Financial disclosures timing affects predictions in earnings call analysis
When Does "Post-Call" Begin? An Availability Contract for Earnings-Call NLP Benchmarks
Computation and LanguageMachine Learning
Summary
Financial companies share important information during earnings calls, but this information comes out in stages over time. The authors point out that models predicting financial outcomes can get confused because the timing of the data they use and the event they predict don’t line up perfectly. They propose a new way to clearly define when the input ends and when the predicted outcome starts, called an Availability Contract. Testing this on real earnings calls, they show that the choice of timing can change predictions significantly even though models may still be ranked the same.
What this means in practice
- •For financial data teams: Set consistent timing rules for when input data ends and outcomes start to improve prediction clarity in earnings-call-based models.
- •For financial software developers: Implement explicit availability contracts in NLP tools to reduce timing mismatches when analyzing earnings call texts and audio.
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.