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So if something like this came into common use, the obvious follow-on would be "earnings writers" who specialize in phrasing and vocabulary that tickles the AI.

• mlthoughts2018 8y ago
  This technique generally (applying NLP to 10K’s and other corporate filings) has been in practice for many years. One common, highly commoditized text signal is earnings surprise: measuring sentiment in earnings call transcripts, and comparing sentiment scores with similar metrics from analyst publications in reaction to the earnings announcement. The arms race effect you mention absolutely exists. Corporate officers are often coached extensively on key phrases to repeat in earnings calls, and phrases to avoid, and how to steer caller questions away from SEO-like speech patterns they don’t want.
• sriku 8y ago
  Goodhart's law will eventually come around.
• dx034 8y ago
  In earnings calls, management already generally reads off scripts and won't answer anything they didn't prepare for (except for some smaller companies, e.g. Tesla recently). They know that transcripts are available and one wrong word could cause investors to panic. Using machine learning won't really change much there. Maybe picking up on signals humans can't hear (e.g. confidence in voice) but texts are already overanalyzed.