Research on the Investment Value of Guangzhou Baiyun International Airport Based on Multiples Valuation Method
Bibliographic record
Abstract
Aviation has been an industry that has been widely watched by investors. As the upstream of the civil aviation industry chain, the airport industry is an important core asset of the entire industry chain, providing relevant services for global air transport companies and passengers. In recent years, China's aviation industry has been in a state of steady development. Until the outbreak of COVID-19 in 2020, the market focus turned to the impact of the speed of passenger traffic recovery during the epidemic on the recovery of airport performance. For core hub airports, the priority is whether asset values have changed. This paper analyzes Guangzhou Baiyun International Airport in China, and studies whether the investment value of the airport is fair through financial analysis, peer comparison, and multiple valuation methods. The study found that the airport's overall revenue decline in the past two years was mainly due to the epidemic, but it has performed better compared to industry players. Through multiples valuation, the theoretical stock value of Baiyun Airport in 2021 is 16.19 CNY higher than the actual value. The result indicated that the company's stock was undervalued and has good room for growth in the future, suggesting a purchasing opportunity. It is hoped that the research in this paper can expand the research on related aspects of the private airport industry, and also provide some help and reference for the valuation research of Guangzhou Baiyun International Airport.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".