The analysis of corporate governance begins with a central feature of modern capitalism – the separation of ownership and control in large corporations – first empirically documented by Berle and Means (1932). Such separation entails several agency problems reflecting conflicts between managers and shareholders, such as self-dealing by managers, low effort, consumption of perquisites, and excessive growth and diversification. Berle and Means saw self-dealing as the central agency problem and stressed the law as the fundamental mechanism of addressing it. Jensen and Meckling (1976) considered the consumption of perquisites and emphasized private mechanisms, such as financial incentives for managers, to counter wasteful perks. Jensen (1986) instead focused on excessive growth and diversification, which led him to count on leverage and takeovers. The combination of public corporate governance mechanisms, mostly the law, and market governance shaped both theory and practice.

We address the joint hypothesis problem in cross-sectional asset pricing by using measured analyst expectations of earnings growth. We construct a firm-level measure of Expectations Based Returns (EBRs) that uses analyst forecast errors and revisions and shuts down any cross-sectional differences in required returns. We obtain three results. First, variation in EBRs accounts for a large chunk of cross-sectional return spreads in value, investment, size, and momentum factors. Second, time variation in these spreads is predictable from that in EBRs, holding constant scaled price variables (as proxies for time varying required returns). Third, firm characteristics often seen as capturing risk premia predict disappointment of expectations and low EBRs. Overall, return spreads typically attributed to exotic risk factors are explained by predictable movements in non-rational expectations of firms’ earnings growth.

How do people form beliefs about novel risks, with which they have little or no experience? Motivated by survey data on beliefs about COVID we collected in 2020, we build a model based on the psychology of selective memory. When a person thinks about an event, different experiences compete for retrieval, and retrieved experiences are used to simulate the event based on how similar they are to it. The model predicts that different experiences interfere with each other in recall and that non-domain-specific experiences can bias beliefs based on their similarity to the assessed event. We test these predictions using data from our COVID survey and from a primed-recall experiment about cyberattack risk. In line with our theory of similarity-based retrieval and simulation, experiences and their measured similarity to the cued event help account for experience effects, priming effects, and the interaction of the two in shaping beliefs.