Biggest Public Oil Companies: Impact of External and Internal Factors on Capitalization
https://doi.org/10.26794/2587-5671-2019-23-5-87-100
Abstract
Estimate and search for factors that influence the capitalization of public oil companies are of great interest to researchers. The impact of various external and internal factors on the value of oil companies’ stocks was considered. This includes changes in oil prices, stock market index movements, inflation fluctuations, financial and production indicators. The study includes building models with calculated standard errors by the Driscoll-Kraay method based on quarterly data for the eight biggest public oil companies operating in the upstream and downstream segments, from the first quarter of 2006 to the third quarter of 2017. Such indicators as total oil production by OPEC countries, greenhouse gas emissions by companies, and the sum of shareholder’s funds owned by large institutional investors were used for the first time when building the model to identify factors affecting the market capitalization of oil companies. One of the key results is the conclusion that quarterly production volumes turned out to be the most significant factor having a positive impact on the cost of oil firms. That is, investors are laying the idea of compensating for losses from lowering the cost of oil by increasing its production and selling a larger volume in the value of shares in companies. At the same time, such indicators of production efficiency as profitability in the upstream and downstream segments lose their significance depending on the period under consideration.
About the Authors
R. M. Nureev
Financial University;
Higher School of Economics
Russian Federation
Russian Federation
Rustem M. Nureev — Dr. Sci. (Econ.), Professor
Moscow
E. G. Busygin
Higher School of Economics
Russian Federation
Russian Federation
Evgenii G. Busygin — Postgraduate Student
Moscow
References
1. Makarov A.A., Grigor’ev L.M., Mitrova T.A., eds. Energy forecast for the world and Russia 2016. Moscow: ERIRAS, Analytical Center for the Government of RF; 2016. 196 p. (In Russ.).
2. Lanza A., Manera M., Grasso M., Giovannini M. Long-run models of oil stock prices. Environmental Modelling & Software. 2005;20(11):1423–1430. DOI: 10.1016/j.envsoft.2004.09.022
3. Chang C.L., McAleer M., Tansuchat R. Volatility spillovers between returns on crude oil futures and oil company stocks. 2009. URL: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1009.974&rep=rep1&type=pdf
4. Sanusi M.S., Ahmad F. Modelling oil and gas stock returns using multi factor asset pricing model including oil price exposure. Finance Research Letters. 2016;18:89–99. DOI: 10.1016/j.frl.2016.04.005
5. Diaz E.M., de Gracia F.P. Oil price shocks and stock returns of oil and gas corporations. Finance Research Letters. 2017;20:75–80. DOI: 10.1016/j.frl.2016.09.010
6. Kang W., de Gracia F. P., Ratti R. A. Oil price shocks, policy uncertainty, and stock returns of oil and gas corporations. Journal of International Money and Finance. 2017;70:344–359. DOI: 10.1016/j.jimonfin.2016.10.003
7. Swaray R., Salisu A.A. A firm-level analysis of the upstream-downstream dichotomy in the oil-stock nexus. Global Finance Journal. 2018;37:199–218. DOI: 10.1016/j.gfj.2018.05.007
8. Edwards K., Jackson J.D., Thompson H.L. A note on vertical integration and stock ratings of oil companies in the U.S. The Energy Journal. 2000;21(2):145–151.
9. Bhaskaran K. R., Sukumaran S. K. An empirical study on the valuation of oil companies. OPEC Energy Review. 2016;40(1):91–108. DOI: 10.1111/opec.12064
10. MacDiarmid J., Tholana T., Musingwini C. Analysis of key value drivers for major mining companies for the period 2006–2015. Resources Policy. 2018;56:16–30. DOI: 10.1016/j.resourpol.2017.09.008
11. Osmundsen P., Asche F., Misund B., Mohn K. Valuation of international oil companies. The Energy Journal. 2006;27(3):49–64.
12. Ratnikova T.A. Analysis of panel data in the STATA package. Guidelines for computer practical training on the course “Econometric analysis of panel data”. Moscow: HSE Publ.; 2004. 39 p. (In Russ.).
13. Morgunov A.V. Modeling the probability of default of investment projects. Korporativnye finansy = Journal of Corporate Finance Research. 2016;10(1):23–45. (In Russ.). DOI: 10.17323/j.jcfr.2073-0438.10.1.2016.23-45
14. Hoechle D. Robust standard errors for panel regressions with cross-sectional dependence. The Stata Journal. 2007;7(3):281–312. DOI: 10.1177/1536867X0700700301
15. Driscoll J. C., Kraay A. C. Consistent covariance matrix estimation with spatially dependent panel data. The Review of Economics and Statistics. 1998;80(4):549–560. DOI: 10.1162/003465398557825
16. Hashem Pesaran M. General diagnostic tests for cross section dependence in panels. IZA Discussion Paper. 2004;(1229). URL: http://ftp.iza.org/dp1240.pdf
17. De Hoyos R. E., Sarafidis V. Testing for cross-sectional dependence in panel-data models. The Stata Journal. 2006;6(4):482–496. DOI: 10.1177/1536867X0600600403
18. Wooldridge J.M. Econometric analysis of cross section and panel data. Cambridge, MA: The MIT Press; 2010. 1096 p.
19. Baum C. F. Residual diagnostics for cross-section time series regression models. The Stata Journal. 2001;1(1):101–104. DOI: 10.1177/1536867X0100100108
Review
For citations:
Nureev R.M., Busygin E.G. Biggest Public Oil Companies: Impact of External and Internal Factors on Capitalization. Finance: Theory and Practice. 2019;23(5):87-100. https://doi.org/10.26794/2587-5671-2019-23-5-87-100
Views:
1111
Email this article 