Algorithmic portfolio choice: lessons from panel survey data

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• 作者：Bernd Scherer
• 关键词：Robo ; advice ; Household portfolio choice ; Panel data ; Regression trees
• 刊名：Financial Markets and Portfolio Management
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：31
• 期：1
• 页码：49-67
• 全文大小：
• 刊物主题：Business and Management, general; Finance, general; Management;
• 出版者：Springer US
• ISSN：2373-8529
• 卷排序：31

文摘

Automated asset management offerings algorithmically assign risky portfolios to individual investors based on investor characteristics such as age, net income, or self-assessment of risk aversion. Using new German household panel data, we investigate the key household characteristics that drive private asset allocation decisions. This information allows us to assess which set of variables should be included in algorithmic portfolio advice. Using heavily cross-validated classification trees, we find that a combination of household balance sheet variables—describing the ability to take risks (e.g., net wealth)—and household personal characteristics—describing the willingness to take risks (e.g., risk aversion)—best explain the cross-sectional variation in household portfolio choice. Our empirical evidence is in line with models of portfolio choice under decreasing relative risk aversion and fixed investment costs. The results suggest the utility of a more holistic modeling of household characteristics. Including background risks in the form of household leverage not only makes investment sense, but is also the new regulatory reality under MIFID II rules. Robo-advisors are strongly advised to act accordingly.