Abnormal Returns and Fundamental Analysis in Institutional Investors’ Decision-making: An Agency Theory Approach
Bibliographic record
Abstract
The purpose of this paper is to investigate the abnormal returns achieved by institutional investors. Distinguishing between institutional investors operating with a specific mandate to invest and those that operate their own choices independently from such a specific delegation, we show that the former achieve higher abnormal returns than the latter. The conceptual explanation of this result is attributable to the use of the fundamental analysis that the first type of institutional investors realized in a higher and more effective way than the second. This different approach in selecting securities might be due to the relationship between the institutional investor and the savers who provided capital. This different agency relationship might have been reflected in the institutional investor's investment policies through the agent behaviour, which changes depending on the nature of the principal who has given the mandate. The empirical analysis has been conducted on a sample of 5,500 institutional investors operating all around the world in 2014, drawing data from institutional investor's annual report, from their investment relations and from Bloomberg, Thomson Reuters, Bankscope, Eurostat and through Computer Assisted Telephone Interviews.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".