Investor Behavior Modeling by Analyzing Financial Advisor Notes: A Machine Learning Perspective

TL;DR

This paper uses NLP to analyze financial advisor notes, uncovering investor behavior patterns and predicting coaching needs during market volatility, pioneering the use of unstructured data in this context.

Contribution

It introduces a novel approach employing NLP and topic modeling to analyze advisor notes for predicting investor coaching needs during market downturns.

Findings

Identified key topics and trends in advisor notes.

Developed a classification model predicting coaching needs.

First to explore advisor-investor interactions using unstructured data.

Abstract

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.