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Oil prices, earnings, and stock returns

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Abstract

Research has failed to document a consistent association between oil prices and stock prices. We propose and examine whether that failure is due to the need to link oil price changes to firm-level changes in earnings and investments. We find that the impact of oil prices on a firm’s earnings and investments varies significantly by industry and by whether the firm is an oil producer or oil consumer. Nevertheless, firm fixed effects explain more than 10 times the variation between oil prices and a firm’s earnings and investments than industry and time fixed effects combined, indicating that aggregation by industry and time can mask the unique impact of oil prices on an individual firm’s earnings and investments. We also find that investors react more strongly to oil-related earnings than non-oil-related earnings, particularly for oil consumers. Investor reaction to oil-related earnings also spills over to the stock prices of industry peers. By providing a firm-level mapping of the impact of oil prices on earnings, investments, and stock prices, our paper extends studies that have examined the impact of oil prices on the aggregate economy and stock markets.

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Notes

  1. See “U.S. Stocks Rise as Oil Prices Rally” (The Wall Street Journal, Sept. 28, 2016), “Stocks Pare Gains as Oil Prices Slide” (Morningstar, Jan. 3, 2017), “Stocks slide as jump in oil prices renews worries” (Fox News, March 1, 2011), and “World stocks rise, but Wall Street dips with oil ahead of jobs report” (Reuters, July 7, Reuters 2016).

  2. Energy expenditures as a percentage of GDP in the United States have fluctuated between 6% and 14% since 1970 (https://www.eia.gov/todayinenergy/detail.php?id=36754).

  3. The authors also find that, except for the energy and transportation industries, stock returns in all industries follow oil prices with a lag of two to five days, consistent with the underreaction to oil price news.

  4. Firms’ hedges complicate these relations. If an individual firm hedges its oil exposure, changes in oil prices may affect the firm’s earnings differently than otherwise expected. For instance, oil and gas drillers and major fuel consumers, such as airlines, aggressively hedge their exposure to oil prices. In robustness analysis, our inferences remain unchanged when conducting analysis using the set of firms that hedge.

  5. The WTI price is the leading oil price indicator in the United States and strongly correlates with international oil price series, such as Brent oil.

  6. We considered including changes in oil future prices in Equation (2) and found that changes in oil future prices are highly correlated with changes in current oil prices during a quarter. (Spearman correlation coefficient is 90%.) Consequently, we do not include changes in oil future prices to avoid multicollinearity.

  7. Yet another strand of literature predicts oil prices using various macroeconomic variables, such as past oil prices, crude oil production, and global economic activity (Kilian and Murphy 2014; Baumeister and Kilian 2016a). Even if oil prices are predictable, the accuracy of those predictions relies on the predictability of its determinants (Baumeister and Kilian 2016a, p.16). Expectations of oil prices that seem reasonable at a time may easily be rendered obsolete by unforeseen political and economic events.

  8. If ΔOilk is replaced with daily oil price volatility during quarter k, sales, expenses, management forecasts, and investments decrease with oil price volatility. This finding, which contrasts with the reported relations, is expected given that oil price volatility should increase uncertainty and reduce economic activity and investments, in line with the literature (Bernanke 1983; Elder and Serletis 2010; Cherif and Hasanov 2013).

  9. The median (ΔEarnings/ΔOil)t of firm-quarter observations with management forecasts is 0.11%. This median is larger than the overall sample median of −0.01% reported in Table 2, Panel A, suggesting that firms with management forecasts exhibit earnings that are more sensitive to oil prices. Yet this median is statistically lower than median (ΔMF/ΔOil)t, 0.23%, suggesting that managers overestimate the effects of oil prices.

  10. We count the number of times the following words are mentioned in 10-K filings: oil, gas, natural gas, petroleum, and gasoline. We exclude mentions of oil preceded by the following food-based words: hydrogenated, fruit, palm, corn, fish, soybean, flax, cooking, vegetable, canola, and shortening.

