In the current world of the Big Data revolution, where a vast amount of information is available to investors, attention is a scarce resource. The time, effort, and skill required to identify, acquire, and process all relevant information for decision-making can be substantial. Consider, for example: the large volume of information available about a single firm from mandated financial statements and voluntary disclosures produced by the firm itself; reports generated by intermediaries such as analysts; news articles by business journalists; postings on social media by investors and other stakeholders; and data publicly available on the Internet or curated from proprietary sources. Even reading a single document, such as this chapter on limited attention, requires time and attention. Moreover, investors must evaluate and compare information for many other firms before they can make informed investment decisions. On top of this, firms compete for their attention. During a busy earnings announcement season, hundreds of firms will announce their earnings on the same day. This deluge of information and the limits of attention create a bottleneck. To understand how capital markets function when this bottleneck occurs, we need to know how investors, managers, and other stakeholders make decisions when faced with limited attention. The traditional theory assumes that information processing costs are negligible, and that all publicly available information is incorporated into stock prices immediately and fully. In contrast, limited attention theory posits that investors have finite attention and processing power, and that this constraint can prevent them from making optimal decisions. Investors cannot fully attend to all public news. Instead, only a subset of investors heeds any specific piece of information at any given time, while the attention of others is consumed by other news. In this market with limited investor attention, the key prediction is that the stock price cannot fully incorporate all available information.

In this chapter, we first provide a simple example of a theory model to illustrate testable predictions for the consequences of limited attention for capital markets. Then we discuss empirical studies examining a wide set of predictions from limited attention theory. Limited attention theory can explain a broad set of empirical findings, including misreaction to public information, post-earnings announcement drift (PEAD), effects of news salience, and managerial choices regarding the format and timing of disclosures. In the next section, we discuss a simple limited attention model and its predictions about investor responses to firms’ earnings announcements. The model illustrates how investor reaction to a firm’s announcement depends on the degree of investor attention and the amount of supplemental information disclosed by the firm in the announcement. We then discuss empirical evidence on the factors that influence investors’ attention and responses to earnings news. We discuss the effects of distraction due to competing stimuli,