Corporations are tailoring their financial reports to be read by machines.

What’s new: Automated systems download far more company financial reports than humans, according to a study by the U.S. nonprofit National Bureau of Economic Research. Consequently, companies are filling those reports with data that looks good to computers.

What they did: The study analyzed 50 years of quarterly and annual financial reports submitted by public companies to the U.S. Securities and Exchange Commission.

  • Drawing on SEC download logs, the authors examined the IP address associated with each download to determine whether a person or a machine initiated it. They found that automated downloads grew from 360,862, or 39 percent of the total, in 2003 to around 165 million, or 78 percent, in 2016.
  • Companies that served large numbers of machines-initiated downloads were more likely to make their reports machine-readable by, say, adhering to ASCII standards, separating tables from text, and ensuring that documents contained all the information required to interpret them.
  • Moreover, these companies use language more likely to produce positive scores from sentiment-analysis models. For instance, they tend to avoid words associated with negative emotions, lawsuits, or uncertainty.

Behind the news: Computer systems increasingly drive the stock market. Last year, Deutsche Bank estimated that automated systems made buying and selling decisions for 80 percent of equity trading and 90 percent of equity futures trading. Corporate financials are following suit.

Why it matters: The study found that the more easily a computer can digest a company’s financial reports, the faster its stock is traded after a report has been published. This suggests that the market’s pace, already lightning-fast, is bound to accelerate.

We’re thinking: Companies have every incentive to tweak their reports to impress their audience, whether readers consist of wetware or software. But there’s a slippery slope between painting a rosy picture and exaggerating in ways that border on fraud. Regulators, analysts, and AI practitioners alike have a responsibility to guard against market manipulation.

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