Cryptocurrencies that are based on Proof-of-Work (PoW) often rely on special purpose hardware to perform so-called mining operations that secure the system, with miners receiving freshly minted tokens as a reward for their work. A notable example of such a cryptocurrency is Bitcoin, which is primarily mined using application specific integrated circuit (ASIC) based machines. Due to the supposed profitability of cryptocurrency mining, such hardware has been in great demand in recent years, in-spite of high associated costs like electricity. In this work, we show that because mining rewards are given in the mined cryptocurrency, while expenses are usually paid in some fiat currency such as the United States Dollar (USD), cryptocurrency mining is in fact a bundle of financial options. When exercised, each option converts electricity to tokens. We provide a method of pricing mining hardware based on this insight, and prove that any other price creates arbitrage. Our method shows that contrary to the popular belief that mining hardware is worth less if the cryptocurrency is highly volatile, the opposite effect is true: volatility increases value. Thus, if a coin's volatility decreases, some miners may leave, affecting security. We compare the prices produced by our method to prices obtained from popular tools currently used by miners and show that the latter only consider the expected returns from mining, while neglecting to account for the inherent risk in mining, which is due to the high exchange-rate volatility of cryptocurrencies. Finally, we show that the returns made from mining can be imitated by trading in bonds and coins, and create such imitating investment portfolios. Historically, realized revenues of these portfolios have outperformed mining, showing that indeed hardware is mispriced.
Blockchain represents a technology for establishing a shared, immutable version of the truth between a network of participants that do not trust one another, and therefore has the potential to disrupt any financial or other industries that rely on third-parties to establish trust. Recent trends in computing including: prevalence of Free and Open Source Software (FOSS); easy access to High Performance Computing (HPC i.e. 'The Cloud'); and increasingly advanced analytics capabilities such as Natural Language Processing (NLP) and Machine Learning (ML) allow for rapidly prototyping applications for analysis of trends in the emergence of Blockchain technology. A scaleable proof-of-concept pipeline that lays the groundwork for analysis of multiple streams of semi-structured data posted on social media is demonstrated. Preliminary analysis and performance metrics are presented and discussed. Future work is described that will scale the system to cloud-based, real-time, analysis of multiple data streams, with Information Extraction (IE) (ex. sentiment analysis) and Machine Learning capability.
Recent years have seen the emergence of a new class of currencies, called\ncryptocurrencies. These currencies use cryptography to provide security\nand peer-to-peer networking to provide a decentralized system. Bitcoin is\nthe most popular of these currencies. It uses a two-pass\nSHA-256 hash at its core. Producing new bitcoins is done through a process\nreferred to as "mining", which involves a brute-force search for a hash with\na specific value. This process requires large amounts of computing power.\n\nCurrent-generation hardware for bitcoin mining includes highly-optimized\nASIC chips which provide huge amounts of performance. However, designers of\nsuch chips are having problems with delivering enough power and cooling\nto the chips. To alleviate this problem, this thesis looks at the possibilities\nof using heterogeneous computing to reduce power consumption and produce a more\nenergy-efficient mining solution.\n\nA SHA-256 accelerator and a DMA module is developed and integrated into a tile for\nthe Single-ISA Heterogeneous MAny-core Computer, SHMAC, and a system with\nmultiple cores is used to exploit the thread-level parallelism provided by\nthe platform. The system is tested using a benchmark to find out what performance\nand energy efficiency can be expected when using the system for bitcoin mining.\n\nThe results show a maximum performance of 175,7 kH/s when running the benchmark\napplication on 14 cores using the SHA-256 accelerator and the DMA module. The best\nenergy efficiency was obtained when running on 14 cores without the DMA enabled,\nat 163,2 kH/J. The results does not compare well to specialized FPGA-based\nbitcoin miners, but demonstrates the SHMAC platform's large degree of thread-level parallelism\nwhich can be better exploited in other applications.
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 Bitcoin’s 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.
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.