Bibliographic record
Abstract
This Ph.D. dissertation investigates various areas in financial economics: market microstructure, corporate finance, asset pricing, and financial econometrics. The three comprising essays have a common ground: cross-border finance. Chapter One documents the impact of differential private information on relative asset pricing across borders by studying the probability of informed trading (PIN) for Canadian shares traded on exchanges separated by Niagara Falls. Relative to the New York Stock Exchange (NYSE), the Toronto Stock Exchange (TSX) has more informed trades and accounts for a larger information share, indicating that informed traders contribute to cross-border price discovery. The information imbalance across the two markets is associated with small but positive price premiums for New York trades. The dynamics of these premiums depends on trade informedness. Lastly, the PIN of a TSX -listed share typically rises upon cross-listing on the NYSE, which is consistent with negative abnormal returns of the original listing. The theory of corporate governance suggests that managers of poorly governed firms are more likely to make poor investment decisions, and the evidence on high antitakeover provision (ATP) firms is consistent. In Chapter Two, I study the effect of domestic and foreign takeovers by U.S. firms and find that high-ATP bidders tend to pay relatively high premiums for either targets. While this suggests that these firms make poor decisions, high-ATP bidders also experience relatively high event study returns at times of foreign takeover news. This contradicts the findings of Masulis et al. (2007) for domestic takeovers. Finally, Chapter Three explores the convergence between the prices of American Depositary Receipts (ADRs) listed by Asia-Pacific firms and their original shares listed on home exchanges. Instead of relying on conventional parametric approaches that carry embedded model-specification errors, I contribute to the literature by introducing a nonparametric technique to estimate the convergence speed parameter. I present the time-varying characteristics of both firm and country-level convergence speed parameters. Furthermore, I empirically verify and visually corroborate the comparative dynamics of convergence with respect to short sales restrictions, trading time differences, and market-tier measures proxied by the Morgan Stanley Capital International indices. I conclude that enhancement in market efficiency accelerates the reversion to the parity of ADR -pairs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".