We use a production-based asset pricing model to investigate whether financing constraints are quantitatively important for the cross-section of returns. Specifically, we use GMM to explore the stochastic Euler equation imposed on returns by optimal investment. Our methods can identify the impact of financial frictions on the stochastic discount factor with cyclical variations in cost of external funds. We find that financing frictions provide a common factor that improves the pricing of cross-sectional returns. Moreover, the shadow cost of external funds exhibits strong procyclical variation, so that financial frictions are more important in relatively good economic conditions.

We examine investment behavior when firms face costs in the access to external funds. We find that despite the existence of liquidity constraints, standard investment regressions predict that cash flow is an important determinant of investment only if one ignores q. Conversely, we also obtain significant cash flow effects even in the absence of financial frictions. These findings provide support to the argument that the success of cash-flow-augmented investment regressions is probably due to a combination of measurement error in q and identification problems.

We use a production-based asset pricing model to investigate whether financing constraints are quantitatively important for the cross-section of returns. Specifically, we use GMM to explore the stochastic Euler equation imposed on returns by optimal investment. Our methods can identify the impact of financial frictions on the stochastic discount factor with cyclical variations in cost of external funds. We find that financing frictions provide a common factor that improves the pricing of cross-sectional returns. Moreover, the shadow cost of external funds exhibits strong procyclical variation, so that financial frictions are more important in relatively good economic conditions.

We develop a general equilibrium model linking the pricing of stocks and corporate bonds to endogenous movements in corporate leverage and aggregate volatility. The model features heterogeneous firms making optimal investment and financing decisions and connects fluctuations in macroeconomic quantities and asset prices to movements in the cross section of firms. Empirically plausible movements in leverage produce realistic asset return dynamics. Countercyclical leverage drives predictable variation in risk premia, and debt‐financed growth generates a high value premium. Endogenous default produces countercyclical aggregate volatility and credit spread movements that are propagated to the real economy through their effects on investment and output.

This paper revisits the theoretical relation between financial leverage and stock returns in a dynamic world where both corporate investment and financing decisions are endogenous. We find that the link between leverage and stock returns is more complex than static textbook examples suggest, and depends on the investment opportunities available to the firm. In the presence of financial market imperfections, leverage and investment are generally correlated so that highly levered firms are also mature firms with relatively more (safe) book assets and fewer (risky) growth opportunities. A quantitative version of our model matches several stylized facts about leverage and returns.

The demand for durable goods is more cyclical than that for nondurable goods and services. Consequently, the cash flows and stock returns of durable‐good producers are exposed to higher systematic risk. Using the benchmark input‐output accounts of the National Income and Product Accounts, we construct portfolios of durable‐good, nondurable‐good, and service producers. In the cross section, an investment strategy that is long on the durable‐good portfolio and short on the service portfolio earns a risk premium exceeding 4 percent annually. In the time series, an investment strategy that is long on the durable‐good portfolio and short on the market portfolio earns a countercyclical risk premium. We explain these findings in a general equilibrium asset‐pricing model with endogenous production.

We propose a new method to estimate the marginal value of capital under minimal assumptions. By combining asset prices with fundamentals, our method provides a quasi‐model‐free marginal q together with a simple correction for measurement error in (average) Tobin's Q using linear regressions. Marginal q yields plausible and robust estimates of adjustment costs and investment sensitivities to fundamentals. The widening gap between marginal q and Tobin's Q is driven primarily by market power and intangible capital. Our novel findings strongly support the neoclassical theory of investment and challenge the widespread use of Tobin's Q as proxy for investment opportunities.

A growing literature shows that credit indicators forecast aggregate real outcomes. While researchers have proposed various explanations, the economic mechanism behind these results remains an open question. In this paper, we show that a simple, frictionless model explains empirical findings commonly attributed to credit cycles. Our key assumption is that firms have heterogeneous exposures to underlying economy-wide shocks. This leads to endogenous dispersion in credit quality that varies over time and predicts future excess returns and real outcomes. Received August 7, 2017; editorial decision June 26, 2018 by Editor Stijn Van Nieuwerburgh.

This paper suggests that skill accumulation through past work experience, or “learning-by-doing” (LBD), can provide an important propagation mechanism in a dynamic stochastic general-equilibrium model, as the current labor supply affects future productivity. Our econometric analysis uses a Bayesian approach to combine micro-level panel data with aggregate time series. Formal model evaluation shows that the introduction of the LBD mechanism improves the model's ability to fit the dynamics of aggregate output and hours.