Econometric Analysis of Market Responses to Corporate Reports of a Multinational Gold Producer
Bibliographic record
Abstract
This thesis explores the advances to mandatory corporate disclosures and reporting systems since 1996, including the growing prevalence of optional reporting in Corporate Social Responsibility (CSR) and Technology and Innovation (T&I), specifically by the gold sub-industry which has increased year-over-year. Many multinational mining corporation executive officers preach that this optional reporting generates greater value for shareholders and is critical to corporate success. This research explores whether this claim is supported by market data on regulated trading platforms. While the social benefits of a strong company-community relationship are immeasurable, the value can be contextualized through econometric techniques which quantify market reactions to various types of corporate disclosures. A custom event study model was built and tested on a case study of a major multinational gold producer. This case study quantified the economic value of mandatory corporate disclosures and evaluated them relative to optional reports. The methodology used benchmarks the company’s equity price to a reconstructed variant of the "NYSE Arca Gold BUGS" (HUI) Index and calculates the abnormal returns for all corporate disclosures. Each abnormal return is then categorized into a performance indicator category (production, financial, corporate, technical), and a T&I or CSR category. Finally, statistical analysis and sentiment analysis determine any economic impacts and trends. Disclosure system shortcomings are discussed along with recommendations provided on improving electronic disclosure systems, from both a policy and practical perspective. Potential further applications of this custom model include stock price prediction given corporate disclosures as an input, anomalous trading behaviour identification for regulatory tools, and communicating social value on equity valuation to corporate board executives.
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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 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".