We show that the level of market-efficiency in the five largest cryptocurrencies is highly time-varying. Specifically, before 2017, cryptocurrency-markets are mostly inefficient. This corroborates recent results on the matter. However, the cryptocurrency-markets become more efficient over time in the period 2017–2019. This contradicts other, more recent, results on the matter. One reason is that we apply a longer sample than previous studies. Another important reason is that we apply a robust measure of efficiency, being directly able to determine if the efficiency is significant or not. On average, Litecoin is the most efficient cryptocurrency, and Ripple being the least efficient cryptocurrency.
Pankaj K. Jain, Thomas H. McInish, Jonathan Miller
Abstract We examine commonality in returns and volume for Bitcoin–fiat currency pairs, each trading in a country with a single time zone. Bitcoin has substantial volume and obeys the theory related to commonality, liquidity, and price discovery. We find evidence that one common factor explains 68% of the variance in hourly volume. Though trading is higher on weekdays, there is substantial weekend trading, reflecting high retail participation. Volume is higher on exchanges during local working hours, as seen in forex markets, supporting the view that trading patterns depend on the location of trade rather than the location of the asset traded.
This paper examines the relationship between Inverse Perpetual Swap contracts, a Bitcoin derivative akin to futures and the margin funding interest rates levied on BitMEX. This paper proves the Heteroskedastic nature of funding rates and goes onto establish a causal relationship between the funding rates and the Bitcoin inverse Perpetual swap contracts based on Granger causality. The paper further dwells into developing a predictive model for funding rates using best-fitted GARCH models. Implications of the results are presented, and funding rates as a predictive tool for gauging the market trend is discussed.
Abstract This study analyses the price movements of a select sample of cryptocurrencies and examines whether they are cointegrated and predictable using machine learning algorithm and Johansen Test. The study used daily historical trading data of 76 cryptocurrencies sourced from different cryptocurrency exchanges. A sub‐sample of six cryptocurrencies were chosen for the cointegration and machine learning analysis based on their market share, attractiveness to the investors and availability of data for the full sample period. The data records starting from April 29, 2013 to February 7, 2019 were considered for the study. An error correction model was estimated to investigate both the long‐run and short‐run dynamics between the cryptocurrency prices. The evidence from the error correction model estimates shows that there is a long‐run association between the prices of crypto currencies. The machine learning algorithm involving neural networks (multilayer perception) was used to comprehend the data patterns in the cryptocurrency price series, and the results show that the model fits well in identifying and predicting the data patterns. The study also examines the possible value drivers of cryptocurrencies by estimating a linear regression with a set of covariates, which include the cryptocurrency demand and supply interaction variables and financial variables such as the NZX/S&P 50 index and exchange rates. The linear model estimates confirm that cryptocurrency market fundamentals have an important impact on cryptocurrency prices; however, they do not support the prediction that financial fundamentals are the major value drivers of cryptocurrencies.
Cryptomonnaies et efficience des marchés Les innovations apportées par les cryptomonnaies et leur technologie sous-jacente, la blockchain, ouvrent de nouvelles voies de recherches en finance. Cette thèse de doctorat est composée de trois essais portant sur les cryptomonnaies et est centrée autour de la notion d’efficience informationnelle des marchés. La première étude vise à expliquer comment la blockchain, développée au sein de communautés informelles, est adoptée et intégrée par les organisations. Cette étude apporte un cadre théorique à la technologie blockchain, cadre qui s’appuie sur les approches contractuelle et cognitive de la théorie des organisations. Grâce à une revue de la littérature illustrée, une analyse à deux dimensions présente les possibles utilisations de la blockchain fondées sur l’accès à l’information pour les participants. L’objectif de la seconde étude est double. Premièrement, elle soulève la problématique de la réelle nature du Bitcoin. Après avoir comparé le Bitcoin aux monnaies, à l’or et aux actions, nous basons notre analyse sur l’hypothèse que les cryptomonnaies peuvent être assimilées aux actions. Deuxièmement, la performance financière (la rentabilité ajustée au risque) du Bitcoin est mesurée en utilisant des modèles traditionnels tels que le MEDAF et le model de Fama-French à trois facteurs. Nous trouvons que l’intégration du Bitcoin dans un portefeuille améliore considérablement sa diversification, tout en apportant des rentabilités ajustées au risque positives et significatives dans le monde, l’Europe et l’Asie-Pacifique. La forte volatilité du Bitcoin ainsi que sa haute performance nous conduisent à analyser le caractère de bulle spéculative des cryptomonnaies, ce qui est l'objet de la troisième étude. Nous analysons cet aspect en utilisant le modèle PSY de Phillips and Shi, 2018. Deuxièmement, nous analysons le plus important pic/éclatement du marché des cryptomonnaies à la fin des années 2017 à l’aide du modèle LPPL (Log Periodic Power Law). Les résultats suggèrent des périodes de bulles avec effet de contagion entre les cryptomonnaies. Les analyses théoriques et empiriques de cette thèse contribuent à la littérature académique sur les cryptomonnaies. Nos résultats sont également importants pour les entreprises et pour les investisseurs qui s’intéressent au potentiel des cryptomonnaies et de la blockchain, ainsi que pour les décideurs politiques responsables de leur régulation.
