Follow blockchain research across journals, conferences, and preprint repositories.
135 results · page 2 of 6
Johannes Fuchs, Paul P. Momtaz
No abstract is available for this record.
Syed Arslan Abbas, Nor Shaipah Abdul Wahab, Firdous Mohd Farouk
No abstract is available for this record.
Min-Bin Lin, anon anon, Ruitong Wang, Daniel Traian Pele
No abstract is available for this record.
Lin Cong, Yilei Dong, Yunbo Lu, Qingsong Ruan · 5 authors
No abstract is available for this record.
Nikhil Kalidasu
No abstract is available for this record.
Jungsuk Han, Jongsub Lee, Tao Li
No abstract is available for this record.
Pegah Beikzadeh, Maedeh Mosharraf
Amidst the frenzy surrounding Non-Fungible Tokens (NFTs) in 2021, the concept of digital assets and trading was redefined. Although the initial hype may have subsided, NFTs continue to drive innovation in ownership, with substantial revenue streams flowing through the market. This transformative shift underscores the importance of discerning the factors that shape this ecosystem. This paper delves into the intricate dynamics of the NFT market, particularly focusing on the impact of creation methods—whether hand-drawn or artificial intelligence (AI)-generated—on market behavior. In a comprehensive analysis of the NFT market, we have analyzed a vast dataset comprising 1,478,556 transactions of NFT art from the OpenSea marketplace in 2023 to explore correlations and patterns between key transactional features. Furthermore, we employed regression models to predict the sales of an NFT and classification models to distinguish between hand-drawn and AI-generated NFTs. Finally, by comparing different machine learning models, we identified the most appropriate model for analyzing the market, considering the non-linear relationships and complex nature of the NFT market. Overall, the results provided in this research can lead to making more informed decisions regarding investment, creation, and trading.
Unyime Ufok Ibekwe, Uche M. Mbanaso, Nwojo Agwu Nnanna, Umar Adam Ibrahim
Smart contracts have emerged as a transformative technology within the blockchain ecosystem, facilitating the automated and trustless execution of agreements. Their adoption spans diverse sectors such as education, agriculture, healthcare, government, real estate, transportation, supply chain, and global initiatives like Central Bank Digital Currencies (CBDCs). However, the security of smart contracts has become a significant concern, as vulnerabilities in their design and implementation can lead to severe consequences such as financial losses and system failures. This systematic review consolidates findings from 78 selected research articles, identifying key vulnerabilities affecting smart contracts and categorizing them into a taxonomy encompassing code-level, environment-dependent, and user-related vulnerabilities. It also examines the threats that exploit these vulnerabilities and the most effective detection techniques. The domain-based classification presented in this review aims to assist researchers, software engineers, and developers in identifying and mitigating significant security flaws related to the design, implementation, and deployment of smart contracts. A comprehensive understanding of these issues is essential for enhancing the security and reliability of the blockchain ecosystem, ultimately fostering the development of more secure and robust decentralized applications for end users.
Minh Hong Nguyen, Binh Nguyen Thanh, Huy Pham, Thi Thu Tra Pham
Decentralized lending in the DeFi ecosystem mirrors traditional financial intermediation but poses significant risks, particularly funding liquidity risk, due to the volatility and composbility of digital assets, high leverage, and the absence of regulatory protections. This study applies traditional financial intermediation theories to DeFi lending and empirically test which internal factors such as interest rates and user market power, as well as external factors like the USD Index, influence funding liquidity risk in DeFi lending. Analyzing high-frequency blockchain data using the ARDL model and a novel dynamic ARDL simulation from major pools such as Wrapped Bitcoin (WBTC) and Wrapped Ethereum (WETH), the research finds that current algorithmic interest rate models fail to function as effective self-stabilization mechanisms. Additionally, lower deposit concentration in these pools may exacerbate, rather than mitigate, funding liquidity risk.
Lennart Ante
We investigate the motivations behind non-fungible token (NFT) ownership. Utilizing survey data from NFT owners, we identify four distinct groups based on their primary motivations: (1) Utilizers, who emphasize functional uses; (2) Socializers, motivated by community and networking; (3) Speculators, focused on profit potential; and (4) Aesthetes, who appreciate artistic and cultural aspects. Our analysis indicates that individual traits such as risk-taking, impulsivity, and investment knowledge significantly influence group membership. These findings suggest that NFT users are a diverse cohort with varied motivations rather than a homogeneous group.
Thomas Bourveau, Janja Brendel, Jordan Schoenfeld
Abstract Decentralized finance (DeFi) has emerged to offer traditional financial services such as lending, borrowing, and trading without intermediaries (e.g., banks). DeFi transactions are typically executed using a special digital class of contracts called smart contracts. These contracts are self-executing and hard-coded directly on a blockchain. We observe the emergence of a new class of voluntary audits that evaluate the integrity of these contracts. Using a hand-coded sample of about 8,500 smart contract audit reports, we provide some of the first evidence showing that (1) these audits are pervasive, (2) the audit firm market is composed of new technical audit firms, (3) the scope of these audits can span a variety of contract features, (4) the audit inputs and outputs differ substantively from those of conventional financial audits, and (5) the market reacts positively to the release of these audit reports, suggesting that these reports are value-relevant. These findings highlight the demand for novel assurance services driven by blockchain technology.
