Using unique data on Alipay users' investment accounts, we find that, in addition to generating better performance than investors’ self-directed portfolios, robo-advising has a positive spillover effect on its adopters in terms that it improves their investment behaviors. Investors have more diversified portfolios and exhibit fewer behavioral biases in portfolio management and fund choices in their self-directed accounts after adopting robo-advising. The spillover effect is more prominent for adopters who interact with the service more actively and who were less sophisticated before adopting the app. We also find that adopters learn from the robo-advisor by simply imitating its portfolios or strategies. Collectively, this study provides large-sample, non-laboratory evidence that robo-advising effectively plays a role in educating investors through repeated interactions with its adopters and setting investment models that are easy to follow.

In this study, we adjust the hierarchical risk parity (HRP) model by introducing hierarchical information on assets to help manage portfolio risk. The adjusted HRP model with hierarchical information considers both correlation and hierarchical information. Compared with other models, the HRP model with hierarchical information has better out-of-sample robustness for simulation data. Moreover, this model achieves better out-of-sample performance using Chinese industry indices data. The results reveal that the adjusted HRP model is an efficient tool to control out-of-sample portfolio risk.