In Chapter One, we examine the following question. Have retail investors become the ants that move the log? Social media has proved instrumental for effective coordination that might lead to extreme returns. To study this effect, I construct a novel crash risk measure by estimating ex-ante crash probabilities via logit and machine learning techniques. Stocks with high ex-ante crash risk tend to have lower returns, especially when lagged sentiment is high. Robinhood traders tend to over-buy high crash-risk stocks, consistent with the optimal expectations theory. By exploiting the staggered first appearances of ticker names on ``Wallstreetbets'', I document a causal effect of social transmission on crash risk. This effect is significantly more substantial for smaller stocks. To further bolster the finding, I exploit the entire history of Reddit to construct a novel instrument and show that social transmission is likely to cause elevated crash risk on a daily basis.In Chapter Two, we examine the following issue. Cyber risk is an important but latent source of risk in the economy. To estimate its impact on the asset market, we use machine learning techniques to develop a firm-level measure of cyber risk. The measure aggregates information from a rich set of firm characteristics and shows superior ability to forecast future cyberattacks on individual firms. We find that firms with higher cyber risk earn higher average stock returns. When these firms underperform, cybersecurity experts tend to have higher concerns about cyber risk, and cybersecurity exchange-traded funds outperform. Further tests strengthen the identification of the cyber risk premium.
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