Abstract In recent years, the scrutiny of bitcoin and other cryptocurrencies as legal and regulated components of financial systems has been increasing. Bitcoin is currently one of the largest cryptocurrencies in terms of capital market share. Therefore, this study proposes that sentiment analysis can be used as a computational tool to predict the prices of bitcoin and other cryptocurrencies for different time intervals. A key characteristic of the cryptocurrency market is that the fluctuation of currency prices depends on people's perceptions and opinions, not institutional money regulation. Therefore, analysing the relationship between social media and web search is crucial for cryptocurrency price prediction. This study uses Twitter and Google Trends to forecast the short‐term prices of the primary cryptocurrencies, as these social media platforms are used to influence purchasing decisions. The study adopts and interpolates a unique multimodel approach to analyse the impact of social media on cryptocurrency prices. Our results prove that people's psychological and behavioural attitudes have a significant impact on the highly speculative cryptocurrency prices.
Driven by innovative information technologies, the financial industry is facing a recent disruptive fintech revolution. One emerging technology within this field is cryptocurrency, aiming to change the future means of payment. In this paper, we study Bitcoin exchange trading and examine what factors influence the behavior of different cryptocurrency investor types. To answer this question, market bids are considered in form of investors' offers and orders as a proxy for their trading behavior. First, an unsupervised clustering technique is applied in order to group different types of investors based on similarities in trading behavior. Second, a supervised classification mechanism is used on social media news to measure the sentiment influencing trading decisions. Among other indicators this bullishness is integrated in an autoregressive distributed lag (ARDL) model to identify the factors influencing the trading behavior of investor types. Besides large investors, foreign traders and speculators, cryptocurrency-specific market participants are characterized in the form of miners. With identifying indicators driving investors' actions (i.e., macro-financial fundamentals, technical trading indicators, technological measures and market sentiment), this study contributes to recent research by explaining the trading behavior on cryptocurrency markets and its impact on exchange rates.
In the past years, cryptocurrencies have received a lot of attention in popular media. Having attracted significant speculation, prices have soared in 2017, fell in 2018 and are generally known to be very volatile. However, some of the price changes have been due to organized manipulation. Traditionally known in the world of penny stocks and made illegal in most countries, pump and dump schemes are frequent in cryptocurrencies, and mostly unregulated. In this paper, we perform quantification and detection of pump and dump schemes that are coordinated through Telegram chats and executed on Binance - one of the most popular cryptocurrency exchanges. We detail how pumps are organized on Telegram, and quantify the properties of 149 confirmed events with respect to market capitalization, trading volume, price impact and profitability. Based on this ground truth, and regular trading intervals obtained from twitter timestamps, we optimize a binary classifier in order to be able to detect additional suspicious trading activity. Our results indicate that pump and dump schemes occur frequently in cryptocurrencies with market capitalizations below $50 million, that scheme operators often organize their actions across multiple channels, that such activity tends to lead to inflated prices over longer time periods and machine learning can help to identify activity that is similar to known pump and dump schemes.
