Evaluation of the Predictive Validity of the CapitalCube™ Market Navigation Platform
Bibliographic record
Abstract
IntroductionThis is the fourth research report where various aspects of the CapitalCube™ Market Navigation Platform [CCMNP] of AnalytixInsight™ have been examined.Previous ResultsIn our previous three studies, we have tested many of the CCMNP-variables as expressed through the S&P500; we have rejected the Nulls of their inter-and intra-group association in favor of the likelihood that the variables that constitute the CCMNP are not produced by random generating processes. This suggests that the CCMNP is capable of creating market relevant information that may inform the investment decision.Current Study The previous results beg the question that is the focus of this report: Given the Non-Random character of the various CCMNP panel variables, does this panel of information enable the identification of a particular stock that will, in the near future, experience a turning-point?Results:We find no evidence that the CCMNP aids in detecting turning-points for the S&P500 Panel of data tested. Various caveats to this study are detailed in the summary section of this research report. Finally, we offer that the methodology used in investigating the CCMNP is a simple, transparent, and useful model for evaluating the acuity of a MNP in detecting turning-points.
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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 | 0.015 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".