Blockchain Papers

Leili Pour Rostami

No abstract is available for this record.

Jingrui Li, Ruming Liu

No abstract is available for this record.

Andrew Morin, Tyler Moore

No abstract is available for this record.

Farhan Junayed, Dev R. Mishra, George F. Tannous

No abstract is available for this record.

Florentina Şoiman

This paper provides an analysis of the negative investor attention impact on bitcoin’s performance. By negative investor attention, we mean investor attention preceding a negative event, such as for example, a cyber-attack. Since their creation, the crypto-market has been numerous times the target of various attacks, which lead to important financial losses. Thus, we propose this study, in which we aim to capture the investor’s reaction and impact on the bitcoin’s performance as a consequence of these negative events happening. We are proxying the negative investor attention by using Google volume searches and splitting the search terms into ’specialist’ and ’non-specialist’ investors. The results obtained show that our Google searches and implicitly the negative investor attention impact bitcoin’s performance. Moreover, the non-specialist-considered keywords seem to drive returns more than the ones of a specialist. This result suggests that the majority of crypto-investors are, in fact, amateur or non-specialists.

Kose John, Jingrui Li, Ruming Liu

No abstract is available for this record.

Yuanli Cai, Bingqiao Luo, Qian Wang, Nuo Chen · 6 authors

The utilization of Large Language Models (LLMs) in financial trading has primarily been concentrated within the stock market, aiding in economic and financial decisions.Yet, the unique opportunities presented by the cryptocurrency market, noted for its on-chain data's transparency and the critical influence of offchain signals like news, remain largely untapped by LLMs.This work aims to bridge the gap by developing an LLM-based trading agent, CryptoTrade, which uniquely combines the analysis of on-chain and off-chain data.This approach leverages the transparency and immutability of on-chain data, as well as the timeliness and influence of off-chain signals, providing a comprehensive overview of the cryptocurrency market.CryptoTrade incorporates a reflective mechanism specifically engineered to refine its daily trading decisions by analyzing the outcomes of prior trading decisions.This research makes two significant contributions.Firstly, it broadens the applicability of LLMs to the domain of cryptocurrency trading.Secondly, it establishes a benchmark for cryptocurrency trading strategies.Through extensive experiments, CryptoTrade has demonstrated superior performance in maximizing returns compared to time-series baselines, but not compared to traditional trading signals, across various cryptocurrencies and market conditions.Our code and data are available at https://github. com/Xtra-Computing/CryptoTrade.CryptoTrade makes day-to-day trading decisions.

Juliane Proelss, Denis Schweizer, Bastien Buchwalter

The cryptocurrency market operates continuously, leading to frequent price fluctuations and information dissemination. This can hinder investors from reacting promptly to market changes, a phenomenon attributed to investors' limited attention. Research in traditional markets shows that the limited attention bias allows successful implementation of momentum strategies. However, past research on cryptocurrency markets finds mixed results. To resolve the puzzle, we utilize a survivorship bias-free dataset while accounting for variations in market capitalization and trading volume. This differentiation is crucial given young and tech affine retail investors' inclination toward smaller-capitalized cryptocurrencies, due to their higher risk tolerance and limited attention. More risk averse investors such as institutional investors, in contrast, focus more on top cryptocurrencies. In line with expectations, we find effective momentum strategies among larger-capitalized cryptocurrencies.

Douglas J. Cumming, Johannes Fuchs, Paul P. Momtaz

Abstract We explore the risk–return trade‐off in international regulation of cryptocurrency markets using a unique sample of regulations implemented between July 2018 and April 2023. Various regulation types have reduced risk in cryptocurrency markets while having differential impacts on raw and risk‐adjusted returns. Given the legal challenges for national jurisdictions in regulating international markets, we develop a digital asset regulatory strength index (DARSI) and study the impacts of national regulatory enforcement quality on the risk and return effects of cryptocurrency regulations. We find that strong enforcement quality, measured based on the strength of formal institutions, amplified the regulations' intended effects. The amplification effect is more pronounced for regulations announced by a financial regulator and for more liquid tokens. Consistent with the view that normative compliance‐seeking facilitates the adoption of norms, we also find that cultural uncertainty avoidance amplifies regulations' intended effects.

Wang Chun Wei, Dimitrios Koutmos, Min Zhu

No abstract is available for this record.

Lukas Mueller

No abstract is available for this record.

Florent Rouxelin, Brice V. Dupoyet, Afak Nazim

No abstract is available for this record.

