Institutional investors, monitoring and corporate finance policies
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the influence of institutional investors’ investment horizons (IIIH) on a wide variety of key corporate policies. Design/methodology/approach The authors perform regression analysis to a panel data set of quarterly financial statement data for US firms over the 1981-2014 using several measures of IIIH. Findings The authors argue that an increase in the presence of long-term investors contributes to more effective monitoring and information quality. This results in a reduction in agency costs and informational asymmetry problems for firms that are more heavily influenced by long-term investors, which in turn influences the corporate policies they pursue. Consistent with these arguments, the evidence suggests that firms with a greater long-term institutional investor base maintain lower investment outlays, higher dividend payments, lower levels of cash and higher levels of leverage. All results hold after controlling for potential endogeneity issues. Originality/value The authors show that a greater presence of long-term institutional investors leads to higher dividends, lower investment outlays, lower cash holdings and higher leverage. The comprehensive nature of the predictions with respect to overall corporate finance policies and the supporting evidence provided represents an important contribution, as previous studies have tended to focus on one specific area of corporate behavior (i.e. such as cash holdings).
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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 | 0.000 | 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 | 0.001 | 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 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".