A privacy-preserving robo-advisory system with the Black-Litterman portfolio model: A new framework and insights into investor behavior

• Published in: Journal of international financial markets, institutions & money institutions & money, 2023-12, Vol.89, p.101873, Article 101873
• Main Authors: Ko, Hyungjin, Byun, Junyoung, Lee, Jaewook
• Format: Article
• Language: English
• Subjects: Black-Litterman model; Expected utility theory; Homomorphic Encryption; Privacy-preserving; Prospect theory; Robo-advisory system
• ISSN: 1042-4431
• DOI: 10.1016/j.intfin.2023.101873

Recent financial sector changes, including strict privacy regulations, challenge robo-advisory companies with cybersecurity and data privacy. This study proposes a new framework integrating Homomorphic Encryption into the Black-Litterman portfolio model to safeguard robo-advisory investment strategies. The framework effectively balances privacy and accuracy while maintaining an acceptable level of privacy optimization error. Novel evaluation methods are also proposed to assess the trade-off between losses from privacy optimization and strategy leakage, from an economic viewpoint based on Expected Utility and Prospect Theory. It provides valuable insights into human behavior concerning privacy protection in portfolio management. •The first empirical study on privacy preservation in the robo-advisory systems.•Propose a new framework to integrate homomorphic encryption into the Black-Litterman model.•Achieve an acceptable level of privacy optimization error, balancing privacy and accuracy.•Propose novel evaluation methods to assess the trade-off between protection and strategy leakage.•Offer economic insights into human behavior in privacy protection in portfolio choice.