Can AI Detect Wash Trading? Evidence from NFTs
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866917941334769664 |
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| author | Falk, Brett Hemenway Tsoukalas, Gerry Zhang, Niuniu |
| author_facet | Falk, Brett Hemenway Tsoukalas, Gerry Zhang, Niuniu |
| contents | Existing studies on crypto wash trading often use indirect statistical methods or leaked private data, both with inherent limitations. This paper leverages public on-chain NFT data for a more direct and granular estimation. Analyzing three major exchanges, we find that ~38% (30-40%) of trades and ~60% (25-95%) of traded value likely involve manipulation, with significant variation across exchanges. This direct evidence enables a critical reassessment of existing indirect methods, identifying roundedness-based regressions à la Cong et al. (2023) as most promising, though still error-prone in the NFT setting. To address this, we develop an AI-based estimator that integrates these regressions in a machine learning framework, significantly reducing both exchange- and trade-level estimation errors in NFT markets (and beyond). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_18717 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Can AI Detect Wash Trading? Evidence from NFTs Falk, Brett Hemenway Tsoukalas, Gerry Zhang, Niuniu General Economics Economics Cryptography and Security Multiagent Systems Trading and Market Microstructure Applications Existing studies on crypto wash trading often use indirect statistical methods or leaked private data, both with inherent limitations. This paper leverages public on-chain NFT data for a more direct and granular estimation. Analyzing three major exchanges, we find that ~38% (30-40%) of trades and ~60% (25-95%) of traded value likely involve manipulation, with significant variation across exchanges. This direct evidence enables a critical reassessment of existing indirect methods, identifying roundedness-based regressions à la Cong et al. (2023) as most promising, though still error-prone in the NFT setting. To address this, we develop an AI-based estimator that integrates these regressions in a machine learning framework, significantly reducing both exchange- and trade-level estimation errors in NFT markets (and beyond). |
| title | Can AI Detect Wash Trading? Evidence from NFTs |
| topic | General Economics Economics Cryptography and Security Multiagent Systems Trading and Market Microstructure Applications |
| url | https://arxiv.org/abs/2311.18717 |