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2,329 results · page 92 of 98
Shivani Aggarwal, Mayank Santosh, Prateek Bedi
No abstract is available for this record.
Robert Ślepaczuk, Maryna Zenkova
Abstract This study investigates the profitability of an algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low predicted returns. A tail set is defined to be a group of coins whose volatility-adjusted returns are in the highest or the lowest quintile. Each cryptocurrency is represented by a set of six technical features. SVM is trained on historical tail sets and tested on the current data. The classifier is chosen to be a nonlinear support vector machine. The portfolio is formed by ranking coins using the SVM output. The highest ranked coins are used for long positions to be included in the portfolio for one reallocation period. The following metrics were used to estimate the portfolio profitability: %ARC (the annualized rate of change), %ASD (the annualized standard deviation of daily returns), MDD (the maximum drawdown coefficient), IR1, IR2 (the information ratio coefficients). The performance of the SVM portfolio is compared to the performance of the four benchmark strategies based on the values of the information ratio coefficient IR1, which quantifies the risk-weighted gain. The question of how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.
Paraskevi Katsiampa, Κωνσταντίνος Γκίλλας, François Longin
No abstract is available for this record.
Wee Seng Wong, Dennis Saerbeck, Dante Delgado Silva
No abstract is available for this record.
Shaen Corbet, Charles Larkin, Brian M. Lucey, Andrew Meegan · 5 authors
No abstract is available for this record.
Cathy Yi‐Hsuan Chen, Wolfgang Karl Härdle, Ai Jun Hou, Ning Wang
The CRIX (CRyptocurrency IndeX) has been constructed based on a number of cryptos and provides a high coverage of market liquidity, hu.berlin/crix. The crypto currency market is a new asset market and attracts a lot of investors recently. Surprisingly a market for contingent claims hat not been built up yet. A reason is certainly the lack of pricing tools that are based on solid financial econometric tools. Here a first step towards pricing of derivatives of this new asset class is presented. After a careful econometric pre-analysis we motivate an affine jump diffusion model, i.e., the SVCJ (Stochastic Volatility with Correlated Jumps) model. We calibrate SVCJ by MCMC and obtain interpretable jump processes and then via simulation price options. The jumps present in the cryptocurrency fluctutations are an essential component. Concrete examples are given to establish an OCRIX exchange platform trading options on CRIX.
Daniele Bianchi, Alexander Dickerson
We provide empirical evidence within the context of cryptocurrency markets that the returns from liquidity provision, proxied by the returns of a short-term reversal strategy, are primarily concentrated in trading pairs with lower levels of market activity. Empirically, we focus on a moderately large cross section of cryptocurrency pairs traded against the U.S. Dollar from March 1, 2017 to March 1, 2022 on multiple exchanges. Our findings suggest that expected returns from liquidity provision are amplified in smaller, more volatile, and less liquid cryptocurrency pairs, where fear of adverse selection might be higher. A panel regression analysis confirms that the interaction between lagged returns and trading volume contains significant predictive information for the dynamics of cryptocurrency returns. This is consistent with theories that highlight the roles of inventory risk and adverse selection for liquidity provision.
Sinan Krueckeberg, Peter Scholz
No abstract is available for this record.
Sergey Nasekin, Cathy Yi‐Hsuan Chen
We study investor sentiment on a non-classical asset such as cryptocurrency using machine learning methods. We account for context-specific information and word similarity by using efficient language modelling tools such as construction of featurized word representations (embeddings) and recursive neural networks (RNNs). We apply these tools for sentence-level sentiment classification and sentiment index construction. This analysis is performed on a novel dataset of 1220K messages related to 425 cryptocurrencies posted on a microblogging platform StockTwits during the period between March 2013 and May 2018. Both in- and out-of-sample predictive regressions are run to test significance of the constructed sentiment index variables. We find that the constructed sentiment indices are informative regarding returns' and volatility predictability of the cryptocurrency market index.
Gianna Figà‐Talamanca, Marco Patacca
No abstract is available for this record.
Kyoung Jin Choi, Alfred Lehar, Ryan M. Stauffer
No abstract is available for this record.
Takahiro Hattori, Ryo Ishida
No abstract is available for this record.
