Blockchain Papers

Elie Bouri, Chi Keung Marco Lau, Tareq Saeed, Shixuan Wang · 5 authors

No abstract is available for this record.

Sonia Arsi, Khaled Guesmi, Elie Bouri

In view of explosive trends and excessive trades in the cryptocurrency markets, this paper contributes to the existing literature by bringing in the limelight the effect of liquidity on the herding behavior in the cryptocurrency market. Results from a first applied herding model including contemporaneous and lagged squared market returns demonstrated that market-wide herding exists within falling markets. The incorporation of liquidity highlights further evidences on herding behavior across cryptocurrencies during high and low liquid days, which varies across percentiles. Our findings bring handy implications for topics of portfolio and risk management, as well as regulation.

Kavita Saini

Blockchain as the name says, it&s;s a chain of multiple blocks linked together. As a distributed ledger, Blockchain built from a chain of blocks where each block consists of various exchanges or transactions and block header. Each blockchain has a block header which helps in differentiating the first block and helps in making the chain. Basically a Blockchain is a distributed ledger, distributed across systems and accessible to all and it is stored and updated across the world by many systems. The beautify of the Blockchain technology is that it does not need any central or any third party involvement to manage, approve or control the transaction. Now a days Blockchain technology has become the backbone not for the cryptocurrency but also for all kind of applications where security and transparency is at most priority. Blockchain technology is used where there is a need to gain trust of the application or technology users such as banking, supply chain, drug counterfeit detection, health care data as the entire word is now storing all information on computer system. This technology is helping in building the trust and transparency across the machines and the human. The chapter discuss how Blockchain Technology works, what are the Consensus protocols. The chapter also talks about the Decentralized and Peer-to-Peer transactions in detail. The Proof of Work, Proof of Stake, Proof of Elapsed Time (PoET), and Practical Byzantine Fault Tolerance (PBFT) also discussed in detail.

Gaurang Bansal, Vinay Chamola, Georges Kaddoum, Md. Jalil Piran · 5 authors

No abstract is available for this record.

Oliver Borgards

No abstract is available for this record.

Eyal Beigman, Gerard P. Brennan, Sheng-Feng Hsieh, Alexander J. Sannella

Cryptocurrencies and blockchain technology are disruptive innovations at the vanguard of a new wave of the digital revolution. The far-reaching appeal, global reach, unprecedented mobility of capital, and multitude of trading venues have created a marketplace like no other. The economic fundamentals underlying this market are yet to be fully comprehended, as evidenced by the often-contradicting guidelines recommended by accounting firms, government agencies, and standard setters. Many of the definitions and models used for classical markets cannot be applied directly to cryptocurrency. Basic concepts must be reinterpreted, and models must be modified to fit the mechanics of these markets. In this article, we focus on one such concept: that of fair value. We argue that in light of the fragmentation of cryptocurrency markets and the global dispersion of trading venues, a principal market may be difficult to identify. The primary objective of this article is to present a methodology to dynamically designate principal markets and derive fair value prices for financial reporting using this designation.

Serda Selin Öztürk, M. Emre Bilgiç

There is a vast amount of information flow in social media about bitcoin, which may affect investors’ decisions. This article investigates whether tweets may affect returns or trade volume changes of bitcoin and, more importantly, whether some Twitter accounts are more influential than other Twitter accounts. We conduct two separate analyses based first on all Twitter accounts and then on the most influential 50 Twitter accounts, which have been selected as such by Unitedtraders. We use the number of positive, negative and neutral tweets by Valence Aware Dictionary and Sentiment Reasoner (VADER) in a logistic model to analyse if tweets have any valuable information about the change in both return and trade volume of bitcoin. Our results indicate that tweets can be used to predict bitcoin returns. Notably, the most influential accounts are the drivers of returns, but all Twitter accounts simply introduce some noise in volatility. This result indicates that following only these 50 most influential accounts may provide the information needed for investors.

