Bibliographic record
Abstract
Markets are risky. And risk, as the traders know, involves loss, or the possibility of loss. The connection that is made between risk and loss is intuitive and powerful. Because the probability of equity loss increases as markets fall, there a manager's ability to defend capital against loss is most critically tested during periods of market decline. But rising markets are risky, too. Regardless how well the managers defend against loss during falling markets, if they are unable to earn satisfactory returns, they subject clients to another risk, lost opportunity. Since opportunity-risk is always highest as the broad market advances, that form of risk is best measured when the market is rising. Also, managers’ returns are reported quarterly. This chapter presents an assignment, where the risk measurement is illustrated by using two sets of benchmark returns, one to measure offensive performance and the other to measure defensive performance. Successive 20-quarter periods are analyzed; along with it offensive performance and defensive performance are also measured. The author shows how the trend of a manager's relative performance is tracked against the background of the illustrated matrix by linking successive calculations of offensive-defensive performance.
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.
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.080 | 0.013 |
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; both teacher heads agree on what is shown here.
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".