Defining, Modeling, and Measuring Investor Sentiment

This thesis attempts to come closer at resolving three highly contested issues involving investor sentiment. First are the theoretical issues of defining and modeling sentiment in an intuitive and parsimonious way. To date, there has been no commonly accepted definition, let alone theory, of investor sentiment since the term may be used in different ways depending on the context. In this thesis, I narrow the scope by focusing on a definition of sentiment expressed in terms of investors’ erroneous beliefs relative to fundamentals. Using this definition, I derive a model of individual sentiment in which individual biases or systematic tendencies to overvalue private “pseudo-information” signals may lead to the formation of erroneous beliefs. I argue intuitively that so long as beliefs are correlated among investors and limited arbitrage is present, sentiment can affect asset prices in equilibrium. Furthermore, defining and modeling sentiment in this way also paves the way for resolving the third main issue regarding investor sentiment: how to measure it. Evaluated within the context of my definition and model, I assess two general approaches to measuring sentiment: an indirect approach using financial proxies and a direct approach using survey results. I argue that at best, only the latter approach captures investor sentiment as I have defined it. I conclude by suggesting a “second-best” measure of sentiment consistent with the initial definition I set forth, while also taking into account empirical and methodological constraints. ∗This thesis would not be possible without the helpful discussions, thoughtful comments, and continued support of my advisor, Professor Botond Kőszegi. Thanks, Botond, for facilitating this endeavor and for making the thesis-writing process an enjoyable experience.