Equity valuation: Netflix, Inc.
Bibliographic record
Abstract
This thesis aims to determine the fair value of the Netflix, Inc. share with reference to the date of December 31, 2022. To this end, and based on the literature review, the analysis of the entertainment and media industry, and the historical financial and operational performance of the streamer, an equity valuation was developed in order to guide investors with a recommendation on the option to buy, sell or hold the company's shares, based on the potential appreciation or devaluation of the market and the potential risks, given the volatility of the stock exchange. To this end, the Discounted Cash Flow model was adopted as the primary valuation methodology, using WACC as the discount rate alongside, with a Relative Valuation. Furthermore, a sensitivity analysis was conducted on the most significant components of our projection to consolidate the robustness of the analysis. Consequently, the application of the DCF model culminated in a target price of $319.97 compared to the Relative Valuation, where the price was ~14.7% lower. However, given the specificities of the model and the projected long-term assumptions, it was decided to consider only the first approach for recommendation proposed in this thesis. Accordingly, we considered that Netflix was undervalued, recommending investors take a Hold with tendency to Buy position in relation to the asset in their investment portfolio.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.141 | 0.045 |
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".