The first paper investigates the predictive power of investors' sentiment and attention for the stock returns' volatility. We introduce a novel and extensive dataset that combines information from social media platforms, news articles, search engine data, and information consumption. Applying a state-of-the-art sentiment classification technique, we construct measures of investors' sentiment and attention for 18 U.S. stocks and the financial market in general. We identify investors' attention, as measured by the number of Google searches on financial keywords (e.g. «financial market» and «stock market»), and the daily volume of company-specific short messages posted on the social media platform StockTwits to be the most relevant variables. The second paper investigates a potential driver of the predictive power documented in the first paper. We focus on news releases of 360 U.S. companies from the S&P 500 universe and analyze how investors' attention affects the speed at which new information is incorporated in stock prices. Our results show that higher investors' attention around news releases is related to higher contemporaneous volatility. Further, retail investor attention increases the post-announcement volatility, whereas institutional investor attention has a small but negative impact on volatility on days following news releases. The third paper extends the analysis of the first paper to the multivariate stock return volatility. Building on the theoretical and empirical evidence that links the price comovements with retail investors' behavior, we analyze the predictive power of retail investors' sentiment and attention for the realized correlation matrix of 35 Dow Jones stocks. We propose a new model of realized covariances that allows exogenous predictors to influence the correlation dynamics while ensuring the predicted matrices' positive definiteness. Using this model, we find retail investors' attention to have predictive power for return correlations, especially for longer forecasting horizons and during the COVID-19 pandemic. The last paper analyzes in more detail the time-series properties of the daily online investor sentiment measures used in the first two papers. We detect structural breaks in the sentiment series for most of the 360 U.S. companies considered in this paper. We illustrate the economic significance of this finding with a return prediction exercise.

Chapter 1. If investors choose consumption and investment levels jointly to maximize expected utility or value, then investor sentiment about stock returns should be reflected in consumption choices. I find a positive contemporaneous relationship between aggregate consumption of nondurables and investor stock sentiment. Investors' false perceptions of changes in stock market wealth appear to move consumption in the same direction initially. But as expected stock returns do not materialize, sentiment-based consumption is reversed. On average, this reversal occurs two to four years later, which coincides with the time it takes for sentiment to correct from prior levels. Sentiment does not positively predict returns as a positive proxy of rational expectations of risk would. Nor does sentiment negatively predict the covariance between consumption growth and returns as an inverse proxy for rational expectations of risk would. The results suggest that bias in investor expectations is an important factor in consumption-based asset pricing models. Chapter 2. I hypothesize that directly observable past returns drive housing investment more so than fundamentals because the difference between price and fundamental value---sentiment---is not directly observable. Housing sentiment only becomes recognizable when it is extreme, so the magnitude of sentiment must be large enough relative to recent returns in order for prices to correct. I construct indices of housing sentiment and use the measures to calibrate a specification of home price growth driven by momentum investing. I find that home price growth is persistent even when prices are moving away from fundamental value, and reversals in home price growth are only likely when the housing sentiment measures are extreme.