Evidence and Effects of Social Referencing Investor Behaviour during Market Bubbles
Bibliographic record
Abstract
Market bubbles often occur around the same time that new means of investing become available to enable increased market participation. An important aspect of increased market participation is the possible introduction of new investors who behave differently from existing traditional investors. Preliminary evidence from a new data set constructed from publicly available information suggests that these new investors display social referencing behaviour - their investment decisions are based more on social information (e.g., members of their peer group have purchased a stock) and less on typical financial information (e.g., the price of a stock). During the internet bubble of the late 1990s, our collected data show how investors using newly introduced on-line brokerages may have invested differently than investors using traditional and established brokerages. Using this model, we simulate an influx of these new social referencing investor agents in a proportion that is similar to the market weight that new on-line investors had during the internet bubble. The ability of our model to cause a quantitatively accurate, multi-agent simulation of traditional investors to similarly produce a price bubble demonstrates the potential that multi-agent models can have to produce quantitative results for qualitative investor models.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".