Antecedents and consequences of financial analyst turnover
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the factors leading to turnover among sell‐side financial analysts and the consequences of turnover. Design/methodology/approach The paper identifies two types of turnover, voluntary and involuntary, and defines voluntary (involuntary) as when analysts leave their employment at one brokerage firm and find (do not find) employment at another brokerage firm. Logistic models are estimated relating the probability of turnover to factors that explain turnover for both voluntary and involuntary turnover. Findings The paper finds that job performance is positively (negatively) related to voluntary (involuntary) turnover. This finding is consistent with Jackofsky's theory predicting U‐shaped relationship between performance and turnover. For voluntary turnover, analysts' performance and job conditions at the new brokerage firm are examined and related to the factors leading to turnover. It was found that turnover analysts move to smaller brokerage firms and become more accurate. They have lighter workload and enjoy more prestige at the new brokerage firm as they follow larger firms and fewer firms and industries. Originality/value This is the first study to apply Jackofsky's theory to the financial analysts' profession. Also, it is the first study to document the consequences to voluntary analyst turnover.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".