The effects of social media sentiment on stock price : a study of the Canadian energy sector
Bibliographic record
Abstract
This thesis investigates the intricate relationship between digital discourse, specifically social media sentiment, and stock price within the Canadian oil and gas industry from 2020-2021. The research comprises two distinct yet complementary studies that collectively shed light on the complex dynamics at play. The first study examines the predictive capacity of non-financial Twitter sentiment on stock prices across nine prominent Canadian oil and gas companies. Employing Granger causality tests, the findings reveal no significant influence of social media sentiment on stock prices within the sample. This contradicts previous studies suggesting a predictive relationship and highlights the potential dominance of market-level sentiment over individual company sentiment, as well as the diminished impact of non-financial discourse on investor decisions. The study bolsters past research which indicates the diminishing power of external stakeholders over firms in the digital era. The second study delves deeper into the relationship between digital discourse and stock price, focusing on Enbridge Inc. and TC Energy Corp. Granger causality tests reveal statistically significant forecasting abilities of Twitter sentiment on stock prices during shortened timeframes surrounding peak Twitter volume dates for these two companies. However, qualitative analysis suggests that stock price movement may be affected more by traditional media coverage than social media sentiment. The findings suggest that the impact of digital discourse on stock price may be company-specific and depend on the nature and intensity of the discourse. Nationally viewed political events or controversies appear to be potential catalysts for movement on stock prices. Additionally, specific RepRisk categories related to environmental, social, and governance (ESG) factors were shared between Enbridge and TC Energy, indicating their potential salience in this relationship.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".