Blockchain Papers

Benjamin M. Blau

No abstract is available for this record.

Mariusz Tarnopolski

The long-term dependence of Bitcoin (BTC), manifesting itself through a Hurst\nexponent $H>0.5$, is exploited in order to predict future BTC/USD price. A\nMonte Carlo simulation with $10^4$ geometric fractional Brownian motion\nrealisations is performed as extensions of historical data. The accuracy of\nstatistical inferences is 10\\%. The most probable Bitcoin price at the\nbeginning of 2018 is 6358 USD.\n

Zhengyao Jiang, Dixing Xu, Jinjun Liang

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

Pavel Ciaian, Miroslava Rajčániová, d’Artis Kancs

This paper empirically examines interdependencies between BitCoin and altcoin markets in the short- and long-run. We apply time-series analytical mechanisms to daily data of 17 virtual currencies (BitCoin + 16 alternative virtual currencies) and two altcoin price indices for the period 2013–2016. Our empirical findings confirm that indeed BitCoin and altcoin markets are interdependent. The BitCoin-altcoin price relationship is significantly stronger in the short-run than in the long-run. We cannot fully confirm the hypothesis that the BitCoin price relationship is stronger with those altcoins that are more similar in their price formation mechanism to BitCoin. In the long-run, macro-financial indicators determine the altcoin price formation to a slightly greater degree than BitCoin does. The virtual currency supply is exogenous and therefore plays only a limited role in the price formation.

Fahad Almudhaf

This article examines the pricing efficiency of Bitcoin Investment Trust. We investigate the deviation between prices and net asset values and find that there is a significant and persistent premium with an average of 44%. Such evidence points to pricing inefficiency of the currently available trust and encourages practitioners to introduce better instruments such as Exchange Traded Funds as alternatives to investors interested in having exposure to bitcoins and the digital currencies market.

John P. Conley

Blockchain startups have embraced initial coin offerings (ICOs) as a vehicle to raise early capital. The crypto-tokens offered in these sales are intended to fill a widely varied set of roles on different platforms. Some tokens are similar to currencies, others are more like securities, and others have properties that are entirely new. Each company's technological vision calls for a token with unique properties and uses. The main point of this paper is that designing a successful token must take into account certain aspects of monetary theory, financial economics, and game theory. Failing to do so can put an otherwise excellent project at risk. We also explore what economics tells us about how to assess the value of tokens offered for sale, how startups should structure their ICOs, and what the implications of assigning various roles to tokens on a platform might be.

Sha Wang, Jean‐Philippe Vergne

Cryptocurrencies have become increasingly popular since the introduction of bitcoin in 2009. In this paper, we identify factors associated with variations in cryptocurrencies' market values. In the past, researchers argued that the "buzz" surrounding cryptocurrencies in online media explained their price variations. But this observation obfuscates the notion that cryptocurrencies, unlike fiat currencies, are technologies entailing a true innovation potential. By using, for the first time, a unique measure of innovation potential, we find that the latter is in fact the most important factor associated with increases in cryptocurrency returns. By contrast, we find that the buzz surrounding cryptocurrencies is negatively associated with returns after controlling for a variety of factors, such as supply growth and liquidity. Also interesting is our finding that a cryptocurrency's association with fraudulent activity is not negatively associated with weekly returns-a result that further qualifies the media's influence on cryptocurrencies. Finally, we find that an increase in supply is positively associated with weekly returns. Taken together, our findings show that cryptocurrencies do not behave like traditional currencies or commodities-unlike what most prior research has assumed-and depict an industry that is much more mature, and much less speculative, than has been implied by previous accounts.

Hélder Sebastião, António Portugal Duarte, Gabriel Guerreiro

This paper analyses the price discovery process in the USD/Bitcoin market since the Mt.Gox bankruptcy until the aftermath of the hack attack on Bitfinex (01-Mar-2014 until 30-Nov-2016).The Geweke feedback measures, estimated pairwise using hourly returns, show that there is a positive relationship between the total feedback and market share, measured by trading volume, that most of the information is transmitted between exchanges within an hour, at least for the main four exchanges (Bitfinex, Bitstamp, BTC-e and ItBit), while lagged feedback runs mainly from the major exchange.Other minor exchanges seem to react to price information with some delay and are thus considered as merely satellite exchanges.Bitfinex stands out as the most important exchange in transmitting information to the market: the relative importance of the lagged feedback from Bitfinex to the market is 18.29% while the lagged feedback from the market to Bitfinex accounts only for 0.60% of the total feedback.The volatility in the major exchange in each pair is the main factor explaining the feedback measures, sustaining the claim that the information-based component of volatility increases with the relative dimension of the exchange.

