Bibliographic record
Abstract
This paper studies how critical entrepreneurial finance outcomes such as the investment return and equity division are shaped by venture characteristics, financier risk preferences and competitive searching. Our analysis uses a double-hazard agency model in which financiers determine the equity division to maximize the expected utility of their investment return while entrepreneurs search for the best deal. Model results provide several novel insights on the role of risk, the venture funding cycle and coexistence of angels/VCs. The model provides theoretical justification for the pattern of venture funding activity by predicting that financiers with higher funding capacity (e.g. VC firms) will benefit by funding ventures at later stages of development as their expected investment return rises with the venture’s initial value and financier productivity. Its also shows that competitive searching by entrepreneurs enables financiers with a diverse set of risk preferences to coexist profitably by reducing the advantage (disadvantage) of lower (higher) risk-aversion financiers and making investment returns more similar. Lastly, the model’s prediction that the financier expected investment return will decline with venture risk means that only angels and VCs with lower levels of risk-aversion can profitably finance riskier projects (for risk-neutral financiers, venture risk has no affect on the equity division and expected return).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".