01 / IMPLEMENTATION & CONTRIBUTION
What the work involves
Local research implementations compare a signed temporal graph, a dynamic latent GNN, and a Student-t HMM choice model, with separate data and evaluation contracts.
Technical depth
Within-event ranking, matured-label graph updates, temporal encoders, filtered HMM posteriors, weighted Plackett-Luce choice, out-of-fold calibration, lower-confidence-bound expected value and abstention.
The project family
A_Share/RotationA_Share/Rotation_hmm_v14_bg02 / RESULTS
What came out of it
Alternative ranking paths use matured labels, filtered states, and explicit feature-availability rules. The GNN path excludes present-day industry classifications because they are not historical point-in-time labels.
03 / SUPPORTING EVIDENCE
Follow the source
Implementation notes, project records, and supporting artifacts.
Source context & project scope
No leaderboard or trading-performance claims. Production wording in local model docs does not establish deployment. Do not combine experiment variants into a single validated algorithm.
Defines an event-level ranking question rather than independent stock forecasts.
SOURCE · 2026-09-17Filtered Student-t HMM, choice calibration, sizing and PIT data clock modules.
SOURCE · 2026-09-17Dynamic latent rotation graph and exclusion of non-PIT industry classifications.
SOURCE · 2026-09-17