Using proprietary data from a major fund data provider, we analyze the screening activity of investment consultants (ICs). We find that ICs frequently shortlist funds using threshold screens clustered at $500MM for AUM, 0% for benchmark-adjusted return, and quartiles for return percentile rank screens. Funds just above the $500MM AUM threshold get 14%–18% more page views and 5–9 pp greater flows over the next year compared to similar funds just below the threshold. Our results are consistent with ICs using a two-stage consider-then-choose decision-making process and cognitive reference numbers in selecting screening thresholds. (JEL G41, G11, G14, G29) Received January 8, 2021; editorial decision September 3, 2021.

We empirically study how inventory constraints of underwriters affect corporate bond offerings. Using underwriter-insurer-level transaction data, we find that a more constrained underwriter is more likely to place a bond and increases the allocation in the primary market to an insurer with a stronger preexisting relationship. The same underwriter is also more likely to buy back part of an allocation from the same insurer within 6 to 12 months after an offering. Overall, by “parking” inventory to relationship investors in the primary market, underwriters mitigate the effect of their inventory constraints on firms’ bond financing costs.

This paper explains that anticipated market liquidity is an important concern for arbitrageurs considering entry into a market, a concern that can generate self-fulfilling asset prices. In the model, fixed investment costs turn a market illiquid and generate an arbitrage opportunity. The worst-case return on pledged collateral constrains arbitrageurs’ leverage. The interaction between this return and arbitrageurs’ capital makes entry decisions complementary and can create multiple equilibria. When arbitrageurs enter with capital, the market becomes more liquid; the worst-case return rises; and more arbitrageurs enter with capital. When arbitrageurs withhold capital, the market stays illiquid; the worst-case return falls; and other arbitrageurs stay out

What distribution best characterizes the time series and cross-section of individual stock returns? To answer this question, we estimate the degree of cross-sectional return skewness relative to a benchmark that nests many models considered in the literature. We find that cross-sectional skewness in monthly returns far exceeds what this benchmark model predicts. However, cross-sectional skewness in long-run returns in the data is substantially below what the model predicts. We show that fat-tailed idiosyncratic events appear to be necessary to explain skewness in the data. (JEL, G10, G11, G12, G13, G14).

Using a dynamic selection model, we obtain consistent and unbiased estimates of risk and returns for infrequently traded bonds and conduct the first comprehensive asset pricing test of municipal bonds using the multifactor approach. Correction for sample selection and infrequent trading problems results in substantially higher beta estimates. Besides conventional risk factors, illiquidity and taxes are important for the pricing of municipal bonds. Importantly, bond returns contain a significant liquidity risk premium. Failing to account for sample selection bias leads to erroneous inference on the magnitude of systematic risk and substantial underestimation of risk premiums.

Valuation risk of a security—uncertainty about its fair value—is a subject of considerable concern in the mutual fund industry. If funds report different values for identical securities, investors cannot easily compare their performance. Yet it is not unusual to see identical illiquid stocks, small-cap stocks, stocks with high analyst dispersion, stocks with less analyst coverage, and newly listed stocks valued differently across mutual funds. An equity fund that has positive price dispersion in its portfolio holdings, that performs poorly, that belongs to a fund family with an inclination for aggressive reporting, that holds more stocks subject to stale prices, that holds more pre-IPO firms, or that experiences net outflows will tend to show positive price dispersion again in the next quarter. This behavior is significant in a volatile market. Aggressive reporting helps funds gain in the mutual fund tournament.

We study the extent of cross-asset learning in financial markets by examining the spillover effects around mutual fund fire sales, which lead to a well-documented impact-reversal pattern in returns. We find that the returns of fire sale stocks spill over onto the stock returns of economic peers with a magnitude of around one-third of the original effect. These spillovers extend to liquidity and are not explained by common funding shocks or the hedging activity of liquidity providers. We conclude that they represent information spillovers due to learning from prices, thus identifying cross-asset learning as an important driver for the commonality in returns and liquidity. (JEL G11, G12, G14, G23) Received July 1, 2021; editorial decision August 30, 2021.

We provide novel evidence that arbitrageurs use exchange-traded funds (ETFs) as an avenue to circumvent short-sale constraints at the stock level. Using a large sample of U.S. equity ETF holdings, we document that shorting activity on ETFs rises with the difficulty of shorting underlying stocks. Stocks heavily shorted via their holding ETFs underperform those that are lightly shorted. The return predictability of ETF shorting is distinct from stock-level shorting measures and is concentrated among stocks that face severe arbitrage constraints. These findings suggest that ETFs allow arbitrageurs to target overpriced stocks that are otherwise difficult to short.

At a given point in time, bitcoin prices are different on exchanges located in different countries, or against different currencies. While existing literature attributes the largest price differences to frictions, like market segmentation, trading platforms advertize how to execute trades based on this information. We provide a novel risk-based explanation of these price differences for a sample containing the most reputable exchanges and after accounting for all transaction costs and limitations to trade. Bitcoin prices for more expensive pairs are riskier because they depreciate more in bad times for cryptocurrency investors, when aggregate liquidity and investor sentiment are lower.

We exploit detailed transaction and position data for a sample of long-short equity hedge funds to study the trading activity of fundamental investors. We find that hedge funds exhibit skill in opening positions, but that they close their positions too early, thereby forgoing about one-third of the trades’ potential profitability. We explain this behavior with the limits of arbitrage: hedge funds close positions early in order to reallocate their capital to more profitable investments and/or to accommodate tightened financial constraints. Consistent with this view, we document that hedge funds leave more money on the table after opening new positions, negative returns, or increases in funding constraints and volatility.