External vs. In-House Advising Service: Evidence from the Financial Industry Acquisitions
Bibliographic record
Abstract
This study analyzes the wealth impact on M&A deals when the acquirers in the financial industry utilize external versus in-house advising services. A quasi-natural observatory setting is applied to investigate the costs and benefits of retaining a financial advisor. Based on agency theory, information asymmetry and conflict of interest both exist in the setting of M&A deals when acquirers use advisory services. We first find that almost 40% of financial acquirers are more likely to use in-house advising services, the frequency of which is significantly higher than that of non-financial acquisitions previously documented. Further, we find that in certain complex deals of greater information asymmetry, the frequency of retaining advisory services in-house is even higher. This finding suggests that for financial acquirers who possess expertise in the M&A market, the concern of conflict of interests (i.e., misaligned incentives) between the acquirers and their advisors are more salient than the concern of information asymmetry. More importantly, using the two-stage regressions method controlling the endogeneity of the choice between in-house versus external advisory services, this study finds that the three-day abnormal returns around the acquisition announcements are 4.5% higher for the acquirers retaining in-house advisory services, 18.7% higher for the corresponding target, and the combined merger gains are 2.2% higher. Overall, our findings provide direct evidence of the agency cost when an external advisor is hired and document the incremental values that the financial acquirers’ in-house advisory services may create.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".