Blockchain Papers

Elisa Facciotti, Domenica Federico, Antonella Notte

No abstract is available for this record.

Mianmian Zhang, Bing Zhu, Ziyuan Li, Siyuan Jin · 5 authors

Abstract The cryptocurrency market is a complex and rapidly evolving financial landscape in which understanding the inter- and intra-asset dependencies among key financial variables, such as return and liquidity, is crucial. In this study, we analyze daily return and liquidity data for six major cryptocurrencies, namely Bitcoin, Ethereum, Ripple, Binance Coin, Litecoin, and Dogecoin, spanning the period from June 3, 2020, to November 30, 2022. Liquidity is estimated using three low-frequency proxies: the Amihud ratio and the Abdi and Ranaldo (AR) and Corwin and Schultz (CS) estimators. To account for autoregressive and persistent effects, we apply the autoregressive integrated moving average-generalized autoregressive conditional heteroscedasticity (ARIMA-GARCH) model and subsequently utilize the copula method to examine the interdependent relationships between the return on and liquidity of the six cryptocurrencies. Our analysis reveals strong cross-asset lower-tail dependence in return and significant cross-asset upper-tail dependence in illiquidity measures, with more pronounced dependence observed in specific cryptocurrency pairs, primarily involving Bitcoin, Ethereum, and Litecoin. We also observe that returns tend to be higher when liquidity is lower in the cryptocurrency market. Our findings have significant implications for portfolio diversification, asset allocation, risk management, and trading strategy development for investors and traders, as well as regulatory policy-making for regulators. This study contributes to a deeper understanding of the cryptocurrency marketplace and can help inform investment decision making and regulatory policies in this emerging financial domain.

David Krause

No abstract is available for this record.

Yiran Huang, Tinghang Xu, Chunxiao Xue, Jianing Zhang

The efficient market hypothesis encounters scrutiny from behavioral finance insights, highlighting the pronounced influence of investor emotions on market dynamics, a phenomenon especially evident in the tumultuous cryptocurrency markets. This investigation utilizes the autoregressive distributed lag (ARDL) model and the error correction model (ECM) to examine the impact of the Bitcoin Sentiment Index (BSI), also known as the Crypto Fear & Greed Index (CFGI), on Bitcoin returns, leveraging monthly data spanning from 2016 to 2021. The ARDL analysis identifies a positive and statistically significant correlation between BSI and Bitcoin returns, indicating that strong sentiment may beneficially affect Bitcoin’s long-term returns. Concurrently, the ECM analysis reveals that fluctuations in the BSI positively influence the changes in Bitcoin returns in the short term. The error correction term demonstrates a significantly negative value, signifying an expedient adjustment toward long-term equilibrium following transient disturbances. These findings remain robust upon the integration of additional macroeconomic control variables. Unlike prior studies centered on singular sentiment indicators or limited temporal analyses, this research employs an extensive sentiment measure over an extended duration. The integrated application of ARDL and ECM methodologies facilitates a thorough and rigorous examination of short-term fluctuations alongside long-term equilibrium dynamics.

Otabek Sattarov, Jaeyoung Choi

The rapid evolution of cryptocurrency markets and the increasing complexity of trading strategies necessitate a comprehensive understanding of price-prediction models and their direct impact on trading efficacy. While extensive research has been conducted separately on price prediction methods and trading strategies, there remains a significant gap in studies explicitly correlating precise price forecasts with successful trading outcomes. This review paper addresses this gap by critically examining the role of accurate cryptocurrency price predictions in enhancing trading strategies. We conducted a systematic review of sufficient scholarly articles and web resources, focusing on the methodologies and effectiveness of various predictive models and their integration into cryptocurrency trading strategies. Our selection criteria ensured the inclusion of papers that demonstrate methodological rigor, relevance, and recent contributions to the field, spanning from economic theories and statistical models to advanced machine learning techniques. The findings reveal that precise price predictions significantly contribute to the development of adaptive and risk-managed trading strategies, which are crucial in the highly volatile cryptocurrency market. The review also identifies current challenges and proposes directions for future research, emphasizing the need for interdisciplinary approaches and ethical considerations in predictive modeling. This synthesis aims to bridge the existing research gap and guide future studies, thereby fostering more sophisticated and profitable trading strategies in the cryptocurrency domain.

Yu‐Lun Chen, Ke Xu, J. Jimmy Yang

No abstract is available for this record.

