Deploying Narrative Economics to Understand Financial Market Dynamics: An Analysis of Activist Short Sellers’ Rhetoric
Bibliographic record
Abstract
We investigate how Activist Short Sellers (AShSs) expose publicly listed firms in an increasingly popular form of “research reports” openly denouncing alleged frauds, flawed business models, accounting irregularities, and wrongdoings. We focus on six AShSs that issued research reports that often led to a strong negative market reaction. Our empirical analysis exploits both qualitative and quantitative methods for a comprehensive dataset of 383 research reports targeting 171 unique firms, and three first-hand interviews with AShSs. Drawing on Aristotle’s rhetoric, we first examine how AShSs use narratives in striving to convince other investors that the target firms are overvalued. Specifically, we search the documents produced by AShSs for stylized narratives related to credibility-based (ethos), emotions-based (pathos), and logic-based (logos) rhetorical strategies. To assess the impact of these strategies, we examine the extent to which the AShSs’ rhetorical strategies resonate in 3,665 press articles. As expected, the press often refers to logos-based arguments. Interestingly, the press also brings up frequently pathos-based and ethos-based statements. Considering the importance of the press in shaping investors’ opinions, our study points to AShSs’ narratives playing a major role in policing financial markets. Theoretically, we show that AShSs, as dissenting market participants, produce narratives that go beyond the language of formal rationality – as they strive to reveal new information and frame it persuasively, in order to destabilize the extent of trustworthiness surrounding target firms.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".