This paper is a new incarnation of the defunct work previously circulated under the titles "Neoclassical Factors," "An equilibrium three-factor model," "Production-based factors," "A better three-factor model that explains more anomalies," and "An alternative three-factor model." We are extremely grateful to Robert Novy-Marx

A new methodology for equity valuation arises from the perspective of managers' supply of capital assets. Under q-theory, managers optimally adjust the supply of assets to changes in their market value. The first-order condition of investment then provides a valuation equation that infers asset prices from managers' costs of supplying the assets. This equation fits well the Tobin's q levels across many testing assets, including portfolios formed on q. With current investment-to-capital as the only input, the supply approach does not require cash flow forecasts or discount rate estimates, both of which are notoriously difficult to obtain in practice.

An investment factor, long in low-investment stocks and short in high-investment stocks, helps explain the new issues puzzle. Adding the investment factor into standard factor regressions reduces the SEO underperformance by about 75%, the IPO underperformance by 80%, the underperformance following convertible debt offerings by 50%, and Daniel and Titman's (2006) composite issuance effect by 40%. The reason is that issuers invest more than nonissuers, and the investment factor earns a significantly positive average return of 0.57% per month.

In the investment theory, firms with high expected investment growth earn higher expected returns than firms with low expected investment growth, holding investment and expected profitability constant. Building on cross-sectional growth forecasts with Tobin’s q, operating cash flows, and change in return on equity as predictors, an expected growth factor earns an average premium of 0.84% per month (t = 10.27) in the 1967–2018 sample. The q5 model, which augments the Hou–Xue–Zhang (2015, Rev. Finan. Stud., 28, 650–705) q-factor model with the expected growth factor, shows strong explanatory power in the cross-section and outperforms the Fama–French (2018, J. Finan. Econom., 128, 234–252) six-factor model.

Disequilibrating macro shocks affect different firms’ prospects differently, increasing idiosyncratic variation in forward-looking stock returns before affecting economic growth. Consistent with most such shocks from 1947 to 2020 enhancing productivity, increased idiosyncratic stock return variation forecasts next-quarter real GDP growth, industrial production growth, and consumption growth both in-sample and out-of-sample. These effects persist after controlling for other leading economic indicators.

A detailed treatment of aggregation and capital heterogeneity substantially improves the performance of the investment CAPM. Firm-level predicted returns are constructed from firm-level accounting variables and aggregated to the portfolio level to match with portfolio-level stock returns. Working capital forms a separate productive input besides physical capital. The model simultaneously fits the value, momentum, investment, and profitability premiums and partially explains positive stock-fundamental return correlations, the procyclical and short-term dynamics of the momentum and profitability premiums, and the countercyclical and long-term dynamics of the value and investment premiums. However, the model falls short in explaining momentum crashes. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online..

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 construct firm-specific measures of expected equity returns using corporate bond yields, and replace standard ex post average returns with our expected-return measures in asset pricing tests. We find that the market beta is significantly priced in the cross section of expected returns. The expected size and value premiums are positive and countercyclical, but there is no evidence of positive expected momentum profits. The Author 2008. 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 study the effect of financial constraints on risk and expected returns by extending the investment‐based asset pricing framework to incorporate retained earnings, debt, costly equity, and collateral constraints on debt capacity. Quantitative results show that more financially constrained firms are riskier and earn higher expected stock returns than less financially constrained firms. Intuitively, by preventing firms from financing all desired investments, collateral constraints restrict the flexibility of firms in smoothing dividend streams in the face of aggregate shocks. The inflexibility mechanism also gives rise to a convex relation between market leverage and expected stock returns.

We construct a dynamic general equilibrium production economy to explicitly link expected stock returns to firm characteristics such as firm size and the book-to-market ratio. Stock returns in the model are completely characterized by a conditional capital asset pricing model (CAPM). Size and book-to-market are correlated with the true conditional market beta and therefore appear to predict stock returns. The cross-sectional relations between firm characteristics and returns can subsist even after one controls for typical empirical estimates of beta. These findings suggest that the empirical success of size and book-to-market can be consistent with a single-factor conditional CAPM model.