Date of Award


Document Type

Honors Thesis (Open Access)


Colby College. Economics Dept.


Randy Nelson

Second Advisor

Lindsey Novak


This paper utilizes data from Google searches in an attempt to utilize online investor sentiment as a predictor of sector exchange traded fund (ETF) performance. The paper tests the assumptions of the Efficient Market Hypothesis that all known information about a stock is incorporated into the price of the stock. With the emergence of ETFs as a popular form of investment for casual investors, there is a possibility that these investors may use Google as a way to collect information about potential stock picks. Thus, this paper investigates the association between online search interest and excess ETF returns by collecting data using Google’s Trends search functionality to calculate investor sentiment for sector ETFs over a five-year time span. Empirical results from this paper suggest that Google search interest has no association with excess returns, supporting the theory associated with the Efficient Market Hypothesis.


Investing, Google Trends, ETF, Investor Sentiment, Investment Theory, Portfolio management