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Oil Price Exposure and the Cross-Section of Stock Returns

The Review of Asset Pricing Studies 2024 14(2), 274-309

Abstract We provide evidence that equity investors are slow to process information about how current oil price changes affect future earnings announcements. Stock prices respond to lagged quarterly oil price changes when firms start announcing earnings in the next quarter. A cross-sectional equity trading strategy that exploits this predictability yields an annualized Sharpe ratio of 0.50. Our oil-response forecast strategy earns especially high returns after large absolute oil price changes, in recessions or bear markets, and during peak earnings season. The predictability we document is consistent with limited attention, is not driven by risk factor exposure, and survives several robustness tests. (JEL G10, G11, G14, G40, Q41)

The effect of capital inflows on the imports of capital goods in developing countries

Jan Fidrmuc; Khusrav Gaibulloev; Ali Mirzaei; Tomoe Moore

Journal of Corporate Finance 2024 84, 102531 open access

This paper examines the relationship between capital inflows and import of capital goods to credit-constrained industries in developing countries. Using data of 11 industrial sectors in 57 countries for 2000–2020, we find that financially dependent industries import disproportionately more capital goods if they operate in countries that receive more foreign funds. A host of robustness tests, including instrumental variables estimation, confirm our main finding. We also document that: (i) the established nexus breaks down during the global financial crisis, (ii) the observed relationship is mainly due to the direct investment via equity, and (iii) host countries tend to import relatively more capital goods from G7 economies. Overall, our results suggest that one channel through which capital inflows affect economic growth is by alleviating firms' financial constraints, thereby enabling firms to acquire more advanced capital goods.

ABSTRACT In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

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Oil Price Exposure and the Cross-Section of Stock Returns

The effect of capital inflows on the imports of capital goods in developing countries

Nonstandard Errors