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financial analytics providers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Audio signals reveal evasive managers during earnings calls

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

Abstract: 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 temporal hidden states are read by a DeepVoice-style sequential reader to produce an audio representation h. Late fusion of (X_text, h) yields a call-level risk score p. On n=1,039 calls (212 late filings) with firm-grouped 5-fold CV, fusion reaches AUROC approx. 0.89, versus 0.55 for the text judge and 0.71 for duration alone. These results show that conversational audio dynamics encode managerial evasiveness beyond lexical content and call length, yielding a stronger early-warning signal of adverse SEC outcomes.

Sat 12 SeptMachine LearningComputational Engineering, Finance, and Science

The gist

When company leaders talk to analysts on earnings calls, sometimes they try to avoid answering questions directly. The authors found that by combining what is said with how it is said—using both text and audio analysis—they can better spot when managers are being evasive. This helps predict when companies might face serious regulatory problems, like late financial filings. Their method works better than just looking at the words or call length alone.

Open → 2609.13893v1