Trapped in the Here and Now - New Insights into Financial Market Analyst Behavior
Bibliographic record
Abstract
This study deals with the phenomenon of topically oriented trend adjustment in the time series of financial market forecasts. A total of 1, 182 time series with altogether 158, 022 interest rate predictions are examined. Forecasts refer to three-month interest rates and ten-year government bond yields in the USA, Japan, Germany, France, the United Kingdom, Italy, Spain, Canada, the Netherlands, Switzerland, Sweden and Norway. The forecasts are generated for horizons of four and thirteen months. Topically oriented trend adjustments arise in 1, 164 of the 1, 182 forecast time series. Thus 98.5% of these forecast time series reflect the present rather than the future. In other words, they correlate more strongly with the time series of naïve forecasts than with actual events. Independent of the forecast object, forecast horizon and countries concerned, the overwhelming majority of the forecast time series is distinguished by topically oriented trend adjustment. Five explanation patterns for topically oriented trend adjustment are meanwhile under discussion: 1. Anchoring heuristics, 2. The tendency to underestimate the variability of reality, 3. Avoidance of major blunders through protective estimates, 4. Normative herd behavior such as externally triggered herding, and 5. Information-based herd behavior.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| 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".