We propose that fund performance can be predicted by its R2, obtained from a regression of its returns on a multifactor benchmark model. Lower R2 indicates greater selectivity, and it significantly predicts better performance. Stock funds sorted into lowest-quintile lagged R2 and highest-quintile lagged alpha produce significant annual alpha of 3.8%. Across funds, R2 is positively associated with fund size and negatively associated with its expenses and manager's tenure.

This paper establishes liquidity linkage between stock and Treasury bond markets. There is a lead-lag relationship between illiquidity of the two markets and bidirectional Granger causality. The effect of stock illiquidity on bond illiquidity is consistent with flight-to-quality or flight-to-liquidity episodes. Monetary policy impacts illiquidity. The evidence indicates that bond illiquidity acts as a channel through which monetary policy shocks are transferred into the stock market. These effects are observed across illiquidity of bonds of different maturities and are especially pronounced for illiquidity of short-term maturities. The paper provides evidence of illiquidity integration between stock and bond markets.

In this study, using data from 46 markets and a 34-year time period, we examine the impact of the illiquidity of U.S. Treasuries on global asset valuation. We find that it predicts equity returns in both developed and emerging markets. This predictive relation remains intact after controlling for various world- and country-level variables. Asset pricing tests further reveal that bond illiquidity is a priced factor even in the presence of other conventional risks. Since the illiquidity of Treasuries is known to reflect monetary and macroeconomic shocks, our results suggest that it can be considered a proxy for aggregate worldwide risks.

We estimate investor disagreement from synthetic long and short stock trades in the equity options market. We show that high disagreement predicts low stock returns after positive earnings surprises and high stock returns after negative earnings surprises. The negative effect is stronger for high-beta stocks and stocks that are more difficult to sell short. In the cross-section of all stocks and the subset of the 500 largest companies, high disagreement robustly predicts low monthly and weekly stock returns.

Previous studies of Treasury market illiquidity span short time periods and focus on particular maturities. In contrast, we study the time series of illiquidity for different maturities over an extended period of time. We also compare time-series determinants of on-the-run and off-the-run illiquidity. Illiquidity increases and the difference between spreads of long- and short-term bonds significantly widens during recessions, suggesting a “flight to liquidity,” wherein investors shift into the more liquid short-term bonds during economic contractions. Macroeconomic variables such as inflation and federal funds rates forecast off-the-run illiquidity significantly but have only modest forecasting ability for on-the-run illiquidity. Bond returns across maturities are forecastable by off-the-run but not on-the-run bond illiquidity. Thus, off-the-run illiquidity, by reflecting macro shocks first, is the primary source of the liquidity premium in the Treasury market.

Given the key role of liquidity in finance research, identifying high quality proxies based on daily (as opposed to intraday) data would permit liquidity to be studied over relatively long timeframes and across many countries. Using new measures and widely employed measures in the literature, we run horseraces of annual and monthly estimates of each measure against liquidity benchmarks. Our benchmarks are effective spread, realized spread, and price impact based on both Trade and Quote (TAQ) and Rule 605 data. We find that the new effective/realized spread measures win the majority of horseraces, while the Amihud [2002. Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets 5, 31–56] measure does well measuring price impact.