In public market investing, the robots have taken over — and in many ways, that’s a good thing. When will private company investing follow suit?
The stock market was once dominated by brokers crunching numbers and doing deals at physical finance hubs such as Wall Street. Today, computer-automated “quantitative funds” are doing much of the analysis, buying, and selling that humans used to do — and are quickly eclipsing their analog predecessors.
According to Bloomberg, quant funds have doubled over the past decade to control some $500 billion in assets. Even firms focused on traditional “fundamental” trading practices are increasingly incorporating computer automation into their workflows.
There are many upsides to this new approach: Purely data-driven investing is more efficient and often more successful than traditional stock investing methods centered primarily around human judgement and relationships. They can be more meritocratic, too: Quant investing focuses purely on numbers and performance, without the social biases and over-reliance on personal connections that often surfaces when people do business.
A Growing Trend, but a Shrinking Sector
But quant investing is a growing trend in a shrinking sector. The number of companies listed on public stock exchanges in the U.S. has plunged 46% over the past 20 years, from more than 8,000 in 1996 to around 4,300 in 2016. Meanwhile, the quantity and quality of private companies has blossomed. Increasing government regulations for public companies coupled with the stock market’s potential for volatility have compelled many successful and growing companies to stay out of the public markets altogether.
This growing private market has not seen the rise of the robots quite yet. Deals in this area are still largely done with a human touch: Analysts comb through data to compile information, due diligence is done through a series of phone calls and in-person meetings, and investments are finalized with a handshake and a wire transfer. While there are nearly 30 million private businesses in the United States today, there’s virtually no standardized technology used to optimize private equity investing.
The Data Gap
Why is private equity so far behind its public market counterparts when it comes to data-driven investing? For one thing, there is a lack of clear data to crunch.
The public market has an abundance of clean, structured data that can be used to predict company performance: The SEC requires that public companies disclose precise information about sales growth, revenue, profits, employee headcount, and more on a quarterly basis in a format that’s accessible to the general public. The rise of relatively cheap processing power means that computers can obtain and analyze this data faster than ever before. In addition, third party data providers that structure data for public investors (including quant funds) to digest easily have existed for many years.
This kind of information is much less readily available from private companies. One of the biggest perks of the private market is, well, the privacy: the ability to grow away from the glare of the public spotlight. Private companies generally only share key data — revenues, user numbers, profits, growth — with their existing investors.
This creates a chicken-and-the-egg scenario: In order to create a data-oriented investing fund, you need access to data. But historically, the only truly reliable way to obtain valuable data about a private company is to become an investor in it. Additionally, quant funds by definition require powerful technology infrastructure, which can only be built through a significant investment of time and resources — both of which upstart firms typically don’t have.
And without any private equity firms leading the way in building a comprehensive dataset to inform a quant approach, there is little competition and incentive to shake up the status quo and deviate from the tried-and-true (but more high-touch and expensive) human-powered methods.
Until, of course, a pioneer takes a risk and starts doing things differently.
Consumer: An Ideal Entry Point
It’s clear, then, that the first firm to bring a quant approach to private investing will have to also be a data company. Certain industries lend themselves to this approach more readily than others.
The tech industry, for example, provides a number of challenges, since data around tech startups is not easily accessed and quantified. Web and software companies don’t typically adhere to uniform business models, they don’t always “sell” a distinct product, and revenue figures (or the lack thereof) don’t correlate in a linear way to a company’s valuation and exit potential.
At CircleUp, we believe that the consumer product space is an ideal entry point for applying quant methods to private investing. The consumer space is relatively data rich, and business models are relatively uniform. The consumer industry traffics in clear numbers such as manufacturing costs, product distribution, pricing, sales, and revenue, all of which have a distinct correlation to a company’s growth, valuation, and ultimate success.
Of course, this data is still not nearly as easily accessible as public company financials. There is no clear template for firms to follow for developing a quant investing strategy. A specialized focus and unique datasets are required to create actionable insights, and build a solid investing thesis.
To that end, CircleUp has spent the past five years creating Helio, our internal data science platform designed to analyze the entire U.S. consumer goods industry and identify its rising stars.
Currently, Helio monitors a wide swath of consumer sectors including food, personal care, beverages, and apparel. Helio organizes and analyzes billions of data points across more than one million consumer brands — all data that is nonstandardized outside of our platform — providing a detailed and dynamic perspective on the industry that automatically surfaces the most exciting investment opportunities, well before they can be sussed out by traditional research methods.
Building Helio has been a massive effort, but one that we believe is critical for truly tackling private investing in the most modern and effective way possible. CircleUp’s unique depth of expertise and years of experience in the consumer product space have allowed us to crack the chicken-and-egg problems that have historically prevented quant methods from penetrating the private market. We firmly believe that this kind of quantitative approach is the future of not just consumer investing, but private investing as a whole.
The robots are here. Now what?
So what happens with all the time and human resources suddenly freed up when computers are doing much of the work that used to be done by teams of analysts? Ideally, they focus on more important things.
Right now, the staff at the typical private investment fund spends a majority of its time sourcing potential investments—leaving very little time for deal execution, and even less for actually helping the companies succeed after an investment has been made. A private quant fund can upend that distribution. When that flips, and a vast majority of investors’ time and resources are spent on working together with companies to help them grow and achieve their goals, it should be better for everyone—entrepreneurs, end users, investors, and LPs.
Winners and Losers
Any time a major sea change comes to an industry, some boats are buoyed, and others founder. If data science successfully disrupts the private investment sphere, existing industry investors will feel the pressure. Many traditional job roles will shift.
It’s a dynamic we’ve seen play out in the public markets already, with the rise of quant funds: The firms that embrace new technology and leverage it as a tool to enhance their existing human resources and expertise will likely emerge as the winners in this new environment. Firms that ignore or laugh off the quant approach and insist on maintaining the completely human-powered status quo may very well be left behind.