Research on the Corporate Value of Ferrari Based on the Valuation Method
Bibliographic record
Abstract
Ferrari as one of the main brands of luxury cars, the company's earnings, has been in focus, according to data show that in 2021, the net loss from a year earlier, the company continued to expand, some investors want to assess the value of the company to evaluate whether to continue to invest, however, the evaluation of some researchers is the lack of a large number of reliable data, the lack of a detailed explanation of the assessment. Therefore, the research topic of this paper is to evaluate the value of Ferrari Company according to its own profit and debt financial situation, and whether investors can get the ideal return. The research method of this paper is as follows: first, the earnings per share and average stock price from 2019 to 2021 are collected, and then the data are analyzed by P/E ratio. In addition, the net profit and revenue data of Ferrari and its competitors from 2019 to 2021 were collected and EV/E valuation analysis was performed on their data. The results of the two research methods show that in terms of valuation, Ferrari is overvalued, enterprise value and P/E ratio are too high. That means investing in Ferrari shares won't necessarily pay off. This may be because Ferrari is not a new automobile brand, but an old one. Secondly, its product types make it difficult for Ferrari to continue to enter a new level. The Fed raised interest rates to ease inflation caused by geopolitical crises and the COVID-19 pandemic. The policy not only affects investors, but also affects Ferrari's borrowing.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".