Krzysztof Gogol, Robin Fritsch, Malte Schlosser, Johnnatan Messias · 6 authors

This paper studies liquid staking tokens (LSTs) on automated market makers (AMMs), both theoretically and empirically. LSTs are tokenized representations of staked assets on proof-of-stake blockchains. First, we model LST-liquidity on AMMs theoretically, categorizing suitable AMM types for LST liquidity and deriving formulas for the necessary returns from trading fees to adequately compensate liquidity providers under the particular price trajectories of LSTs. For the latter, two relevant metrics are considered: (1) losses compared to holding the liquidity outside the AMM (loss-versus-holding, or "impermanent loss"), and (2) the relative profitability compared to fully staking the capital (loss-versus-staking) which is specifically tailored to the case of LST-liquidity. Next, we empirically measure these metrics for Ethereum LSTs across the most relevant AMM pools. We find that, while trading fees often compensate for impermanent loss, fully staking is more profitable for many pools, raising questions about the sustainability of the current LST liquidity allocation to AMMs.

Augustin Valéry

No abstract is available for this record.

Krzysztof Gogol, Johnnatan Messias, Deborah Miori, Claudio J. Tessone · 5 authors

Arbitrage can arise from the simultaneous purchase and sale of the same asset in different markets in order to profit from a difference in its price. This work systematically reviews arbitrage opportunities between Automated Market Makers (AMMs) on Ethereum ZK rollups, and Centralised Exchanges (CEXs). First, we propose a theoretical framework to measure such arbitrage opportunities and derive a formula for the related Maximal Arbitrage Value (MAV) that accounts for both price divergences and liquidity available in the trading venues. Then, we empirically measure the historical MAV available between SyncSwap, an AMM on zkSync Era, and Binance, and investigate how quickly misalignments in price are corrected against explicit and implicit market costs. Overall, the cumulative MAV from July to September 2023 on the USDC-ETH SyncSwap pool amounts to $104.96k (0.24% of trading volume).

Danling Jiang, Lin Sun, Lolita Nazarov, Jiarong Chen · 6 authors

No abstract is available for this record.

Darren Shannon, Michael Dowling, marjan zhaf, Barry Sheehan

Non-fungible tokens (NFTs) rose to prominence as a wide-scale implementation of blockchain technology to support the emergence of crypto-asset markets. These nascent digital markets raise questions about the behaviours of investors in the digital economy and their appetite for risk. Using 28,919 auction listings, 4937 sales, and 30,197 Telegram messages, we conduct a field study on the bidding and selling behaviours of NFT investors in a Dutch auction system. We reveal risk-seeking behaviours in our sample of Dutch auction sales. We document that time pressures and value propositions significantly influence NFT investors: fast clock speeds and greater price separations induce underbidding behaviours and are associated with low value retention for sellers. These results are confirmed using a matched-pairs analysis. Our study raises further questions on the risk preferences of investors in emergent digital marketplaces. We propose value maximisation strategies for marketplace developers and participants, while drawing attention to the presence of potentially exploitable biases and heuristics amongst participants, courtesy of bidding incentivisation schemes significantly altering how investors value NFTs. • We identify the bidding and listing behaviours of NFT investors in Dutch auctions. • 28,919 listings, 4937 sales, and the sentiment of 30,197 messages are examined. • We identify risk-seeking underbidding behaviours from NFT investors. • Time pressures, value propositions, and market experience are influential factors. • Strategies are proposed for NFT developers and traders to maximise profit.

Bin Liu, Tina Prodromou, Sandy Suardi, Caihong Xu

No abstract is available for this record.

Hyung-Eun Choi

No abstract is available for this record.

Umesh Kumar, Biqing Huang

This study scrutinizes the COVID-19 measures and their effect on leading cryptocurrency returns. Our direct measures of COVID-19 show that cryptocurrency returns are significantly influenced by COVID-19 and are most visible throughout pre-vaccination phase. The intraday price movement becomes wider during vaccination period compared to cryptocurrency returns. The findings demonstrate that even negative news of COVID-19 did not deter investors from being optimistic in the pre-vaccination period. Further, COVID-19 impacts on the cryptocurrency market diverge depending on the size of currency once vaccination begins. It reflects a different underlying dynamic process in cryptocurrency trading.

Vahidin Jeleskovic, Claudio Latini, Zahid Irshad Younas, Mamdouh Abdulaziz Saleh Al‐Faryan

The growing interest in cryptocurrencies has drawn the attention of the financial world to this innovative medium of exchange. This study aims to explore the impact of cryptocurrencies on portfolio performance. We conduct our analysis retrospectively, assessing the performance achieved within a specific time frame by three distinct portfolios: one consisting solely of equities, bonds, and commodities; another composed exclusively of cryptocurrencies; and a third, which combines both 'traditional' assets and the best-performing cryptocurrency from the second portfolio.To achieve this, we employ the classic variance-covariance approach, utilizing the GARCH-Copula and GARCH-Vine Copula methods to calculate the risk structure. The optimal asset weights within the optimized portfolios are determined through the Markowitz optimization problem. Our analysis predominantly reveals that the portfolio comprising both cryptocurrency and traditional assets exhibits a higher Sharpe ratio from a retrospective viewpoint and demonstrates more stable performances from a prospective perspective. We also provide an explanation for our choice of portfolio optimization based on the Markowitz approach rather than CVaR and ES.

