“GO STRAIGHT to the source” is a useful rule for anyone seeking accurate information. It suggests that equity investors can best glean insight into a firm by quizzing its chief executive. But bosses are not always reliable narrators. Their position encourages them to be overly optimistic about their company’s outlook. Sometimes they are clueless. And occasionally they are careless about what they tweet.
On February 25th the Securities and Exchange Commission (SEC), America’s financial-market regulator, asked a federal judge to hold Elon Musk, the chief executive of Tesla, a carmaker, in contempt. Mr Musk’s troubles with the SEC began in August when his tweet claiming that he had secured funding to take Tesla private caused the firm’s share price to soar. When the claim proved false, the SEC sued him for securities fraud. They settled in October, when Mr Musk stood down as Tesla’s chairman (he remains chief executive), paid a $20m fine and agreed to have his tweets approved by Tesla’s lawyers. He violated that last condition on February 20th by tweeting that Tesla would produce 500,000 vehicles this year—a claim he later had to clarify—without consulting the firm.
Regulators are not the only ones frustrated by Mr Musk’s antics. Investors have long clamoured for more insight into Tesla’s operations. Happily for investors, new methods of data-gathering present a solution. A growing number of providers now sell “alternative data”—a catch-all term for measures found beyond financial statements and other typical sources. J.P. Morgan, a bank, reckons that asset managers spend up to $3bn a year on such data.
An investor keen to know how many cars Tesla is selling need no longer ask Mr Musk. “When you buy a car, you also buy insurance,” says Tammer Kamel of Quandl, a data provider. His firm asks insurance companies for access to (anonymous versions of) their policy databases. Once Quandl knows how many policies on Tesla cars are being taken out, it can work out how many are hitting the road.
Faced with competition from firms like Quandl, incumbents are doing more. Bloomberg, a data provider, for example, now offers a Tesla-production tracker. This looks at the issuance of Vehicle Identification Numbers (VINs), which every car made in America must have. Output is estimated based on how many VINs Tesla registers.
Investment banks, which often offer research to their clients, are also branching out. In 2014 UBS, a bank, set up Evidence Lab, a research team. It has taken apart a Tesla Model 3, a Chevy Bolt and a BMW i3 to compare their component parts. “If you don’t know what these vehicles cost,” says Barry Hurewitz of Evidence Lab, “you can’t know when they become profitable.” The team found that Tesla’s battery was superior, but its production quality was poorer, and build costs higher, than expected.
Alternative data’s early consumers were mostly quantitative hedge funds, which could easily process the extra information. But as more measures have become available, other investors have become interested. This shifts the power dynamic between companies and their shareholders. If a firm refuses to disclose information, alternative data might fill the gap. Instead of gauging an executive’s tone during an earnings call, investors can assess data on the firm’s job postings. Its hiring plans might better reflect management’s sentiment.
Misleading statements, too, might be caught more quickly. Investors surprised by Mr Musk’s tweet that Tesla would build 500,000 cars can check with other sources. The SEC may struggle to stop Mr Musk making misleading comments, but investors can more easily see through them.