financial-nlp

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topic2 events

papersTODAY 04:00 UTC

Audio encoders detect managerial evasiveness in earnings calls

A new arXiv preprint presents an approach that uses conversational audio encoders to spot evasive language from managers during earnings conference calls. Rather than aggregating vocal and lexical features across an entire call, the method analyzes the conversational dynamics between analysts and executives. Prior research has tied such cues to later negative outcomes for firms, and this work aims to capture them more precisely.

research/speech/finance/benchmarks

papersSEP 12 04:00 UTC

Study tests whether financial sentiment tools validate the same way for labels and market signals

A new arXiv paper examines a common assumption in financial NLP: that a sentiment model validated against human annotations can be trusted to extract market signals. The authors measure both objectives in a setting where they can be compared directly, finding that the two evaluations do not necessarily capture the same thing. The work suggests sentiment tools may need separate validation depending on whether they are used for labeling text or predicting prices.

nlp/finance/sentiment-analysis/evaluation