A Blockchain Securities Tokenization Framework and A Stochastic Analysis of Interbank Payment Networks
Bibliographic record
Abstract
This thesis explores two complementary areas in finance: securities tokenization and interbank payments. First, we propose a tokenization framework extending decentralized finance (DeFi) benefits—accessibility, transparency, efficiency—to real-world securities. While DeFi mechanisms like liquidity pools complicate securities entitlements (e.g., dividends, voting), our solution overcomes this by combining fungible tokens with off-chain accounting and separate smart contracts for entitlements. Implemented on Ethereum, it saves 27% in costs compared to alternatives and supports various securities including stocks and bonds. We also confirm compatibility with liquidity logic in 90% of Ethereum pools. Second, we model Real Time Gross Settlement (RTGS)-based Large Value Payment Systems (LVPS) using queuing theory. RTGS is the most common mechanism for LVPS due to its instant settlement and low risk, but it has high liquidity requirements. Our model yields closed-form solutions for network performance and offers insights into liquidity requirements to aid the design of liquidity saving mechanisms.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".