Bibliographic record
Abstract
Settlement efficiency has always been a concern for regulators and supervisors. This issue has recently gained the attention of all stakeholders in financial markets, however, due to: (1) the introduction of the Settlement Discipline Regime (SDR) in the European Union (EU) in February 2022; and (2) the migration to T+1 in the US, Mexico, Argentina and Canada, along with the consequent discussions in Europe about transitioning from T+2 to T+1. There is a perception in the market that the EU settlement efficiency rate is lower than that of other regions; however, there is no available failure rate for the US, and the methodologies used to assess these rates are neither uniform nor comparable. In fact, the EU methodology established by the Central Depositories Deposition Regulation (CSDR) is the most stringent. Furthermore, there are significant differences depending on asset classes, type of transactions and size of markets, etc. Equities markets usually have worse settlement ratios than bond markets, while exchange traded funds (ETFs) have the worst ratio, indicating possible structural problems. Larger markets generally tend to have worse ratios, likely due to higher levels of cross-border investment and more complex products, such as exchange traded products (ETPs) and ETFs. Settlement failures occur for a variety of reasons, encompassing numerous operational and technical issues along the custody value chain. Additionally, factors such as short selling activities and ‘strategic failures’ may contribute, although these are less frequently discussed by market participants. Determining the precise impact of each cause is challenging, as most are typically present during periods of increased settlement failures. These periods often align with high market volatility, elevated trading volumes, high borrowing costs and/or low interest rates. To improve settlement efficiency rates, a comprehensive set of actions should be undertaken by market participants, central securities depositories (CSDs) and regulators. A realistic and achievable target for settlement failures might be around 2 per cent in terms of value, which, while still ambitious, is more attainable than a 0 per cent target. This paper synthesises various analyses and personal experiences, rather than relying on a singular analytic study. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".