Is Decentralized Always Better? How Market Structure Affects Trading Costs for Tokenized Assets
Bibliographic record
Abstract
Decentralization in financial markets operates at multiple layers, encompassing both the asset level, through tokenization, and the infrastructure level, via novel trading protocols such as Automated Market Makers (AMMs). This paper critically examines these twin dimensions of decentralization, focusing on the introduction of tokenized equities and the adoption of AMM mechanisms in securities market design. We find that the first tokenized equities struggle to gain adoption, resulting in poor liquidity (high price impact) and substantial price dislocations from the underlying shares. In contrast, we show that AMMs can significantly reduce transaction costs, particularly for large size trades in actively traded assets, using both matching and simulation mechanisms between centralized exchanges and AMMs. Our results suggest that while not all decentralized innovations face an easy path to adoption, some have significant potential for application to traditional asset classes. • Transaction cost gap measures implicit market-clearing price of DEX-specific frictions. • Venue-specific cost structures drive trade-size segmentation between AMMs and CEXs. • Median CEX spread 18.6 bps vs 3.1 bps simulated AMM cost for same trades. • AMM execution costs remain stable as CEX spreads widen with volatility • Regulatory clarity and institutional design essential for tokenized equity market success.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".