Order protection, commonality, and herding behaviour in equity markets
Bibliographic record
Abstract
This dissertation consists of three research papers on financial system efficiency and stability. The findings of the thesis expand our understanding of equity market liquidity, efficiency, herding behaviour and potential systemic risks. The results also have significant implications for policy makers and various market participants, in particular those concerned with the impact of changes to trading rules on equity markets . Paper 1 examines the impact of the Order Protection Rule (OPR) on market liquidity and price discovery in North America. The OPR is one of the most influential policies that guarantees that trades are executed at the best available price and provides fair protection for investors. Using the difference - in - differences (DiD) and two - stage least squares (2SLS) methodologies, we find that market liquidity increases and transaction cost decreases. We evaluate two possible channels, market fragmentation and active returns, and find that only the former explains the result. Furthermore, we find a more efficient price discovery process, while the in creased liquidity mainly happens to U.S. lit markets where orders are displayed on order book transparently. We also find a significant improvement in national quoted prices and depth in small trading venues in Canada. Paper 2 examines the impact of the U. S. Securities and Exchange Commission Tick Size Pilot on commonality in liquidity, i.e. the level of co - movement between a security's liquidity and that of the corresponding Tick Size Pilot group. The Tick Size Pilot increases the tick size from 1 cent to 5 cents for a chosen list of small capitalisation stocks. Using a DiD methodology, we find an increase in liquidity commonality due to the higher analyst c overage and institutional ownership across treatment stocks in the pilot period. We further investigate the reactions from stocks not included in the pilot period and find that the tick unconstrained stocks attracted more analysts' attention and institutional investors, resulting in a higher commonality in liquidity. Paper 3 examines herding behaviour among investors in the renewable energy sector in the U.S. Over the last decade, the renewable energy sector demonstrated significant growth in the global economy. However, the sector also has variations in performance, with periods of relatively high active returns and others with substantial underperformance. We examine the relationship between the level of equity return dispersion - measured by the cross - sectional absolute deviation (CSAD) of returns - and the overall market return in the renewable sector. Using data from January 2000 to December 2015, we find significant evidence of excess return dispersion, or dispersion in the renewable energy sector for several sub - periods. We also find evidence of asymmetric return dispersion, indicating a different imp act of positive or negative market returns on return dispersion. These results also hold when considering risk - adjusted returns for the renewable sector based on a CAPM or multifactor model. In addition, we find some evidence of investor herding during periods of low market liquidity. Our results indicate a unique behaviour of returns in the renewable energy sector compared to other equity markets. Overall, investors in renewable energy stocks seem to disagree on their interpretation of large market movements, leading to an even higher return dispersion than predicted by standard asset pricing models.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".