SOURCE-LINKED INTELLIGENCE

Lie to me: Detecting Managerial Evasiveness in Earnings Calls via Conversational Audio Encoders

arXiv · AI, language, vision and robotics · article · Sep 12, 2026 · UTC

Earnings conference calls are a primary channel through which managers disclose information under analyst scrutiny. Prior work has linked vocal and lexical cues to future adverse outcomes, but often pools features over an entire call and underuses the interactive structure of Q&A. We propose a two-branch late-fusion framework for detecting managerial evasiveness as a predictor of extrinsic SEC events (primarily late filings): (i) an LLM-as-a-judge that maps Q&A text to an interpretable call-level vector X_text via a structured binary rubric, and (ii) a frozen conversational encoder whose tempo

Read original source ↗ Open in workspace

recordType

paper

region

Global