Why Listen to Earnings Calls When Artificial Intelligence Can Do It Better?
Prattle has started providing real-time, quantitative analysis of company earnings calls.
Venture-capital backed Prattle will begin offering a new service Monday that quantifies and scores the language used in earnings calls and reports filed by 3,000 U.S. public companies.
The company, which is backed by venture-capital firms including New Enterprise Associates and GCM Grosvenor, uses natural language processing and machine learning techniques to improve on current methods of analyzing text and sentiment. Traditional text analysis often uses a simple calculation of the number of positive and negative words in a report or transcript.
Analysts and fundamental portfolio managers are trying to shift from decision-making that relies on human subjectivity to a quantitative approach or a mix of both quant and fundamental analysis, according to Evan Schnidman, Prattle’s chief executive officer.
“We’re quantifying what wasn’t quantifiable before,” said Schnidman, a game theorist who taught at Brown University and Harvard University before founding Prattle.
After an earnings call, for example, Prattle provides a score, as well as the sentiment of every speaker. Schnidman expects its methodology to be highly accurate based on its experience distilling language used by central banks into quantitative sentiment data that projects policy outcomes. So far Prattle has predicted outcomes from the nuanced language of central bankers with 98 percent accuracy in the year and a half that it has offered the service.
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Schnidman says the company is not looking for new patterns or investment insights in big data. Instead, Prattle is making it more efficient to mine proven sources of market-moving information by using advanced computing techniques.
For example, if a company’s numbers are positive, but the tone is downbeat during an earnings call, Prattle’s equity analytics platform can quantify that gap in a way that is difficult for a human analyst to do.
The provider of sentiment data that predicts market reactions understands that as its services become popular, they may also become less effective as investors flock to the same trades. This hasn’t yet happened to its central bank service, according to Schnidman, who said Prattle will keep investing in the offering to prevent a loss in effectiveness.
“When does this cease to be alpha and baked into beta?” he said. “There is more risk of crowding in mid- and small-cap stocks than large caps, but we’ll just have to become more sophisticated.”