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SUMMARY:Corporate Earnings Calls and Analyst Beliefs
DTSTART:20260512T121500
DTEND:20260512T131500
DTSTAMP:20260531T010026Z
UID:6026008359f084fb2caf82cda4faeb8d3772593a4b60d177eac9ed70
CATEGORIES:Conferences - Seminars
DESCRIPTION:Giuseppe Matera - SFI@EPFL PhD student \nI study how linguist
 ic dimensions of corporate disclosure shape earnings expectations. I devel
 op a methodology that uses large language models to generate counterfactua
 l versions of earnings call transcripts that vary in language while holdin
 g underlying quantitative content fixed\, creating a form of counterfactua
 l variation that is impossible to obtain experimentally. I first show that
  textual features from earnings calls improve out-of-sample predictions of
  analyst forecasts and realized earnings above and beyond a rich set of qu
 antitative fundamentals. I then vary each call along six linguistic dimens
 ions---confidence\, sentiment\, uncertainty\, forward guidance\, technical
  jargon\, and macroeconomic focus---and trace how these changes affect pre
 dicted analyst revisions. The results show that analysts respond systemati
 cally to linguistic variation: they place excessive weight on optimistic a
 nd macro-focused language and do not fully incorporate information embedde
 d in language emphasizing risks and uncertainty. Linguistic variation also
  changes which fundamentals drive predictions: optimistic language amplifi
 es the role of recent earnings surprises\, while risk-laden language dimin
 ishes it\, evidence that presentation shapes not just the level of analyst
  beliefs but the information they attend to.
LOCATION:UNIL\, Extranef\, room 126
STATUS:CONFIRMED
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