Innovation in Trading Activity: Should Stock Markets be More Transparent?
Bibliographic record
Abstract
The aim of the paper is to test whether different PTT levels are able to affect the volatility and liquidity of a Stock Exchange. Much research carried out over the last few years has attempted to describe these relationships, yet their empirical results have sometimes contradicted one another (see Section 2). Nevertheless, the innovative contribution of this paper is to study, on a large international scale and through a wide set of indicators, each of the three different PTT dimensions (specifically PTT1, PTT2 and PTT3) in relation to liquidity, on the one hand, and to volatility, on the other. Our attention is focused upon the equity division of the following 18 Stock Exchanges: the Hong Kong Stock Exchange, the Singapore Stock Exchange, the Australian Stock Exchange, the Toronto Stock Exchange, the New York Stock Exchange (NYSE), the NASDAQ, the American Stock Exchange (AMEX), the London Stock Exchange, Euronext (Paris, Amsterdam, Brussels and Lisbon), Deutsche Bourse (Xetra), the Madrid Stock Exchange, Borsa Italia, the Stockholm Stock Exchange, the Copenhagen Stock Exchange and the Helsinki Stock Exchange. All these stock markets are electronic order driven or hybrid markets. Pure quote driven Stock Exchanges are not typically attended by high frequency traders, because they admit orders sent only by market makers.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".