Bibliographic record
Abstract
This paper investigates the relationship between managerial risk-taking and firm value as measured by Tobin’s Q. We conduct OLS regressions to examine the relationship between firm value and managerial risk-taking. To consider differential behaviors by different levels of risk-taking, we conduct separate tests on different risk-taking levels and on a dummy variable for risk-taking. We also adjust for industry fixed effects and address potential endogeneity issues with a two-stage least square (2SLS) approach. We find that risk-taking is positively related to firm value, and that this relationship is driven mainly by firms with relatively higher levels of risk-taking. We confirm the findings with various sub-periods, with industry fixed effects, and with two-stage linear regressions. Finally, we show that excessive risk-taking is not value-destroying. Subsequent tests report that higher risk-taking manifests as positive but slightly reduced capital allocation efficiency. When managers engage in more risk-taking, they increase the efficiency of capital allocation, suggesting that both immediate payoffs as well as long-term gains contribute to the boost in firm value. While the prior studies examine managerial risk-taking, none of them explicitly investigate its relation to a variable of particular importance to shareholders: firm value measured as Tobin’s Q. Furthermore, prior studies do not examine whether high levels of risk-taking can lead to the acceptance of negative NPV investments and consequently destroy firm value.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.064 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".