We examine the bidding patterns and auction profits in the Japanese Government Bond (JGB) auctions and empirically test the predictions of auction theory. We find that the average profit in JGB auctions is not reliably different from zero, and the degree of competition and the level of uncertainty are insignificant in determining auction profits. The winning shares of the U.S. dealers are positively related to auction profits, whereas the winning shares of their Japanese counterparts show a negative association. We also find that the share of winnings of Japanese dealers tends to be correlated with the share of winnings of their compatriot dealers but a similar relation is not found for U.S. dealers.

This article develops and implements a new test to investigate whether sell-side analysts herd around the consensus when they make stock recommendations. Our empirical results support the herding hypothesis. Stock price reactions following recommendation revisions are stronger when the new recommendation is away from the consensus than when it is closer to it, indicating that the market recognizes analysts’ tendency to herd. We find that analysts from larger brokerages, analysts following stocks with smaller dispersion across recommendations, and analysts who make less frequent revisions are more likely to herd.

Recent evidence indicates that market model alphas are stronger predictors of mutual fund flows than alphas with other models. Some recent papers have interpreted this evidence to mean that CAPM is the best asset pricing model, but some others have interpreted it as evidence against investor sophistication. We evaluate the merits of these mutually exclusive interpretations. We show that no tenable inference about the validity of any asset pricing model can be drawn from this evidence. Rejecting the investor sophistication hypothesis is tenable, but the appropriate benchmark to judge sophistication is different from that used in this literature.

Closing auctions account for about 10% of daily trading volume and offer a potentially attractive alternative to trading in the continuous market. We find that the price impact is lower in closing auctions than in the continuous market for all stocks except Nasdaq microcaps. Opening auctions are illiquid. We compute trading costs for anomalies based strategies by strategically placing orders in the lower cost mechanism. The annualized trading costs for long/short portfolios based on financial ratios such as profitability and investment range from 17 to 41 basis points (bps). Excluding microcaps, these costs fall to 9–21 bps in closing auctions.

We estimate the risk and expected return of private equity using market prices of publicly traded funds of funds holding unlisted private equity funds and of publicly traded private equity funds participating directly in private equity transactions. We find that the market expects unlisted private equity funds to earn abnormal returns between −0.5% and 2% per year. In addition, private equity has a market beta close to one and a positive beta on the SMB factor. These listed funds exhibit greater systematic risk than an index based on the self-reported net asset value of unlisted private equity funds.

To attenuate an inherent errors-in-variables bias, portfolios are widely employed to test asset pricing models; but portfolios might mask relevant risk- or return-related features of individual stocks. We propose an instrumental variables approach that allows the use of individual stocks as test assets, yet delivers consistent estimates of ex post risk premiums. This estimator also yields well-specified tests in small samples. The market risk premium under the capital asset pricing model (CAPM) and the liquidity-adjusted CAPM, premiums on risk factors under the Fama–French three- and five-factor models, and the Hou et al. (2015) four-factor model are all insignificant after controlling for asset characteristics.

We show that analysts from sell‐side firms generally recommend “glamour” (i.e., positive momentum, high growth, high volume, and relatively expensive) stocks. Naïve adherence to these recommendations can be costly, because the level of the consensus recommendation adds value only among stocks with favorable quantitative characteristics (i.e., value stocks and positive momentum stocks). In fact, among stocks with unfavorable quantitative characteristics, higher consensus recommendations are associated with worse subsequent returns. In contrast, we find that the quarterly change in consensus recommendations is a robust return predictor that appears to contain information orthogonal to a large range of other predictive variables.