Asset Pricing Restrictions on Predictability

U.S. stock portfolios sorted on size, momentum, transaction costs, M/B, I/A and ROA ratios, and industry classication show considerable levels and variation of return predictability, inconsistent with asset pricing models. This means that a predictable risk premium is not equal to compensation for systematic risk as implied by asset pricing theory (Kirby 1998). We show that introducing market frictions relaxes these asset pricing moments from a strict equality to a range. Empirically, it is not short sales constraints but transaction costs (below 35 basis points) that help to reconcile the observed predictability with the Fama-French-Carhart four- factor model and the Chen-Novy-Marx-Zhang three factor model, and partly with the Durable Consumption model. Across the sorts, predictability in industry returns can be reconciled with all models considered with only 25 basis points transaction costs, whereas for momentum and ROA portfolios up to 115 basis points are needed

Disentangling Persistence from Predictability in Asset Pricing

We find persistent predictors known to bias predictive regressions for stock re- turns to matter also for asset pricing models. For example, deciding whether hedge funds offer an expansion of the investment opportunity set depends, among other things, on the persistence levels of predictors used to create managed returns. Us- ing simulations to disentangle the effects of persistence from predictability, we find highly persistent predictors to bias asset pricing models and tests even if managed portfolios and conditioning information are used optimally. Our framework enables us to construct tests that are robust in the presence of persistent predictors, and we find it to be more difficult to construct such robust tests for linear than for the non-linear ways of utilizing conditioning information

The Information Content of Commodity Futures Markets

We find that commodity futures returns contain information relevant to stock market returns and macroeconomic fundamentals for a large number of countries. Commodity futures returns predict stock market returns in 59 out of 70 countries and macroeconomic fundamentals in 62 countries. This predictability is not concentrated in the Energy and Industrial Metals sectors, as it is economically and statistically significant across all sectors. Surprisingly, we find that the role of countries’ dependence on commodity trade is limited in its ability to account for this predictability. This holds true even when considering new measures that take into account indirect exposures through financial and trade linkages between countries. We find much stronger evidence of predictability being related to the ability of commodities to forecast inflation rates. Overall, our evidence is consistent with commodity markets having a truly global information discovery role in relation to financial markets and the real economy

The Price of Commodity Risk in Stock and Futures Markets

We find that commodity risk is priced in the cross-section of US stock returns. Following the financialization of commodities, investors hedge commodity price risk directly in the futures market, primarily via commodity index investments, whereas before they gained commodity exposure mainly via the stock market. As a result, we find that the annualized average returns of high-minus-low commodity beta stocks change from -8% pre-financialization to 11% post-financialization. As stock market investors increasingly participate in commodity futures markets, stock market risk is also priced in the cross-section of commodity futures returns