Three Essays in Financial Economics: the Interactions of Stocks and Fixed Income Securities
Bibliographic record
Abstract
This dissertation consists of three essays in the field of financial economics, which examine the interactions of stocks and fixed income securities, at the individual bond level and aggregate market level. The first chapter provides a general introduction for the whole thesis. The second chapter studies asynchronous and contemporaneous links between values of individual stocks and bonds issued by the same firm. These correlations offer indications on how firm-specific information streams between the stock and bond markets. We examine those links using a novel database which contains bonds issued by Canadian firms over three decades. The overall result provides strong evidence of information flows streaming from the stock market to the bond market, and suggests that significant bidirectional information flows were triggered by the 2007 financial crisis. Further, information regarding the mean of firm's value, rather than its volatility, prevails in driving contemporaneous variations in stocks and bonds. The third chapter examines flights from stocks to three types of safe-haven assets: long-term Treasuries, T-Bills, and top-grade corporate bonds. We propose an innovative data-driven approach to identify flight-to-quality, and thus eliminate the exogenous identification of the crisis period. The chapter then examines the role of asset performance, volatility, illiquidity, and monetary policy activities on the flight-to-quality episode. The results indicate that illiquidity shocks appear to diversely affect different types of flights. Monetary policy announcements, both past and contemporaneous, are shown to decrease the incidence of flight-to-quality. In addition, this chapter establishes a strong link between the profitability of the momentum strategy and flight-to-quality. In Chapter 4, we check the robustness of the methodology to identify flight-to-quality proposed in Chapter 3. We find that flight indicators obtained by employing sub-samples, crisis periods or benchmark periods with different numbers of observations are highly correlated with each other. Our flight to long-term Treasuries indicators are robust to inclusion of the two other safe-haven assets. Results based on a series of data simulations with correlation changes of various possible sizes indicate that when a correlation change is about 5 times as large as the benchmark correlation level, our model can identify a flight in 90% of the data simulations. The last chapter concludes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".