trust a world-scale free enterprise money system. We propose an "Ontologically Networked Exchange" (ONE), with purpose as its highest order domain. Each purpose is defined in a contract, and the entire economy of contracts is structured in a unified ontology. We claim to secure the ONE network using economically non-arbitrary methodologies and economically incented human behaviour. Decisions influenced by reputation help to secure the network without an untrusted third party. The stack of contracts, organised in a unified ontology, functions as a super recursive algorithm, with individual use programming the algorithm, acting as the "oracle". The state of the algorithm becomes the "memory" of a scalable and trustable artificial intelligence (AI). This AI offers a new platform for what we call the "Autonomy-of-Things" (AoT).
Bitcoin (BTC) is a major virtual currency. Using weekly data over the 2010-2013 period, we analyze a BTC investment from the standpoint of a US investor with a diversified portfolio including both traditional assets (worldwide stocks, bonds, hard currencies) and alternative investments (commodities, hedge funds, real estate). Over the period under consideration, BTC investment had highly distinctive features, including exceptionally high average return and volatility. Its correlation with other assets was remarkably low. Spanning tests confirm that BTC investment offers significant diversification benefits. We show that the inclusion of even a small proportion of BTCs may dramatically improve the risk-return trade-off of well-diversified portfolios. Results should however be taken with caution as the data may reflect early-stage behavior that may not last in the medium or long run.
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 transactionâ the first Bitcoin transactionâmarked 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 âblockâ of transactions, which incorporates a reference to previous blocks. This creates a long chain of blocksâa âblockchainââthat 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
The main subject of this thesis is a paradigm of instability and stabilization in coalition forming among countries as rational actors, presented through a Statistical Physics inspired model.This is an interdisciplinary work involving the fields of applied mathematics and sociophysics, as well as the political applications in the real cases from past and present. Applied to political,economic and social problems, the models can be used to analyze a wide variety of real cases -- international alliances, economic or business alliances, coalitions of political parties, socialnetworks, and organizational structures.In the first part of this thesis we present and analyze the coalition forming and the instability among rational actors coupled with pairwise static historical propensity bonds that have evolvedindependently. Such organization leads to discordant associations into coalitions and the instability as a consequence of decentralized maximization of the individual benefits gained fromjoining or leaving the coalitions. We define Natural Model of coalition forming and address the questions of instability and stabilization among actors possessing different levels of rationality. The framework presented here allows to analytically calculate the optimal and non-optimal stable configurations of actors' coalitions. We then investigate the coalition forming and the stabilization under the influence of externally-set opposing global alliances, which are represented in Global Alliance Model. The stabilization is produced through new cooperations based on the effect of polarization of several distinct interests shared by actors, which generates interest-based propensities and enables a planned coalition forming. We then investigate the effect of dissolution of a global alliancewhich, together with the competing alliance, has previously generated stable coalitions.A special section of the thesis is devoted to investigation and illustration of coalition forming in real historical cases. This part presents the analysis of unstable coalitions in Europe - cycling in the England-Spain-France conflicting triangle and creation of the Italian state, as well as of the remarkable historical cases of the Soviet global alliance collapse, of the recent internal conflict in Syria, and of the "paradoxical stability" in the Eurozone.In the second part of this thesis, we present a simulation of the coalition forming models. The simulation allows to follow graphically the coalition forming processes. We present the methodology used in the simulation, as well as its application in the illustration of coalition forming in the prototypes of real case systems. Given exact propensity values, which is fairly consideredto be the most difficult part of coalition forming modeling, the simulation tool can be used to predict optimal and non-optimal spontaneous stabilizations and globally motivated stabilities inreal cases.An independent part of the thesis is devoted to the subject of viability correction in dynamic network of actors. The model is a finite set of autonomous actors with states that evolveindependently and connected into a network via their connection operators, which evolve independently as well. The network is defined to be viable if a joint evolution satisfies the centralized scarcity constraints set by the environment. In order to restore the viability of these decentralized dynamics, we apply to the method of correction by viability multipliers used in Viability Theory, where the multipliers play the role of decentralizing prices. Standing apart from the main course of the thesis, the subject of viability correction in dynamic network of actors suggests an interesting theoretic dynamical generalization of coalition stabilization in our models inspired from Statistical Physics.
