Abstract A blockchain replaces central counterparties with time-consuming consensus protocols to record the transfer of ownership. This settlement latency slows cross-exchange trading, exposing arbitrageurs to price risk. Off-chain settlement, instead, exposes arbitrageurs to costly default risk. We show with Bitcoin network and order book data that cross-exchange price differences coincide with periods of high settlement latency, asset flows chase arbitrage opportunities, and price differences across exchanges with low default risk are smaller. Blockchain-based trading thus faces a dilemma: Reliable consensus protocols require time-consuming settlement latency, leading to arbitrage limits. Circumventing such arbitrage costs is possible only by reinstalling trusted intermediation, which mitigates default risk.
This research studies cryptocurrency trading with an emphasis on technical analysis. In particular, the classic Turtle Trading System is modified to suit cryptocurrency trading. Based on backtest results on main cryptocurrencies, the extended Turtle Trading System yielded 114.41 % average net profit margin and 52.75% average profitability, which were higher than those of the original system. The trading strategy was next implemented in a mobile application. The application prototype is also presented in the paper.
While pump-and-dump schemes have attracted the attention of cryptocurrency observers and regulators alike, this paper represents the first detailed empirical query of pump-and-dump activities in cryptocurrency markets. We present a case study of a recent pump-and-dump event, investigate 412 pump-and-dump activities organized in Telegram channels from June 17, 2018 to February 26, 2019, and discover patterns in crypto-markets associated with pump-and-dump schemes. We then build a model that predicts the pump likelihood of all coins listed in a crypto-exchange prior to a pump. The model exhibits high precision as well as robustness, and can be used to create a simple, yet very effective trading strategy, which we empirically demonstrate can generate a return as high as 60% on small retail investments within a span of two and half months. The study provides a proof of concept for strategic crypto-trading and sheds light on the application of machine learning for crime detection.
This paper examines the day of the week effect in the cryptocurrency market using a variety of statistical techniques (average analysis, Student's t-test, ANOVA, the Kruskal–Wallis test, and regression analysis with dummy variables) as well as a trading simulation approach. Most crypto currencies (LiteCoin, Ripple, Dash) are found not to exhibit this anomaly. The only exception is BitCoin, for which returns on Mondays are significantly higher than those on the other days of the week. In this case the trading simulation analysis shows that there exist exploitable profit opportunities; however, most of these results are not significantly different from the random ones and therefore cannot be seen as conclusive evidence against market efficiency.
This paper develops the optimal causal path algorithm and applies it within a fully-fledged statistical arbitrage framework to minute-by-minute data of the S&P 500 constituents from 1998 to 2015. Specifically, the algorithm efficiently determines the optimal non-linear mapping and the corresponding lead–lag structure between two time series. Afterwards, this study explores the use of optimal causal paths as a means for identifying promising stock pairs and for generating buy and sell signals. For this purpose, the established trading strategy exploits information about the leading stock to predict future returns of the following stock. The value-add of the proposed framework is assessed by benchmarking it with variants relying on classic similarity measures and a buy-and-hold investment in the S&P 500 index. In the empirical back-testing study, the trading algorithm generates statistically and economically significant returns of 54.98% p.a. and an annualized Sharpe ratio of 3.57 after transaction costs. Returns are well superior to the benchmark approaches and do not load on any common sources of systematic risk. The strategy outperforms in the context of cryptocurrencies even in recent times due to the fact that stock returns contain substantial information about the future bitcoin returns.
The narrative of a Bitcoin is a bubble is very common. We employ statistical techniques to empirically evaluate such claim. A branch of literature links the existence of a bubble in some financial asset’s price to strict local martingales — a finitely lived asset has a bubble if, and only if, it is a strict local martingale under the equivalent risk-neutral measure. A diffusion process is a strict local martingale if its volatility increases faster than linearly as its level grows. We apply a nonparametric method to estimate the volatility function of Bitcoin daily and high frequency prices, as well as of more traditional financial assets. We then estimate the stochastic volatility model of Andersen and Piterbarg (2007), whose parameter space has a specific subset under which the asset’s price is a strict local martingale. Results suggest the existence of a bubble in Bitcoin prices from early 2013 to mid 2014, but, interestingly, not in late 2017.
