Driven by Emotion? A Study on Investor Sentiment and the US Stock Market

Abstract

This thesis examines the extent to which investor sentiment can predict stock market movements in the United States, with focus on the S&P 500 Index. Using a quantitative approach, the study analyzes data from 2006 to 2023 and applies both Ordinary Least Squares and GARCH regression models. Two sentiment indicators are central to the analysis: the CNN Fear and Greed Index, which captures overall market mood, and the American Association of Individual Investors Sentiment Survey, which reflects individual investor expectations. The purpose is to explore whether emotional factors, such as fear and greed, can help explain deviations from traditional financial theories like the Efficient Market Hypothesis and Modern Portfolio Theory. The empirical findings reveal a negative correlation between sentiment and future market volatility. While sentiment alone cannot fully predict market trends, the results indicate that emotionally driven investor behavior plays a significant role, particularly during periods of high uncertainty. These insights support the relevance of behavioral finance and suggest that incorporating sentiment into financial analysis may improve the understanding of market dynamics. The study concludes that sentiment indicators, especially when combined, can serve as valuable tools for identifying emotionally driven market movements and contribute to more informed investment strategies.