On Tokenizing Securities in Contemporary Decentralized Finance Ecosystems
Bibliographic record
Abstract
This paper presents a securities tokenization solution allowing real-world securities to benefit from the accessibility, transparency, efficiency, and innovation of blockchain and decentralized finance. While existing solutions limit their scope to specific types of securities, our solution is generalizable to the tokenization of any securities with any holding rights by representing the security by a fungible token and using separate smart contracts for shareholders to redeem the holding rights. Furthermore, unlike existing solutions, our solution addresses the complications regarding ownership accounting that arise from the decentralized nature of liquidity pools, a pivotal component of the decentralized finance ecosystem. Our solution achieves this by performing accounting off-chain with additional logic for liquidity pools. In this paper, we implement our solution on Ethereum, measuring its gas costs to demonstrate that it is 27% cheaper than existing solutions. We also analyse the liquidity logic of over 90% of liquidity pools on Ethereum, confirming their compatibility with our solution. Moreover, we demonstrate the application of our solution for three use cases: dividend-paying stocks, common stock, and mergers and acquisitions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".