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Status
Forthcoming
Venue
The Journal of Performance Measurement
Accepted · Winter issue
Year
2026
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Page one public · full text on request
Soto, A. B. (2026). Hierarchical Reconciliation with Narrative Preservation: A convex framework for multi-level arithmetic attribution linking. The Journal of Performance Measurement (forthcoming). https://adambsoto.com/research/hierarchical-reconciliation-narrative-preservation
02 / 07Forthcoming

Hierarchical Reconciliation with Narrative Preservation

A convex framework for multi-level arithmetic attribution linking

Executive overview

Real attribution reports are hierarchies: total, asset class, region or sector, and security. A linked report is defensible only if every parent equals the sum of its children and the top equals cumulative active return. Linking each level independently routinely breaks that, because the adjustments disagree between levels.

HLD parametrizes the linked report by its bottom level, so coherence holds by construction. Top-level reconciliation is a hard constraint, distortion is controlled at every node, and narrative penalties apply level by level. The base formulation is a weighted projection of the raw tree onto the coherent, reconciled set.

99.6%of coherent scenarios broken by per-level linking
69.4%HLD8S lowest or jointly lowest gated loss (500 scenarios)
1,500mixed-depth hierarchies in production hardening

Findings, in order

  1. Per-level linking does not compose. Independent optimization at each level violated coherence in 99.6% of 500 coherent Monte Carlo scenarios.
  2. Coherence becomes structural. Solving for the bottom level through a summing matrix guarantees that parents equal the sum of their children.
  3. Base-HLD is a weighted projection. This ties attribution linking to the hierarchical forecast reconciliation literature (Hyndman et al.; Wickramasuriya et al.).
  4. Strong comparative results. Under reconciliation- and coherence-gated loss, HLD8S won or tied in 69.4% of scenarios and HLD7S in 65.8%.
  5. Multi-system inputs expose leaf-only methods. When levels come from different systems, leaf-only methods fail reconciliation itself.
  6. Hardened at production scale. 1,500 mixed-depth and ragged hierarchies, 3,000 production solves and 400 independent objective-oracle comparisons.

Novelty

Extends convex-QP linking from a flat vector to a full reporting tree and places attribution linking inside hierarchical forecast reconciliation, while keeping an economically motivated diagonal weighting suited to production.

ScopeThe narrative-augmented variants are not pure projection estimators, and discrete ranking is treated as a diagnostic, not a hard guarantee.
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