Institutional Ownership Heterogeneity and Firm Performance: Evidence from Malaysia
Bibliographic record
Abstract
The roles of institutional owners in alleviating agency problem and its effect on firms have been studied extensively in corporate finance. However, these researches have mixed results because most have treated institutional owners as a homogenous group. Drawing on previous research, this study differentiates institutional owners as transient or dedicated owners. These two groups of institutional owners differ significantly in terms of size of holdings, purposes, goals as well as monitoring efforts. The objective of this study is to determine how the institutional ownership structure i.e. transient or dedicated ownership affect firm performance within the context of agency theory. To determine whether treating the sample homogenously or heterogeneously has different effects on firm performance, this study also examine the effect of total ownership (whereby the sample is treated homogeneously) on firm performance. Using panel data that span from 2002 to 2006, several hypotheses are tested based on data of firms listed on Bursa Malaysia’s Kuala Lumpur Composite Index (KLCI) using hierarchical regression analysis methodology. The empirical results indicate that total institutional ownership and dedicated ownership have no significant effect towards firm performance. Transient ownership, however, has statistically significant effect on firm performance. These results show that institutional ownership is heterogeneous with transient ownership structure monitor the management themselves. The establishment of the differing effect of institutional ownership due to the heterogeneous nature of ownership suggests that future research in this area must take into account the heterogeneity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".