Aleksi Aaltonen

This thesis is a descriptive statistical analysis of cryptocurrency market and its relation within cryptocurrencies and across asset classes, using correlation functions, orthogonalized impulse response functions and OLS regressions. Consistent with Wang (2014), bitcoin does not suffer from a liquidity trap, even though bitcoin is a decentralized system. This thesis concludes that bitcoin has a lead effect on only 2 out of 8 of the top cryptocurrencies, endowing diversification benefits within cryptocurrency market. This paper provides evidence on cryptocurrency market’s and US equity market’s impulse response dynamics which are insignificant, consistent with Gangwal’s (2016) results that adding cryptocurrencies to a diversified portfolio will yield to a higher Sharpe ratio. Lastly, the study reports bitcoin momentum factor having an impact on banking and financial industries’ excess returns.

Andreas Bjordal, Espen Opdahl

In this paper, we rigorously investigate the benefit of utilizing an active investment strategy\nbased on momentum when investing in cryptocurrencies. We also examine how including\ncryptocurrencies in a more traditional asset allocation can optimize an investment portfolio.\nFirst, we create strategies with the use of exponential moving averages and simple average\nfilters to generate a trading signal. Second, we provide evidence that the active strategies\nreceive positive return, but significantly less than the passive buy-and-hold\nalternative/benchmark. Third, we find evidence that including a portion of cryptocurrency in\na portfolio with more traditional assets will improve the risk-adjusted return, due to low\nhistorical correlation. And fourth, we look at and evaluate the extreme volatility and risk\nrelated to cryptocurrencies and the suggested cryptocurrency bubble. Our results have\nimportant implications for portfolio managers and first-time investors alike.

Kartik Hegadekatti

A Company's Brand image is an intangible asset. Though Initial Public Offerings (IPOs) try to capture Brand Value, a company's share value is a result of several factors like performance, initial capital, investor identity etc. Moreover, time needed for a company to be listed runs into several months. Therefore, immediate capitalization of Brand Value is not possible. Initial Coin Offerings on the other hand deliver a wide range of possibilities not provided by IPOs. Most important among them is Brand Tokenization and Monetization. This paper explores Brand Tokenization and Monetization through ICOs (Initial Coin Offerings). Firstly, the concept of Brands and cryptocurrencies are explained. Then the concept of ICOs is discussed. I envisage a scenario where a company tokenizes its Brand and attempts to monetize it. We then evaluate the advantages that can accrue from such a venture. The paper concludes as to how Brand Tokenization and monetization can be realized through cryptocurrencies and its impact on future businesses.

Ryan J. Davies, Erik R. Sirri

No abstract is available for this record.

Joerg Osterrieder, Martin Strika, Julian Lorenz

Cryptocurrencies became popular with the emergence of Bitcoin and have shown an unprecedented growth over the last few years. As of November 2016, more than 720 cryptocurrencies exist, with Bitcoin still being the most popular one. We provide both a statistical analysis as well as an extreme value analysis of the returns of the most important cryptocurrencies. A particular focus is on the tail risk characteristics and we will provide an in-depth univariate and multivariate extreme value analysis. The tail dependence of cryptocurrencies is investigated (using both empirical and Gaussian copulas). For investors¡ªespecially institutional ones¡ªas well as regulators, an understanding of the risk and tail characteristics are of utmost importance. For cryptocurrencies to become a mainstream investable asset class, studying these properties is necessary. Our findings show that cryptocurrencies exhibit strong non-normal characteristics, large tail dependencies, depending on the particular cryptocurrencies and heavy tails. Statistical similarities can be observed for cryptocurrencies that share the same underlying technology. This has implications for risk management, financial engineering (such as derivatives on cryptocurrencies)¡ªboth from an investor¡¯s as well as from a regulator¡¯s point of view. To our knowledge, this is the first detailed study looking at the extreme value behaviour of cryptocurrencies, their correlations and tail dependencies as well as their statistical properties.

Navroop K. Sahdev

Blockchain, the technology behind Bitcoin, promises to be nothing less than Internet 2.0. The financial services industry, in particular, is preparing for the disruption blockchain/distributed ledger technology promises to cause. In the current business environment, the majority of startups and small businesses have to look for alternative sources of funding given that ‘going public’ is increasingly expensive. The crowdfunding space has seen tremendous growth as an alternative way to raise capital by businesses. However, these crowdfunded shares cannot be traded for 7-10 years on average on any given platform in the U.S. currently. To build a trading platform on the blockchain which is completely P2P, immutable, fully transparent and low cost presents some key design issues. In particular, the issue of liquidity - and price discovery - on the blockchain continues to be a puzzle. At the same time, the proposition of removing middlemen from equities trading is a very attractive one, streamlining the process of capital formation with higher market efficiency. The current paper addresses the following key questions: How can a DLT trading platform ensure adequate liquidity? What would be the process of price discovery? While some recent studies hail blockchain technology as a boom for market liquidity, it is not immediately clear what the impact of P2P trading would be on the prices of various stocks. There are no ‘solutions’ just yet. At the same time, the lack of regulation around trading on the blockchain creates an environment of uncertainty for all players. In particular, the implementation of such a platform can revolutionize capital formation and build robust markets in both developing and developed countries where crowdfunding has proven to be a successful model. While my research is targeted at solving a very specific pain point for both researchers and companies working on distributed ledger technology, ultimately, it would be a significant step forward towards onboarding underserved communities across the world who don’t have access to financial services.

