Paper: arXiv 2610.21659

Authors: Xiaoyu Ma, Kelvin J. L. Koa, Yuncong Liu, Ke-Wei Huang

Abstract

Corporate financial disclosures often unfold over time, with announcements, prepared remarks, and discussion spanning multiple high-frequency return intervals. Financial NLP benchmarks can therefore suffer from a within-event timing mismatch when the prediction target begins before the textual or audio input used by a model could be complete. Chronological train–test splits do not prevent this mismatch, because it occurs inside individual events rather than across samples. We introduce an Availability Contract, which requires a benchmark to state the input boundary, evidence for input availability, the outcome start, and the claims supported by that evidence. We apply this framework to 552 earnings calls from 24 firms. For each call, we hold the return endpoint fixed and compare the previous close, a documentary call-start proxy, and a completion proxy based on reconstructed audio end plus a stipulated processing allowance. At this baseline, moving from the call-start proxy to the completion proxy flips return signs for 9.4% of calls. Holding completion-clock cutpoints fixed, 28.3% change quintile. In an independent release-session analysis of a public earnings-call volatility benchmark, shifting before-market target windows forward by one trading session raises three-day mean squared error by 18.1-21.4% across four frozen prediction streams, while their point rankings remain unchanged. Together, the results show that labels and losses can depend materially on the declared outcome clock even when model rankings appear stable. The Availability Contract makes this assumption explicit and ties post-input claims to the available timing evidence.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper introduces a novel and critical concept, the ‘Availability Contract,’ to address a significant timing mismatch issue in financial NLP benchmarks. It demonstrates strong empirical rigor through a detailed audit of earnings calls and a sensitivity analysis, showing material impacts on labels and losses. The mathematical complexity is moderate, focusing on precise definitions and statistical analysis rather than advanced theoretical derivations.

Research Flowchart

  flowchart TD
    A[Research Goal: Address Timing Mismatch in Financial NLP Benchmarks] --> B{Introduce Availability Contract Framework};
    B --> C[Apply Framework to 552 Earnings Calls (24 Firms)];
    C --> D{Compare Return Signs with Different Call Endpoints};
    C --> E{Analyze Quintile Changes with Fixed Completion-Clock Cutpoints};
    E --> F{Independent Release-Session Analysis of Volatility Benchmark};
    D & E & F --> G[Key Findings: Significant Impact of Outcome Clock on Labels/Losses, even if Model Rankings Seem Stable];