  11. We also calculated median (ΔEarnings/ΔOil)t and (ΔMF/ΔOil)t for the lowest tercile of (ΔEarnings/ΔOil)t to see whether managers are pessimistic for the firms that experience severe decreases in earnings when oil prices rise. Median (ΔMF/ΔOil)t is −0.26%, suggesting that managers indeed make lower forecasts when oil price increases severely reduce corporate earnings.

  12. In unreported analysis, we separate firm-quarter observations based on terciles of (ΔEarnings/ΔOil)t. For firms in the lowest (highest) tercile, sales decrease (increase), expenses increase (decrease), management forecasts decrease (increase), capital expenditures increase (increase substantially), and R&D increases (stays constant). Interestingly, R&D increases the most for firms negatively affected by oil price increases.

  13. In response to higher oil prices, R&D expenditures increase the most among firms in holding and investment industry and decrease the most among firms in utilities, eating and drinking, and finance industries. However, these firms do not have high levels of R&D expenditures (less than 0.1% of sales).

  14. The internet appendix contains a table showing the results for all 117 SIC four-digit industries that have at least 750 observations.

  15. The upstream sector refers to the exploration of crude oil and natural gas fields as well as drilling and operating wells that bring crude oil and raw natural gas to the surface. The downstream sector refers to the refining of petroleum crude oil and processing and purifying of raw natural gas as well as marketing and distribution of products derived from crude oil and natural gas. Sometimes, transportation, storage, and wholesale marketing of crude or refined petroleum products are distinguished from other downstream activities and called the midstream sector. We do not distinguish the midstream sector from the downstream sector.

  16. Inferences are unchanged if we calculate CAR using the Fama-French three-factor methodology.

  17. The remaining econometric problems of the Collins et al. model, such as errors in measuring earnings expectations or the risk profile of a firm, also exist for the firm’s oil-related and non-oil-related components of ΔEarningsk. This diminishes the severity of econometric problems in our comparisons.

  18. Equation (7) does not include Invk, which proxies for managers’ earnings expectations in the Collins et al. model, because values are missing for a large number of observations. Sensitivity checks show that including Invk in Equation (7) reduces the sample size but does not alter inferences.

  19. The average difference, 0.90, is statistically insignificant. However, we focus on the median to discuss the effects of oil prices on a typical firm in the sample.

  20. In a separate set of tests, we re-estimate the above equations after inflation-adjusting all raw variables. We find similar results to those tabulated.

References

  • Abarbanell, J., & Park, H. (2017). Do bright-line earnings surprises really affect stock price reactions? Management Science, 63(4), 1063–1084.

    Article  Google Scholar 

  • Alfaro, I., Bloom, N., & Lin, X. (2016). The finance-uncertainty multiplier. Working paper: Ohio State University.

    Google Scholar 

  • Alquist, R., & Kilian, L. (2010). What do we learn from the price of crude oil futures? Journal of Applied Econometrics, 25(4), 539–573.

    Article  Google Scholar 

  • Bailey, W., & Chan, K. C. (1993). Macroeconomic influences and the variability of the commodity futures basis. Journal of Finance, 48(2), 555–573.

    Article  Google Scholar 

  • Baumeister, C., & Peersman, G. (2013). Time-varying effects of oil supply shocks on the U.S. economy. American Economic Journal: Macroeconomics, 5(4), 1–28.

    Google Scholar 

  • Baumeister, C., & Kilian, L. (2016a). Forty years of oil price fluctuations: Why the price of oil may still surprise us. Journal of Economic Perspectives, 30(1), 139–160.

    Article  Google Scholar 

  • Baumeister, C., & Kilian, L. (2016b). Lower oil prices and the U.S. economy: Is this time different? Brookings Papers on Economic Activity, Fall, 2016, 287–336.

    Article  Google Scholar 

  • Bernanke, B. (1983). The determinants of investment: Another look. American Economic Review, 73(2), 71–75.

    Google Scholar 

  • Bertrand, M., & Mullainathan, S. (2001). Are CEOs rewarded for luck? The ones without principals are. Quarterly Journal of Economics, 116(3), 901–932.