Myungwan Kim, Ye Jin Jeong, Jaehong Jeong

This study examines the impact of incorporating cryptocurrencies into global asset portfolios using ensemble approaches and a tracing strategy. We considered cryptocurrency ratios of 1%, 3%, and 5% for including cryptocurrencies. Benchmarking was performed using classical portfolio optimization strategies such as minimum variance portfolio (MVP), maximum diversification portfolio (MDP), equal risk contribution portfolio (ERCP), and hierarchical risk parity (HRP). The ensemble methods and tracing strategies we evaluated were the equally weighted portfolio (EWP), the linear combination portfolio (LCP), the return tracing portfolio (RTP), and the return volatility tracing portfolio (RVTP). EWP averages the weights of classical methods, while LCP combines the objective functions of three optimization methods. RTP and RVTP represent tracing strategy portfolios with monthly rebalancing, selecting the best-performing portfolio based on cumulative returns or a combination of cumulative returns and annualized volatility. Our findings reveal that increasing the cryptocurrency allocation improves performance metrics in ensemble portfolios but also leads to higher risk. In addition, including cryptocurrencies reduces transaction fees, especially evident in the LCP with a 5% allocation. In the case of a 3-month RTP, HRP emerged as the preferred strategy, outperforming the use of HRP alone. In the case of a 6-month RVTP, MVP remained the preferred choice, consistently achieving lower volatility.

Jasmina Džafić, Emir Hečimović

The cryptocurrency market has attracted considerable attention from investors and researchers alike. This paper examines the volatility patterns of two major cryptocurrencies utilizing GARCH modeling: Bitcoin, based on a proof-of-work mechanism, and Cardano, operating on a proof-of-stake mechanism. Our findings reveal differences in the volatility structures of the two cryptocurrencies, with Cardano demonstrating a reduced long-term volatility compared to Bitcoin. This study suggests that transitioning from proof-of-work to proof-of-stake mechanisms might lead to a decrease in market volatility.

Ye Liu, Chengxuan Zhang, Yi Li

Smart contracts are computer programs running on blockchains to automate the transaction execution between users. The absence of contract specifications poses a real challenge to the correctness verification of smart contracts. Program invariants are properties that are always preserved throughout the execution, which characterize an important aspect of the program behaviors. In this paper, we propose a novel invariant generation framework, INVCON+, for Solidity smart contracts. INVCON+ extends the existing invariant detector, InvCon, to automatically produce verified contract invariants based on both dynamic inference and static verification. Unlike INVCON+, InvCon only produces likely invariants, which have a high probability to hold, yet are still not verified against the contract code. Particularly, INVCON+ is able to infer more expressive invariants that capture richer semantic relations of contract code. We evaluate INVCON+ on 361 ERC20 and 10 ERC721 real-world contracts, as well as common ERC20 vulnerability benchmarks. The experimental results indicate that INVCON+ efficiently produces high-quality invariant specifications, achieving a recall of 80%, which can be used to secure smart contracts from 17 types of common vulnerabilities.

David Krause

No abstract is available for this record.

Josephine Nartey

No abstract is available for this record.

An‐Sing Chen, Huong Thi Nguyen

No abstract is available for this record.

Mieszko Mazur, Efstathios Polyzos

Inflows to the newly-established bitcoin exchange traded funds (ETFs) surpassed $20 billion in the first several weeks of trading and are considered historic high by ETF standards. In this paper we provide early examination of the bitcoin spot ETFs listed on US exchanges, and their effect on bitcoin price formation. We establish several empirical facts: (1) daily capital flows to spot bitcoin ETFs exceed $500 million or roughly 10,000 bitcoins and surpass daily production of bitcoin by the factor of 5; (2) net flows to ETFs are a strong positive predictor of bitcoin price with R-squared of 95%; (3) most of bitcoin price appreciation is generated outside of the ETF trading hours; (4) increase in bitcoin price leads to an abnormal ETF trading volume; (5) inflows to bitcoin ETFs witness outflows from gold ETFs. Overall, during the period studied, capital flows to spot bitcoin ETFs emerge as a dominant single factor predicting positive valuation effects of bitcoin.

David Ardia, David Ardia, Keven Bluteau, Keven Bluteau

We study the relation between the promotion of a cryptocurrency on Twitter and its return dynamics around pump-and-dump events. By analyzing abnormal returns, trading volume, and tweet activity, we uncover that Twitter effectively garners attention for pump-and-dump schemes, leading to notable effects on abnormal returns before the event. Our results indicate that investors relying on Twitter information exhibit delayed selling behavior during the post-dump phase, resulting in significant losses compared to other participants. We also find that, while tweets directly promoting pump schemes align with anticipated market phases, a noteworthy portion of indirect, non-pump-aware tweets significantly influence market movements pre-event.

Thomas Conlon, Shaen Corbet, Les Oxley

ABSTRACT The introduction of regulated CME futures contracts on Bitcoin in 2017 raised an expectation that cryptocurrencies would become part of mainstream financial markets. This also heightened links between traditional markets and Bitcoin, implying that the cryptocurrency would be subject to systematic spillovers. This paper uses high‐frequency data to examine whether Bitcoin basis risk is linked to investor sentiment from established financial markets. Our findings indicate that extreme investor sentiment, as reflected by the tail risk in various volatility indices, including the VIX, consistently correlates with a negative Bitcoin basis, where Bitcoin futures prices are lower than spot prices. Fluctuations significantly influence this relationship in the trading volume of Bitcoin futures and are more pronounced during periods of substantial unexpected inflation and deflation. These results underline the complex dynamics between market sentiment and cryptocurrency pricing, offering insights with substantial implications for investors and policymakers.

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.