Investment Decisions with Endogeneity: A Dirichlet Tree Analysis

Abstract

Ignoring endogeneity when assessing investors’ decisions carries the risk of biased estimates for the influence of exogeneous marketing variables. This study shows how to overcome this challenge by using Polya trees in the quantification of impacts on investors’ decisions. A total of 2255 investors recruited for this study received and opened a digital marketing newsletter about investing daily. Given the nature of investors’ decisions characterized by heterogeneity and endogeneity, the response model is assessed with the Dirichlet process mixture and estimated with the Markov chain Monte Carlo method. Digital marketing substantially exceeds the impact of investor experience, but both have a significant positive impact on investors’ trading volume. Findings obtained with the Dirichlet process mixture as a flexible model indicate that digital marketing even with latent endogenous factors makes an underlying contribution to the investors’ actions in the stock market.