Thomas Heine Felix, Henk von Eije
Purpose The purpose of this paper is to analyze underpricing in initial coin offerings (ICO). It bridges the gap between findings in initial public offering (IPO) literature and empirical results from ICOs. Design/methodology/approach The sample set consists of 279 ICOs between April 2013 and January 2018. A regression analysis is performed with data from the ICOs. Findings The results show an average level of underpricing of ICOs of 123 percent in the USA and 97 percent in the other countries. The results for the US ICOs are significantly higher than for US IPOs on average and also higher than US IPOs at the beginning of the dot.com bubble. The authors also study the determinants of ICO underpricing. The authors use proxies based on asymmetric information from the IPO literature as well as ICO-related variables. First-day trading volume and a good sentiment on the ICO market go together with more ICO underpricing. Moreover, hot markets make first-day investors to benefit less. Finally, companies that use a large issue size or a pre-ICO (a sale of cryptocurrencies before the ICO) leave less money on the table. Research limitations/implications A first restriction is that the authors focus on ICOs and not on crowdfunding, though there are similarities in that both of them are novel ways to finance projects. A second restriction is that the authors had to decide on the definition of a listing day. Cryptocurrencies are traded on many exchanges, and if the exchange is tailored to the cryptocurrency itself, the data on, e.g., close prices are not necessarily to be trusted. The authors, therefore, decided to use close price data from coinmarketcap.com, which requires a listing on two exchanges. This choice implies that there may have been trades before the listing day itself. A third restriction arises from the relative newness of the ICO phenomenon. The authors gathered data on underpricing from coinmarketcap.com and combined that with project information from icobench.com. However, the data were not simply matched and they required manual adjustments based on several other sources. The authors hope that in due time data on ICOs will be as adequate as data on IPOs and that they become more readily available. It might help if regulators or the crypto community would institute publication requirements. Adherence to such requirements would also reduce the extent of fraud and of asymmetric information, so that solid issuers with good projects might benefit from less underpricing. Practical implications The research may help in reducing underpricing, as the authors find that issuers can reduce it by holding a pre-ICO and by considering larger issue sizes. If they do so, investors will get fewer opportunities to benefit from underpricing. Investors can, nevertheless, also profit from the knowledge generated in this paper. When market sentiment is positive and first-day trading volume is expected to be high, investing in ICOs is likely to give them higher first-day returns. Finally, the authors hope that this paper will serve as a basis for further research into the exciting and dynamic world of cryptocurrencies. Originality/value There is hardly any research on underpricing of ICOs. The paper is interesting for its table with a brief comparison of ICOs and IPOs. It also searches for variables from the asymmetric information theory behind IPOs to be applied in explaining ICOs. It shows high levels of ICO underpricing in comparison to IPOs. It also gives suggestions for issuers of (and investors in) ICOs.
Tim Leung, Hung Cuong Nguyen
Purpose This paper aims to present a methodology for constructing cointegrated portfolios consisting of different cryptocurrencies and examines the performance of a number of trading strategies for the cryptocurrency portfolios. Design/methodology/approach The authors apply a series of statistical methods, including the Johansen test and Engle–Granger test, to derive a linear combination of cryptocurrencies that form a mean-reverting portfolio. Trading systems are designed and different trading strategies with stop-loss constraints are tested and compared according to a set of performance metrics. Findings The paper finds cointegrated portfolios involving four cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Bitcoin Cash (BCH) and Litecoin (LTC), and the corresponding trading strategies are shown to be profitable under different configurations. Originality/value The main contributions of the study are the use of multiple altcoins in addition to bitcoin to construct a cointegrated portfolio, and the detailed comparison of the performance of different trading strategies with and without stop-loss constraints.
Ayesha Afzal, Aiman Asif
The evolution of money has accompanied the development of civilizations and technological innovations, leading to today’s cryptocurrencies. Cryptocurrencies have become a popular mode of payment globally because of their low cost, high-speed transferability and a decentralized tracking network that provides secure transactions and a high degree of anonymity. However, the decentralized system of cryptocurrencies has made global monetary systems more dynamic and therefore more prone to misuse as well as posing a threat to financial stability. Cryptocurrencies are also gaining popularity in Pakistan: its first cryptocurrency, named ‘Pakcoin’, was launched in 2015. The State Bank of Pakistan does not recognize any digital currency, and the Federal Board of Revenue and Federal Investigation Agency have taken legal action against local and internationally traded cryptocurrencies. This article reviews these risks and provides various regulatory solutions so that methods can be developed to improve the management of financial innovations and create a safer environment in which financial innovation can continue. Furthermore, developing countries such as Pakistan can take advantage of distributed ledger technology (used in cryptocurrencies) in applications including: microfinance to help the unbanked, in data identification systems and in land registries to help enforce property rights.
Christian Masiak, Joern Block, Tobias Masiak, Matthias Neuenkirch · 5 authors
No abstract is available for this record.
Hoje Jo, Haehean Park, Hersh Shefrin
Abstract Baker and Wurgler identify high sentiment betas with small startup firms that have great growth potential. On the surface, cryptocurrencies share important features in common with high sentiment beta stocks. This paper investigates the degree to which, during the period July 18, 2010–February 26, 2018, the return to bitcoin displayed the characteristics of a high sentiment beta stock. Using a sentiment‐dependent factor model, the analysis indicates that in large measure, bitcoin returns resembled returns to high sentiment beta stocks. Additionally, we show that bitcoin's expected returns are low when sentiment measured by Volatility Index is high while expected returns are high when sentiment is low.
Sinan Krueckeberg, Peter Scholz
Using tick-level bitcoin data from February 2013 through April 2018, we show substantial arbitrage spreads between global bitcoin markets. Spreads follow multiple consistent patterns. Minimum and maximum prices show significant clustering. Spreads increase during the early hours of a day (according to coordinated universal time), when new exchanges enter markets, and following bitcoin heists and hacks. The full year 2017 and the first quarter of 2018 each had exploitable net arbitrage profit opportunities of at least USD380 million that smart money failed to capture. Based on long-term analyses, we also found that bitcoin market inefficiency has increased over time.
