Blockchain Papers

Hà Thanh Nguyễn, Bin Liu, Nirav Y. Parikh

No abstract is available for this record.

Nino Antulov-Fantulin, Tian Guo, Fabrizio Lillo

We study the problem of the intraday short-term volume forecasting in cryptocurrency exchange markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the outperformance of our model by comparing its outcomes with those obtained with different time series and machine learning methods. Finally, we discuss the predictions conditional to volume and we find that also in this case machine learning methods outperform econometric models.

Huaigang Long, Adam Zaremba, Ender Demir, Jan Jakub Szczygielski · 5 authors

This study presents the first attempt to examine the cross-sectional seasonality anomaly in cryptocurrency markets. To this end, we apply sorts and cross-sectional regressions to investigate daily returns on 151 cryptocurrencies for the years 2016 to 2019. We find a significant seasonal pattern: average past same-weekday returns positively predict future performance in the cross-section. Cryptocurrencies with high same-day returns in the past outperform cryptocurrencies with a low same-day return. This effect is not subsumed by other established return predictors such as momentum, size, beta, idiosyncratic risk, or liquidity.

Massimo La Morgia, Alessandro Mei, Francesco Sassi, Julinda Stefa

In the last years, cryptocurrencies are increasingly popular. Even people who are not experts have started to invest in these securities and nowadays cryptocurrency exchanges process transactions for over 100 billion US dollars per month. However, many cryptocurrencies have low liquidity and therefore they are highly prone to market manipulation schemes. In this paper, we perform an in-depth analysis of pump and dump schemes organized by communities over the Internet. We observe how these communities are organized and how they carry out the fraud. Then, we report on two case studies related to pump and dump groups. Lastly, we introduce an approach to detect the fraud in real time that outperforms the current state of the art, so to help investors stay out of the market when a pump and dump scheme is in action.

Ahmed S. Baig, Omair Haroon, Nasim Sabah

Economic theory suggests that introduction of derivative contracts can improve the informational efficiency of the underlying asset prices (Danthine, 1978). In this study, we examine the impact of the introduction of Bitcoin futures on price clustering in Bitcoin. Our findings suggest that price clustering in Bitcoin meaningfully decreases post the introduction of its futures contracts.

Akther Uddin, Md Hakim Ali, Mansur Masih

Abstract This study explores whether Bitcoin constitutes as a hedging instrument whilst seeking portfolio diversification opportunities among sustainable, conventional and Islamic asset classes since Bitcoin emerges as a distinct alternative investment and asset class across the world. We apply multivariate generalised autoregressive conditional heteroscedastic‐dynamic conditional correlation and continuous wavelet transforms based on the recent data set ranging from August 18, 2011, to September 10, 2018. First, our findings show that Bitcoin returns are mean‐reverting which implies that its value tends to come down to mean value in the long run and not completely crushed to zero irrespective of price changes suggesting Bitcoin as a sustainable asset class. Second, the time‐invariant model shows that Bitcoin offers portfolio diversification opportunities with almost all equity indices, in particular, Dow Jones Islamic followed by FTSE 4 Good index. Finally, the time‐variant analysis reconfirms that Bitcoin offers portfolio diversification benefits both in the short and long run. These findings carry meaningful policy considerations for fund managers and cross‐country investors.

Mina Sami, Wael Abdallah

The main goal of this study is to examine whether the cryptocurrency market impacts the stock market returns in the Gulf countries. Understanding this impact is quite interesting to clarify whether the cryptocurrency market and the stock market are substitutes or complements for investors. The author compiles the data on the stock market of the Gulf countries with the cryptocurrency data on a daily basis over the period 2014-2019. Generalized Method of Moments with Instrumental Variable (IV - GMM) approach has been implemented as the main strategy to fulfill the objective of the paper. The results of this paper show that the Stock market and the cryptocurrency market are substitutes for investors in Gulf countries. In fact, each 10 percent increase in the cryptocurrency returns is associated with a decline in the stock market returns by 0.17 percent. The cryptocurrency market hampers the stock market indices in the Gulf countries. Having agreed upon in the literature that the stock market is affected by fundamental factors, market sentiment, technical factors, and anomalies, this study offers robust evidence that the cryptocurrency should be introduced as one of the main determinants of stock market prices and returns.

Adrian D. Lee, Mengling Li, Huanhuan Zheng

No abstract is available for this record.

Alexander Brauneis, Roland Mestel, Erik Theissen

No abstract is available for this record.

Jonathan Sadighian

There has been a recent surge in interest in the application of artificial intelligence to automated trading. Reinforcement learning has been applied to single- and multi-instrument use cases, such as market making or portfolio management. This paper proposes a new approach to framing cryptocurrency market making as a reinforcement learning challenge by introducing an event-based environment wherein an event is defined as a change in price greater or less than a given threshold, as opposed to by tick or time-based events (e.g., every minute, hour, day, etc.). Two policy-based agents are trained to learn a market making trading strategy using eight days of training data and evaluate their performance using 30 days of testing data. Limit order book data recorded from Bitmex exchange is used to validate this approach, which demonstrates improved profit and stability compared to a time-based approach for both agents when using a simple multi-layer perceptron neural network for function approximation and seven different reward functions.