    Article  Google Scholar 

  • Bonsall, S. B., Bozanic, Z., & Fischer, P. E. (2013). What do management earnings forecasts convey about the macroeconomy? Journal of Accounting Research, 51(2), 225–266.

    Article  Google Scholar 

  • Bushman, R., Lerman, A., & Zhang, X. (2016). The changing landscape of accrual accounting. Journal of Accounting Research, 54, 41–78.

    Article  Google Scholar 

  • Chen, N., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. Journal of Business, 59, 383–403.

    Article  Google Scholar 

  • Cherif, R., & Hasanov, F. (2013). Oil exporters’ dilemma: How much to save and how much to invest. World Development, 52, 120–131.

    Article  Google Scholar 

  • Chuk, E., Matsumoto, D., & Miller, G. S. (2013). Assessing methods of identifying management forecasts: CIG vs. researcher collected. Journal of Accounting and Economics, 55, 23–42.

  • Coleman, L. (2012). Explaining crude oil prices using fundamental measures. Energy Policy, 40, 318–324.

    Article  Google Scholar 

  • Collins, D., Kothari, S. P., Shanken, J., & Sloan, R. (1994). Lack of timeliness and noise as explanations for the low contemporaneous return-earnings association. Journal of Accounting and Economics, 18(3), 289–324.

    Article  Google Scholar 

  • Dargay, J. M., & Gately, D. (2010). World oil demand’s shift toward faster growing and less price-responsive products and regions. Energy Policy, 38(10), 6261–6277.

    Article  Google Scholar 

  • Davis, S. J., & Haltiwanger, J. (2001). Sectoral job creation and destruction response to oil price changes. Journal of Monetary Economics, 48, 465–512.

    Article  Google Scholar 

  • DeHaan, E., Shevlin, T., & Thornock, J. (2015). Market (in)attention and the strategic scheduling and timing of earnings announcements. Journal of Accounting and Economics, 60(1), 36–55.

    Article  Google Scholar 

  • Driesprong, G., Jacobsen, B., & Maat, B. (2008). Striking oil: Another puzzle? Journal of Financial Economics, 89(2), 307–327.

    Article  Google Scholar 

  • Edelstein, P., & Kilian, L. (2009). How sensitive are consumer expenditures to retail energy prices? Journal of Monetary Economics, 56, 766–779.

    Article  Google Scholar 

  • Elder, J., & Serletis, A. (2010). Oil price uncertainty. Journal of Money, Credit and Banking, 42, 1138–1159.

    Article  Google Scholar 

  • Ferson, W. E., & Harvey, C. R. (1993). The risk and predictability of international equity returns. Review of Financial Studies, 6, 527–566.

    Article  Google Scholar 

  • Foster, G. (1981). Intra-industry information transfers associated with earnings releases. Journal of Accounting and Economics, 3, 201–232.

    Article  Google Scholar 

  • Fox News (2011). Stocks slide as jump in oil prices renews worries. www.foxnews.com/us/2011/03/01/stocks-headed-higher-ahead-economic-news.html/. Accessed 01/06/2017).

  • Greenspan, A. (2005). Remarks before the Japan Business Federation, the Japan Chamber of Commerce and Industry, and the Japan Association of Corporate Executives. www.federalreserve.gov/boarddocs/speeches/2005/20051017/. Accessed 01/06/2017.

  • Hamilton, J. D. (1983). Oil and the macroeconomy since world war II. Journal of Political Economy, 92(2), 228–248.

    Article  Google Scholar 

  • Haushalter, G. D. (2000). Financing policy, basis risk, and corporate hedging: Evidence from oil and gas producers. Journal of Finance, 50(1), 107–152.

    Article  Google Scholar 

  • Huang, R., Masulis, R., & Stoll, H. (1996). Energy shocks and financial markets. Journal of Futures Markets, 16(1), 1–27.

    Article  Google Scholar 

  • Hughes, J. E., Knittel, C. R., & Sperling, D. (2008). Evidence of a shift in the short-run elasticity of gasoline demand. Energy Journal, 29, 113–134.

