The transition to T+1: Accelerated settlement cycles and progress so far
Bibliographic record
Abstract
This paper examines the current momentum driving faster settlements in financial markets, specifically focusing on the shift from trade date + 2 (T+2) to trade date + 1 (T+1) settlement cycles. The U.S. Securities and Exchange Commission (SEC) and the Canadian Capital Markets Association plan to implement it in May 2024. His Majesty’s Treasury in the UK and the Association for Financial Markets in Europe (AFME) have both established taskforces to assess the feasibility of transitioning to T+1 settlement. This paper aims to provide readers with a comprehensive understanding of the accelerated settlement movement and its potential implications for global market participants. It will delve into the reasons behind the simultaneous adoption of this change across various markets, highlight the key changes being introduced in the US market, and explore its impact on market participants within the US. It will also address the consequences of accelerated settlement for international markets, raising critical factors that all market participants need to consider when facing settlement cycle changes. Practical recommendations to prepare for T+1 readiness will be offered. Readers can expect insights into the motivations driving the accelerated settlement movement, the key changes unfolding in major markets and the potential effects on international markets, ensuring preparedness for the forthcoming T+1 settlement era.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".