[I argue that the slow diffusion of industry information is a leading cause of the lead-lag effect in stock returns. I find that the lead-lag effect between big firms and small firms is predominantly an intra-industry phenomenon. Moreover, this effect is driven by sluggish adjustment to negative information, and is robust to alternative determinants of the lead-lag effect. Small, less competitive and neglected industries experience a more pronounced lead-lag effect. The lead-lag effect is related to the post-announcement drift of small firms following the earnings releases of big firms within the industry.]

We study the effect of a bond’s place in its issuer’s maturity structure on credit risk. Using a structural model as motivation, we argue that bonds due relatively late in their issuers’ maturity structure have greater credit risk than do bonds due relatively early. Empirically, we find robust evidence that these later bonds have larger yield spreads and greater comovement with equity and that the magnitude of the effects is consistent with model predictions for investment-grade bonds. Our results highlight the importance of bond-specific credit risk for understanding corporate bond prices.

We parsimoniously characterize the severity of market frictions affecting a stock using the delay with which its price responds to information. The most delayed firms command a large return premium not explained by size, liquidity, or micro-structure effects. Moreover, delay captures part of the size effect, idiosyncratic risk is priced only among the most delayed firms, and earnings dirft is monotonically increasing in delay. Frictions associated with investor recognition appear most responsible for the delay effect. The very small segment of delayed firms, comprising only 0.02% of the market, generates substantial variation in average returns, highlighting the importance of frictions.

An empirical q-factor model consisting of the market factor, a size factor, an investment factor, and a profitability factor largely summarizes the cross section of average stock returns. A comprehensive examination of nearly 80 anomalies reveals that about one-half of the anomalies are insignificant in the broad cross section. More importantly, with a few exceptions, the q-factor model's performance is at least comparable to, and in many cases better than that of the Fama-French (1993) 3-factor model and the Carhart (1997) 4-factor model in capturing the remaining significant anomalies.

Journal Article Industry Information Diffusion and the Lead-lag Effect in Stock Returns Get access Kewei Hou Kewei Hou Department of Finance, Fisher College of Business, The Ohio State University Address correspondence to Kewei Hou, Department of Finance, Fisher College of Business, Ohio State University, 2100 Neil Avenue, Columbus, OH 43210, or e-mail: [email protected] Search for other works by this author on: Oxford Academic Google Scholar The Review of Financial Studies, Volume 20, Issue 4, July 2007, Pages 1113–1138, https://doi.org/10.1093/revfin/hhm003 Published: 22 January 2007

We study the effect of a bond’s place in its issuer’s maturity structure on credit risk. Using a structural model as motivation, we argue that bonds due relatively late in their issuers’ maturity structure have greater credit risk than do bonds due relatively early. Empirically, we find robust evidence that these later bonds have larger yield spreads and greater comovement with equity and that the magnitude of the effects is consistent with model predictions for investment-grade bonds. Our results highlight the importance of bond-specific credit risk for understanding corporate bond prices. Received January 1, 2016; editorial decision June 6, 2017 by Editor Robin Greenwood.

[Using monthly returns for over 27,000 stocks from 49 countries over a three-decade period, we show that a multifactor model that includes factor-mimicking portfolios based on momentum and cash flow-to-price captures significant time-series variation in global stock returns, and has lower pricing errors and fewer model rejections than the global CAPM or a popular model that uses size and book-to-market factors. We find reliable evidence that the global cash flow-to-price factor is related to a covariance risk model. In contrast, we reject the covariance risk model in favor of a characteristic model for size and book-to-market factors.]

We parsimoniously characterize the severity of market frictions affecting a stock using the delay with which its price responds to information. The most delayed firms command a large return premium not explained by size, liquidity, or microstructure effects. Moreover, delay captures part of the size effect, idiosyncratic risk is priced only among the most delayed firms, and earnings drift is monotonically increasing in delay. Frictions associated with investor recognition appear most responsible for the delay effect. The very small segment of delayed firms, comprising only 0.02% of the market, generates substantial variation in average returns, highlighting the importance of frictions.

Many studies report that the size effect in the cross-section of stock returns disappeared after the early 1980s. This paper shows that its disappearance can be attributed to negative shocks to the profitability of small firms and positive shocks to big firms. After adjusting for the price impact of profitability shocks, we find a robust size effect in the cross-section of expected returns after the early 1980s. Our results highlight the importance of in-sample cash-flow shocks in understanding cross-sectional return predictability.Received April 2, 2014; editorial decision August 6, 2018 by Editor Laura Starks.

Using monthly returns for over 27,000 stocks from 49 countries over a three-decade period, we show that a multifactor model that includes factor-mimicking portfolios based on momentum and cash flow-to-price captures significant time-series variation in global stock returns, and has lower pricing errors and fewer model rejections than the global CAPM or a popular model that uses size and book-to-market factors. We find reliable evidence that the global cash flow-to-price factor is related to a covariance risk model. In contrast, we reject the covariance risk model in favor of a characteristic model for size and book-to-market factors.