Miguel Ángel Echarte Fernández, Sergio Luis Náñez Alonso, Javier Jorge-Vázquez, Ricardo Francisco Reier Forradellas

This article analyzes the monetary policy of major central banks during the economic crisis generated by the COVID-19 pandemic. Rising public debt in many countries is being financed through asset purchases by monetary authorities. Although these stimulus policies predate the pandemic, they have been significantly boosted as many governments face large financing needs. We have been in a low interest rate environment for years and some governments have issued debt securities at negative rates. In addition, the rise of decentralized cryptocurrencies, based on blockchain technology, has created greater competition in the international monetary system and many governments have considered the creation of centralized virtual currencies, known as central bank digital currencies (CBDCs). We will analyze some relevant cases, with an emphasis on the digital euro project. The methodology is based on the analysis of the evolution of monetary variables. Pearson’s correlation will be used to establish some relationships between them. There is a strong similarity in the expansionary monetary policies of central banks. Although the growth of the money supply has not been passed on to the CPI, it has been passed on to the financial markets and the price of assets such as Bitcoin or gold.

Benjamin M. Blau, Todd G. Griffith, Ryan J. Whitby

No abstract is available for this record.

Chetan G. Shinde, Atharav Upare, Vishal Pawar, Ajay Raut

We propose a new blockchain-based framework for a completely decentralized stock market and bitcoin exchange in this paper. By proposing a groundbreaking framework utilizing blockchain to build a decentralized bitcoin and stock exchange network, this paper discusses the shortcomings of conventional centralized stock exchange platforms, High transaction costs, vulnerable centralized governance, and a lack of clarity in consumer behavior and algorithms are just a few of the problems. Blockchain technology consists of a large number of computer nodes that share a shared ledger securely without the need for intermediaries of any sort. The proposed blockchain-based solution addresses the disadvantages of the centralized stock exchange architecture by ensuring the integrity and security of the properties and orders of the owner, by self-enforcing intelligent agreements between parties, and by consensus algorithms, by achieving democratic and effective decisions on the execution and settlement of orders. Intelligent contracts are used in the proposed architecture to enforce the validation of the owner's rights as well as the proper execution and settlement of orders, reducing the need for a central authority to ensure that the stock exchange process is accurate. The proposed system proposes a hybrid platform that incorporates cryptocurrency and stock trading. The solution was tested for a subset of rules for the Stock Exchange by implementing a prototype in Ethereum.

Ali Al-Ameer, Fouad M. AL‐Sunni

This paper discusses securities and cryptocurrency trading using artificial intelligence (AI) in the sense that it focuses on performing Exploratory Data Analysis (EDA) on selected technical indicators before proceeding to modelling, and then to develop more practical models by introducing new reward loss function that maximizes the returns during training phase. The results of EDA reveal that the complex patterns within the data can be better captured by discriminative classification models and this was endorsed by performing back-testing on two securities using Artificial Neural Network (ANN) and Random Forests (RF) as discriminative models against their counterpart Naïve Bayes as a generative model. To enhance the learning process, the new reward loss function is utilized to retrain the ANN with testing on AAPL, IBM, BRENT CRUDE and BTC using auto-trading strategy that serves as the intelligent unit, and the results indicate this loss superiorly outperforms the conventional cross-entropy used in predictive models. The overall results of this work suggest that there should be larger focus on EDA and more practical losses in the research of machine learning modelling for stock market prediction applications.

Natividad Blasco, Pilar Corredor

This paper studies the herding behaviour among different exchanges trading bitcoin. The analysis allows us to conclude that the size of the exchange is an influencing parameter. Since 2018, when the significant growth in the number of exchanges became a reality, smaller exchanges have shown strong herding behaviour, whereas large exchanges seem to respond to their own information and beliefs and lead the process of price definition. This result may originate some temporary profitable strategies in the process of evolution towards efficiency according to the Adaptive Markets Hypothesis.

Chuanzhen Wu

No abstract is available for this record.

Dorsa Mohammadi Arezooji

First, a big data analysis of the transactions and smart contracts made on\nthe Ethereum blockchain is performed, revealing interesting trends in motion.\nNext, these trends are compared with the public's interest in Ether and\nBitcoin, measured by the volume of online searches. An analysis of the crypto\nprices and search trends suggests the existence of big players (and not the\nregular users), manipulating the market after a drop in prices. Lastly, a\ncross-correlation study of crypto prices and search trends reveals the pairs\nproviding more accurate and timely predictions of Ether prices.\n

Fan Fang, Waichung Chung, Carmine Ventre, Michail Basios · 7 authors

The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using machine learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a machine learning approach to predict the direction of the mid-price changes on the upcoming tick. We show that there are universal features amongst cryptocurrencies which lead to models outperforming asset-specific ones. We also show that there is little point in feeding machine learning models with long sequences of data points; predictions do not improve. Furthermore, we solve the technical challenge to design a lean predictor, which performs well on live data downloaded from crypto exchanges. A novel retraining method is defined and adopted towards this end. Finally, the trade-off between model accuracy and frequency of training is analyzed in the context of multi-label prediction. Overall, we demonstrate that promising results are possible for cryptocurrencies on live data, by achieving a consistent 78% accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs. US dollars.

