Oil and Efficient-Market Theory

Rodney Schulz, Principal, Schulz Financial

In 2003, well-known economist Eugene Fama was nominated for the Nobel Prize in economics for work pertaining to his efficient-market hypothesis. Since the 1960s, that hypothesis has been researched, applied, and debated mostly in the context of stock markets. But the theory has measurable applicability to how we go about our daily business in the oil and gas industry.

In short, the efficient-market hypothesis states that a free and open market, such as the New York Stock Exchange, adjusts its prices almost instantaneously to all publicly available information. That suggests that one cannot predictably, consistently outperform market returns through fundamental or technical analysis of publicly available information. But what is considered “publicly available information”? Just about everything that happens outside a boardroom and much of what happens inside a boardroom. The market reacts, quickly and efficiently. It may not always be “right,” but it will react and continually readjust almost instantaneously. Another part of the hypothesis states that price movements in the market follow a “random walk” pattern (i.e., price movements are random and the best predictor of tomorrow’s price is today’s price).

Taking the hypothesis to its next logical step implies that analysts and active stock investors cannot predictably, consistently outperform market returns by selectively placing bets on particular publicly traded investments. Although this flies against the beliefs and marketing strategies of most mutual funds and Wall Street firms, an extensive number of studies over the years indicate that passive, market-driven investments outperform active, arbitrary investments.

Applications to the Oil and Gas Industry

Looking at the oil and gas industry, one immediately finds evidence of market efficiency with oil and gas prices. First, if the market were not efficient, firms that did nothing but trade oil and gas futures would be as ubiquitous as independent producers. Moreover, they would perform as well in down markets as in up markets. This would be an easy business to start, as there are almost no barriers to entry. However, firms that do nothing other than trade oil and gas futures are practically nonexistent. One sees few, if any, traders who do nothing other than trade futures paper or buy crude oil in a Cushing, Oklahoma, tank today and sell it in the same Cushing tank tomorrow, for a profit.

One may argue that the oil market is not efficient because a few large players, such as some of OPEC’s largest producers, have the ability to move prices. And that is true, as well as the fact that insiders in those organizations can take advantage of certain information. But this is true in many industries, particularly those dominated by a few large concerns that everyone else follows. However, having dominant industry players does not mean that the market of buyers and sellers does not incorporate all publicly available information almost instantaneously. The world reacts to OPEC’s moves as soon as they are discovered.

Implications on Decision Making

The next question is how to apply this theory to improve decision making in the oil and gas business. First, we need to look at how we evaluate economic flow streams. Currently, almost all oil and gas producers evaluate projects and make decisions by assuming a smooth or semismooth commodity-price flow stream into the future. Many, if not all, evaluate projects and budgets with several different price assumptions, and that is a start, but it is still giving the decision maker only a glimpse of what actually might happen.

Alternatively, one could apply the random-walk model and statistically model the probability of various random price movements in the future. We are in a volatile industry, and statistically modeling price swings would help us understand and better prepare for future cash flows.

Although yesterday’s volatility does not predict tomorrow’s volatility, looking at price volatility statistically over the past 5 years improves understanding. By putting price on a relative-value basis (a U.S. $7/bbl movement in oil prices is a greater percentage of $30/bbl oil than of $70/bbl oil), one gets a mathematical feel for uncertainty. During the past 5 years ending in September 2006, the average monthly crude-oil price change was 6.6% (Fig. 1). But there is a significant uncertainty in our uncertainty. More specifically, the 95% confidence range of the uncertainty ranges from 5.4 to 7.7%. In other words, we cannot even be certain of our uncertainty.

This is just the mean monthly crude price change. To find the real range of uncertainty, we need to consider the standard deviation of monthly crude price changes on top of the mean change. In other words, and again assuming a normal distribution, the 95% certainty level for the absolute value of oil-price changes is approximately 6.6% plus two times 4.5%. So we can be 95% confident that next month’s crude-oil price will equal this month’s crude-oil price plus or minus 16%. That is volatility.

Does the chart bear out the statistical calculation? Three times out of 60, the absolute value of the monthly crude-price change was 16–17%. So, 5% of the time, crude-oil prices moved 16% in one month. In other words, at a crude price of $60, one can expect the price to change $10 or more per barrel in a month once every 20 months, with the typical monthly price change being approximately $4/bbl.

Fig. 1—A histogram of monthly crude-oil price changes.

Pragmatic Applications Going Forward

So how could not even being able to know our uncertainty level help the industry going forward? First, we do have the option of improving our statistical model of the future. Instead of simply predicting low-, medium-, and high-price cases, which may or may not be based on statistical analysis, we can model the uncertainty. Fortunately, the statistical models and software for doing this are readily available and inexpensive.

Second, it makes sense to quantify future price uncertainty and incorporate it into daily decision making. Should we forward sell a portion of future production to get price volatility to a manageable level? What are the future capital needs of contractual commitments, and can the organization withstand the volatility that history has demonstrated? Is the organization’s staffing strategy incorporating the expected volatility? And, finally, do the organization’s leaders know their risk tolerance for dealing with the inherent uncertainty?

The efficient-market hypothesis has applications to daily endeavors. The number of firms that consistently have made a profit by purchasing and selling oil and gas, in both down and up markets—and without adding value—is limited at best. This holds true despite almost nonexistent barriers to entry into such a business. However, oil and gas companies can quantify their risk statistically and make their decisions accordingly. Incorporating this methodology has the potential to smooth daily business functions across a range of decision-making spectrums.

Rodney Schulz, founder of Schulz Financial, has held finance and engineering positions for major and independent oil and gas companies in addition to his experience as a financial adviser. He has held reservoir-engineering positions in five different regions of North America for a major oil company. Additionally, Schulz has served as Vice Chairman of the Corpus Christi, Texas, Gas Processors’ Assn. and was Secretary-Treasurer of the Alaska Well Logging Soc. Outside of the oil and gas industry, he has been Financial Director/Chief Financial Officer for an organization with 150 employees and served on the Investment Committee, along with senior oil and gas executives, for a multimillion-dollar endowment fund. Schulz earned a BS degree in petroleum engineering from the U. of Kansas and an MBA degree from Duke U.