Abstract
The household finance literature has found that investors often deviate from optimal investment behavior and suffer wealth losses due to factors such as incomplete information, lack of financial knowledge, and behavioral biases. Investment advisors perform multiple functions including information provision, investor education, and asset allocation recommendations, serving as crucial means to assist investors. Currently, there are two popular logics for investment advisor’s asset allocation recommendations: catering to investor preferences and educating investor. Exploring which asset allocation logic and design are more comprehensive, trustworthy, and beneficial to investors’ welfare holds significant implications for the upgrade of advisory services, development of financial markets, and enhancement of social welfare.
The experiment will randomly divide users of a certain bank’s APP into four groups: control group, preference catering group, loss aversion education group, and mental accounting education group. All users will first report their current asset allocation and complete a series of questionnaires on basic information and behavioral preferences. The control group will not receive any recommendation. The preference catering group will be recommended an asset allocation that is algorithmically calculated to be optimal based on the investor’s behavioral parameters. The loss aversion education group will be recommended an asset allocation that is algorithmically calculated to be optimal but with the modification of the loss aversion parameter to be fully rational while keeping the investor’s other behavioral parameters unchanged. The mental accounting education group will be recommended an asset allocation that is algorithmically calculated to be optimal but with the modification of the mental accounting parameter to be fully rational while keeping the investor’s other behavioral parameters unchanged. Finally, all users will report the asset allocation they consider to be most appropriate.