Ferrari Analysis Based on Multiples Valuation Method
Bibliographic record
Abstract
In recent years, with the development of World multi-polarization and the impact of the COVID-19 epidemic on the economy, Economic Globalization and Trade Protectionism parallel. In this case, a world-famous cars manufacturing company, Ferrari, which provides luxury sports cars and racing cars, is facing a tough situation with the instability of its share price. Thus, an accurate evaluation of Ferrari's value appears essential. Based on this, this paper uses the multiples valuation method to evaluate Ferrari Company and will show whether Ferrari is under-valued, over-valued, or fair. And the authors choose several peer-comparable companies of the same type to provide an informative reference point, which can help to determine its value situation more accurately, and focus on P/E ratio and EV/EBITDA, then verify its actual valuation. The investigation shows that investors overvalued the share price of Ferrari and creditors overvalued the EV of Ferrari at the same time. At the end of the paper, some preliminary suggestions about Ferrari's sustainable development are given. The methods used are conducive to evaluating Ferrari's value more accurately, and the showed results can help investors and investigators get more exact information about Ferrari's current situation and future prospects to make more reasonable investment decisions.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".