This paper offers cross-sectional and data-intensive insights into Robo-advisory portfolio structures. For this purpose, we scrape portfolio recommendations for 16 German Robo-advisors. Our sample accounts for about 78% of assets in the German Robo-advisory market. We analyze about 243.000 pairs of recommended portfolios and their corresponding client characteristics. Our results show that current Robo-advice offers limited individualization. Variables that matter in modern portfolio choice like the amount and nature (beta) of human capital or shadow assets are largely ignored. Instead, portfolio recommendations are designed to meet investor preconceptions or the regulator’s understanding of portfolio choice. While ensuring consumer trust and regulatory approval makes business sense, it also limits the economic benefits of Robo-advisors.\(^1\)

本文提供了对机器人咨询投资组合结构的横截面和数据密集型见解。为此，我们为 16 位德国机器人顾问收集了投资组合建议。我们的样本约占德国机器人咨询市场资产的 78%。我们分析了大约 243.000 对推荐的投资组合及其相应的客户特征。我们的研究结果表明，当前的机器人建议提供有限的个性化。现代投资组合选择中的重要变量，例如人力资本或影子资产的数量和性质（贝塔），在很大程度上被忽略了。相反，投资组合建议旨在满足投资者的先入之见或监管机构对投资组合选择的理解。虽然确保消费者信任和监管批准具有商业意义，但它也限制了机器人顾问的经济利益。\(^1\)