Analyzing Financial Smart Contracts for Blockchain
Bibliographic record
Abstract
The recent advancement in Blockchain technology and cryp-tocurrencies like Bitcoin and Ethereum has captured interest of many researchers in academia and industry. Since its advent, the Blockchain was inherently supposed to be used in combination with cryptocurrencies but currently this technology is being used in other areas as well. One particular area based on the distributed ledger technology that has grabbed the attention of many technologist and financial marketers is “Smart Contracts”. Based on a cryptocurrency framework known as Ethereum, these smart contracts can be used for different applications such as Option Pricing, Currency Exchange, Revenue Management System, Crowd-funding and Peer-to-Peer networking. In our current effort, we have designed a smart contract and demonstrate that this smart contract could be used to take various possible positions in an European style option. We have analyzed the contract for potential security vulnerabilities when implemented in Blockchain. These option based smart contracts could be used for collateral contract services among finance industries.
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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".