Yang Yang

No abstract is available for this record.

Xitong Zhang, He Zhu, Jiayu Zhou

With the proliferation of blockchain projects and applications, cryptocurrency exchanges, which provides exchange services among different types of cryptocurrencies, become pivotal platforms that allow customers to trade digital assets on different blockchains. Because of the anonymity and trustlessness nature of cryptocurrency, one major challenge of crypto-exchanges is asset safety, and all-time amount hacked from crypto-exchanges until 2018 is over $1.5 billion even with carefully maintained secure trading systems. The most critical vulnerability of crypto-exchanges is from the so-called hot wallet, which is used to store a certain portion of the total asset of an exchange and programmatically sign transactions when a withdraw happens. Whenever hackers managed to gain control over the computing infrastructure of the exchange, they usually immediately obtain all the assets in the hot wallet. It is important to develop network security mechanisms. However, the fact is that there is no guarantee that the system can defend all attacks. Thus, accurately controlling the available assets in the hot wallets becomes the key to minimize the risk of running an exchange. However, determining such optimal threshold remains a challenging task because of the complicated dynamics inside exchanges. In this paper, we propose Shoreline, a deep learning-based threshold estimation framework that estimates the optimal threshold of hot wallets from historical wallet activities and dynamic trading networks. We conduct extensive empirical studies on the real trading data from a trading platform and demonstrate the effectiveness of the proposed approach.

Hans‐Peter Burghof, Achim Fecker, Patrick Jaquart, Benedikt Notheisen

The 5th European Retail Investment Conference was hosted at Börse Stuttgart, Germany, from April 10th to 12th 2019. The conference chairs invited academics and practitioners to participate and discuss empirical and theoretical research focusing on retail investor products and services, the impact of technology on retail investors, investors’ decision-making, investor protection schemes, and market microstructure. Albert Menkveld, Professor of Finance at Vrije Universiteit Amsterdam and Fellow at the Tinbergen Institute, held the keynote about the fundamental value of bitcoin.

Ramzi Nekhili, Jahangir Sultan

This paper aims at identifying a validated risk model for the cryptocurrency market. We propose a stochastic volatility model with co-jumps in return and volatility (SVCJ) to highlight the role of jumps in returns and volatility in affecting Value-at-Risk (VaR) and Expected Shortfall (ES) in cryptocurrency market. Validation results based on backtesting show that SVCJ model is superior in terms of statistical accuracy of VaR and ES estimates, compared to alternative models such as TGARCH (Threshold GARCH) volatility and RiskMetrics models. The results imply that for the cryptocurrency market, the best performing model is a stochastic process that accounts for both jumps in returns and volatility.

Fan Fang, Carmine Ventre, Michail Basios, Leslie Kanthan · 7 authors

Abstract In recent years, the tendency of the number of financial institutions to include cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included by asset managers. Although they have some commonalities with more traditional assets, they have their own separate nature and their behaviour as an asset is still in the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading ( e . g ., cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects (contents/properties) and technologies, concluding with some promising opportunities that remain open in cryptocurrency trading.

Aurelio F. Bariviera

This letter expands the studies of the informational efficiency in the cryptocurrency market. Most studies have focused on Bitcoin, the foremost known cryptocurrency, and a few more coins. However, this market is more diverse, with cryptocurrencies entering and leaving the market on a weekly basis. This letter fills an important gap in the literature, by studying the informational efficiency using a multi-scaling methodology, which represents a new approach. We compute the generalized Hurst exponent of eighty-four cryptoassets daily returns. The multi-scaling methodology used in this paper find compelling evidence that cryptocurrencies have different degree of long range dependence, and --more importantly -- follow different stochastic processes. Some of them follow traditional monofractal models consistent with fractional Brownian motion, while others exhibit complex multifractal dynamics.

Shazia Salamat, Lixia Niu, Sobia Naseem, Muhammad Mohsin · 6 authors

This study measures the volatility of cryptocurrency by utilizing the symmetric (GARCH 1, 1) and asymmetric (EGARCH, TGARCH, PGARCH) model of GARCH family using a daily database designated in different digital monetary standards. The results for an explicit set of currencies for entire period provide evidence of volatile nature of cryptocurrency and in most of the cases, the PGARCH is a better-fitted model with student’s t distribution. The findings show positive shocks heavily affected conditional volatility as a contrast with negative stuns. Those additional analyses can be provided further support their findings and worthwhile information for economic thespians who are engrossed in adding cryptocurrency to their equity portfolios or are snooping about the capabilities of cryptocurrency as a financial asset.

