Who Benefits from Robo-advising? Evidence from Machine Learning

Abstract

We study the effects of a large U.S. hybrid robo-adviser on the portfolios of previously self- directed investors. Across all investors, robo-advising reduces investors’ holdings in money market mutual funds and increases bond holdings. It also reduces idiosyncratic risk by lowering the holdings of individual stocks and US and international active mutual funds and raising exposure to low-cost indexed mutual funds. It further eliminates home bias by significantly increasing international equity and fixed income diversification. Finally — over our sample period — it increases investors’ overall risk-adjusted performance, mainly by lowering investors’ portfolio risk. We use a machine learning algorithm, known as Boosted Regression Trees (BRT), to explain the cross-sectional variation in the effects of advice on portfolio allocations and performance. Investors who benefit from advice are those with little self-directed investment experience on the platform, those with prior high cash holdings, and those with high trading volume before adopting advice. Individuals invested in high-fee active mutual funds also display significant performance gains. Finally, we study the determinants of investors’ sign-up and attrition. Investors who benefit more from robo-advising are also more likely to sign-up and less likely to quit the service.