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.
We show that the behaviour of Bitcoin has interesting similarities to stock\nand precious metal markets, such as gold and silver. We report that whilst\nLitecoin, the second largest cryptocurrency, closely follows Bitcoin's\nbehaviour, it does not show all the reported properties of Bitcoin. Agreements\nbetween apparently disparate complexity measures have been found, and it is\nshown that statistical, information-theoretic, algorithmic and fractal measures\nhave different but interesting capabilities of clustering families of markets\nby type. The report is particularly interesting because of the range and novel\nuse of some measures of complexity to characterize price behaviour, because of\nthe IRS designation of Bitcoin as an investment property and not a currency,\nand the announcement of the Canadian government's own electronic currency\nMintChip.\n
We present an agent based stock flow consistent macroeconomic model with heterogeneous agents interacting through a decentralized matching process across multiple markets with multiple assets. The model is consistent across both the micro and macroeconomic levels, by providing a detailed, comprehensive, and rigorous accounting of real and financial flows and stocks. We implement the model using a brand new Java programming platform, explicitly designed for AB-SFC models.
Bitcoin is a decentralized, open-source cryptocurrency used to make private, peer-to-peer transactions anywhere across the world. Although the individuals involved are (mostly) anonymous, every Bitcoin transaction is a matter of public record; anyone can view every Bitcoin transaction ever made. Following the methodology developed by Evans and Lyons (2002), this paper adapts and estimates a FX microstructure model that emphases order flow, the difference between buyer- and seller-initiated trading volume, to the Bitcoin market Using a data set consisting of all major currency transactions occurring on the Mt. Gox exchange, our results are quite similar to prior microfinance research on traditional currencies insofar order flow is a significant determinant of Bitcoin spot rates.
In the history of money bitcoin represents an outstanding medium of exchange, independent from central authorities. Therefore, it has experienced impressive demand which, combined with inelastic supply, has led to huge price appreciation. Nonetheless, transaction volume has not been increasing accordingly. At the core of this conundrum is the very poor performance of bitcoin as unit of account: dramatic deflationary price instability makes bitcoin just impractical for commerce, but completely unserviceable for salaries, mortgages, and deferred payments in general. Ametrano (2014a) has championed as Hayek Money the proposal to engineer cryptocurrencies with fully automatic algorithmic non-discretionary elastic supply: the monetary rule of pegging to a price index would dynamically rebase the outstanding amount of money and achieve price stability. It is proposed here to implement Hayek Money as multiple coexisting units of account wrapped around the unmodified bitcoin (or any other cryptocurrency). Prices would be stable in terms of these rebased-bitcoin units of account: different coexisting cryptocurrencies all backed by the same bitcoins, each one with its own floating bitcoin-equivalent rebasing index. These cryptocurrencies would define a new monetary standard, with striking resemblance to the gold standard as improved by the compensated dollar proposed by Fisher (1913). In this Fisher Money scenario bitcoin would be digital crypto-gold and exchange rates would be floating, not fixed, being just the relative prices of the respective cryptocurrency price indexes.
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.
David GarcĂa, Claudio J. Tessone, Pavlin Mavrodiev, Nicolas Perony
What is the role of social interactions in the creation of price bubbles? Answering this question requires obtaining collective behavioural traces generated by the activity of a large number of actors. Digital currencies offer a unique possibility to measure socio-economic signals from such digital traces. Here, we focus on Bitcoin, the most popular cryptocurrency. Bitcoin has experienced periods of rapid increase in exchange rates (price) followed by sharp decline; we hypothesise that these fluctuations are largely driven by the interplay between different social phenomena. We thus quantify four socio-economic signals about Bitcoin from large data sets: price on on-line exchanges, volume of word-of-mouth communication in on-line social media, volume of information search, and user base growth. By using vector autoregression, we identify two positive feedback loops that lead to price bubbles in the absence of exogenous stimuli: one driven by word of mouth, and the other by new Bitcoin adopters. We also observe that spikes in information search, presumably linked to external events, precede drastic price declines. Understanding the interplay between the socio-economic signals we measured can lead to applications beyond cryptocurrencies to other phenomena which leave digital footprints, such as on-line social network usage.
