The Small-Sized Premium: Is It Really Relevant? Evidence from the European Equity Market
Bibliographic record
Abstract
The valuation of a company reflects the expected return or equivalently, the cost of capital that investors demand in exchange for the risk assumed. Despite the ex-ante nature of the problem, the majority of empirical analysis has focused on factors explaining expected returns from an ex-post perspective. In this paper, we take a different approach and try to identify which factors are ex-ante included in discount rates, with particular attention to the so-called size premium. Starting from observed market capitalisations and company fundamentals, we obtain the implied cost of capital from the reverse engineering of a carefully designed fundamental valuation model. Panel data regressions are used to investigate the existence of a relation between the implied cost of capital and the firm’s size, including other control variables representative of the most cited asset pricing “anomalies”. Our sample comprises European non-financial stocks listed on primary markets, with half-yearly observations starting from the aftermath of the 2008 global financial crisis. Contrary to common wisdom, we find that the firm’s size has no tangible impact to explain the implied cost of capital.
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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 | 0.003 | 0.012 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 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".