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Some VCs Use Algorithm to Pick Start-ups for Investments
Robots and algorithms
Is this like a robot vetting out which start-ups are worthy?
By RBR Staff



Venture Capital Re-imagined?

Technology Review (MIT): Correlation Ventures, calls itself a “new breed of venture capital firm”—one driven by predictive analytics software—built over the last six years by founder David Coats and his partner Trevor Kienzle.

The company’s catch phrase: “Venture Capital Re-imagined.” The partners say that the effort adds efficiency to the investment process. And for entrepreneurs, it means far faster answers: rejections come in as little time as two days.

algorithm

To run its model, Correlation Ventures, which is based in San Diego and Palo Alto, California, asks start-ups to submit five basic planning, financial, and legal documents. It enters these into a program similar in function to credit rating software.

The 30-minute interview

A top-ranked score leads to a 30-minute interview with both the start-up CEO and the outside venture firm leading the investment, plus a quick legal review and background check. As a co-investor, Correlation Ventures always relies on some vetting by the primary investor.

Correlation Ventures will then often deliver a check from its $165 million fund, closed last November, in less than two weeks. “That’s unheard of in the venture industry,” says Coats.

For example, Aldea Pharmaceuticals, a biotechnology start-up developing an emergency treatment for alcohol poisoning, seemed like an attractive investment to Coats. But he didn’t rely on a hunch—he consulted the computer model he’d built.

Analytics show that companies with more than two VCs on the board are less likely to be successful

Two weeks and a few phone calls later, he cut the company a $1.25 million check. “A decision like that would have normally taken a minimum of three months,” says Tim Shannon, a partner with Canaan Partners, the firm that had led Aldea’s $7 million fund-raising round.

Once it makes an investment, Correlation backs off and doesn’t take a board seat. That policy is itself data driven: the firm’s analytics show that companies with more than two VCs on the board are less likely to be successful.

What’s not yet clear is whether this system works

Correlation Ventures has so far invested in 26 companies in diverse sectors but says it is too early to report successes or failures.

None of this might have been possible a decade ago. Harvard Business School professor Matthew Rhodes-Kropf, who advises Correlation Ventures and is now an investor in the fund, says the venture capital industry has only recently worked through enough business cycles to look for subtle trends.

There was also no complete, accurate, public set of venture capital data, so Correlation Ventures hustled for it. To build and maintain its database, it partnered with Dow Jones, scoured the Internet, signed nondisclosure agreements with more than 20 venture funds to see their internal statistics, and called hundreds of companies.

Predicting the next Facebook, not so easy

While so-called Big Data companies have attracted plenty of investors, the reputation-driven venture capital industry itself has yet to embrace their tools. (There are exceptions, such as Google Ventures, which uses quantitative analysis to help guide decisions.)

Rhodes-Kropf says venture capitalists are good at identifying companies that will have the best chances at success but not as good at predicting which will be the next Facebook.

And one finding from Coats’s research is that while top-tier firms do invest in a disproportionate share of “winning” companies, the majority of successful investments are led by firms that don’t even crack the top 50. So it makes logical sense for Correlation Ventures to focus equal time and energy on many companies and co-invest with a diverse set of venture capital firms, he says.

To explain his project, Coats cites Moneyball, the book and movie about how Oakland Athletics general manager Billy Beane rejected the conventional wisdom on evaluating baseball players and built a winning franchise by letting a computer tease out variables that others overlooked.

He believes the averages will work out. “We’re not claiming to have a magic crystal ball,” he says. “We’re tilting the odds a little in our favor with each investment.”

Circa 1987: Peterffy’s robot, an algorithm and a cool $25 million

Technology Review (MIT): Here’s a robot and algorithm coupling that really made a bang.

In Automate This, author and entrepreneur Christopher Steiner tells the story of stockbroker Thomas Peterffy, the creator of the first automated Wall Street trading system.

Using a computer to execute trades, without humans entering them manually on a keyboard, was controversial in 1987—so controversial that Nasdaq pressured him to unplug from its network.

Automate_this

Then, with a wink, Peterffy built an automated machine that could tap out the trades on a traditional keyboard—technically obeying Nasdaq rules. Peterffy made $25 million in 1987 and is now a billionaire.

Today, automated trading bots account for nearly three-quarters of U.S. equity trading by volume. Trading houses plow millions into fiber optics and microwave dishes so their algorithms can send trades a millisecond faster than the next guys’.

And although the first trading robot was built 25 years ago, most of the change on Wall Street has occurred during just the last few years.

When it comes to automation, we may be in the elbow of an exponential curve.


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