Bibliographic record
Abstract
This thesis examines topics in corporate finance and behavioral finance. First, I examine the effects of ownership structure on the amount of firm-specific information in stock prices, measured using synchronicity. With a unique dataset of 6,184 firm-year observations for Canadian companies listed on the Toronto Stock Exchange during 2000-2012, I find evidence of a significant, non-linear relationship between the size of the largest shareholder and synchronicity. Using propensity score matching (PSM) to isolate the effect of family firms on synchronicity, I find no evidence of a significant difference in synchronicity for matched pairs of family and non-family firms. Finally, I find evidence of a negative relationship between firms with multiple large controlling shareholders and synchronicity. Second, in a co-authored paper with Dr. Richard Deaves (McMaster University) and Dr. Brian Kluger (University of Cincinnati) we investigate the relationship between path-dependent behaviors (i.e., the disposition effect, house money effect and break-even effect) and investor characteristics (e.g., overconfidence and emotional stability) using experimental trading sessions. The majority of our subjects exhibit path-dependent biases and there are significant correlations between these biases. The correlations hint at the possibility that a common underlying factor may be driving all path-dependent behaviors. We also find some evidence that the existence of psychological bias (overconfidence and negative affect) leads to more bias in financial decision-making. Third, in co-authored work with Dr. Lucy Ackert (Kennesaw State University), Dr. Richard Deaves (McMaster University) and Dr. Quang Nguyen (Middlesex University) we report the results of an experiment designed to explore whether both cognitive ability (IQ) and emotional stability (EQ) impact risk preference and time preference in financial decision-making, finding evidence in support. Specifically, IQ impacts risk preferences and EQ impacts time preferences. Our results are primarily driven by our male participants. Most interestingly, EQ plays a role that is almost as meaningful as IQ when it comes to explaining preferences.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".