Robo-advisors will soon be under the same pressure to outperform the market as their human counterparts. Investor expectations for robo-advisors will inevitably rise, sparking the next wave of disruption on Wall Street. To survive, robo-advisors will have to adopt robo-analyst technology.

Today’s robo-advisors lower the price of investment advice but it’s the same advice human advisors offer. The value today’s robo-advisors add is predominantly a reduction in costs. Just like their human counterparts though, robo-advisors will eventually be expected to generate alpha. 

To do this at scale firms will have to transform how research is performed.

They have little choice but to do so. It’s similar to the way the world’s largest wealth managers were forced to replace old-school money managers with robo-advisors. We dubbed this transition Crossing the Chasm 2.0 in May 2017. To cross the next chasm, many highly-paid, underperforming analysts will be replaced with lower cost technological solutions as the wealth management business disrupts itself once again.

To generate market-beating insight at scale, firms utilizing robo-advisors will increasingly turn to robo-analyst technology that leverages machines to analyze, test, and produce systematic investing strategies. Instead of relying on the intuition and subjectivity of human analysts, robo-analysts offer unmatched analytical rigor, objectivity, and speed, as recently featured in Bloomberg.

For the first time ever, academic research shows that robo-analysts are better stock pickers than humans. The research suggests robo-analysts are less optimistic, revise their recommendations more often, and outperform their human Wall Street counterparts by 4% on an annualized basis. What the robo-analyst may lack in intuition, they more than make up for with higher analytical rigor, discipline, and independence.

Robo-analysts also outperform humans when it comes to exposing corporate deception.  Research from the Harvard Business School and the Massachusetts Institute of Technology’s Sloan School of Management reveals managers bury unflattering earnings data in lengthy filings to intentionally mislead investors. 

By instantly parsing the footnotes in these filings, robo-analysts unearth the earnings adjustments companies hide and often use to artificially inflate earnings. Unlike human analysts, robo-analysts discover these adjustments the moment reports are filed for thousands of stocks and help investors make smarter decisions. Robo-analysts offer more accurate earnings models with more scale and speed.

One of the new research tools from robo-analysts is Earnings Distortion Scores, which predict which companies are most likely to beat or miss consensus earnings forecasts. The scores are based on a company’s historical adjusted core earnings which strip out unusual gains/losses. Companies with higher levels of distortion overstate earnings by including unusual or non-core gains and are more likely to miss estimates. Companies with lower levels of distortion understate earnings and are likely to beat. It’s a simple strategy, but it only works with the unique data uncovered by robo-analysts.

For example, robo-analyst technology recently detected a high level of distortion in Copart’s earnings due, in part, to the $732 million in income statement and balance sheet adjustments our robo-analyst made in FY19. We rated the company a “Strong Miss” ahead of its earnings February 19, 2020. Following CPRT’s earnings announcement, shares declined nearly 7%. 

Investors can generate alpha from the recommendations robo-analysts make by buying stocks that are likely to beat consensus forecasts and selling companies likely to miss. The HBS-MIT research backtested this strategy over twenty years and found it produced abnormal returns of 7-to-10% a year.

Human analysts can’t generate insights like these at scale. It’s why today’s robo-advisors that rely solely on human generated insight aren’t able to consistently provide market beating insight. Once again, wealth management firms find themselves on the brink. Crossing this chasm successfully is a choice all of them can make. How else can they differentiate their offerings from their competitor?

Tomorrow’s top performing robo-advisors are incorporating the analytical rigor robo-analysts provide today. Generating actionable insight at scale with proven ability to generate alpha will position robo-advisors to better serve and retain clients. It’s likely a prerequisite if robo-advisors are to ultimately fulfill their dual mandate of reducing costs and improving investment outcomes.

News of the robo-advisor’s death may be premature. But they’ll soon be under the same pressure as the humans they’re replacing.

David Trainer is founder and CEO of New Constructs, LLC., a financial technology and equity research firm that provided the data on which the Harvard-MIT research is based.