The purpose of this study is to investigate whether stock prices are influenced by investor attention and how this, in turn, can be used to better advise the financial decisions of the everyday investor. Using weekly adjusted close data, weekly traded volumes, and weekly company searches using Google Trends, I tested my hypothesis that including the frequency of company searches, found through consumers using Google, in financial models will help better predict stock returns. Using S&P 500 company data from February 2012 to February 2017, frequency is a better predictor of price in comparison to trading volumes. But, to maximize predictability, both frequency and volume should be used to predict price. Further investigation revealed that the Health Care and Energy sectors tend to have the strongest correlation between frequency and volume, compared to the Consumer Staples and Utilities sectors, which tend to attract individual investors.
Recommended CitationRodier, Anna, "Exploring Investor Attention in Financial Models" (2017). Honors Projects in Finance. Paper 34.