Using Machine Learning to Model Advised-Investor Behavior

Abstract

During periods of extreme market volatility, such as that experienced during the COVID-19 pandemic, advised investors may consider impulsive and inappropriate investment decisions like moving all assets to cash. Financial advisors, through proactive behavioral coaching, can help their clients avoid such decisions. But which clients need the most help? A predictive model that better identifies the clients most likely to react to market volatility can be an invaluable tool for financial advisors. Such a model requires insight into the investors’ mindset. In previous work, the authors focused on the perspective of the financial advisor and used natural language processing to explore advisors’ summary notes to extract such investor insights. They then used this novel data source as input for a machine-learning model to predict the investors most in need of intervention during volatile market periods. In this article, the authors further expand the model to include a unique dataset of investors’ digital activity, including investor-initiated contacts (via web, email, and phone) and web activity (page view and browsing history), to better reveal investor intention. Using machine-learning techniques, the authors build a model using this novel dataset as well as advisor notes, transaction activity, and a market volatility index to identify advised investors most in need of proactive intervention. The authors further describe the implication such work has for both traditional and robo-advisory service models.