Big Data and Individual Trading Behavior
Big Data and Individual Trading Behavior

Abstract: 

The first part of our research project and talk explores the determinants of investors trading behavior and returns using a large US dataset of individual trades and account. We will explore the impact of external factors (information that arrives at the market, environment) and endogenous factors such as behavioral and economic factors. Another angle we will be examining is whether there are differences in investment behavior across gender, demographics and across cohorts (i.e., gen X vs. gen Y), and, if so, what are those and what explains them (socioeconomic factors, psychological factors, supply driven factors such as differential advising, products offered, communication styles, etc.). Previous research has identified some common investment patterns, such as inertia, trend-chasing, and the disposition effect, the phenomenon by which investors hold on to winners and dispose of losers, among others. Nevertheless, there is very limited evidence on gender or cohort differences and on investment product choices and decisions of these different categories of investors. Understanding the mechanism behind differences in investors’ trading and expectations is valuable to design new products, and marketing strategies. More generally, women investors differ from men for the source of their wealth, risk attitudes, income and demographics, and possibly their comfortableness with financial decisions. They might also have different investment objectives, a different communication style and different concerns and preferences than men. Understanding the differences between male and female investors and their sources is valuable to design new products, marketing strategies to target women investors. The same is true for investors born in different generations. The second project aims to understand if the same investor behaves differently (in things like rebalancing, trading, risk taking, etc.) across different accounts (brokerage accounts, taxable mutual fund accounts, or retirement accounts) and, if so, how one can understand that differential behavior.

Bio: 

Enrichetta Ravina is Assistant Professor of Finance and Economics at Columbia Business School. Her research interests include Behavioral Finance, Fine-Tech, Consumption and Credit Markets, and Private Wealth Management. Professor Ravina’s current research examines individual portfolio and investment decisions in 401(k) plans, their determinants and the way they are affected by government policy, firm characteristics and individual demographics. She is also working on financial anxiety and robo advisors. Her prior work has examined the financial decisions of high net worth U.S. households and their interactions with their wealth managers; investors’ preferences and decision making; the consumption, borrowing decisions, credit card usage of U.S. households; and the effect of appearance, persuasion and personal characteristics on the terms of financial transactions. She received a Ph.D. in Economics from Northwestern University and B.A. in Economics and Business from University of Torino.

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