Equity valuation : Canadian National Railway
Bibliographic record
Abstract
This dissertation with the title Equity Valuation – Canadian National Railway was written by Oliver Franz-Hermann Pach. The master thesis deals with the determination of the fair share value of Canadian National Railway. First, the author reviews the state-of-the-art methods for equity valuation in a literature review. Then, an analysis of the industry and the company itself is conducted and risks are highlighted. In the valuation section, a DCF valuation, sensitivity analysis and the modeling of two different scenarios are applied. In the DCF valuation, revenues and costs are forecasted, and the free cash flow to firm (FCFF) is determined, which in turn is discounted to the current value using the weighted average cost of capital (WACC). In addition, a valuation is performed using relative valuation approaches. Finally, the calculation of the value-at-risk for different time periods is intended to illustrate the risk of an investment. Both methods achieve slightly different results, the DCF and the scenarios indicate a slight undervaluation of CNR whereas the multiples rather show a slight downside. All in all, a target price of CAD 151 and a HOLD recommendation is arrived at. The own analysis will also be compared with an existing analyst report from Vertical Research Partners to identify similarities and differences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".