High growth rate of labor hours per worker signals low future stock returns and high future hiring rate. As labor hours are substituted for hiring, hiring becomes less responsive to future discount rate. The growth rate of the number of labor hours per worker does not appear to be related to future profitability. Our findings are largely consistent with a dynamic labor hours asset pricing model that features large asymmetric costs in adjusting the number of workers and small costs in adjusting the number of hours per worker.

We consider asset pricing in a monetary economy where liquid assets are held to lower transaction costs. The ensuing model extends the capital asset pricing model (CAPM) and the consumption CAPM by deriving real money growth as an additional factor determining returns. Empirically, the two model versions compare favorably to other theoretical asset pricing models along several dimensions, supporting the traditional intertemporal asset pricing perspective. A value premium arises because value firms are sensitive to liquidity shocks but growth firms are not. Although no alternative factor drives out the money growth factor, the conditioning CAY factors of Lettau and Ludvigson (2001b) add explanatory power.

Winner stocks have higher changes in sales order backlogs and a sales order backlog factor is significant in explaining various winner minus loser returns and often reduces the αs by big margins. We argue that this factor is a proxy for innovation in demand in the economy and it is likely to relate to expected growth risks and future business conditions.

We use an investment-based asset pricing model to examine the effect of firms’ investments relative to cash holdings on stock returns, assuming holding cash lowers transaction costs. We find that mimicking portfolios based on investments relative to non-cash capital and based on investments relative to cash capital are priced for various testing portfolios. On average, momentum stocks and growth stocks are more sensitive to the factor constructed using investment relative to cash.

In a general real business cycle model, we derive a pricing kernel that involves only production function arguments. The productivity shock is the single factor and the capital stock relative to a productivity measure is the conditioning variable. The model compares favorably with the complementary consumption-based and market-based approaches and with the Fama-French three-factor model. A size premium arises from differences in unconditional sensitivities—small firms are more sensitive to productivity shocks—and a value premium from differences in conditional sensitivities to productivity shocks—growth firms are more sensitive to productivity shocks when the productivity risk premium is low.

We present a theoretical perspective that motivates the use of the Generalized Least Squares R-Square, prominently advocated by Lewellen et al. [Lewellen, J., Nagel, S., Shanken, J., forthcoming. A skeptical appraisal of asset-pricing tests. Journal of Financial Economics], as an evaluation measure for multivariate linear asset pricing models. Adapting results from Shanken [Shanken, J., 1985. Multivariate tests of the zero-beta CAPM. Journal of Financial Economics 14, 327–348] and Kandel and Stambaugh [Kandel, S., Stambaugh, R.F., 1995. Portfolio inefficiency and the cross-section of expected returns. Journal of Finance 50, 157–184], we provide various interpretations and a graphical account in mean-variance space of this measure, facilitating a better understanding of its properties. We furthermore relate it to another leading evaluation metric, the HJ-distance of Hansen and Jagannathan [Hansen, L.P., Jagannathan, R., 1997. Assessing specification errors in stochastic discount factor models. Journal of Finance 52, 557–590]. Additionally, we present a comparison between these evaluation measures using mean-variance mathematics in risk-return space, and we provide a simple formula for calculating both model evaluation measures that involves only the parameters of the mean-variance asset and factor frontiers.

We examine institutional investors' responses to corporate disclosure quality conditional on market states. Transient institutions react more positively to corporate disclosure quality during market downturns than during normal market periods, as better disclosure practices lower information asymmetry and are thus associated with reduced uncertainty, enhanced liquidity, and weakened impacts of crises, which are the most desirable features of assets during market downturns. Dedicated institutions are insensitive to corporate disclosure quality in both normal and market downturn periods, as they have access to inside information and rely less on public disclosures. Their reliance on corporate disclosures in market downturns, however, increases sharply after the implementation of Regulation Fair Disclosure, which removes their inside information advantage. We further show that corporate disclosure reduces information asymmetry to a greater extent in market downturns than in normal market periods and that transient ownership in market downturns provides strong price support and stabilizes return volatility, whereas dedicated ownership does not possess such functions. Finally, we show that the results are not simply driven by endogeneity and are robust to alternative corporate disclosure quality measure and to the control of other determinants of institutional holdings.

The growth rate of industrial electricity usage predicts future stock returns up to 1 year with an R 2 of 9%. High industrial electricity usage today predicts low stock returns in the future, consistent with a countercyclical risk premium. Industrial electricity usage tracks the output of the most cyclical sectors. Our findings bridge a gap between the asset pricing literature and the business cycle literature, which uses industrial electricity usage to gauge production and output in real time. Industrial electricity growth compares favorably with traditional financial variables, and it outperforms Cooper and Priestley’s output gap measure in real time.

Short selling efficiency (SSE), measured each month by the slope coefficient of cross-sectionally regressing abnormal short interest on a mispricing score, significantly and negatively predicts stock market returns both in-sample and out-of-sample, suggesting that mispricing gets corrected after short sales are executed on the right stocks. We show conceptually and empirically that SSE has favorable predictive ability over aggregate short interest, as SSE reduces the effect of noises in short interest and better captures the amount of aggregate short selling capital devoted to overpricing. The predictive power is stronger during the periods of recession, high volatility, and low public information. In addition, low SSE precedes the months when the CAPM performs well and signals an efficient market. Overall, our evidence highlights the importance of the disposition of short sales in stock markets.

We examine net arbitrage trading (NAT) measured by the difference between quarterly abnormal hedge fund holdings and abnormal short interest. NAT strongly predicts stock returns in the cross-section. Across ten well-known stock anomalies, abnormal returns are realized only among stocks experiencing large NAT. Exploiting Regulation SHO, which facilitated short selling for a random group of stocks, we present causal evidence that NAT has stronger return predictability among stocks facing greater limits to arbitrage. We also find large returns for anomalies that arbitrageurs chose to exploit despite capital constraints during the 2007–09 financial crisis. We confirm our findings using daily data. Received September 1, 2016; editorial decision May 28, 2018 by Editor Andrew Karolyi.