The diversification and welfare effects of robo-advising ☆

• 成果类型: Article
• 署名作者: Rossi, Alberto G.; Utkus, Stephen
• 署名单位: Georgetown University; University of Pennsylvania
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2024.103869
• 发表日期: 2024
• 关键词: FinTech; portfolio choice; Machine Learning; individual investors; financial literacy; technology adoption

摘要

We study the diversification and welfare effects of a large US robo-advisor on the portfolios of previously self -directed investors and document five facts. First, robo-advice reshapes portfolios by increasing indexing and reducing home bias, number of assets held, and fees. Second, these portfolio changes contribute to higher Sharpe ratios. Third, those who benefit most from robo-advice are investors who did not have high exposure to equities or indexing and had poorer diversification levels. Fourth, robo-advice decreases the time investors dedicate to managing their investments. Fifth, those investors who benefit most are more likely to join the service and not quit it.