This paper constructs and analyzes various measures of trading costs in US equity markets covering the period 1926–2015. These measures contain statistically and economically significant predictive signals for stock market returns and real economic activity. We decompose illiquidity proxies into a component capturing aggregate volatility and a residual. The predictive content of these components differs in important ways. Specifically, we find strong evidence that the component of illiquidity uncorrelated with volatility forecasts stock market returns. Both the volatility and residual components of illiquidity contain information regarding future economic activity.

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.

Security trading now fragments into more than ten almost identical stock exchanges in the United States. We show that discrete pricing is one economic force that prevents the consolidation of trading volume. The uniform one-cent tick size (minimum price variation), imposed by the SEC's Rule 612, leads to more dispersed trading for lower priced securities. When a security reverse splits, its price increases and relative tick size (one cent divided by the price) decreases. We find that reverse splits consolidate trading of securities, using securities with identical underlying fundamentals that do not reverse split as the control group.

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.