[The output gap, a production-based macroeconomic variable, is a strong predictor of U. S. stock returns. It is a prime business cycle indicator that does not include the level of market prices, thus removing any suspicion that returns are forecastable due to a "fad" in prices being washed away. The output gap forecasts returns both in- sample and out-of-sample, and it is robust to a host of checks. We show that the output gap also has predictive power for excess stock returns in other G7 countries and U. S. excess bond returns.]

Abstract We study the predictability of stock returns using a pure macroeconomic measure of the world business cycle, namely the world's capital to output ratio. This variable tracks variation in expected stock returns in a group of the major industrial economies in the presence of world financial market–based predictor variables. The world's capital to output ratio exhibits strong out-of-sample predictive power in almost all countries studied. This is in contrast to financial market–based variables that almost never have out-of-sample forecasting power. Using the stock return predictability that we uncover, we find that international versions of conditional asset pricing models perform well. The world capital to output ratio also predicts bond returns, interest rate changes, and credit spreads. The results highlight the importance of world business conditions for financial markets.

ABSTRACT This paper derives a real options model that accounts for the value premium. If real investment is largely irreversible, the book value of assets of a distressed firm is high relative to its market value because it has idle physical capital. The firm's excess installed capital capacity enables it to fully benefit from positive aggregate shocks without undertaking costly investment. Thus, returns to equity holders of a high book‐to‐market firm are sensitive to aggregate conditions and its systematic risk is high. Simulations indicate that the model goes a long way toward accounting for the observed value premium.

Characteristics play a similar role in describing returns in private firms as in public firms. This evidence suggests a causal effect of optimal investment underlying the role of characteristics, as private firms do not have stock prices to over- or under-react on. Common factor models largely describe the cross section of investment returns of both types of firms, suggesting that the common factors are likely aggregate risk factors. Finally, the cost of capital and firm valuations are similar across private and public firms.

We ask to what extent the negative relation between investment and average stock returns is driven by risk. We show that: (i) the average return spread between low and high asset growth and investment portfolios is largely accounted for by their spread in systematic risk, as measured by the loadings on the Chen, Roll, and Ross (1986) factors; (ii) as predicted by q-theory and real options models, systematic risk falls during large investment periods; (iii) the returns of factors formed on the investment-to-assets, asset growth, and investment growth all forecast aggregate economic activities. Our evidence suggests that risk plays an important role in explaining the investment-return relation.

The output gap, a production-based macroeconomic variable, is a strong predictor of U.S. stock returns. It is a prime business cycle indicator that does not include the level of market prices, thus removing any suspicion that returns are forecastable due to a “fad” in prices being washed away. The output gap forecasts returns both in-sample and out-of-sample, and it is robust to a host of checks. We show that the output gap also has predictive power for excess stock returns in other G7 countries and U.S. excess bond returns.

Abstract Value and momentum returns and combinations of them across both countries and asset classes are explained by their loadings on global macroeconomic risk factors. These loadings describe why value and momentum have positive return premia, although being negatively correlated. The global macroeconomic risk factors also perform well in capturing the returns on other characteristic-based portfolios. The findings identify a global macroeconomic source of the common variation in returns across countries and asset classes.

We estimate conditional multifactor models over a large cross section of stock returns matching 25 CAPM anomalies. Using conditioning information associated with different instruments improves the performance of the Hou, Xue, and Zhang (HXZ) (2015) and Fama and French (FF) (2015), (2016) models. The largest increase in performance holds for momentum, investment, and intangibles-based anomalies. Yet, there are significant differences in the performance of scaled models: HXZ clearly dominates FF in explaining momentum and profitability anomalies, while the converse holds for value–growth anomalies. Thus, the asset pricing implications of alternative investment and profitability factors (in a conditional setting) differ in a nontrivial way.

Abstract We examine the consistency of several prominent multifactor models from the empirical asset pricing literature with the arbitrage pricing theory (APT) framework. We follow the APT-related literature and estimate the common factor structure from a rich cross-section (associated with 42 major CAPM anomalies) by employing the asymptotic principal components method. Our benchmark model contains six statistical factors and clearly dominates, in both economic and statistical terms, most of the empirical multifactor models proposed in the literature by a good margin. These results represent a critical challenge to the current workhorse models in terms of explaining large-scale equity risk premiums. (JEL G10, G12) Received December 27, 2019; editorial decision October 20, 2020 by Editor Thierry Foucault. 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.