Characterizing the Initial and Subsequent NFT Sales Market Dynamics: Perspectives From Boom and Slump Periods
Bibliographic record
Abstract
The NFT phenomenon has disrupted the traditional notion of digital ownership. As distinct digital assets, NFTs serve as proof of ownership for crypto assets, for example, art, music, trading cards, or in-game items. The NFT market experienced unprecedented growth in 2021, with NFTs solidifying their position as a transformative technology in the digital realm. The market growth peaked during the boom period in January 2022, and has since declined, experiencing a major slump in June 2022. The market is not yet commonplace for everyone. Within the market, the initial sales market is more challenging to enter than the subsequent sales market since the former requires a higher seller entry cost. This paper decouples the initial and subsequent sales markets on the largest NFT market platform, OpenSea, and examines the markets across the two distinct periods in the NFT market history: the boom period of January 2022 and the slump period of June 2022. For the study dataset, sales transaction records are extracted from Opensea. This study discovers many properties which are invariant between the boom and slump: higher pricing in subsequent sales than in initial sales, rapid market movement (more in the initial sales), skewed revenue generation, and statistically strong regressors from Linear Discriminant Analysis (LDA) to distinguish between different resale return types, ranging from large loss to large profit. The finding indicates that the initial and subsequent sales markets are not identical. However, the composition and behaviour of the markets listed prior would be scale-free to the markets’ economics stages. When we simulated a resale return with a zero hidden cost, the number of profit returns in resale inflated, suggesting that hidden costs should be surfaced and/or minimised to improve an investor’s experience in the subsequent sales market. Our study sheds light on the dynamics of the NFT initial and subsequent sales markets across the boom and slump periods. By evaluating the two market types separately, we contribute to demystifying the subsequent sales market, which can be veiled without the market type distinction due to the initial sales market’s over-representation. Ordinary people, who are generally incapable of affording a seller’s entry cost in the initial NFT sales market, would notably benefit from this study. Since the study covers the two extreme periods, its finding will provide certainty, even in an atypical period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".