This chapter provides an overview of studies in finance and economics that use automated textual analysis algorithms to analyze the informational content of a wide variety of texts, including journalist’s coverage of news events, management-issued statements, and Internet stock message boards. In these studies, researchers quantify qualitative information with one or more of the following textual tone variables: textual negativity, positivity, and uncertainty. The studies show that textual negativity and positivity conveyed by managers and journalists helps predict future firm level and aggregate economic activity. Textual negativity and positivity, in turn, affect asset prices, although the information is sometimes incorporated with some delay. Textual uncertainty of management-issued information is associated with future cash flow volatility and asset price volatility. In contrast, the textual tone of stock market message board postings is, on average, not very informative in explaining asset prices. The use of automated textual analysis algorithms in finance and economics is a relatively new phenomenon and research in this area is expected to continue to grow.