Lou and Shu decompose Amihud’s illiquidity measure (ILLIQ) proposing that its component, the average of inverse dollar trading volume (IDVOL), is sufficient to explain the pricing of illiquidity. Their decomposition misses a component of ILLIQ that is related to illiquidity. We find that this component affects stock returns significantly, both in the cross-section and in time-series. We show that the ILLIQ premium is significantly positive after controlling for mispricing, sentiment, and seasonality. In addition, the aggregate market ILLIQ outperforms market IDVOL in estimating the effect of market illiquidity shocks on realized stock returns.

We investigate how market participants react when corporate executives strategically blame the economy or industries for poor firm performance. In the quarters subsequent to earnings conference calls, we find that the “blame sentences,” which capture executives’ blaming tactics, predict negative and non-reverting abnormal returns, negative earnings surprises, and analyst recommendation downgrades. These blaming tactics also reduce the sensitivity of executives’ turnover to their performance. Our findings imply that executives strategically inject the negative information about future cash flows into blame sentences. Market participants need to combine distinct categories of information (firm-specific vs. economy-wide) to understand the implications of blame sentences, which consumes more cognitive resources and delays their reactions to the blame sentences.

Speculative language in corporate disclosures can convey valuable information about firms’ fundamentals. We evaluate this idea by developing a measure for speculative statements based on sentences marked with the “weasel tag” on Wikipedia. In the 16-week test period after filing, greater use of speculative statements in 10-Ks predicts higher and nonreverting abnormal returns, more insider and informed buying, and higher news sentiment. These findings imply that managers’ usage of speculative language in 10-Ks reflects voluntary disclosure of their private information about the positive prospects of events when market implications of the events are uncertain and thus have room for (re)interpretation.

To attenuate an inherent errors-in-variables bias, portfolios are widely employed to test asset pricing models; but portfolios might mask relevant risk- or return-related features of individual stocks. We propose an instrumental variables approach that allows the use of individual stocks as test assets, yet delivers consistent estimates of ex post risk premiums. This estimator also yields well-specified tests in small samples. The market risk premium under the capital asset pricing model (CAPM) and the liquidity-adjusted CAPM, premiums on risk factors under the Fama–French three- and five-factor models, and the Hou et al. (2015) four-factor model are all insignificant after controlling for asset characteristics.