    Article  Google Scholar 

  • Jin, Y., & Jorion, P. (2006). Firm value and hedging: Evidence from U.S. oil and gas producers. Journal of Finance, 61(2), 893–919.

    Article  Google Scholar 

  • Jones, C. M., & Kaul, G. (1996). Oil and the stock market. Journal of Finance, 51(2), 463–491.

    Article  Google Scholar 

  • Kilian, L. (2008). The economic effects of energy price shocks. Journal of Economic Literature, 46(4), 871–909.

    Article  Google Scholar 

  • Kilian, L., & Murphy, D. P. (2014). The role of inventories and speculative trading in the global market for crude oil. Journal of Applied Econometrics, 29(3), 454–478.

    Article  Google Scholar 

  • Lee, K., & Ni, S. (2002). On the dynamic effects of oil shocks: A study using industry level data. Journal of Monetary Economics, 49, 823–852.

    Article  Google Scholar 

  • Lundholm, R., & Myers, L. (2002). Bringing the future forward: The effect of disclosure on the returns-earnings relation. Journal of Accounting Research, 40(3), 809–839.

    Article  Google Scholar 

  • Miao, H., Ramchander, S., Wang, T., & Yang, D. (2017). Influential factors in crude oil price forecasting. Energy Economics, 68, 77–88.

    Article  Google Scholar 

  • Morningstar (2017). Stocks pare gains as oil prices slide. www.morningstar.com/ advisor/t/117981257/stocks-pare-gains-as-oil-prices-slide. Accessed 01/06/2017.

  • Muslu, V., Radhakrishnan, S., Subramanyam, K. R., & Lim, D. (2015). Forward-looking MD&a disclosures and the information environment. Management Science, 61(5), 931–948.

    Article  Google Scholar 

  • Narayan, P. K., & Sharma, S. (2011). New evidence on oil price and firm returns. Journal of Banking and Finance, 35, 3253–3262.

    Article  Google Scholar 

  • Pescatori, A., & Mowry, B. (2008). Do oil prices directly affect the stock market? Economic Trends, September, 12, 13.

    Google Scholar 

  • Pincus, M., & Rajgopal, S. (2002). The interaction between accrual management and hedging: Evidence from oil and gas firms. The Accounting Review, 77(1), 127–160.

    Article  Google Scholar 

  • Ready, R. C. (2018). Oil prices and the stock market. Review of Finance, 22(1), 155–176.

  • Reuters (2016). World stocks rise, but Wall Street dips with oil ahead of jobs report. www.reuters.com/article/us-global-markets-idUSKCN0ZM021. Accessed 01/06/2017.

  • Srivastava, A. (2014). Why have measures of earnings quality changed over time? Journal of Accounting and Economics, 57, 196–217.

    Article  Google Scholar 

  • The Wall Street Journal (2016). U.S. Stocks rise as oil prices rally. http://www.wsj.com/articles/europe-stocks-lifted-by-firmer-oil-price-475048685. Accessed 01/06/2017.

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Acknowledgement

We thank Anup Srivastava, Gus De Franco, Shivaram Rajgopal, Gerry Lobo, Max Mueller, Chris Rigsby, and Terry Skantz for helpful comments. We also thank workshop participants at the 2017 Lone Star Accounting Research Conference at UT Arlington, the 2017 AAA FARS Midyear Meeting, University of Houston, LUISS, VU Amsterdam, and the Free University of Bozen-Bolzano.

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Authors and Affiliations

  1. University of Houston, 4750 Road Houston, Calhoun, TX, 77204, USA

    Steve Crawford & Volkan Muslu

  2. WHU, Burgplatz 2, Vallendar, Germany

    Garen Markarian

  3. University of Oklahoma, 307 W. Brooks Ave, Norman, OK, 73019, USA

    Richard Price

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Correspondence to Volkan Muslu.

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Crawford, S., Markarian, G., Muslu, V. et al. Oil prices, earnings, and stock returns. Rev Account Stud 26, 218–257 (2021). https://doi.org/10.1007/s11142-020-09556-7

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