Robo-Advisers and Investor Behavior

Topic and Objectives

The current rise of robo-advisers provides a new, potentially more cost-effective approach to offering financial advice. Using unique data from a large German retail bank, we plan to investigate the effects of robo-advisers on three aspects of clients' portfolios: i) stock market participation and share of risky assets; ii) behavioral biases and portfolio efficiency; and iii) learning and spillover effects between robo-advised and non-robo-advised accounts. Preliminary evidence suggests that after joining a robo-advising service, clients increase financial risk-taking, hold more diversified portfolios with a larger fraction of index funds, exhibit lower home bias and increase their (buy) turnover. Our research has the potential to provide key insights into the trade-offs associated with using robo-advisers. Many households rely on financial advisors for investment guidance. Despite the widespread use of financial advice, academic research has raised concerns about the cost and quality of this service. In recent years, a technology-based form of advice, commonly referred to as robo-advice, has emerged as an alternative. By providing low-cost access to passive equity investments, robo-advisers could reduce the cost of participating in the stock market and, more generally, the costs of holding equity. Robo-advisers may also be able to reduce client biases by proposing cost-effective, passive, and well-diversified investments while leaving no room for the potential biases that human advisors can pass on to their clients. Additionally, robo-advisers may draw attention to the fees that their clients are paying on their other, non-robo-advised accounts. Contrary to this positive view, the lack of a human advisor could limit clients' levels of trust in the financial markets and reduce participation and share of risky assets (Guiso, Sapienza, and Zingales, 2008). Moreover, clients could not overcome the anxiety associated with holding risky investments (Gennaioli, Shleifer, and Vishny, 2015) or select unsuitable investment risk.

We have obtained data from a large German retail bank that started offering a robo-advice service. Three features make them particularly suitable for addressing our research questions: i) roughly half of the robo-advised clients had existing accounts at the bank, allowing us to investigate investment behaviors before and after joining; ii) we can observe the behaviors of bank clients that did not join (i.e. clients working with human advisors or making their own investment decisions), and iii) we have information on the marketing campaigns that quasi-randomly targeted existing bank clients to advertise the robo-adviser. In contrast, data from independent robo-advisers would not typically include information on clients' prior investments, the investments of those who don't sign up for the service, or clients' additional investments outside of their robo-advised accounts.

Key Findings

• The project bi-furcated into two papers, namely one on robo-advice and one on the impact of smartphones as trading devices on investor behavior.:
• Robo-advice paper:
  • First-time SP users are more inert and stick to the robo-advisor’s proposed asset allocation while experienced SP users quickly readjust their equity exposure away from the robo-advisor’s recommendation.
  • My results emphasize the power of defaults in all-digital robo-advisory services and highlight how they can improve fund choices while at the same time pushing investors into unsuitable asset allocations.
• Smartphone paper:
  • We find that smartphones increase purchasing of riskier and lottery-type assets and chasing past returns. After the adoption of smartphones, investors do not substitute trades across platforms and buy also riskier, lottery-type, and hot investments on other platforms.
  • Using smartphones to trade specific assets or during specific hours contributes to explaining our results. Digital nudges and the device screen size do not mechanically drive our results. Smartphone effects are not transitory.

Policy Implications

• Many robo-advisers use only a few default settings for asset allocation (e.g., equity shares for 25 , 50, and 75 percent) and product selection (model portfolios).
• Retail clients with preferences that deviate from such defaults, will then end up holding suboptimal portfolios.
• Smartphones might tempt retail investors to trade more impulsively and therefore trade riskier assets than with conventional devices.