How to objectively rate investment experts in absence of full disclosure?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The result of this investigation is an operational model that can be used to accurately identify real stock market time series. In other words, if we are presented with a collection of blinded time series (real-life time series and simulated Random-Walks) then the proposed model will allow us to discriminate between both categories. In addition, it is shown that the type II error of this model quickly converges to zero as the time series length increases. The most remarkable feature of this model is its simplicity: a (bias-reduced) logistic regression with a single exogenous variable (the kurtosis p-value) based on the Quasi Random-Walk model that relates returns of equity and the entire market in times of large market returns. This model can be used as an objective rating benchmark for the models that are used by hedge funds to identify the stocks that should be used in a market neutral arbitrage strategy of long and short positions. In addition, it allows independent auditors to objectively evaluate the added value of statistical and technical analysis techniques that are often used in investment decisions. A rating mechanism that is based on the proposed benchmark, provides valuable information about the investment strategy even in absence of full disclosure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it