This paper establishes a robust link between the trading behavior of institutions and the book-to-market effect. Building on work by Daniel and Titman (2006), who argue that the book-to-market effect is driven by the reversal of intangible returns, I find that institutions tend to buy (sell) shares in response to positive (negative) intangible information and that the reversal of the intangible return is most pronounced among stocks for which a large proportion of active institutions trade in the direction of intangible information. Furthermore, the book-to-market effect is large and significant in stocks with intense past institutional trading but nonexistent in stocks with moderate institutional trading. This influence of institutional trading on the book-to-market effect is distinct from that of firm size. These results are consistent with the view that the tendency of institutions to trade in the direction of intangible information exacerbates price overreaction, thereby contributing to the value premium.

We propose a measure of dispersion in fund managers׳ beliefs about future stock returns based on their active holdings, i.e., deviations from benchmarks. We find that both the level of and the change in dispersion positively predict subsequent stock returns on a risk-adjusted basis. This effect is particularly pronounced among stocks with high information asymmetry and binding short-sale constraints. These results suggest that a subgroup of informed managers drives up the dispersion in active holdings when they place large bets after receiving positive private information. Binding short-sale constraints, however, prevent them from fully using their negative private information, leading to low dispersion in active holdings.

We uncover a negative relation between herding behavior and skill in the mutual fund industry. Our new, dynamic measure of fund‐level herding captures the tendency of fund managers to follow the trades of the institutional crowd. We find that herding funds underperform their antiherding peers by over 2% per year. Differences in skill drive this performance gap: Antiherding funds make superior investment decisions even on stocks not heavily traded by institutions, and can anticipate the trades of the crowd; furthermore, the herding‐antiherding performance gap is persistent, wider when skill is more valuable, and larger among managers with stronger career concerns.

We propose a new measure, active fundamental performance (AFP), to identify skilled mutual fund managers. AFP evaluates fund investment skills conditioned on the release of firms’ fundamental information. For each fund, we examine the covariance between deviations of its portfolio weights from a benchmark portfolio and the underlying stock performance on days when firms publicize fundamental information. Because asset prices on these information days better reflect firm fundamentals, AFP can more effectively identify investment skills. From 1984 to 2014, funds in the top decile of high AFP subsequently outperformed those in the bottom decile by 2% to 3% per year. Received August 25, 2016; editorial decision December 17, 2017 by Editor Andrew Karolyi.

We propose a new measure of time-varying tail risk that is directly estimable from the cross-section of returns. We exploit firm-level price crashes every month to identify common fluctuations in tail risk among individual stocks. Our tail measure is significantly correlated with tail risk measures extracted from S&P 500 index options and negatively predicts real economic activity. We show that tail risk has strong predictive power for aggregate market returns. Cross-sectionally, stocks with high loadings on past tail risk earn an annual three-factor alpha 5.4% higher than stocks with low tail risk loadings. We explore potential mechanisms giving rise to these asset pricing facts.

We study active investment skills in relation to returns to scale in the active mutual fund industry. Using a sample of 13,807 funds from sixteen domicile countries investing in forty-two equity markets from 2001 to 2014, we find that they achieve negative trading performance on average, driven mainly by particularly low returns to their trades in US equities. Exploring their investment environment, we find convincing evidence of decreasing returns to scale around the world, especially for the US market. Based on theory of optimal fund size, we estimate the optimal size of the active mutual fund industry. We find that the active mutual fund industry in USA has exceeded the optimal level, whereas in the international markets, there may still be room for further expansion. Consistent with this view, we find that mutual fund managers have been gradually reallocating their assets away from the USA and more into international equity markets.

How do corporate bond mutual funds manage liquidity to meet investor redemptions? We show that during tranquil market conditions, these funds tend to reduce liquid asset holdings to meet redemptions, temporarily increasing relative exposures to illiquid asset classes. When aggregate uncertainty rises, however, they tend to scale down their liquid and illiquid assets proportionally to preserve portfolio liquidity. This fund-level dynamic management of liquidity appears to affect the broad financial market: Redemptions from the corporate bond fund sector lead to more corporate bond selling during high-uncertainty periods, which generates price pressures and predicts strong return reversals.

We study how passive investing affects asset prices. Flows into passive funds disproportionately raise the stock prices of the economy’s largest firms, especially those large firms in high demand by noise traders. Because of this effect, the aggregate market can rise even when flows are entirely due to investors switching from active to passive funds. Intuitively, passive flows increase the idiosyncratic risk of large firms in high demand, which discourages investors from correcting the flows’ effects on prices. Consistent with our theory, prices and idiosyncratic volatilities of the largest S&P500 firms rise the most following flows into that index.

We propose a novel high-frequency decomposition of daily stock returns into news- and non-news-driven components, and uncover evidence of pervasive stock market underreaction to firm news. Prices tend to drift in the same direction as the initial market response for several days after the news arrival without reversals. A trading strategy exploiting the return drift generates high abnormal returns and remains profitable after transaction costs. To understand the economic mechanism, we find that the return drift is stronger when investors are distracted. Analysts’ slow adjustments of market expectations following firm news also contribute to the market underreaction.