Abe Alexander, Lars Fritz
In the ever evolving landscape of decentralized finance automated market makers (AMMs) play a key role: they provide a market place for trading assets in a decentralized manner. For so-called bluechip pairs, arbitrage activity provides a major part of the revenue generation of AMMs but also a major source of loss due to the so-called 'informed orderflow'. Finding ways to minimize those losses while still keeping uninformed trading activity alive is a major problem in the field. In this paper we will investigate the mechanics of said arbitrage and try to understand how AMMs can maximize the revenue creation or in other words minimize the losses. To that end, we model the dynamics of arbitrage activity for a concrete implementation of a pool and study its sensitivity to the choice of fee aiming to maximize the revenue for the AMM. We identify dynamical fees that mimic the directionality of the price due to asymmetric fee choices as a promising avenue to mitigate losses to toxic flow. This work is based on and extends a recent article by some of the authors.
Asma Alawadi, Nada Kakabadse, Andrew Kakabadse, Sam Zuckerbraun
No abstract is available for this record.
Abe Alexander, Lars Fritz
In the ever evolving landscape of decentralized finance automated market makers (AMMs) play a key role: they provide a market place for trading assets in a decentralized manner. For so-called bluechip pairs, arbitrage activity provides a major part of the revenue generation of AMMs but also a major source of loss due to the so-called informed orderflow. Finding ways to minimize those losses while still keeping uninformed trading activity alive is a major problem in the field. In this paper we will investigate the mechanics of said arbitrage and try to understand how AMMs can maximize the revenue creation or in other words minimize the losses. To that end, we model the dynamics of arbitrage activity for a concrete implementation of a pool and study its sensitivity to the choice of fee aiming to maximize the value retention. We manage to map the ensuing dynamics to that of a random walk with a specific reward scheme that provides a convenient starting point for further studies.
Shen-Ning Tung, Cheuk Yin Lee, Tai‐Ho Wang
We study how trading fees and continuous-time arbitrage affect the profitability of liquidity providers (LPs) in Geometric Mean Market Makers (G3Ms). We use stochastic reflected diffusion processes to analyze the dynamics of a G3M model under the arbitrage-driven market [Milionis et al. 2022a. “Automated Market Making and Loss-Versus-Rebalancing.” arXiv e-prints]. Our research focuses on calculating LP wealth and extends the findings of Tassy and White [Tassy and White. 2020. “Growth Rate of a Liquidity Provider's Wealth in xy = c Automated Market Makers.”] for the constant product market maker (Uniswap v2) to a broader range of G3Ms, including Balancer. This allows us to calculate the long-term expected logarithmic growth of LP wealth, offering new insights into the complex dynamics of AMMs and their implications for LPs in decentralized finance.
Rongda Chen, Jingjing Yu, Chenglu Jin, Xinyang Chen · 6 authors
Abstract Although extensive research has examined the credit risk of real estate enterprises, the relationship between the political connection of real estate enterprises and these enterprises’ credit risk has not been formally studied. Using the panel data of 123 real estate listed companies in the Chinese stock market from 2008 to 2021, this paper finds a significant positive correlation between the political connection of private real estate listed companies and their credit risk. This phenomenon is attributed to the excessive debt that benefits from political connections since it may raise the credit risk of any real estate firm. Interestingly, considering that 2013 is the first year of China’s Internet finance era, we find that the popularity of Internet finance and other decentralized lending financing channels may enhance the impact of political connections on real estate credit risk. Our findings provide new micro evidence for the influencing factors and mechanism of credit risk of real estate enterprises during the recent “credit crisis” in the real estate market in China.
Kaitao Lin
No abstract is available for this record.
Yakun Liu, Yan Chen
No abstract is available for this record.
Valeria Fedyk, De-Rong Kong, Daniel Rabetti
No abstract is available for this record.
Angelo Aspris, Jiří Švec
ABSTRACT Using comprehensive transaction level loan data for the MakerDAO protocol (2019–2023), this study investigates decentralized finance (DeFi) lending dynamics, focusing on the deter- minants of loan demand and the interplay between leverage, skill, and user performance. We document a counterintuitive positive relationship between the cost of borrowing and loan demand, consistent with yield seeking behavior. Moreover, blockchain- and protocol-specific frictions, such as gas fees shape borrowing activity. At the vault level, leverage universally reduces returns and amplifies liquidation risk, with unskilled users incurring significantly greater losses than skilled counterparts under extreme leverage. While skilled users mitigate moderate leverage risks through active management, excessive leverage erodes performance across all skill levels, with forced liquidations accounting for a significant proportion of this decline. The findings reveal critical trade-offs in DeFi permissionless architecture. While skilled participants exploit leverage strategically, systemic design features disproportionately penalize less sophisticated users.
Xiaomeng Zhou
No abstract is available for this record.
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.
Lisa De Simone, Peiyi jin, Daniel Rabetti
No abstract is available for this record.
David Krause
No abstract is available for this record.