Chinese Super League stock prices and team performance
Bibliographic record
Abstract
Purpose Utilizing the capital asset pricing model (CAPM), the purpose is to analyze whether the stock prices of the corporation that owns sport teams fluctuate based on team performance in the Chinese Super League (CSL). Design/methodology/approach Several CSL teams are publicly owned corporations. As such, the authors look to see if on-field performance impacts the stock price of the firms. Using the news model from previous research, seemingly unrelated regressions are estimated on CSL games from 2014 through 2017. Findings The results from the main models indicate some evidence of a statistical relationship between on-field team performance and stock price. Furthermore, the findings for individual teams across markets did not hold consistent across different markets. More specifically, the authors found some instances where successful on-field performance led to a decline in stock prices. Originality/value The present study further contributes to the growing literature related to on-field performance and stock prices. Unlike previous research, the use of the CSL as the empirical setting provides the opportunity to use multiple stock markets which provides an opportunity to further examine this relationship. Finally, the study contributes broadly to the literature on professional sports ownership structures around the world.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".