Informed Trading and Momentum in the Corporate Bond Market
Bibliographic record
Abstract
Abstract Taking advantage of the different trading behaviors of investors on same-issuer bonds, we show that informed trading lies at the core of the momentum effect for corporate bonds. We split the firm-level bond cross-section into top (nontop) bonds that are characterized by higher (lower) volumes of institution-sized trades. We show that top bonds attract more informed trading and transmit information faster than nontop bonds. We design specific top and nontop bond momentum strategies to capitalize on this informational heterogeneity. The results indicate that fast news spreading yields short-lived momentum in top bonds, whereas momentum in nontop bonds is strong and drawn-out due to slow information diffusion. These differences are concentrated in bond-level information-intensive periods and are not explained by differences in liquidity levels, systematic risk (including liquidity risk), bond characteristics, and market states. In particular, bond-level liquidity affects the momentum effect only by altering the rate at which news spreads.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".