This research aims to identify how Bitcoin-related news publications and\nonline discourse are expressed in Bitcoin exchange movements of price and\nvolume. Being inherently digital, all Bitcoin-related fundamental data (from\nexchanges, as well as transactional data directly from the blockchain) is\navailable online, something that is not true for traditional businesses or\ncurrencies traded on exchanges. This makes Bitcoin an interesting subject for\nsuch research, as it enables the mapping of sentiment to fundamental events\nthat might otherwise be inaccessible. Furthermore, Bitcoin discussion largely\ntakes place on online forums and chat channels. In stock trading, the value of\nsentiment data in trading decisions has been demonstrated numerous times [1]\n[2] [3], and this research aims to determine whether there is value in such\ndata for Bitcoin trading models. To achieve this, data over the year 2015 has\nbeen collected from Bitcointalk.org, (the biggest Bitcoin forum in post\nvolume), established news sources such as Bloomberg and the Wall Street\nJournal, the complete /r/btc and /r/Bitcoin subreddits, and the bitcoin-otc and\nbitcoin-dev IRC channels. By analyzing this data on sentiment and volume, we\nfind weak to moderate correlations between forum, news, and Reddit sentiment\nand movements in price and volume from 1 to 5 days after the sentiment was\nexpressed. A Granger causality test confirms the predictive causality of the\nsentiment on the daily percentage price and volume movements, and at the same\ntime underscores the predictive causality of market movements on sentiment\nexpressions in online communities\n
We study the efficiency of cryptocurrencies by measuring the price’s reaction time to unexpected relevant information. We find the average price delay to significantly decrease during the last three years. For the cross-section of 75 cryptocurrencies we find delays to be highly correlated with liquidity.
Investoren und Vermögensverwalter suchen nach Finanzinstrumenten, die die erwartete Rendite ihrer Investition bei gleichzeitiger Minimierung des potenziellen Risikos erhöhen. In der Praxis diversifizieren Vermögensverwalter ihre Vermögensallokation auf verschiedene Anlageklassen allen voran Aktien, Anleihen und Rohstoffe. In den letzten Jahren gewinnt die neue Anlageklasse der Kryptowährungen immer mehr an Einfluss - Anlagekapital. Die erste unter ihnen, Bitcoin, wurde wegen ihrer Technologie sehr berühmt: dezentrale Datenhaltung, sicheres und schnelles elektronisches Tausch- und Zahlungsmittel. Diese Arbeit konzentriert sich auf die Leistung der Core-Satellite Strategie mit diesen neuen digitalen Währungen als Satellit. Die Herausforderung besteht darin, die Kryptowährungen zu finden, die Abwärtstrends kompensieren kann und dem Anleger bessere Renditen erwirtschaftet. Die Korrelationsstruktur der Kryptowährungen muss untersucht werden, sodass gegenläufige Kryptowährungen ausgewählt werden können. Dazu verwenden wir TEDAS – Tail Event Driven Asset allocation, eine aktive Investitionsstrategie zur Auswahl der Kryptowährungen. Diese Methode untersucht die Abhängigkeit von Kryptowährungen in verschiedenen Quantilen am linken Rand der Verteilung. Die Arbeit vergleicht verschiedene auf TEDAS basierenden Investitionsstrategie.
Proposed is a communication system, integrating a distributed ledger with ambient intelligence and a data fusion scheme. It introduces a clear distinction between the financial profitability of investment projects and their contribution to environmental sustainability while ensuring performance measurability and flexibility for both aspects of project management as well as the possibility to trade emission certificates. Two key benefits of the system are the possibility to implement a reliable and straightforward methodology for the evaluation of investments, both ongoing and planned, and the potential to connect the emissions trading scheme with pollution control in the real time. Moreover, the system brings about stability where the revisions of governmental subsidy policies tend to move the break-even costs significantly, thus incentivising for innovative or small-scale projects.
