Browsing by Subject "Cryptocurrency"
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Item Embargo Can investors’ informed trading predict cryptocurrency returns? Evidence from machine learning(Elsevier Inc., 2022-05-24) Wang, Y.; Wang, C.; Şensoy, Ahmet; Yao, S.; Cheng, F.As an emerging asset, cryptocurrencies have attracted more and more attention from investors and researchers in recent years. With the gradual convergence of the investors in cryptocurrency and traditional financial markets, the research on investor trading behavior from the perspective of microstructure has become increasingly important in cryptocurrency market. In this paper, we study whether investors’ informed trading behavior can significantly predict cryptocurrency returns. We use various machine learning algorithms to verify the contribution of informed trading to the predictability of cryptocurrency returns. The results show that informed trading plays a role in the prediction of some individual cryptocurrency returns, but it cannot significantly improve the prediction accuracy in an average sense of the whole market. The lack of market supervision of cryptocurrency market may be the main factor for relatively low efficiency of this market, and policymakers need to pay attention to it.Item Open Access Correction to: High frequency multiscale relationships among major cryptocurrencies: portfolio management implications(SpringerOpen, 2021-10-29) Mensi, Walid; Rehman, Mobeen Ur; Shafiullah, Muhammad; Al-Yahyaee, Khamis Hamed; Şensoy, AhmetThis paper examines the high frequency multiscale relationships and nonlinear multiscale causality between Bitcoin, Ethereum, Monero, Dash, Ripple, and Litecoin. We apply nonlinear Granger causality and rolling window wavelet correlation (RWCC) to 15 min—data. Empirical RWCC results indicate mostly positive co-movements and long-term memory between the cryptocurrencies, especially between Bitcoin, Ethereum, and Monero. The nonlinear Granger causality tests reveal dual causation between most of the cryptocurrency pairs. We advance evidence to improve portfolio risk assessment, and hedging strategies.Item Open Access A cryptocurrency incentivized voluntary grid computing platform for DNA read alignment(Bilkent University, 2019-09) Özercan, Halil İbrahimThe main computational bottleneck of High Throughput Sequencing (HTS) data analysis is to map the reads to a reference genome, for which clusters are typically used. However, building clusters large enough to handle hundreds of petabytes of data is infeasible. Additionally, the reference genome is also periodically updated to x errors and include newly sequenced insertions, therefore in many large scale genome projects the reads are realigned to the new reference. Therefore, we need to explore volunteer grid computing technologies to help ameliorate the need for large clusters. However, since the computational demands of HTS read mapping is substantial, and the turnaround of analysis should be fast, we also need a method to motivate volunteers to dedicate their computational resources. For this purpose, we propose to merge distributed read mapping techniques with the popular blockchain technology. Cryptocurrencies such as Bitcoin calculate a value (called nonce) to ensure new block (i.e., \money") creations are limited and di cult in the system, however, this calculation serves no other practical purpose. Our solution (Coinami) introduces a new cryptocurrency called Halocoin, which rewards scienti c work with alternative minting. In Coinami, read alignment problems are published and distributed in a decentralized manner while volunteers are rewarded for their work. Authorities have two main tasks in our system: 1) inject new problem sets (i.e., \alignment problems") into the system, and 2) check for the validity of the results to prevent counterfeit.Item Open Access High-frequency return and volatility spillovers among cryptocurrencies(Routledge, 2021-03-22) Şensoy, Ahmet; Silva, T. C.; Corbet, S.; Tabak, B. M.We examine the high-frequency return and volatility of major cryptocurrencies and reveal that spillovers among them exist. Our analysis shows that return and volatility clustering structures are distinct among different cryptocurrencies, suggesting that return and volatility might have different spillover patterns. Further investigation via minimal spanning trees points out that BTC, LTC and ETH are the most relevant cryptocurrencies in general, serving as connection hubs for linking many other cryptocurrencies. However, their role is challenged lately, potentially due to the increased usage of other cryptocurrencies in time.Item Open Access The impact of blockchain related name changes on corporate performance(Elsevier, 2020) Akyıldırım, E.; Corbet, S.; Şensoy, Ahmet; Yarovaya, L.This paper examines the impact of blockchain and crypto-related name changes on corporate and financial performance of the corporations. We document several pieces of evidence suggesting that companies who partake in such “crypto-exuberant” naming practices become more volatile and offer substantial and persistent stock market premiums as a reward for their corporate identity change. However, the retroactive name changes harm firm's short-term profitability and have a dampening effect on financial leverage of the company. This paper advances the Dotcom effect literature by providing novel results on the changing traditional pathways of price discovery and information flows after the announcement of corporate name changes to blockchain-related names. The identified contagion channels display that crypto-exuberant companies become more susceptible to cryptocurrency markets, which should interest regulators and investors.Item Open Access Investor attention and idiosyncratic risk in cryptocurrency markets(Routledge, 2021-12-18) Yao, S.; Kong, X.; Şensoy, Ahmet; Akyıldırım, E.We explore the impact of investor attention on idiosyncratic risk in the cryptocurrency markets. Taking the Google Trends Index as the measure of investor attention, we find that investor attention can significantly reduce cryptocurrencies’ idiosyncratic risks by increasing the liquidity. We further study possible cross-sectional variations of the effect of investor attention on idiosyncratic risk. Evidence shows that the investor attention effect is more pronounced for smaller-cap and younger cryptocurrencies. Moreover, a relatively stable external market environment and rising market state are conducive to the further play of the attention effect.Item Open Access Prediction of cryptocurrency returns using machine learning(Springer, 2021-02) Akyildirim, E.; Goncu, A.; Sensoy, AhmetIn this study, the predictability of the most liquid twelve cryptocurrencies are analyzed at the daily and minute level frequencies using the machine learning classification algorithms including the support vector machines, logistic regression, artificial neural networks, and random forests with the past price information and technical indicators as model features. The average classification accuracy of four algorithms are consistently all above the 50% threshold for all cryptocurrencies and for all the timescales showing that there exists predictability of trends in prices to a certain degree in the cryptocurrency markets. Machine learning classification algorithms reach about 55–65% predictive accuracy on average at the daily or minute level frequencies, while the support vector machines demonstrate the best and consistent results in terms of predictive accuracy compared to the logistic regression, artificial neural networks and random forest classification algorithms.