Paolo Pagnottoni, Dirk G. Baur, Thomas Dimpfl
Trading of Bitcoin is spread about multiple venues where buying and selling is offered in various currencies. However, all markets trade one common good and by the law of one price, the different prices should not deviate in the long run. In this context we are interested in which platform is the most important one in terms of price discovery. To this end, we use a pairwise approach accounting for a potential impact of exchange rates. The contribution to price discovery is measured by Hasbrouck's and Gonzalo and Granger's information share. We then derive an ordering with respect to the importance of each market which reveals that the Chinese OKCoin platform is the leader in price discovery of Bitcoin, followed by BTC China.
Shaen Corbet, Charles Larkin, Brian M. Lucey, Larisa Yarovaya
Eastman Kodak is an American technology company that produces imaging products. In 2018, it announced its intentions to enter the crytpocurrency market, raising concerns that it could be taking advantage of a potential cryptocurrency bubble for short-term gains. We analyse the relationships between Kodak, crytocurrency and stock market index returns. We find evidence of a significant, sustained increase in both the share price and price volatility of Kodak after the KODAKCoin announcement, with an increased correlation between the price of Kodak shares and Bitcoin.
Krzysztof Kość, Paweł Sakowski, Robert Ślepaczuk
We report the results of investigation of the momentum and contrarian effects on cryptocurrency markets. The investigated investment strategies involve 100 (amongst over 1200 present as of date Nov 2017) cryptocurrencies with the largest market cap and average 14-day daily volume exceeding a given threshold value. Investment portfolios are constructed using different assumptions regarding the portfolio reallocation period, width of the ranking window, the number of cryptocurrencies in the portfolio, and the percent transaction costs. The performance is benchmarked against: (1) equally weighted and (2) market-cap weighted investments in all of the ranked assets, as well as against the buy and hold strategies based on (3) S&P500 index, and (4) Bitcoin price. Our results show a clear and significant dominance of the short-term contrarian effect over both momentum effect and the benchmark portfolios. The information ratio coefficient for the contrarian strategies often exceeds two-digit values depending on the assumed reallocation period and the width of the ranking window. Additionally, we observe a significant diversification potential for all cryptocurrency portfolios with relation to the S&P500 index.
Guglielmo Maria Caporale, Alex Plastun
Purpose The purpose of this paper is to examine price overreactions in the case of the following cryptocurrencies: bitcoin, litecoin, ripple and dash. Design/methodology/approach A number of parametric ( t -test, ANOVA, regression analysis with dummy variables) and non-parametric (Mann–Whitney U -test) tests confirm the presence of price patterns after overreactions: the next day price changes in both directions are bigger than after “normal” days. A trading robot approach is then used to establish whether these statistical anomalies can be exploited to generate profits. Findings The results suggest that a strategy based on counter-movements after overreactions is not profitable, whilst one based on inertia appears to be profitable but produces outcomes not statistically different from the random ones. Therefore, the overreactions detected in the cryptocurrency market do not give rise to exploitable profit opportunities (possibly because of transaction costs) and cannot be seen as evidence against the efficient market hypothesis (EMH). Originality/value The overreactions detected in the cryptocurrency market do not give rise to exploitable profit opportunities (possibly because of transaction costs) and cannot be seen as evidence against the EMH.
David Procházka
The invention of blockchain technology has radically changed the perception of how monetary systems can be structured and operated. Central banks and state authorities mostly refuse to acknowledge that cryptocurrencies are money, yet the number of payment transactions using cryptocurrencies is increasing and cryptocurrencies form a non-negligible stake of wealth. As with other economic phenomena, cryptocurrencies shall be addressed in the financial statements of the entities using them, albeit without any accounting guidance in current financial reporting standards. This paper fills this void by suggesting, comparing, and assessing potential accounting models under IFRS. Based on evidence from literature review, as well as recent time-series data on the price volatility of cryptocurrencies, the paper shows that fair value accounting is the most relevant source of useful information for users of financial statements when cryptocurrencies are acquired for investment purposes. Furthermore, the paper identifies scenarios under which cryptocurrencies shall be treated as (foreign) currencies, even though financial system regulators do not consider cryptocurrencies as being money (fiat currency).
Nicola Borri, Kirill Shakhnov
Abstract At a given point in time, bitcoin prices are different on exchanges located in different countries, or against different currencies. While existing literature attributes the largest price differences to frictions, like market segmentation, trading platforms advertize how to execute trades based on this information. We provide a novel risk-based explanation of these price differences for a sample containing the most reputable exchanges and after accounting for all transaction costs and limitations to trade. Bitcoin prices for more expensive pairs are riskier because they depreciate more in bad times for cryptocurrency investors, when aggregate liquidity and investor sentiment are lower. (JEL G12, G14, G15, F31).