BFO Theory with Variable Profit in Case of Advance Payments of Tax on Profit
Bibliographic record
Abstract
The Brusov–Filatova–Orekhova (BFO) theory is generalized for the simultaneous account of variable company profit and advance tax on income payments. The generalized BFO formula for the WACC, has been derived. The dependence of WACC, discount rate, WACC–g (here g is growth rate), company capitalization, V, the equity cost, ke, on leverage L at various values of g, on debt cost, kd, and on age of the company, n, is studied. It is shown, that WACC, is no longer a discount rate. This role passes to WACC–g, which decreases with g, while the company's value increases with g. The tilt of curve k(L) growths with g. It is found that at the growth rate g < g* the tilt of the curve ke(L) is negative. This changes significantly the company's dividend policy principles. WACC(L) as well as the discount rate, WACC–g, decrease with the increase of debt cost kd. V (L) at all values of kd increases with leverage L, as well V(L) increases with kd. This means that tax shield advantages the decrease of the cost of raising capital. Examining the main financial parameters of the company at the positive (g=0.2) and negative (g=–0.2) growth rates, we found a huge difference in their behavior. This allows you to explore companies with growing profits and companies with decreasing profits, as well investigate the financial state of the companies whose profits rise and fall in different periods.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".