A Fractal View on Losses Attributable to Scams in the Market for Initial Coin Offerings
Bibliographic record
Abstract
Analogous to traditional Initial Public Offerings (IPO), Initial Coin Offerings (ICOs) represent an emerging channel through which firms can access external funding using the new evolving digital financial market for tokens. However, while ICOs represent an alternative funding channel for startups, the ICO market is essentially unregulated, which creates opportunities for fraud such as ‘ICO scams’. This paper addresses the question as to what the expected losses attributable to scams in the market for ICOs are. Using web scrapping techniques, all ICOs launched between August 2014 and December 2019 were first screened for accusations of fraud, before a novel methodological framework was employed to understand the true costs associated with scams. The findings reveal that 56.80% of ICOs were subject to scams, corresponding to 65.80% of the relevant market capitalization, estimated at USD 15.38 billion. Moreover, it is found that the loss distribution due to scam ICOs is governed by a fractal process. Specifically, the power law exponent for the distribution governing losses due to scam ICOs suggests that the second moment is not defined, rendering the sample mean unstable. Taken together, the results in this paper provide evidence that we have not yet seen the largest loss in the market for ICOs and are supportive of an urgent need for ICO market regulations from governments and regulatory agencies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".