Stock exchange operators compete for order flow by setting “make” fees for limit orders and “take” fees for market orders. When traders can quote continuous prices, exchange operators compete on total fee, because traders can choose prices that perfectly neutralize any fee division. The 1-cent minimum tick size, however, prevents traders from neutralizing fee division. The nonneutrality of division between make and take fees (1) allows an exchange operator to establish exchanges that differ in fee structure to engage in second-degree price discrimination and (2) destroys the Bertrand equilibrium, leads to frequent fee changes, and encourages entries of new exchanges. Received May 29, 2016; editorial decision April 19, 2018 by Editor Robin Greenwood.

Short interest is currently required to be disclosed twice per month, but regulators have sought to increase this frequency. Meanwhile, short interest information from private third‐party vendors has emerged to meet investor demand on a daily basis. We find that daily private‐sector data strongly predict bimonthly regulatory disclosure. Furthermore, private‐sector data help price discovery, albeit with modest economic magnitude. Investors tend to underreact to the information content of private‐sector data mainly due to limits to arbitrage rather than market inattention. Despite the costly access to private‐sector data, we find no evidence that retail investors are harmed in their trades. Overall, our findings highlight the interplay between private‐sector and regulatory solutions in enhancing financial market transparency.

We explore a new dimension of fund managers' timing ability by examining whether they can time market liquidity through adjusting their portfolios' market exposure as aggregate liquidity conditions change. Using a large sample of hedge funds, we find strong evidence of liquidity timing. A bootstrap analysis suggests that top-ranked liquidity timers cannot be attributed to pure luck. In out-of-sample tests, top liquidity timers outperform bottom timers by 4.0–5.5% annually on a risk-adjusted basis. We also find that it is important to distinguish liquidity timing from liquidity reaction, which primarily relies on public information. Our results are robust to alternative explanations, hedge fund data biases, and the use of alternative timing models, risk factors, and liquidity measures. The findings highlight the importance of understanding and incorporating market liquidity conditions in investment decision making.

We identify a parsimonious set of factors from a large pool of candidates for explaining hedge fund returns, ranging from equity market, anomaly, and trend-following factors to macroeconomic factors. The resulting 9-factor model, including five anomaly factors, outperforms existing hedge fund models both in sample and out of sample, with a significant reduction in alphas while showing substantial cross sectional performance heterogeneity. Further analysis based on fund holdings confirms the model’s ability to capture returns from arbitrage trading. Overall, the anomaly factors help quantify hedge fund strategies and risk exposures and improve fund performance evaluation.

In the presence of sentiment fluctuations, arbitrageurs may engage in different strategies leading to dispersed sentiment exposures. We find that hedge funds in the top decile ranked by sentiment beta outperform those in the bottom decile by 0.59% per month on a risk‐adjusted basis, with the spread being larger among skilled funds. We also find that about 10% of hedge funds have sentiment timing skill that positively correlates with fund sentiment beta and contributes to fund performance. Our findings show that skilled hedge funds can earn high returns by predicting and exploiting sentiment changes rather than betting against mispricing.