GOVERNANCE THROUGH STOCK TRADING IN BRAZIL: EVIDENCE WITH INSTITUTIONAL INVESTORS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Purpose: This paper analyses the viability of stock trading as a mechanism to promote corporate governance, addressing its effects on abnormal returns, information, and firm performance. Originality/value: The study indicates that competition among institutional investors is important to raise stock price efficiency. Policies that allow capital inflow, increase in liquidity, and a link between managers’ salaries and stock performance are beneficial to reinforce the stock market efficiency. Design/methodology/approach: Hypotheses testing using panel data regressions of 233 stocks between December 2009 to December 2017 from Thomson Eikon, Economatica and ComDinheiro. Findings: The results indicate that the number of institutional investors is not related to abnormal returns. On the other hand, the number of institutional investors increases the amount of firm-specific information into stock prices, rising stock market price efficiency. This relationship is stronger among the preferred stocks (PN), but this mechanism is still not valid to increase firms’ operational performance. Despite the possible increase in stock price efficiency, the investors cannot adopt such a mechanism to exercise governance if there is no remuneration linked to performance.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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 it