Throwing in the towel: What happens when analysts' recommendations go wrong?
Bibliographic record
Abstract
Abstract Every analyst will experience stock recommendation failures during their career. Unlike many other professions, these pivotal moments occur in the full glare of clients, colleagues, equity‐sales teams, and the media. This research explores the practices of analysts up to and beyond the point where, faced with a failing recommendation, they contemplate “throwing in the towel” on their recommendation. Based on empirical evidence gathered from interviews with sell‐side analysts and their key interlocutors—equity‐sales specialists, investors, and investor relations officers—this paper uncovers several new empirical insights into the recommendation practices of analysts. The main argument made in the paper is that capitulation practices emerge from the specific contextual framework of individual recommendations and the analyst's conduct as a knowledgeable, emotional human agent. We identify several contextual contingencies of stock recommendations that underpin how a capitulation episode unfolds, including the temporal proximity of the capitulation to the original recommendation; the importance and profile of the stock to the analyst's reputation (“franchise intensity”); the level of interest/reaction from clients, equity‐sales teams and corporates; the nature/cause of recommendation failure; and recommendation boldness. Our study provides evidence that what an analyst does when faced with a failing recommendation cannot be reduced to a predictable, rational process and informs our understanding of observed practices such as the reluctance of analysts to capitulate and why “recommendation paralysis” often follows a recommendation capitulation.
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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 | 0.014 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".