Social psychologists have documented a tendency for people to overestimate their similarity to others. I investigate whether financial analysts' forecast errors are consistent with this bias. I model the bias by assuming analysts overestimate the correlation of the private signals they receive about a firm's future earnings. My model predicts a positive relationship between (i) the likelihood of an analyst's revised forecast being too close to his earlier forecast and (ii) the number of analysts issuing forecasts during the time interval between his two forecasts. I empirically confirm this prediction and consider several alternative explanations.

We conduct a series of forecasting experiments to examine how people update their beliefs upon observing others’ forecasts. Subjects exhibit “cursedness,” that is, a propensity to underestimate the link between others’ forecasts and others’ information, which causes subjects to underreact. The behavior of sophisticated subjects is not affected by the framing of information, but unsophisticated subjects switch from underreaction to overreaction when they are only provided qualitative (rather than quantitative) forecast information. Our results have important implications for the way that financial analysts aggregate information and the way that financial institutions present forecasts to their clients.

Motivated by research in psychology and experimental economics, we assume that investors update their beliefs about an asset's value upon observing the price, but only when the price clearly reveals that others obtained private information that differs from their own private information. Specifically, we assume that investors learn from the price of an asset in an asymmetric manner—they learn from the price if they observe good (bad) private information and the price is worse (better) than what is justified based on public information alone. We show that asymmetric learning from an asset's price leads to post‐earnings‐announcement drift (PEAD), and that it generates arbitrage opportunities that are less attractive than alternative explanations of PEAD. In addition, our model predicts that PEAD will be concentrated in earnings surprises that are not dominated by accruals, and it also predicts that earnings response coefficients will decline in the magnitude of the earnings surprises.

We analyze the partisanship of Commissioners at the SEC and Governors at the Federal Reserve Board. Using recent advances in machine learning, we identify partisan phrases in Congress, such as “red tape” and “climate change,” and observe their usage among regulators. Although the Fed has remained relatively nonpartisan throughout our sample period (1930–2019), we find that partisanship among SEC Commissioners rose to an all-time high during the 2010-2019 period, driven by more-partisan Commissioners replacing less-partisan ones. Partisanship at the SEC appears in both the language of new SEC rules and the voting behavior of SEC Commissioners.

Non‐deal roadshows (NDRs) are private meetings between management and institutional investors, typically organized by sell‐side analysts. We find that around NDRs, local institutional investors trade heavily and profitably, while retail trading is significantly less informed. Analysts who sponsor NDRs issue significantly more optimistic recommendations and target prices, together with more “beatable” earnings forecasts, consistent with analysts issuing strategically biased forecasts to win NDR business. Our results suggest that NDRs result in a substantial information advantage for institutional investors and create significant conflicts of interests for the analysts who organize them.