Firm age and crude oil returns: Stock price sensitivity of oil-producing and consuming companies

Abstract

This study aims to identify firm characteristics that affect the cross-firm variation in oil–stock interactions. A panel data analysis with a sample of U.S. and Canadian firms reveals that the stock price sensitivity to crude oil price returns is negatively and significantly associated with firm age. Contrary to a common belief, firm size or stock liquidity does not seem to influence heterogeneity in oil–stock relationships. My finding is consistent across oil-producing and consuming companies while the effect of firm age is not observed among financial institutions engaged in commodity trading. An additional test using the panel Granger causality approach shows no lagged effect of oil market movement on the oil and gas extraction firms, suggesting their prompt response to market information.

Keywords:

• Crude oil price
• stock returns
• firm age
• panel causality

GEL Classification:

• C32
• G12
• G13

PUBLIC INTEREST STATEMENT

The valuation of a young firm is often challenging due to a limited earnings history coupled with seemingly unlimited growth potential. This article shows that the stock prices of young oil-producing and consuming companies are more sensitive to changes in the crude oil market than their more mature counterparts. My analysis also confirms that the stock returns of these firms respond to oil market movement without delay. Combined, these findings suggest that investors place a heavier weight on market-wide information when assessing a young firm, and such information is quickly reflected in stock valuations. My study offers useful implication for today’s investors, who manage portfolios including a wide range of asset classes beyond traditional stocks and fixed-income securities.

Notes

1. On the other hand, Hooker (1996) suggests that oil prices no longer precede many macroeconomic indicators in the U.S. after 1973.

2. Some of the studies analyzing the oil-stock relationships at the firm level are discussed in Section 2.

3. The exception in their study is the telecommunications sector.

4. Note that the sample excludes the securities and commodity exchanges.

5. The use of weekly returns reduces noise in daily data and certain statistical biases, such as the non-synchronous trading, while keeping sufficient number of data points (Arouri & Nguyen, 2010; Henriques & Sadorsky, 2008).

6. One major difference between these two tests is how autocorrelation in the errors is corrected. While the ADF test addresses this issue by incorporating lagged values of the first difference of the variable as regressors, the PP test ignores any autocorrelation in the regression model but instead makes a non-parametric correction to the t-statistic.

7. With respect to nine firms in the sample, the null hypothesis of non-stationarity is rejected at the significance level of 5% or better in the ADF test, the PP test, or both. The unit-root test results on the individual firms can be available upon request.

8. Although not reported in this paper, I also tested each firm individually using the standard market model augmented by the oil price factor as following. For the vast majority of the firms, the crude oil price return is statistically significant at a 5% level or better.

9. For example, it has been suggested that the inflow of institutional investors has contributed to dramatic increases in commodity futures prices and cross-commodity correlations in the mid-2000s (see Tang & Xiong, 2012; Singleton, 2014; Basak & Pavlova, 2016 among others).

10. Although not reported, I have conducted a similar analysis using daily oil and stock price returns, which does not alter the main findings in this article.

11. The study by Narayan and Sharma (2011) is based on 560 firms listed on the NYSE while Lv et al. (2020) focus on petrochemical companies in the U.S. and China.

12. The results of these tests are available upon request.

13. It considers heterogeneity of the causal relationships as well as heterogeneity of regression models.

14. This is contrasted with Holtz-Eakin et al. (1988) showing a test of non-causality assumption against causality for all the units.

15. Due to a large T dimension, ${\bar{Z}}_{N,T}$ is preferable to ${\tilde{Z}}_{N,T}$ although the results are almost identical.

Additional information

Funding

The author received no direct funding for this research.

Notes on contributors

Hirofumi Nishi

Hirofumi Nishi is an assistant professor in finance at the Fort Hays State University, USA, where he teaches various finance courses both at the undergraduate and graduate levels. Prior to pursuing an academic career, he spent over 10 years in the energy trading industry as a quantitative analyst. His research interests cover a broad range of topics, including corporate finance, asset pricing, financial intermediation, and commodity markets. He has published articles, among others, in Applied Finance Letters, Managerial Finance, and Research in Finance.