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.
This paper presents an agent-based artificial cryptocurrency market in which heterogeneous agents buy or sell cryptocurrencies, in particular Bitcoins. In this market, there are two typologies of agents, Random Traders and Chartists, which interact with each other by trading Bitcoins. Each agent is initially endowed with a finite amount of crypto and/or fiat cash and issues buy and sell orders, according to her strategy and resources. The number of Bitcoins increases over time with a rate proportional to the real one, even if the mining process is not explicitly modelled. The model proposed is able to reproduce some of the real statistical properties of the price absolute returns observed in the Bitcoin real market. In particular, it is able to reproduce the autocorrelation of the absolute returns, and their cumulative distribution function. The simulator has been implemented using object-oriented technology, and could be considered a valid starting point to study and analyse the cryptocurrency market and its future evolutions.
The Bitcoin has emerged as a fascinating phenomenon in the Financial markets. Without any central authority issuing the currency, the Bitcoin has been associated with controversy ever since its popularity, accompanied by increased public interest, reached high levels. Here, we contribute to the discussion by examining the potential drivers of Bitcoin prices, ranging from fundamental sources to speculative and technical ones, and we further study the potential influence of the Chinese market. The evolution of relationships is examined in both time and frequency domains utilizing the continuous wavelets framework, so that we not only comment on the development of the interconnections in time but also distinguish between short-term and long-term connections. We find that the Bitcoin forms a unique asset possessing properties of both a standard financial asset and a speculative one.
In this paper we revisit some major orthodoxies which lie at the heart of the bitcoin crypto currency and its numerous clones. In particular we look at The Longest Chain Rule, the monetary supply policies and the exact mechanisms which implement them. We claim that these built-in properties are not as brilliant as they are sometimes claimed. A closer examination reveals that they are closer to being... engineering mistakes which other crypto currencies have copied rather blindly. More precisely we show that the capacity of current crypto currencies to resist double spending attacks is poor and most current crypto currencies are highly vulnerable. Satoshi did not implement a timestamp for bitcoin transactions and the bitcoin software does not attempt to monitor double spending events. As a result major attacks involving hundreds of millions of dollars can occur and would not even be recorded. Hundreds of millions have been invested to pay for ASIC hashing infrastructure yet insufficient attention was paid to network neutrality and to insure that the protection layer it promises is effective and cannot be abused. In this paper we develop a theory of Programmed Self-Destruction of crypto currencies. We observe that most crypto currencies have mandated abrupt and sudden transitions. These affect their hash rate and therefore their protection against double spending attacks which we do not limit the to the notion of 51% attacks which is highly misleading. In addition we show that smaller bitcoin competitors are substantially more vulnerable. In addition to small hash rate, many bitcoin competitors mandate incredibly important adjustments in miner reward. We exhibit examples of 'alt-coins' which validate our theory and for which the process of programmed decline and rapid self-destruction has clearly already started.
Bitcoins have emerged as a possible competitor to usual currencies, but other\ncrypto-currencies have likewise appeared as competitors to the Bitcoin\ncurrency. The expanding market of crypto-currencies now involves capital\nequivalent to $10^{10}$ US Dollars, providing academia with an unusual\nopportunity to study the emergence of value. Here we show that the Bitcoin\ncurrency in itself is not special, but may rather be understood as the\ncontemporary dominating crypto-currency that may well be replaced by other\ncurrencies. We suggest that perception of value in a social system is generated\nby a voter-like dynamics, where fashions form and disperse even in the case\nwhere information is only exchanged on a pairwise basis between agents.\n