Unveiling Wash Trading in Popular NFT Markets
- URL: http://arxiv.org/abs/2403.10361v1
- Date: Fri, 15 Mar 2024 14:52:52 GMT
- Title: Unveiling Wash Trading in Popular NFT Markets
- Authors: Yuanzheng Niu, Xiaoqi Li, Hongli Peng, Wenkai Li,
- Abstract summary: We analyze more than 25 million transactions within four non-fungible tokens (NFT) markets.
We propose a algorithm that integrates the network characteristics of transactions with behavioral analysis.
Our findings indicate that NFT markets with incentivized structures exhibit higher proportions of wash trading volume.
- Score: 0.7529855084362796
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As emerging digital assets, NFTs are susceptible to anomalous trading behaviors due to the lack of stringent regulatory mechanisms, potentially causing economic losses. In this paper, we conduct the first systematic analysis of four non-fungible tokens (NFT) markets. Specifically, we analyze more than 25 million transactions within these markets, to explore the evolution of wash trade activities. Furthermore, we propose a heuristic algorithm that integrates the network characteristics of transactions with behavioral analysis, to detect wash trading activities in NFT markets. Our findings indicate that NFT markets with incentivized structures exhibit higher proportions of wash trading volume compared to those without incentives. Notably, the LooksRare and X2Y2 markets are detected with wash trading volume proportions as high as 94.5% and 84.2%, respectively.
Related papers
- When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments [55.19252983108372]
We have developed a multi-agent AI system called StockAgent, driven by LLMs.
The StockAgent allows users to evaluate the impact of different external factors on investor trading.
It avoids the test set leakage issue present in existing trading simulation systems based on AI Agents.
arXiv Detail & Related papers (2024-07-15T06:49:30Z) - The Dark Side of NFTs: A Large-Scale Empirical Study of Wash Trading [28.20696034160891]
We analyze 8,717,031 transfer events and 3,830,141 sale events from 2,701,883 NFTs.
We identify three types of NFT wash trading and propose identification algorithms.
We also provide insights from six aspects, i.e., marketplace design, profitability, NFT project design, payment token, user behavior, and NFT ecosystem.
arXiv Detail & Related papers (2023-12-19T19:29:24Z) - Diffusion Variational Autoencoder for Tackling Stochasticity in
Multi-Step Regression Stock Price Prediction [54.21695754082441]
Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility.
Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions.
We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction.
Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
arXiv Detail & Related papers (2023-08-18T16:21:15Z) - Abnormal Trading Detection in the NFT Market [1.7205106391379026]
The total transaction volume on OpenSea, the largest NFT marketplace, reached 34.7 billion dollars in February 2023.
The NFT market is mostly unregulated and there are significant concerns about money laundering, fraud and wash trading.
The lack of industry-wide regulations, and the fact that amateur traders and retail investors comprise a significant fraction of the NFT market, make this market particularly vulnerable to fraudulent activities.
arXiv Detail & Related papers (2023-05-25T15:12:14Z) - Bubble or Not: Measurements, Analyses, and Findings on the Ethereum
ERC721 and ERC1155 Non-fungible Token Ecosystem [22.010657813215413]
The market capitalization of NFT reached 21.5 billion USD in 2021, almost 200 times of all previous transactions.
The rapid decline in NFT market fever in the second quarter of 2022 casts doubts on the ostensible boom in the NFT market.
By collecting data from the whole blockchain, we construct three graphs, namely NFT create graph, NFT transfer graph, and NFT hold graph, to characterize the NFT traders.
We propose new indicators to quantify the activeness and value of NFT and propose an algorithm that combines indicators and graph analyses to find bubble NFTs.
arXiv Detail & Related papers (2023-01-05T10:17:57Z) - A Game of NFTs: Characterizing NFT Wash Trading in the Ethereum Blockchain [53.8917088220974]
The Non-Fungible Token (NFT) market experienced explosive growth in 2021, with a monthly trade volume reaching $6 billion in January 2022.
Concerns have emerged about possible wash trading, a form of market manipulation in which one party repeatedly trades an NFT to inflate its volume artificially.
We find that wash trading affects 5.66% of all NFT collections, with a total artificial volume of $3,406,110,774.
arXiv Detail & Related papers (2022-12-02T15:03:35Z) - The Fungibility of Non-Fungible Tokens: A Quantitative Analysis of
ERC-721 Metadata [9.812718050900918]
Non-Fungible Tokens (NFTs) have until recently been traded on a highly lucrative and speculative market.
An emergence of misconceptions, along with a sustained market downtime, are calling the value of NFTs into question.
This project describes three properties that any valuable NFT should possess.
arXiv Detail & Related papers (2022-09-29T02:33:31Z) - Macroscopic properties of buyer-seller networks in online marketplaces [55.41644538483948]
We analyze two datasets containing 245M transactions that took place on online marketplaces between 2010 and 2021.
We show that transactions in online marketplaces exhibit strikingly similar patterns despite significant differences in language, lifetimes, products, regulation, and technology.
arXiv Detail & Related papers (2021-12-16T18:00:47Z) - Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market [58.720142291102135]
This paper focuses precisely on the study of these markets makers strategies from an agent-based perspective.
We propose the application of Reinforcement Learning (RL) for the creation of intelligent market markers in simulated stock markets.
arXiv Detail & Related papers (2021-12-08T14:55:21Z) - A Sentiment Analysis Approach to the Prediction of Market Volatility [62.997667081978825]
We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements.
The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility.
We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
arXiv Detail & Related papers (2020-12-10T01:15:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.