Gloria Yang Yu, Jinyuan Zhang

No abstract is available for this record.

ChengYuan Qu

No abstract is available for this record.

Stefan Hubrich

No abstract is available for this record.

Nashirah Abu Bakar, Sofian Rosbi

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

Alessandra Cretarola, Gianna Figg-Talamanca, Marco Patacca

In recent literature it is claimed that BitCoin price behaves more likely to a volatile stock asset than a currency and that changes in its price are influenced by sentiment about the BitCoin system itself; in Kristoufek [10] the author analyses transaction based as well as popularity based potential drivers of the BitCoin price finding positive evidence. Here, we endorse this finding and consider a bivariate model in continuous time to describe the price dynamics of one BitCoin as well as a second factor, affecting the price itself, which represents a sentiment indicator. We prove that the suggested model is arbitrage-free under a mild condition and, based on risk-neutral evaluation, we obtain a closed formula to approximate the price of European style derivatives on the BitCoin. By applying the same approximation technique to the joint likelihood of a discrete sample of the bivariate process, we are also able to fit the model to market data. This is done by using both the Volume and the number of Google searches as possible proxies for the sentiment factor. Further, the performance of the pricing formula is assessed on a sample of market option prices obtained by the website deribit.com.

Shimeng Shi

No abstract is available for this record.

Jim Kyung-Soo Liew, Levar Hewlett

No abstract is available for this record.

Anton Kajtazi, Andrea Moro

This research explores the effects of adding bitcoin to an optimal portfolio (naïve, long-only, unconstrained and semi-constrained) by relying on mean-CVaR in the Chinese market. Then backtesting to compare the performance of portfolios with and without bitcoin for each scenario is perfomed. Results show significant but weak correlations between various asset classes and bitcoin, implying a more mature financial profile of bitcoin in China compared to that in the west. Backtesting results show that the effect of adding bitcoin to optimal portfolios is not consistent over the entire out-of-sample period. The naïve and the long-only strategy improved the risk-reward ratio up until the late 2013 price-crash with no significant advantages thereafter. Shorting strategies on the other hand, with or without leverage, fail to produce more efficient portfolios when bitcoin is added, and this is consistent over the entire out-of-sample period. The results also show that semi-annual rebalancing amplifies the advantages of adding bitcoin to most portfolios except for the semi-constrained portfolio, although the weights analysis show significant shifts in weights which might not represent a feasible strategy in realistic scenarios.

Usman W. Chohan

No abstract is available for this record.

Marco Bianchetti, Camilla Ricci, Marco Scaringi

The growth of peer-to-peer exchanges and the blockchain technology has led to a proliferation of cryptocurrencies and to a massive increase in the number of investors who actually negotiate digital money. Cryptocurrencies trade at prices mainly driven by investor sentiment, becoming a potential source of financial bubbles and instabilities. In this work, we apply quantitative models to the study of Bitcoin and Ether, two of the most famous cryptocurrencies. Our bubble detection methodology combines the Log Periodic Power Law (LPPL) model, originally created by Johansen, Ledoit and Sornette (JLS), and the statistical model developed by Phillips, Shi, and Yu (PSY). In particular, we employ three different versions of JLS model, i.e. Ordinary Least Square (OLS), Generalised Least Squares (GLS) and Maximum Likelihood Estimation (MLE), and two PSY statistical tests (BSADF and BSADF*). We find that, during the sample period 1st December 2016 - 16th January 2018, Bitcoin shows typical hallmarks of a bubble phase in mid December 2017 and in the first half of January 2018, anticipating the large crashes observed thereafter. Also the Ether price dynamics reveals bubble evidence in mid June 2017, anticipating the crash observed on 12th June, and a weaker signal around 12th January 2018, anticipating the crash observed in the same days. This paper confirms the high risk of speculative bubbles associated with cryptocurrencies, related to investor exuberance pumping market prices far away from their fundamental values, thus creating critical situations subject to possible crashes. Our methodology is general and can be applied to virtually any financial time series, and may support investing and risk management strategies.