Shun Liu, Kexin Wu, Chufeng Jiang, Bin Huang · 5 authors

In the realm of cryptocurrency, the prediction of Bitcoin prices has garnered substantial attention due to its potential impact on financial markets and investment strategies. This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. Specifically, linear regression(OLS, LASSO), long-short term memory(LSTM), decision tree regressors are introduced. Through the grounded experiments, we observe linear regressor achieves the best performance among candidate models. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting. We believe this work may derive more attention and inspire more researches in the realm of time-series analysis and its realistic applications.

Karthik Natashekara, Aravind Sampath

No abstract is available for this record.

Fayad Ali, Ravate Suryakant, S. R. Nimbore

This paper investigates an ensemble convolutional and recurrent neural network architecture for cryptocurrency price forecasting. The inherent volatility and noise in cryptocurrency time series pose considerable modeling challenges. The proposed ensemble model integrates convolutional neural networks (CNN) and gated recurrent units (GRU) to jointly discern spatial patterns and temporal dynamics. The model is trained on an extensive dataset comprising daily historical prices for major cryptocurrencies spanning January 2015 to October 2023. The time series data is structured into rolling input sequences of historical prices and target outputs as future price values. Comprehensive hyperparameter tuning is conducted to optimize model performance. Rigorous validation on held-out test data enables analysis of multi-step prediction accuracy. Results demonstrate that the ensemble CNN-GRU model achieves high forecasting proficiency. Evaluation metrics including Root Mean Squared Error quantify the model's efficacy in learning the nuanced volatility signatures of cryptocurrencies. Additionally, the high R-squared scores attained, including 0.99 for Bitcoin and Ethereum and 0.98 for Ripple, underscore the model's exceptional capacity to explain cryptocurrency price fluctuations. This substantiates the model's utility for generating actionable insights for investors and analysts in the cryptocurrency domain.

Aleksandar Tošić, Jernej Vičič, Niki Hrovatin

Wash trading in decentralized markets remains a significant concern magnified by the pseudonymous and public nature of blockchains. In this paper we introduce an innovative methodology designed to detect wash trading activities beyond surface-level transactions. Our approach integrates NFT ownership traces with the Ethereum Transaction Network, encompassing the complete historical record of all Ethereum account normal transactions. By analyzing both networks, our method offers a notable advancement over techniques proposed by existing research. We analyzed the wash trading activity of 7 notable NFT collections. Our results show that wash trading in unregulated NFT markets is an underestimated concern and is much more widespread both in terms of frequency as well as volume. Excluding the Meebits collection, which emerged as an outlier, we found that wash trading constituted up to 25% of the total trading volume. Specifically, for the Meebits collection, a staggering 93% of its total trade volume was attributed to wash trading.

Yensen Ni, Pinhui Chiang, Min-Yuh Day, Yuhsin Chen

Using the Bollinger Bands trading strategy (BBTS), investors are advised to buy (and then sell) Bitcoin and Ethereum spot prices in response to BBTS’s oversold (overbought) signals. As a result of analyzing whether investors would profit from round-turn trading of these two spot prices, this study may reveal the following remarkable outcomes and investment strategies. This study first demonstrated that using our novel design with a heatmap matrix would result in multiple higher returns, all of which were greater than the highest return using the conventional design. We contend that such an impressive finding could be the result of big data analytics and the adaptability of BBTS in our new design. Second, because cryptocurrency spot prices are relatively volatile, such indices may experience a significant rebound from oversold to overbought BBTS signals, resulting in the potential for much higher returns. Third, if history repeats itself, our findings might enhance the profitability of trading these two spots. As such, this study extracts the diverse trading performance of multiple BB trading rules, uses big data analytics to observe and evaluate many outcomes via heatmap visualization, and applies such knowledge to investment practice, which may contribute to the literature. Consequently, this study may cast light on the significance of decision-making through the utilization of big data analytics and heatmap visualization.

