Nonprofessional Investor Judgments: Linking Dependent Measures to Constructs
Bibliographic record
Abstract
ABSTRACT There is limited evidence on the construct validity of the dependent measures commonly used in the literature on nonprofessional investor judgments. In this paper, we first survey the literature to understand the types of dependent measures typically used by researchers. We then conduct factor analyses to uncover linkages between dependent measures and the constructs underlying these nonprofessional investor judgments. Our results suggest that, while the wide variety of dependent measures can appear on their face to represent many nuanced economic constructs, these measures capture three distinct factors. These factors relate to nonprofessional investors’ (1) expectations regarding future firm performance and value, (2) holistic perceptions of the firm, and (3) evaluations of the risk associated with investing in the firm. Next, we provide recommendations for selecting, analyzing, and reporting dependent measures in future research. Finally, we provide directions for future research to further our understanding of the judgments made by investors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.006 |
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".