Cryptocurrency prices have changed very dynamically in the market. Buyers and sellers can trade cryptocurrency on the market without time limits compared to traditional trading markets such as exchange currency markets and stock markets. Also, since it is a cryptocurrency created by an anonymous inventor, cryptocurrency price predictions are not determined by the company’s financial performance. Rather, the cryptocurrency price is related to how many investors participate in the market. In this sense, the prediction of cryptocurrency prices is very difficult and related to market participants. In this study, to better understand cryptocurrency pricing factors, we explore cryptocurrency price forecasts and use deep learning to improve forecasts. Specifically, we collected variables related to the investor’s decision. Use linear regression to select features to find important variables in cryptocurrency price prediction. In regression analysis, three models were created to identify the model that represents the best performance of cryptocurrency price prediction. Model 1 uses all variables without function selection. Model 2 uses only variables that are important in feature selection. Model 3 uses only variables that are not important for feature selection. Our test results show that Model 2 outperforms other models. We conclude that using the appropriate variables can improve cryptocurrency price predictions.
We study the Bitcoin and Ether price series under a financial perspective. Specifically, we use two econometric models to perform a two-layer analysis to study the correlation and prediction of Bitcoin and Ether price series with traditional assets. In the first part of this study, we model the probability of positive returns via a Bayesian logistic model. Even though the fitting performance of the logistic model is poor, we find that traditional assets can explain some of the variability of the price returns. Along with the fact that standard models fail to capture the statistic and econometric attributes—such as extreme variability and heteroskedasticity—of cryptocurrencies, this motivates us to apply a novel Non-Homogeneous Hidden Markov model to these series. In particular, we model Bitcoin and Ether prices via the non-homogeneous Pólya-Gamma Hidden Markov (NHPG) model, since it has been shown that it outperforms its counterparts in conventional financial data. The transition probabilities of the underlying hidden process are modeled via a logistic link whereas the observed series follow a mixture of normal regressions conditionally on the hidden process. Our results show that the NHPG algorithm has good in-sample performance and captures the heteroskedasticity of both series. It identifies frequent changes between the two states of the underlying Markov process. In what constitutes the most important implication of our study, we show that there exist linear correlations between the covariates and the ETH and BTC series. However, only the ETH series are affected non-linearly by a subset of the accounted covariates. Finally, we conclude that the large number of significant predictors along with the weak degree of predictability performance of the algorithm back up earlier findings that cryptocurrencies are unlike any other financial assets and predicting the cryptocurrency price series is still a challenging task. These findings can be useful to investors, policy makers, traders for portfolio allocation, risk management and trading strategies.
With the rise of cryptocurrency tokens as a new asset class, the question of the fair evaluation of a cryptocurrency token has become a question of increasing importance. We estimate the pricing kernel with which users price factors affecting their token holdings. We investigate how traditional risk factors such as market risk are evaluated, as well as how blockchain specific risk factors are priced in. In order to do so, we introduce an asset pricing model and modify its properties to make it applicable to cryptocurrency markets. We group the risk factors into market related and Bitcoin- and Ethereum blockchain specific risk factors. We find that blockchain specific risk factors are priced in. There is evidence that risk factors have moved from Bitcoin to Ethereum specific risk factors with an increasing importance of market factors, providing evidence for a decoupling of on-chain and off-chain trading activity.
We investigate the long run relationship and the short run dynamic between the price and the NAV of Bitcoin Investment Trust (BIT), the world’s first publicly traded bitcoin fund. We examine whether the price and the NAV are cointegrated. We also investigate how the price and the NAV adjust to short-term deviations from long run relationship. Our results find that both the price and the NAV are cointegrated despite significant price premium over the NAV. Further, we find that the BIT prices adjust partially to the short-term deviations while the NAV does not react. The results may provide valuable insight to regulatory bodies when they consider the approval of bitcoin or cryptocurrency ETFs.