Zekai ŞENOL

Kripto varlıklar pay senetleri ve emtialar gibi geleneksel yatırım araçlarıyla karşılaştırıldığında daha az düzenleme, düşük işlem maliyetleri, merkeziyetsizlik gibi bazı avantajlara sahiptirler. Kripto varlıklar ortaya çıkışlarından günümüze kadar fiyat, hacim ve değer bakımından artarak portföylerde kendilerine yer edinmeye başlamışlardır. Kripto varlıkların geleneksel yatırım araçlarıyla olan ilişkileri portföy yönetimi açısından sonuçlar ortaya çıkarabilir. Bu çalışmada bitcoin ile altın, petrol, doğal gaz ve emtia endeksinden oluşan emtialar arasındaki volatilite yayılımları incelenmiştir. Çalışmada 24 Ağustos 2016 – 13 Ocak 2023 dönemine ait günlük veriler varyansta nedensellik ve Lu, Hong, Wang, Lai ve Liu (2014) tarafından geliştirilen zamanla değişen varyansta nedensellik testiyle incelenmiştir. Çalışmada bitcoinden altın ve emtia endeksine doğru ve doğal gazdan bitcoine doğru tek yönlü volatilite yayılımı görülmüştür. Bitcoin ile emtilar arasında düşük düzeyde zamanla değişen volatilite yayılımı belirlenmiştir. Sonuçlar portföy yönetimi, portföy riskinin yönetilmesi, yatırım kararları açısından önem taşımaktadır.

Izz Eddien N. Ananzeh, Mohammad O. Al-Smadi

The purpose of this study is to examine the market efficiency of cryptocurrencies, specifically at a weak level. The study focuses on six prominent cryptocurrencies selected based on their significant market capitalization: Bitcoin (BTC), Tether (USDT), Ethereum (ETH), Binance Coin (BNB-USD), Ripple (XRP-USD), and Cardano USD (ADA-USD). The analysis utilizes unit root, Ljung–Box, variance ratio, runs, and the Brock–Dechert–Scheinkman (BDS) tests to assess different aspects of market efficiency. The data spans from September 2017 to April 2023, encompassing a wide time frame to capture potential shifts in market behavior. The results of all the tests, except the BDS test, indicate that the tested cryptocurrencies' markets are inefficient. However, the BDS test yielded different results, suggesting that BTC and ETH exhibit market efficiency compared to the other cryptocurrencies. This discrepancy indicates that the BDS test may be capturing different aspects of the time series behavior. The practical implication is that investors and market participants should exercise caution and consider the varying levels of efficiency when making decisions regarding these cryptocurrencies. Also, investors should consider a range of factors, including technical and fundamental analyses, when making investment decisions in a dynamic and evolving market.

Afzol Husain

This research studies the dynamic connectedness among digital assets proxied by non-fungible tokens (NFTs), Islamic cryptocurrencies, and conventional cryptocurrencies with the US Economic Policy Uncertainty (EPU) and Geopolitical Risk (GPR) indices. We also examine the hedge and safe haven properties of the aforementioned digital assets against the uncertainties. Using wavelet coherence analysis from 19 January 2018 to 31 October 2023, we show that NFTs react heterogeneously to changes in uncertainties while cryptocurrency reacts inversely. NFTs and conventional cryptocurrencies can only act as diversifiers, but neither as a hedge nor a safe haven against uncertainties. However, Islamic cryptocurrencies have the potential to act as both a hedge and a safe haven against uncertainties. Our findings shed light on the role of emerging digital assets in formulating investment strategies and ensuring stability in the financial markets. Originality/Value: Given the immense potential of digital assets, a remaining research gap concerns their interplay with uncertainty. In other words, given the presence of extreme market turmoil over recent years, no consensus is present in terms of highlighting the dynamic co-movement between digital assets such as NFT, Islamic cryptocurrencies, and global uncertainty factors. In addition to that, the lead-lag relationship among digital assets and uncertainties are also unknown till date. The current study fills this gap by providing robust evidence.

Ahmet Faruk Aysan, Erhan Muğaloğlu, Ali Yavuz Polat, Hasan Tekin

Abstract Using a wavelet coherence approach, this study investigates the relationship between Bitcoin return and Bitcoin-specific sentiment from January 1, 2016 to June 30, 2021, covering the COVID-19 pandemic period. The results reveal that before the pandemic, sentiment positively drove prices, especially for relatively higher frequencies (2–18 weeks). During the pandemic, the relationship was still positive, but interestingly, the lead-lag relationship disappeared. Employing partial wavelet tools, we factor out the number of COVID-19 cases and deaths and the Equity Market Volatility Infectious Disease Tracker index to observe the direct relationship between a change in sentiment and return. Our results robustly reveal that, before the pandemic, sentiment had a positive effect on return. Although positive coherence still existed during the pandemic, the lead-lag relationship disappeared again. Thus, the causal relationship that states that sentiment leads to return can only be integrated into short-term trading strategies (up to six weeks frequency).