Jaroslav Mazanec

Virtual currency represents a specific technological innovation on financial markets. Bitcoin and other cryptocurrencies are popular alternatives to traditional cash and investment. We indicate a research gap in the literature review. We find out that current research focused rarely on portfolio diversification using bibliographic analysis in VOSviewer. We think that portfolio diversification is extremely important on the crypto market for most investors because virtual currencies are very risky compared to traditional assets. The primary aim is to construct an optimal portfolio consisting of several cryptocurrencies without traditional assets using a modern theory portfolio. The total sample consists of 16 virtual currencies from 1 October 2017 to 13 January 2020. We mainly obtain historical data on the daily close price of cryptocurrencies from Yahoo Finance. The results show that the optimal portfolio using Markowitz approach consists of Cardano, Binance Coin, and Bitcoin. In addition, virtual currencies are moderately Correlated, with the exception of Tether based on correlation analysis. The high correlation is dangerous for cryptocurrency in portfolio diversification. However, Tether is an atypical virtual currency compared to other cryptocurrencies.

Mohammad Tariquel Islam, Kumer Pial Das

The study investigates and develops the ability of the extreme value theory (EVT) to predict bitcoin return. EVT is used to deal with rare but extreme events, such as severe losses or excessive damages. It is being used as a powerful statistical tool in various disciplines, including finance, engineering, environmental science, and actuarial science. As the largest among all cryptocurrencies in existence, bitcoin’s behavior is primarily characterized by great volatility. Predicting bitcoin return is complex and important, primarily because of the extreme nature of its return. There is not enough substantial research involving EVT in bitcoin analysis. This study has three objectives. First, confirming the extreme nature of bitcoin return by various statistical tests; second, modeling the bitcoin return using two different EVT approaches (block maxima approach and peak over threshold approach); and third, assessing uncertainties by predicting bitcoin return levels for 5-, 10-, 20-, 50-, and 100-years with a 95% confidence interval using both of these methods. These results could certainly serve policymakers and investors, as these return levels can be useful in characterizing bearish and bullish trends and predicting the same. Moreover, these can serve as starting points for future studies regarding the stationary and non-stationary properties of bitcoin return.

Ch. Sanjeev Kumar Dash, Ajit Kumar Behera, Sarat Chandra Nayak, Satchidananda Dehuri

The cryptocurrency price movement behaves randomly and fluctuates like other stock markets. Prediction of cryptocurrency is a recent area of research interest and budding fast. The underlying nonlinearities in its price series make its prediction challenging. Sophisticated methodologies for accurate prediction of cryptocurrency are highly desired. Artificial neural networks (ANNs) are good approximators, however their accuracy is greatly subjective to optimal network structure and learning method. This article designs optimal ANNs for efficient cryptocurrency prediction using quasi opposition based Rao algorithms, i.e. QORA-ANN. The model explores a set of potential ANNs in the search space and lands at an optimal network through the evolving process. Historical data from four emerging cryptocurrencies such as Bitcoin, Litecoin, Ethereum, and Ripple are used to evaluate the QORA-ANN. The prediction ability of the proposed approach is compared with few similar methods such as ANN trained with genetic algorithm, differential evolution and particle swarm optimization (i.e. ANN-GA, ANN-DE, ANN-PSO), support vector machine (SVM), and multilayer perceptron (MLP). From exhaustive simulation studies and comparative result analysis it is found that the QORA-ANN method performed better than others and hence can be suggested as an efficient tool for cryptocurrencies prediction.

Κωνσταντίνος Γκίλλας, Rangan Gupta, Christian Pierdzioch

We use intraday data to construct measures of the realized volatility of bitcoin returns. We then construct measures that focus exclusively on relatively large realizations of returns to assess the tail shape of the return distribution, and use the heterogeneous autoregressive realized volatility (HAR-RV) model to study whether these measures help to forecast subsequent realized volatility. We find that mainly forecasters suffering a higher loss in case of an underprediction of realized volatility (than in case of an overprediction of the same absolute size) benefit from using the tail measures as predictors of realized volatility, especially at a short and intermediate forecast horizon. This result is robust controlling for jumps and realized skewness and kurtosis, and it also applies to downside (bad) and upside (good) realized volatility.

