Direct Evidence of Non‐trading on the London Stock Exchange
Bibliographic record
Abstract
The extent of non‐trading is shown to be much greater in the UK than in the more heavily researched US equity markets. Over the period 1975 to 1995 we find that almost 44% of all stocks in our sample failed to trade on the last day of a given month, a figure which is significantly higher than for stocks in the US (see Foerster and Keim, 1993). In this paper we investigate the relationship between the non‐trading of UK stocks and the autoregressive and seasonal behaviour of UK stock returns. In addition, we find that stocks are much more likely to be recorded as not having traded on the last day of the month in the period prior to April 1981 than after this date. We trace this result to a reporting requirement change on the London Stock Exchange and investigate whether the change has any real implications for systematic risk estimates over this period. We also find that alternative methods for calculating betas, in the presence of thin trading, are very sensitive to stock size and to non‐trading.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".