Main objective of this study is to develop investment portfolio with diversifications using two different assets. Modern portfolio theory develop investment portfolio to maximize expected return based on a given level of market risk. This study selected cryptocurreny (Bitcoin) and stock price (Petronas Gas Berhad) as the combination in developing investment portfolio. In this analysis, mean return for Bitcoin is 9.890 %. Meanwhile, the mean return for stock price of Petronas Gas Berhad is -0.496 %.The value of correlation is between two assets is -0.372. Result shows the portfolio risk can be reduced with the diversification approach for different assets. Therefore, findings of this study are important for assisting investors to maximize their return for given level of investment risk.
We demonstrate that investors obtain abnormal returns by trading cryptocurrencies daily on the London Stock Exchange from 2014–2017. Excess returns persist once we account for systematic risk, size, value, momentum, profitability and investment. Investor abnormal returns in cryptocurrencies implies inefficiency.
This paper is a sequel to our previous works related to Robo-advisors and cryptocurrencies. Our goal now is to build two application modules for a single Robo-advisor. The first module is a Long short-term memory (LSTM) neural network which forecasts cryptocurrencies prices daily. The second module uses Robo-advising approach to build an investment plan for novice cryptocurrencies investors with different risk attitude investment decisions. The third module does ETL (Extract-Transform-Load) for a statistics dataset and neural networks models. Results of the investigation show that investing in cryptocurrencies can give 23.7% per year for risk-averse, 31.8% per year for risk-seeking investors and 16.5% annually for riskneutral investors.
Rune Tevasvold Aune, Adam Krellenstein, Maureen O’Hara, Ouziel Slama
<h3>Practical Applications Summary</h3> In <b>Footprints on a Blockchain: <i>Trading and Information Leakage in Distributed Ledgers</i></b>, from the Summer 2017 issue of <b><i>The Journal of Trading</i></b>, authors <b>Rune Tevasvold Aune</b>, <b>Adam Krellenstein</b> (both of <b>Symbiont</b>) <b>Maureen O’Hara</b> (<b>Cornell University School of Management</b>) and <b>Ouziel Slama</b> (formerly of Symbiont and now with <b>Legolas</b>), describe a creative solution to a potential limitation to the use of distributed ledger technology in financial market contexts. This complication relates to the disclosure of proprietary trading information that could be used to “front-run” certain investors. The authors outline a solution to this problem, which involves the use of a cryptographic hash to indicate prioritization of transactions. This solution could facilitate widespread usage of blockchain technology in a variety of financial contexts. <b>TOPICS:</b>Quantitative methods, exchanges/markets/clearinghouses
The cryptocurrency market has witnessed significant growth in the past few months. The emergence of hundreds of new digital currencies and the huge increase in the prices of their leading representatives have attracted a lot of attention from investors. However, the financial characteristics of the cryptocurrency markets have not been systematically evaluated yet. As a consequence, there is currently no consensus on whether cryptocurrencies constitute an individual asset class or if they share substantial similarities to stocks, bonds, commodities or foreign exchange. Based on Markowitz et al. (2017) this paper aims to fill this lack of research by evaluating the cryptocurrency market based on seven requirements of an individual asset class. The authors find that the cryptocurrency market distinguishes itself remarkably from established asset classes in terms of risk and return. Additionally, the low correlation between the cryptocurrency markets and these established asset classes induces a diversification potential for investors, leading to more favorable risk/return profiles of their portfolios. But also the emergence of investment services and products provided by the financial industry and the increasingly cost-effective access to cryptocurrencies corroborate the conclusion that cryptocurrencies can be seen as an individual asset class.