Information Behaviour of Sell-side and Other Analysts in Financial Institutions in Toronto, Canada
Bibliographic record
Abstract
The purpose of this dissertation is to examine the information behaviour of sell-side and other analysts in terms of their perception of environmental signals and information goals. I consider how individual, task, and sector factors influence sell-side and other analystsâ information behaviour. The research questions are: 1. What are the information goals of analysts as they seek and use financial and non-financial information to produce research? 2. In what ways do analysts perceive signals in their environment to prompt specific information seeking and information use? 3. What are the factors (individual-, sector-, and task- related factors) that influence the perception of information goals and signals in the environment? To address the research questions, I study the information behaviour of sell-side and other analysts in major financial institutions and private client firms. This study includes analysts in different positions to offer a holistic account of analyst information behaviour. I collected the data using Critical Incident Technique (CIT) interviews, and I analyzed the data using Boyatzis (1998) Thematic Analysis. The main findings of the study are that sell-side and other analysts seek and use information to identify investment opportunities or problematic situations, to establish reputations as trustworthy information sources, and to contribute innovative, value-added research to their industries. Environmental factors that influence how analysts perceive information goals and signals include time, geographical, political, and regulatory changes, competition, and consensus. Analysts establish criteria to select and to analyze quality information and communicate views or recommendations based on their research. Other analysts refer to sell-side analyst research to produce internal research for institutional investors (organizations) and to advise private clients and retail clients (general public). This study advances our knowledge of sell-side and other analysts who work for financial institutions in Toronto, Canada, as influential economic actors in a complex business environment.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".