Two essays on regulation and transparency
Bibliographic record
Abstract
This dissertation includes two essays. The first essay analyses the effect of demutualization on the New York Stock Exchange (NYSE) and the Toronto Stock Exchange (TSX). Exchanges have undergone significant transformations due to increased competition but some changes have created conflicts of interest, particularly when deciding which firms to delist. On the NYSE, delisting is an autonomous decision whereas on the TSX, external regulators have a larger role. After collecting data from involuntarily delisted firms that continue to trade on smaller listing markets between 2002 and 2009, I create a sample of 195 NYSE and 39 TSX firms. I calculate the percentage spreads on firms from both exchanges and find that spreads are larger and more volatile on the NYSE than on the TSX. These results are stronger after 2006, when the NYSE went public. Similar results are obtained when I look at firms that were delisted for trading below minimum quantitative standards. The second essay studies the effect of post-transparency on the NASDAQ. Transparency has been promoted by the SEC as a measure that can reduce transaction costs and increase liquidity. However, empirical studies have shown that the benefits of transparency have not been entirely positive across different markets. This paper contributes to the transparency literature by measuring the effects on transaction costs after the implementation of the National Market System NMS. The paper looks at a sample of 2,882 firms that have trading information 60 days before and after NMS and were incorporated to the NMS between 1982 and 1989. The paper shows that spreads decrease by 5 percent and spreads of a matching sample also decrease but at a smaller magnitude. Another effect of transparency is a reduction of 41 percent in the volatility of returns after firms start trading on the NMS. This paper also shows that the effects of transparency are much stronger after removing firms that are already trading at very low spreads prior to NMS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".