Abstract
The proliferation of robo-advisory platforms represents a significant transformation in the financial services industry, offering automated, algorithm-driven investment advice. This study investigates the intricate relationship between prevalent behavioral biases, investor adoption of these platforms, and subsequent investment performance. Drawing upon established theories in behavioral finance, we explore how cognitive and emotional biases, such as overconfidence, loss aversion, and anchoring, influence an investor's decision to utilize a robo-advisor and the eventual financial outcomes. While robo-advisors are often posited as tools to mitigate these biases, their effectiveness hinges on user interaction and the specific design elements of the platform. Through a hypothetical quantitative study design, employing survey instruments to assess biases and simulated investment scenarios, we analyze how behavioral predispositions mediate the adoption process and moderate portfolio performance. Our findings suggest that while robo-advisors can indeed temper some behavioral pitfalls, certain biases may paradoxically deter adoption or lead to suboptimal engagement, thereby limiting their full potential. The research underscores the necessity for platform developers and regulators to design user interfaces that proactively address these biases, fostering more rational investment behavior and enhancing investor protection.
Keywords
Robo-Advisory, Behavioral Biases, Investor Adoption, Investment Performance, Fintech, Financial Technology, Cognitive Biases, Financial Engineering