21  Methodology

This page indexes where each part of the methodology already lives rather than restating it — the detail is maintained in the platform-substrate and programme chapters, each generated from or grounded in the platform itself.

21.1 Data sources

• Data Catalogue — provenance, licensing, refresh schedules for every dataset the research draws on.
• The multiplex data substrate — whether the platform can source the proposal’s three network layers (factor exposure, supply-chain, institutional ownership).
• research proposal §“Data & reproducibility” — public and commercial sources named for the research specifically (the French data library, CRSP-like sources, institutional 13F holdings, 10-K parsing).

21.2 Statistical methods

• Factor models & benchmarks — factor loadings and returns, the CAPM decomposition, systematic versus idiosyncratic return, benchmark selection.
• The factor layer as multiplex Layer 1 — the bipartite stock–factor layer as built, not hypothetical.
• Systemic-risk & crowding groundwork — prior network work (comomentum, crowding) the proposal builds on.
• Whitepaper §5 — the mathematics of trust — the statistical core (the information coefficient, permutation testing, false-discovery correction, overfitting probability) that any candidate signal — including a network-based early-warning indicator — must clear.

21.3 Validation approach

• Validation & anti-overfitting — how network claims specifically are protected from self-deception, beyond the platform’s existing factor-validation gates.
• Reproducibility & experiment infrastructure — what makes an experiment auditable and repeatable here.
• Gap analysis & research roadmap — an honest per-capability status, stated in prose — implemented, partially built, or proposed but not yet built — so methodology claims do not outrun what is actually in place.

See also: Research Questions for what each of these methods is being used to answer, and Experiments for the running record of applying them.