Nonfinancial Information Preferences of Professional Investors
Bibliographic record
Abstract
ABSTRACT Academics, regulators, and the general business community have called for the assessment of the decision-usefulness of three key categories of nonfinancial information: economic, governance, and corporate social responsibility (CSR). This study builds on prior research by examining the nonfinancial information preferences of professional investors, how their demand for the nonfinancial information categories compares to those of nonprofessional investors, and whether these demands vary across subgroups of professional investors. Based on survey responses from 228 professional investors, the results show that this investor class prefers nonfinancial information that is concise, comprehensive, comparable, and credible. Similar to prior research, the results indicate that professional and nonprofessional investors have similar demand orderings: they are most interested in economic information, then governance information, and then CSR information. However, professional investors demand greater detail than do nonprofessional investors for subcategories of economic and governance information. Further, while both professional and nonprofessional investors' demand is increasing for all subcategories of governance and CSR information, the change in demand is more pronounced for nonprofessional relative to professional investors, and particularly for CSR information subcategories. These variations in preferences suggest potential differences in perspectives between professional and nonprofessional investors. Finally, the findings indicate that institutional investors have recently used less economic information. For the subgroups whose investment research consists of at least a quarter of socially responsible investments (SRI), the higher the SRI investor level, the higher the recent usage of more information categories; and the higher the SRI investor level, the higher the future demand for economic information, but the lower the SRI investor level, and the higher the demand for CSR information. These findings suggest potential differences in investment approaches among subgroups of professional investors. Together, these results provide support for more integrated reporting of nonfinancial information to meet professional investors' multidimensional preferences and differences in demand relative to those of nonprofessional investors.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".