Over-Valuation: Avoid Double Counting when Retaining Dividends in the FCFE Valuation
Why this work is in the frame
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Bibliographic record
Abstract
Valuation based on DCF (Discounted Cash Flow) has been the dominant valuation procedure during the last decades. In spite of this dominance, enterprise valuation using the discounted FCF (Free Cash Flow) model has some practical drawbacks, since there is often some confusion on how to effectively use it. Commonly, the valuation procedures start by estimating future FCF figures from historical data, such as mean FCF, growth and retention ratio, alongside many other variables. These FCF forecasts are discounted at the cost of equity (FCFE – FCF to Equity) or the Weighted Average Cost of Capital WACC (FCFF – FCF to Firm). Implicit in the above mentioned valuation procedures is the expectation that the company puts the retained free cash that is generating to good use, yielding a value capable of rewarding appropriately the level of risk inherent in the way it used. Some poorly performed valuation studies however tend to double count (Damodaran, 2006a) the retained cash’s interest in subsequent values of FCF, or include the accumulated cash build-up in the Terminal Value. This paper discusses how these two common double-counting mistakes are made and evaluates their weight in the final valuation figure for the particular case of retained FCFE (the case for the FCFF is analogous, but we focus on FCFE for simplicity) using projected figures.
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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 | 0.033 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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 it