Marketing capability refers to a firm’s ability to optimally deploy and integrate different marketing inputs to achieve high sales at low cost. This paper examines whether the value of marketing capability is incorporated into stock returns. High-level marketing capability predicts better future operating performance and stock returns. A marketing capability-based long-short portfolio strategy earns an average annual return of 5.16%, a substantial portion of which is earned over subsequent earnings announcements. The return predictability is stronger in stocks with higher valuation uncertainty and lower investor attention. Investors underreact to the value-relevant, but difficult to process, information embedded in marketing capability.

It is generally optimal for risk‐sharing reasons to base a charge for information on the signal realization. When this is not possible, a charge based on the amount of trading, a brokerage commission, may be a good alternative. The optimal brokerage commission schedule is derived for a risk‐neutral information seller faced with risk‐averse purchasers who may differ in their risk aversion. Revenues from the brokerage commission are compared with those from a fixed charge for information and the optimal mutual fund management fee.

This paper explores commonalities across asset pricing anomalies. In particular, we assess implications of financial distress for the profitability of anomaly-based trading strategies. Strategies based on price momentum, earnings momentum, credit risk, dispersion, idiosyncratic volatility, and capital investments derive their profitability from taking short positions in high credit risk firms that experience deteriorating credit conditions. In contrast, the value-based strategy derives most of its profitability from taking long positions in high credit risk firms that survive financial distress and subsequently realize high returns. The accruals anomaly is an exception. It is robust among high and low credit risk firms in all credit conditions.

This paper shows that the puzzling negative cross-sectional relation between dispersion in analysts’ earnings forecasts and future stock returns may be explained by financial distress, as proxied by credit rating downgrades. Focusing on a sample of firms rated by Standard & Poor's (S&P), we show that the profitability of dispersion-based trading strategies concentrates in a small number of the worst-rated firms and is significant only during periods of deteriorating credit conditions. In such periods, the negative dispersion–return relation emerges as low-rated firms experience substantial price drop along with considerable increase in forecast dispersion. Moreover, even for this small universe of worst-rated firms, the dispersion–return relation is non-existent when either the dispersion measure or return is adjusted by credit risk. The results are robust to previously proposed explanations for the dispersion effect such as short-sale constraints and leverage.

This paper documents a strong relationship between short‐run reversals and stock illiquidity, even after controlling for trading volume. The largest reversals and the potential contrarian trading strategy profits occur in high turnover, low liquidity stocks, as the price pressures caused by non‐informational demands for immediacy are accommodated. However, the contrarian trading strategy profits are smaller than the likely transactions costs. This lack of profitability and the fact that the overall findings are consistent with rational equilibrium paradigms suggest that the violation of the efficient market hypothesis due to short‐term reversals is not so egregious after all.

Previous studies of liquidity span short time periods and focus on the individual security. In contrast, we study aggregate market spreads, depths, and trading activity for U.S. equities over an extended time sample. Daily changes in market averages of liquidity and trading activity are highly volatile and negatively serially dependent. Liquidity plummets significantly in down markets. Recent market volatility induces a decrease in trading activity and spreads. There are strong day‐of‐the‐week effects; Fridays accompany a significant decrease in trading activity and liquidity, while Tuesdays display the opposite pattern. Long‐ and short‐term interest rates influence liquidity. Depth and trading activity increase just prior to major macroeconomic announcements.

This paper develops a general equilibrium model and provides empirical support that the market volatility-of-volatility (VOV) predicts market returns and drives the time-varying volatility risk. In asset pricing tests with the market, volatility, and VOV as factors, the risk premium on VOV is statistically and economically significant and robust. Market and volatility risks are not priced in unconditional models, but, consistent with theory, their factor loadings, conditional on VOV, are priced. The pricing impact of VOV strengthens during market crashes, suggesting that VOV is particularly relevant during market turmoil, when investors demand increased compensation for VOV risk.

Building on the growing literature on interfirm links and limited attention, we find evidence of return predictability across alliance partners. A long–short portfolio sorted on lagged returns of strategic alliance partners provides a return of 89 basis points per month that is robust to a number of specifications. Investor inattention and limits to arbitrage may be the source of the underreaction of a firm’s returns to that of its partners.

This paper examines the relation between information transmission and cross-autocorrelations. We present a simple model, where informed trading is transmitted from large to small stocks with a lag. In equilibrium, large stock illiquidity induced by informed trading portends stronger cross-autocorrelations. Empirically, we find that the lead-lag relation increases with lagged large stock illiquidity. Further, the lead from large stock order flows to small stock returns is stronger when large stock spreads are higher. In addition, this lead-lag relation is stronger before macro announcements (when information-based trading is more likely) and weaker afterward (when information asymmetries are lower).