“Being there” and the continued importance of the local in finance
Bibliographic record
Abstract
Abstract We examine the role of tacit knowledge and the need for proximity in shaping the geography of the sell-side equity research, as one of the most knowledge intensive parts of the financial sector, which forecasts the value of firms, and as such has major significance for the whole economy. We use a unique combination of professional experience, a purpose-built quantitative dataset on analysts’ coverage, and extensive expert interview material. Our analysis, focused on three highly globalised sectors (metals & mining, oil & gas, and semiconductors), documents the leading positions of Toronto, Calgary & Houston, and Taipei & San Francisco, respectively, as sell-side equity research centers, matching or exceeding the role of New York or London as global financial centers. We argue that this geography reflects the continued significance of specialised and localised tacit knowledge, which is crucial to sell-side equity analysts for three inter-related reasons: the need for preferential access to local information and knowledge networks in the forecasting process; the importance of individual interpretative and analytical expertise; and the growing pressure for rapid analysis and response to new information. In short, equity analysts have to ‘be there’, at the sources of industry-specific information and knowledge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".