Dominique Guégan, Thomas Renault

No abstract is available for this record.

Shaker Ahmed, Klaus Grobys, Niranjan Sapkota

This paper studies simple moving average trading strategies employing daily price data on the ten most-traded cryptocurrencies that exhibit the ‘privacy function’. Investigating the 2016–2018 period, our results indicate a variable moving average strategy is successful only when applied to Dash generating returns of 14.6%−18.25% p.a. in excess of the simple buy-and-hold benchmark strategy. However, when applying our technical trading rules to the entire set of ten privacy coins shows that, on an aggregate level, simple technical trading rules do not generate positive returns in excess of a buy-and-hold strategy.

Michael Ward

This project takes several common strategies for algorithmic stock trading and tests them on the cryptocurrency market. The three strategies used are moving average crossover, mean reversion, and pairs trading. Data was collected every five minutes for the top one hundred cryptocurrencies between October 5, 2017, and January 24, 2018. Due to the high volatility of the market, the data includes various market situations. Three noted situations are a rising market, falling market, and relatively stable market. The three strategies were modified to optimally follow each market situation. Modifications include adjusting parameters used in each strategy as well as mixing several strategies or dynamically changing between strategies. In each strategy and with each cryptocurrency, the benchmark against which the algorithm is tested is the market's performance, or what an investor would have after buying and holding. Returns are compared with the buying and holding strategy, and different scenarios are analyzed to determine the risk associated with buying and holding compared with an algorithmic strategy. Results will be taken with the market's actual trends and also with some alternate possible trends to test all market scenarios. A web interface will accompany the presentation, allowing users to test the strategies by entering their own parameters and instantly see the results.

Stephen Dipple, Abhishek Choudhary, James Flamino, Bolesław K. Szymański · 5 authors

Abstract The growing interconnectivity of socio-economic systems requires one to treat multiple relevant social and economic variables simultaneously as parts of a strongly interacting complex system. Here, we analyze and exploit correlations between the price fluctuations of selected cryptocurrencies and social media activities, and develop a predictive framework using noise-correlated stochastic differential equations. We employ the standard Geometric Brownian Motion to model cryptocurrency rates, while for social media activities and trading volume of cryptocurrencies we use the Geometric Ornstein-Uhlenbeck process. In our model, correlations between the different stochastic variables are introduced through the noise in the respective stochastic differential equation. Using a Maximum Likelihood Estimation on historical data of the corresponding cryptocurrencies and social media activities we estimate parameters, and using the observed correlations, forecast selected time series. We successfully analyze and predict cryptocurrency related social media and the cryptocurrency market itself with a reasonable degree of accuracy. In particular, we show that our method has impressive accuracy in predicting whether a cryptocurrency market will increase or decrease a day in the future, a significant result with regards to investing and trading cryptocurrencies.

Dionisis Philippas, Νικόλαος Φίλιππας, Panagiotis Tziogkidis, Hatem Rjiba

No abstract is available for this record.

Paz Grimberg, Tobias Lauinger, Damon McCoy

Algorithmic trading is well studied in traditional financial markets. However, it has received less attention in centralized cryptocurrency exchanges. The Commodity Futures Trading Commission (CFTC) attributed the $2010$ flash crash, one of the most turbulent periods in the history of financial markets that saw the Dow Jones Industrial Average lose $9\%$ of its value within minutes, to automated order "spoofing" algorithms. In this paper, we build a set of methodologies to characterize and empirically measure different algorithmic trading strategies in Binance, a large centralized cryptocurrency exchange, using a complete data set of historical trades. We find that a sub-strategy of triangular arbitrage is widespread, where bots convert between two coins through an intermediary coin, and obtain a favorable exchange rate compared to the direct one. We measure the profitability of this strategy, characterize its risks, and outline two strategies that algorithmic trading bots use to mitigate their losses. We find that this strategy yields an exchange ratio that is $0.144\%$, or $14.4$ basis points (bps) better than the direct exchange ratio. $2.71\%$ of all trades on Binance are attributable to this strategy.

Taurai Muvunza

We investigate the behaviour of cryptocurrencies using data for bitcoin, ethereum and ripple which account for over 70% of the cryptocurrency market. We demonstrate that $α$-stable distribution is an appropriately sufficient model for highly speculative cryptocurrencies which outperforms other heavy tailed distributions that are used in financial econometrics. We find that the maximum likelihood method proposed by DuMouchel (1971) produces estimates that fit the cryptocurrency return data much better than the quantile based approach of McCulloch (1986) and sample characteristic method by Koutrouvelis (1980). The empirical results show that the leptokurtic feature presented in cryptocurrency return data can be captured by an $α$-stable distribution. The findings highlight that $α$-stable distribution is not only parsimonious with its four free parameters but also a creative model that is close to reality. This paper covers early reports and literature on cryptocurrencies and stable distributions.