Three Essays on US Corporate Bond Market and Canadian Newspaper Market
Bibliographic record
Abstract
This thesis presents an empirical study of the US Corporate Bond market and the consolidation of the Canadian Community Newspaper Market. First, we focus on the effects of post-crisis regulations designed to reduce risk exposure on dealers’ trading behaviors and market liquidity. A simple model is developed to illustrate the relationship between dealers’ trading behaviors and market liquidity, specifically predicting that optimizing relationships with long-term trading partners enables dealers to maintain the same level of liquidity while reducing inventory risk. Empirical regression analysis, using the Trace Academic dataset, was conducted to test this hypothesis. The findings support the notion that since the implementation of the Dodd-Frank Act, dealers have indeed optimized their trading partnerships to provide consistent liquidity levels under regulatory pressure, thereby enhancing market efficiency. The thesis also delves into the origins of the centrality premium in the US corporate bond market, which operates on an over the counter basis. In this market, dealers are positioned within a network, with more connected, central dealers at the core and less connected, peripheral dealers at the edges. A common observation is that core dealers typically charge higher markups than their peripheral counterparts, resulting in a centrality premium. The study investigates the factors contributing to this premium. It reveals that core dealers capitalize on their search efforts, retain bonds for longer periods, and engage with higher-valued clients, leading to a greater trading surplus. Finally, I explore the consolidation of the Canadian community newspaper market. In 2017, Postmedia and Metroland engaged in a swap involving dozens of newspapers across several local regional markets, followed by closing most of the swapped publications. This led to an increased concentration in these markets, potentially resulting in anti-competitive impacts. My investigation delves into the details of advertising rates before and after this event. My findings reveal that while there is indeed a spike in the average advertising rate in the treatment markets following the event. However, this is attributable to the strategic shutdown of lower-rate newspapers following the swap. There is no evidence of always existing newspapers increasing rates after the swap events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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 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".