Anwar Hasan Abdullah Othman, Adam Abdullah, Razali Haron
As crypto-currencies hold dual nature of a medium of exchange (currency) and an investment asset, some questions may arise about the potentiality of including crypto-currencies as liquid investment asset in financial institutions particularly in the banking sector to enhance their liquidity risk management and improve their portfolio diversification investment strategy. The objective of this study therefore is to examine the characteristics of Bitcoin currency based on the requirements of High-Quality Liquid Assets (HQLA) standards of Basel III and compare its volatility structure with other traditional asset classes that are already recommended by Basle III as HQLA. The study utilizes both descriptive and quantitative analysis using the GARCH family models to examine the volatility structures of these assets. The findings show that Bitcoin currency holds the same characteristics of HQLA, however; the risk of legality and recognition is still under consideration by legal authorities around the world and this risk will be eradicated in the future as crypto-currencies derive their legality from their real intrinsic value, multi-economic usefulness and not by law as in the case of fiat money currency. Furthermore, the symmetric volatility structure analysis shows the continuing persistence of volatility and predictability behavior in return series of Bitcoin currency and other- traditional asset classes in the U.S. market. However, Bitcoin’s stability has gradually improved over time. With regard to the asymmetric informative response, Bitcoin returns respond more to negative shock but it has no statistical significance, thus suggesting the lack of leveraging effect in Bitcoin market but this effect was found to be statistically persistent in other traditional asset class markets. In addition, Bitcoin returns show very low correlation with other traditional asset classes. All these imply that Bitcoin is a potential candidate as a hedge and asset diversifier, which is recommended to be included in the HQLA. This study provides some support to recent theoretical work on crypto asset return behaviour and liquidity risk management. The findings provide appropriate information about Bitcoin asset behaviour compared to other traditional asset classes which will enable them to make the right investment decision with regard to hedging, diversification and liquidity risk management. The findings of this study may assist in evaluating the suitability of including crypto assets into HQLA to improve the liquidity requirement standards and ensure that banks have an adequate amount of HQLA specifically during times of financial turmoil.
This article examines whether the investment strategies of cryptocurrency market involve high-risk gambling. Results show that the cryptocurrency risk premiums co-move closely with the return on CBOE Volatility Index (VIX). As such, the strategies of cryptocurrency trading closely resemble that of high-risk gambling. In other words, traders' expectations co-move closely (significantly) with the expected future payoffs from gambling. The co-movement is more pronounced when the gambling offers gains rather than losses and the payoffs are above average. VIX index returns significantly Granger-cause CSAD of returns (with and without Bitcoin) indicates that the cryptocurrency trading constitutes a form of gambling where the motivation for gambling comes from the amount of variation (i.e. riskiness) in the gambling payoffs. These findings warrant policymakers of countries to revisit the existing regulatory framework governing the conduct of electronic finance in the financial services industry.
This research presents a novel mechanism of digital asset trading system on blockchain called JSP-DATS. The JSP-DATS includes (1) a novel mechanism of trading, and (2) a novel mechanism of blockchain which will be the infrastructure of the system. The proposed novel mechanism of blockchain uses the "Random-Checker Proof of Stake" consensus model which can decrease transaction time. The blockchain of the JSP-DATS has been designed to multiple layers. This design is easy to develop, and can be used for further research. The internal mechanism of the proposed system including steps of encoding/decoding, key management, and the storage of encrypted digital assets on the blockchain has will be discussed in this paper. In addition, we have implemented the designed model using Microsoft Visual C++ and encryption libraries from the MSDN web-site to create a software prototype. The prototype is used to study and measure the speed of the proposed scheme. The results show that transaction time of the proposed scheme is lower than that in BitCoin and Ethereum blockchain. With the proposed scheme, the seller (digital asset owners) can see transactions of the trading system transparently, and they can receive their percentage share immediately. In addition, we expect that buyers will indirectly benefit from purchasing digital assets at a lower price.
This paper examines the size effect in the cryptocurrency market with a sample of more than 1800 cryptocurrencies over the period from January 2014 to May 2019. We find that cryptocurrencies with small market value tend to perform better in the future, which challenges the Efficient Market Hypothesis. The size effect is stable over the sample period and robust to the sample size. The prior returns and liquidity also have an impact on the size effect. In addition, our findings provide practical implications for cryptocurrency investors.