Market Microstructure and Securities Values Evidence from the Tel Aviv Stock Exchange＊
Bibliographic record
Abstract
Introduction Securities markets around the world are making major investments to improve their trading technology. The London Stock Exchange is phasing in an automated, order-driven trading system to improve liquidity and reduce trading costs (see, London Stock Exchange, 1996). The current plan is to implement the system for the FTSE-100 stocks while preserving the traditional quote-driven system for large block trades. Other European stock exchanges (e.g., the French, Italian, Spanish, and Swedish markets) have converted their trading systems from the traditional call market to computer-based continuous trading alternatives. The stock exchanges of Toronto, Montreal, Vancouver, and Alberta have recently changed their trading systems to support decimal pricing ‘in a bid to increase liquidity’ (Reuters, 1996). In the United States, the Securities and Exchange Commission has introduced new rules requiring market-makers to incorporate in their public quotes both customer limit orders and orders they quote on electronic communication networks such as Instinet and Selectnet. These rules are intended to improve liquidity through increased market transparency and enhanced quality of execution (see, Securities and Exchange Commission, 1996). These reforms were partly triggered by the studies of Christie and Schultz (1994) and Christie et al. (1994), who presented evidence that market makers implicitly colluded to maintain high bid--ask spreads, and Huang and Stoll (1996), who showed that Nasdaq spreads exceeded NYSE benchmarks. Further, emerging markets are evaluating and implementing new trading systems, and are making considerable investments in improving their microstructure. A question of interest for both financial economists and practitioners is whether investments in improving the market microstructure have positive value. The answer relates to the raison d’être of market microstructure research, and it can quantify the benefits of improving trading mechanisms. This paper examines the value of an improvement in the market microstructure for selected stocks on the Tel Aviv Stock Exchange (TASE).
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".