Characteristics of price related fluctuations in Non-Fungible Token (NFT) market

ArXiv ID: 2310.19747 “View on arXiv”

Authors: Unknown

Abstract

A non-fungible token (NFT) market is a new trading invention based on the blockchain technology which parallels the cryptocurrency market. In the present work we study capitalization, floor price, the number of transactions, the inter-transaction times, and the transaction volume value of a few selected popular token collections. The results show that the fluctuations of all these quantities are characterized by heavy-tailed probability distribution functions, in most cases well described by the stretched exponentials, with a trace of power-law scaling at times, long-range memory, and in several cases even the fractal organization of fluctuations, mostly restricted to the larger fluctuations, however. We conclude that the NFT market - even though young and governed by a somewhat different mechanisms of trading - shares several statistical properties with the regular financial markets. However, some differences are visible in the specific quantitative indicators.

Keywords: Non-Fungible Tokens (NFT), Heavy-Tailed Distributions, Stretched Exponentials, Long-Range Memory, Fractal Organization, Digital Assets/NFTs

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced statistical physics concepts (stretched exponentials, power-law scaling, long-range memory, fractal analysis) to analyze NFT market data, indicating high math complexity. Empirical rigor is high as it analyzes real blockchain transaction data for specific NFT collections, computes heavy-tailed distributions, and compares findings to established financial market stylized facts, though it lacks explicit backtesting or code.

  flowchart TD
    A["Research Goal<br>Analyze NFT Market Dynamics<br>& Compare with Financial Markets"] --> B["Methodology & Data"]
    B --> C["Selected Pop. Token Collections"]
    C --> D["Analyze Key Metrics<br>Floor Price, Transactions, Volume, etc."]
    D --> E["Statistical Analysis<br>Heavy-Tailed & Stretched Exponential Distributions"]
    D --> F["Memory & Fractal Analysis<br>Long-Range Memory & Organization"]
    E & F --> G["Outcomes"]
    G --> H["NFTs share statistical properties<br>with traditional markets"]
    G --> I["Unique quantitative indicators<br>reflect different trading mechanisms"]