Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2210.14942

The Art NFTs and Their Marketplaces

Lanqing Du·Michelle Kim·Jinwook Lee
View original source ↗Read source document ↗Export metadata ↓

Abstract

Non-Fungible Tokens (NFTs) are crypto assets with a unique digital identifier for ownership, powered by blockchain technology. Technically speaking, anything digital could be minted and sold as an NFT, which provides proof of ownership and authenticity of a digital file. For this reason, it helps us distinguish between the originals and their copies, making it possible to trade them. This paper focuses on art NFTs that change how artists can sell their products. It also changes how the art trade market works since NFT technology cuts out the middleman. Recently, the utility of NFTs has become an essential issue in the NFT ecosystem, which refers to the owners' usefulness, profitability, and benefits. Using recent major art NFT marketplace datasets, we summarize and interpret the current market trends and patterns in a way that brings insight into the future art market. Numerical examples are presented.

Explore related subjects

q-fin.STcs.CRcs.LG

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lanqing Du, Michelle Kim, Jinwook Lee. 2022-10-23. The Art NFTs and Their Marketplaces. https://arxiv.org/abs/2210.14942

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Cross-Market Alpha: Testing Short-Term Trading Factors in the U.S. Market via Double-Selection LASSO

We test whether 168 short-horizon price-volume signals from the Alpha191 library, originally developed for China's retail-dominated A-share market, contain pricing information for S&P 500 stocks from 2002 to 2022 beyond 153 established U.S. factors. Using the double-selection LASSO of Feng et al. (2020), 17 signals receive significant stochastic discount factor (SDF) loadings in the baseline test-asset design. Their robustness is uneven. Only three signals (a multi-horizon moving-average ratio, a directional-pressure ratio, and a price-gap correlation) remain significant with a finer test-asset grid and under Elastic Net and principal-component control selection; six more pass most checks, and the remaining eight depend on the specification. Robust signals are concentrated in volume-price interaction and short-term mean reversion, whereas volatility-based signals are fragile.

q-fin.ST↗
arXiv2026

The Endogenous Constraint: Hysteresis, Stagflation, and the Structural Inhibition of Monetary Velocity in the Bitcoin Network (2016-2025)

Bitcoin operates as a macroeconomic paradox: it combines a strictly predetermined, inelastic monetary issuance schedule with a stochastic, highly elastic demand for scarce block space. This paper empirically validates the Endogenous Constraint Hypothesis, positing that protocol-level throughput limits generate a non-linear negative feedback loop between network friction and base-layer monetary velocity. Using a verified Transaction Cost Index (TCI) derived from Blockchain.com on-chain data and Hansen's (2000) threshold regression, we identify a definitive structural break at the 90th percentile of friction (TCI ~ 1.63). The analysis reveals a bifurcation in network utility: while the network exhibits robust velocity growth of +15.44% during normal regimes, this collapses to +6.06% during shock regimes, yielding a statistically significant Net Utility Contraction of -9.39% (p = 0.012). Crucially, Instrumental Variable (IV) tests utilizing Hashrate Variation as a supply-side instrument fail to detect a significant relationship in a linear specification (p=0.196), confirming that the velocity constraint is strictly a regime-switching phenomenon rather than a continuous linear function. Furthermore, we document a "Crypto Multiplier" inversion: high friction correlates with a +8.03% increase in capital concentration per entity, suggesting that congestion forces a substitution from active velocity to speculative hoarding.

q-fin.ST↗
arXiv2026

Wasserstein-Barycentric Interaction Fields for Spatial Factor Models: Evidence from Language-Model Representations

Spatial asset-pricing models take the structure of inter-firm interaction as given. We infer that structure from firms' information environments using language-model representations. Each firm is represented as a distribution of news-article embeddings, and a target-anchored Wasserstein barycentric reconstruction selects, for every firm, the weighted combination of other firms whose information footprints jointly reconstruct its own. The resulting directed peer field enters a quadratic exposure-adjustment model in which the spatial coefficient indexes alignment with information peers relative to stand-alone exposure. Using fields built from 2018-2022 news and frozen before 2023-2026 returns, we find that the constructed field organizes cross-sectional return dependence beyond the Fama-French five factors and momentum and raises the held-out mean Gaussian quasi-log score relative to a matched factor-only model. Because factor betas are unchanged, the gain lies in residual covariance. The field outperforms pairwise distance weighting and equal weighting of the same peers, and remains incrementally informative beside persistent news co-mentions under the primary factor-conditioned specification. Linear and quadratic transport generate nearly identical peer-return signals and equivalent held-out predictive performance. The barycentric-proximity ordering persists across alternative embedding models, and a pre-period encoder preserves the held-out advantage under the primary specification. Language-model representations thus serve as a measurement instrument for latent inter-firm information structure in capital markets.

q-fin.ST↗