Bibliographic record
Abstract
"Investors who trade based on good research are said to be the backbone of stock markets: They conduct research to discover the value of stocks and, through their trading, guide financial prices to reflect true value. What can make their job difficult is that high-speed, short-term traders could use machine learning and other technologies to infer when informed investors are trading. These short-term traders can then buy when informed investors are buying or sell when they are selling, and (in a sense) steal some of their profits. This behaviour could discourage informed investors from trading, thus making prices less informative—which could have wider ramifications for the economy. To assess this possibility, we investigate an 11-year sample of stock trading and study investors we identify as informed in the long term. We ask whether informed investors are healthy and whether they are affected by shorter-term traders. We find, in contrast to some empirical studies, that informed investors have roughly constant trading and profits in the sample. Also, our findings show no sign prices are getting worse, as our metrics of price informativeness are flat. This is despite a slow growth (though later, a fall) in the presence of shorter-term trading. Moreover, informed investors are sophisticated in how they trade, which might disguise their presence in markets from a detection algorithm. In general, rather than being the “prey” in financial markets, informed traders appear to be the “apex predators.” They seem to be so good at their trading that (as far as we can measure) relatively little of what they know makes it into prices. Thus, it is unclear that efforts to protect informed investors from high-speed traders are really needed. Instead, regulators might think of ways to increase competition in the financial sector among informed investors themselves."
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".