Investors vs. Matched Robo-investors

Would retail investors improve portfolio performance by using robo-advisors to manage holdings they have selected? In their July 2019 paper entitled “Artificial Intelligence Alter Egos:Who benefits from Robo-investing?”, Catherine D’Hondt, Rudy De Winne, Eric Ghysels and Steve Raymond compare performances of portfolios held by each of a large sample of actual individual investors to that of a robo-investor constrained to the stocks and exchange-traded funds (ETF) held by that investor over a rolling 2-year historical window. They consider three robo-investor strategies:

1. Mean-variance optimization with guiding average and variance estimates based straightforwardly on 2-year rolling historical windows and parameters set to maximize Sharpe ratio.
2. Mean-variance optimization guided by machine learning algorithms and sophisticated covariance estimators, with two variations in variance estimation.
3. Equal weight.

Robo-investors may hold cash, but they may not sell short, with focus on quarterly portfolio rebalancing. They measure portfolio performance monthly and exclude trading frictions. Using common stock/exchange-traded fund (ETF) trading records for 20,622 individual Belgian brokerage accounts during January 2003 through March 2012, they find that:

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