Understanding Belgian Individual Investors: Complementarity of Qualitative and Quantitative Methodologies in a Grounded Theory Approach
Bibliographic record
Abstract
This article describes the methodological reasoning followed while studying Belgian individual investors and shows how two methodological approaches, one qualitative and one quantitative, can together allow to build a real inductive process within priority is given to data and to returns from the field.How do individual investors experience their investment? Are they one or several investor’s profiles?Our research explores an unknown territory (Bouchard, 200). Many researches focus on investor behaviour bias and their underperformance. No researches studied Belgian individual investors, few studies used mixed methodologies (qualitative and quantitative) and few studies used primary data. Our research proposes to fill that gap.Thanks to the qualitative phase (17 interviews of Belgian investors) we highlighted the importance of family tradition and the influence of environment regarding investment decisions; the difference of perception between investors and their environment, qualities of a good investor and their perception of financial intermediaries.The quantitative phase (706 questionnaires) allowed to discover 5 investors’ profiles in term of behaviour: the followers, the traditionalists, the sleeping investors, the experts and the gamblers.This article also pinpoints all difficulties met during the research using grounded theory and proposed the solutions used by the authors.
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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 | 0.067 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".