We explore the trading decisions of equity mutual funds during ten periods of extreme market uncertainty. We find that mutual funds reduced their aggregate holdings of illiquid stocks. Exploring the drivers behind this result reveals that this is mainly driven by larger withdrawals from funds that hold less liquid stocks. We further find that the sell-off of illiquid stocks occurred only after initial deterioration in market conditions, consistent with retail investors’ response to bad performance. At a broader level, this shows that mutual funds consumed liquidity during periods where liquidity was most valuable. Moreover, the fact that fund managers traded in response to these withdrawals suggests a potentially magnifying channel for the drop in illiquid stock prices, also known as flight-to-liquidity.

Previous research documents differences in trading desk skills across management companies that result in significant variation in execution costs. In this paper, we utilize links between management companies and their institutional clients to explore variation in execution costs within management companies. For a subset of management companies, we find that systematic differences in execution costs exist across clients; these differences are comparable to the variation documented across management companies and persist over time. Clients who receive lower execution costs reward management companies with an increase in dollar trading volume. We find no evidence that the results are driven by broker commissions or differences in trading practices. Given the economic significance of our findings and implications for institutional investors, this aspect of execution should be recognized regardless of the ultimate source of the differences.

Using new monthly data, we investigate open-market repurchase executions of US firms. We find that firms repurchase at prices that are significantly lower than average market prices. This price discount is negatively related to size and positively related to market-to-book ratio. Firms’ repurchase activity is followed by a positive and significant abnormal return. Importantly, the market response occurs when firms disclose their actual repurchase data in earnings announcements, and this positive response is followed by a 1-month drift. Consistent with these results, we find that insider trading is positively related to actual repurchases.

Using a unique database of aggregate daily flows to equity mutual funds in Israel, we find strong support for the “temporary price pressure hypothesis” regarding mutual fund flows: Mutual fund flows create temporary price pressure that is subsequently corrected. We find that flows are positively autocorrelated, and are correlated with market returns ( R 2 of 20%). Our main finding is that approximately one-half of the price change is reversed within 10 trading days. This support for the “temporary price pressure hypothesis” complements microstructure research concerning price impact and price noise in stocks by indicating price noise at the aggregate market level.

We investigate a proxy for monthly shifts between bond funds and equity funds in the USA: aggregate net exchanges of equity funds. This measure (which is negatively related to changes in VIX) is positively contemporaneously correlated with aggregate stock market excess returns: One standard deviation of net exchanges is related to 1.95% of market excess return. Our main new finding is that 85% (all) of the contemporaneous relation is reversed within four (ten) months. The effect is stronger in smaller stocks and in growth stocks. These findings support the notion of “noise” in aggregate market prices induced by investor sentiment.

We propose a direct measure of abnormal institutional investor attention (AIA) using news searching and news reading activity for specific stocks on Bloomberg terminals. AIA is highly correlated with institutional trading measures and related to, but different from, other investor attention proxies. Contrasting AIA with retail attention measured by Google search activity, we find that institutional attention responds more quickly to major news events, leads retail attention, and facilitates permanent price adjustment. The well-documented price drifts following both earnings announcements and analyst recommendation changes are driven by announcements to which institutional investors fail to pay sufficient attention.

Stock liquidity has improved over the recent 4 decades. This improvement was accompanied by a dramatic increase in trading activity. The net effect on the liquidity premium is ambiguous. We show that the characteristic liquidity premium of U.S. stocks has significantly declined over the past 4 decades. In recent years, characteristic liquidity is significantly priced only for the smallest common stocks. This decline stems from an improvement in liquidity and from a lower sensitivity of expected returns to liquidity. By contrast, systematic liquidity has not been trending down and is still significantly priced primarily among NASDAQ stocks.

We propose a direct measure of abnormal institutional investor attention (AIA) using news searching and news reading activity for specific stocks on Bloomberg terminals. AIA is highly correlated with institutional trading measures and related to, but different from, other investor attention proxies. Contrasting AIA with retail attention measured by Google search activity, we find that institutional attention responds more quickly to major news events, leads retail attention, and facilitates permanent price adjustment. The well-documented price drifts following both earnings announcements and analyst recommendation changes are driven by announcements to which institutional investors fail to pay sufficient attention. Received February 24, 2016; editorial decision December 29, 2016 by Editor Andrew Karolyi.

Several measures of credit-market booms are known to precede downturns in real economic activity. We offer an early indicator for all known measures of credit booms. Our measure is based on intra-family flow shifts towards high-yield bond mutual funds. It predicts indicators such as growth in financial intermediary balance sheets, increase in shares of high-yield bond issuers, and downturns of various measures of credit spreads. It also directly predicts the business cycle by positively predicting GDP growth and negatively predicting unemployment. Our results provide support for the investor demand-based narrative of credit cycles and can be useful for policymakers.

We consider how images (i.e., photos but not graphs, charts, or infographics) in annual reports provide users with information and use machine-learning algorithms to assess their informativeness. We develop a metric of content reinforcement, defined as the degree to which information investors extract from images complements and reinforces details in textual narratives. We find that firms are more likely to use images when they experience greater asset growth, have greater business complexity, and provide less readable textual disclosures—suggesting images are used more often when information processing costs are high. Our main results indicate that increases in visual prevalence and the extent to which images reinforce text are associated with greater analyst forecast accuracy and lower dispersion, suggesting that images improve users’ information processing. Firms also increase image use after an exogenous decline in analyst coverage. Overall, firms use images when their information environment is poorer, and visual informativeness facilitates information assimilation.