Equity Market Pricing and Central Bank Interventions: A Panel Data Approach
Bibliographic record
Abstract
This paper analyzes the effects of central bank interventions via large-scale purchases of government debt securities on the pricing of stock market indices. This study examines the effects of changes in the size of the Federal Reserve’s balance sheet in three intervention scenarios: during the 2008–2013 period, the 2020–2022 period, and in the years between by using the instrumental variables three-stage least squares (3SLS) method for a time series approach, and calculates the effects of these interventions on each index in a fund of funds setup using the panel data strategy. This study confirms that large-scale purchases of government debt securities in response to the Great Recession and COVID-19 crises influenced the pricing of equity markets via their effect on the pricing of treasury bonds, with different degrees of sensitivity of each index to the effects on yields. Although the findings apply to the U.S. market, the results indicate that the pricing of small capitalization indices such as the Russell 2000 are less sensitive to changes in treasury yields caused by central bank interventions than large capitalization indices such as the DJIA. This research contributes to the understanding of financial asset pricing, particularly by identifying price distortions within equity market portfolios.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".