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arXiv Machine Learning By Xiaoyu Ma, Kelvin J. L. Koa, Yuncong Liu, Ke-Wei Huang

When Does "Post-Call" Begin? An Availability Contract for Earnings-Call NLP Benchmarks

Read the original on arXiv Machine Learning →

The paper introduces an Availability Contract to address timing mismatches in financial NLP benchmarks, requiring explicit declaration of input boundaries, evidence of availability, outcome start, and supported claims. Applying this framework to 552 earnings calls, the authors show that shifting from a call‑start proxy to a completion proxy changes return signs in 9.4% of calls and alters quintile rankings in 28.3%. In a volatility benchmark, moving target windows forward by one session increases mean squared error by 18.1‑21.4% while leaving point rankings unchanged, illustrating that labels and losses can be sensitive to the declared outcome clock.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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