  Modeling investor behavior is crucial to identifying behavioral coaching
opportunities for financial advisors. With the help of natural language
processing (NLP) we analyze an unstructured (textual) dataset of financial
advisors' summary notes, taken after every investor conversation, to gain first
ever insights into advisor-investor interactions. These insights are used to
predict investor needs during adverse market conditions; thus allowing advisors
to coach investors and help avoid inappropriate financial decision-making.
First, we perform topic modeling to gain insight into the emerging topics and
trends. Based on this insight, we construct a supervised classification model
to predict the probability that an advised investor will require behavioral
coaching during volatile market periods. To the best of our knowledge, ours is
the first work on exploring the advisor-investor relationship using
unstructured data. This work may have far-reaching implications for both
traditional and emerging financial advisory service models like robo-advising.
