Behavioral Heterogeneity in the Stock Market Revisited: What Factors Drive Investors as Fundamentalists or Chartists?
Bibliographic record
Abstract
This paper reexamines the issue of behavioral heterogeneity in the stock market. In contrast to previously documented contemporaneous results, we test the issue by identifying and testing four new determinants of the proportion of fundamentalists and chartists in the stock market. Our empirical results are consistent with the following notions. First, the proportion of fundamentalists increases for stocks with incremental information involved in accounting reporting as proxied by discretionary accruals. Second, the proportion of fundamentalists is positively related to the degree of dispersion in financial analysts' forecasts, which implies that stock investors care more about the intrinsic value of firms obtained with fundamental analysis when encountering information asymmetry or uncertainty. Third, the proportion of fundamentalists increases for stocks with higher volatility in prices. For the proportion of chartists, the reverse of these arguments holds true. Fourth, the proportion of chartists versus fundamentalists is related to the investment horizon. Investors give more weights to technical analysis when considering short-term investments. For long-term investments, investors increase the weighting given to the fundamental analysis.
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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 | 0.002 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".