I study the cross-sectional variation of stock returns and technological progress using a dynamic equilibrium model with production. Technological progress is endogenously driven by research and development (R&D) investment and is composed of two parts. One part is devoted to product innovation; the other, to increasing the productivity of physical investment. The latter is embodied in new tangible capital. The model breaks the symmetry assumed in standard models between tangible and intangible capital, in which the accumulation processes of tangible and intangible capital stock do not affect each other. Qualitatively and, in many cases, quantitatively, the model explains well-documented empirical regularities.

In standard production models, wage volatility is far too high, and equity volatility is far too low. A simple modification–sticky wages because of infrequent resetting together with a constant elasticity of substitution (CES) production function leads to both smoother wages and higher equity volatility. Further, the model produces several other hard-to-explain features of financial data: high Sharpe ratios, low and smooth interest rates, time-varying equity volatility and premium, a value premium, and a downward-sloping equity term structure. Procyclical, volatile wages are a hedge for firms in standard models; smoother wages act like operating leverage, making profits and dividends riskier.

In standard production models, wage volatility is far too high, and equity volatility is far too low. A simple modification-sticky wages because of infrequent resetting together with a constant elasticity of substitution (CES) production function leads to both smoother wages and higher equity volatility. Further, the model produces several other hard-toexplain features of financial data: high Sharpe ratios, low and smooth interest rates, time-varying equity volatility and premium, a value premium, and a downward-sloping equity term structure. Procyclical, volatile wages are a hedge for firms in standard models; smoother wages act like operating leverage, making profits and dividends riskier.

[Previous studies show that firms with low inventory growth outperform firms with high inventory growth in the cross-section of publicly traded firms. In addition, inventory investment is volatile and procyclical, and inventory-to-sales is persistent and countercyclical. We embed an inventory holding motive into the investment-based asset pricing framework by modeling inventory as a factor of production with convex and nonconvex adjustment costs. The augmented model simultaneously matches the large inventory growth spread in the data, as well as the time-series properties of the firm-level capital investment, inventory investment, and inventory-to-sales. Our conditional single-factor model also implies that traditional unconditional factor models such as the CAPM should fail to explain the inventory growth spread, although not with the same large pricing errors observed in the data.]

Previous studies have identified a negative relation between firms’hiring rates and future stock returns in the cross-section. We document that this relation is significantly steeper in industries that rely relatively more on high-skill workers than low-skill workers. A longshort portfolio sorted on firm-level hiring rate earns an average annual return of 8.6 % in high-skill industries, and only 0.9 % in low-skill industries. Moreover, this pattern is not explained by the standard CAPM. These findings are consistent with a neoclassical model with labor force heterogeneity and labor market frictions if it is more costly to replace high-skill than low-skill workers.

Previous studies show that firms with low inventory growth outperform firms with high inventory growth in the cross-section of publicly traded firms. In addition, inventory investment is volatile and procyclical, and inventory-to-sales is persistent and countercyclical. We embed an inventory holding motive into the investment-based asset pricing framework by modeling inventory as a factor of production with convex and nonconvex adjustment costs. The augmented model simultaneously matches the large inventory growth spread in the data, as well as the time-series properties of the firm-level capital investment, inventory investment, and inventory-to-sales. Our conditional single-factor model also implies that traditional unconditional factor models such as the CAPM should fail to explain the inventory growth spread, although not with the same large pricing errors observed in the data. The Author 2011. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: [email protected]., Oxford University Press.

We show that labor market frictions are first-order for understanding credit markets. Wage growth and labor share forecast aggregate credit spreads and debt growth as well as or better than alternative predictors. They also predict credit risk and debt growth in a cross section of international firms. Finally, high labor share firms choose lower financial leverage. A model with labor market frictions and risky long-term debt can explain these findings, and produce large credit spreads despite realistically low default probabilities. This is because precommitted payments to labor make other committed payments (i.e., interest) riskier.

We develop a dynamic model with time variation in external equity financing costs and show that variation in these costs is important for the model to quantitatively capture the joint dynamics of firms’ asset prices, real quantities, and financial flows in the U.S. economy. Growth firms and high investment firms are less risky in equilibrium, because they can substitute more easily debt financing for equity financing when it becomes more costly to raise external equity, which are high marginal utility states. Using a model-implied proxy of aggregate equity issuance cost shocks, we provide empirical support for the model’s economic mechanism. Received August 7, 2017; editorial decision September 24, 2018 by Editor Stijn Van Nieuwerburgh.

We show how real and financial frictions amplify, prolong, and propagate the negative impact of uncertainty shocks. We use a novel instrumentation strategy to address endogeneity in estimating the impact of uncertainty by exploiting differential firm exposure to exchange rate, policy, and energy price volatility. We show that financially constrained firms cut investment more than unconstrained firms following an uncertainty shock. We then build a general equilibrium heterogeneous firms model with real and financial frictions, finding that financial frictions (i) amplify uncertainty shocks by doubling their impact on output; (ii) increase persistence by doubling the duration of the drop; and (iii) propagate uncertainty shocks by spreading their impact onto financial variables.

We study the impact of labor market frictions on asset prices. In the cross section of US firms, a 10 percentage point increase in the firm’s hiring rate is associated with a 1.5 percentage point decrease in the firm’s annual risk premium. We propose an investment-based model with stochastic labor adjustment costs to explain this finding. Firms with high hiring rates are expanding firms that incur high adjustment costs. If the economy experiences a shock that lowers adjustment costs, these firms benefit the most. The corresponding increase in firm value operates as a hedge against these shocks, explaining the lower risk premium of these firms in equilibrium.