Natalya Apopo, Andrew Phiri

The legitimacy of virtual currencies as an alternative form of monetary exchange has been the centre of an ongoing heated debated since the catastrophic global financial meltdown of 2007-2008. Our study tests the informational market efficiency of cryptomarkets by investigating the weak-form efficiency of the top-five cryptocurrencies using random walk testing procedures which are robust to asymmetries and unobserved smooth structural breaks. Moreover, our study employs two frequencies of cryptocurrency returns, one corresponding to daily returns and the other to weekly returns. Our findings validate the random walk hypothesis for daily series hence validating the weak-form efficiency for daily returns. On the other hand, weekly returns are observed to be stationary processes which is evidence against weak-form efficiency for weekly returns. Overall, our study has important implications for market participants within cryptocurrency markets.

F. Chudzyński, Zbigniew R. Struzik

In this paper, we present an analysis of the non-Gaussianity of the Bitcoin market, in which we focus on the scale dependence of the variance 2 estimated by fitting the Castaing equation to the detrended price series. Our analysis showed scale invariance across a large range of scales in the years 2012-2019, which indicates that the Bitcoin market is in a critical state.

Florian Horky, Mihai Mutaşcu, Jarko Fidrmuc

With its rising popularity, the Bitcoin has also become increasingly independent from global financial markets. Recently, it has joined the class of alternative assets. We use the newly developed wavelet methodology to analyze daily data to compare the COVID-19 pandemic at the beginning of 2020 with the bear market episode at the end of 2018. In both cases, attention signals and a general panic are the main drivers of the Bitcoin fluctuations. We show that the Bitcoin’s dynamic is more complex than the dynamics of standard financial assets. The Bitcoin is, on the one hand, subject to pandemic shocks but also represents an important source of attention signals. On the other hand, because the Bitcoin additionally reacts on an emotional basis, it might react faster than other assets and thus creates a market signal itself. Moreover, we identify short cycles (of several days), which may possibly be related to demand factors, while long cycles (of several weeks) seem to mirror supply factors and might be related to Bitcoin mining in China. Finally, the analysis underlines the importance of continuous financial education and communication by the supervisory authorities about new, alternative financial assets.

Sun-Woong Kim

디지털화폐로서의 비트코인에 대한 법적 논쟁에도 불구하고 암호화폐 시장에서는 가격이 급등락을 거듭하면서 많은 투자자들을 암호화폐 투자의 세계로 끌어들이고 있다. 본 연구는 투자자 관점에서 “과연 기술적 분석을 이용하여 수익성 있는 비트코인 투자 전략을 찾을 수 있을까?”에 초점을 맞추었다. 주식시장에서 투자자들이 주로 활용하고 있는 기술적 분석 지표에 기초한 트레이딩시스템들을 1340일 동안의 비트코인 거래에 적용해본 결과는 긍정적인 답을 얻을 수 있었다. 분석 기간 동안 비트코인의 가격이 32,273,000원 오르는 동안 제안된 추세 트레이딩시스템은 최대 48,451,000원의 수익을 실현하였다. 반면, 역추세 트레이딩시스템의 최대 수익은 6,409,000원에 그치고 있다. 한편 투자 위험 척도인 MDD는 52% 이상의 위험 감소 효과가 나타났다. 주식시장과 비교하여 추세가 더 강한 비트코인에 대한 추세 추종형 시스템의 투자 결과는 높은 수익과 더불어 투자에 따른 위험을 크게 낮출 수 있음을 보여주었다. 본 연구의 결론은 투자 규칙 없이 암호화폐 시장에 뛰어들고 있는 투자자들에게 암호화폐 투자의 나침판을 제시하고 있다.

Thomas Leirvik

In this paper I document a positive relation between the volatility of liquidity and expected returns. Specifically, I analyze the relationship between the idiosyncratic volatility of market liquidity and the returns of the five largest cryptocurrencies by market capitalization. I find that the correlation between liquidity volatility and returns is overall significantly positive, but highly time-varying. This implies that investors demand a premium for a high variation in liquidity volatility. I furthermore find that the correlation between returns and the level of liquidity is mostly positive, thus, when liquidity is low, expected returns are high. The results corroborates results from other financial markets.