Financial risk-taking and performance: A 20 year look at the petroleum industry
Risk and risk management are fundamental elements of the E&P competitive landscape. Managing financial risks associated with E&P activities is a complex and critical task, with both immediate and lasting effects on firm performance. The primary motivation for the work discussed in this article is to better understand the performance effects, if any, associated with different levels of risk-taking by E&P companies. The results of this study also have broader prescriptive implications with regard to setting corporate risk policies and managing financial risks in the E&P firm.
Dr. Michael R. Walls, Colorado School of Mines, Golden, Colo.
This study provides an up-to-date look at the risk propensities of the 50 largest US-based oil companies. It utilizes a previously developed decision analysis framework to evaluate the capital allocation decisions of these firms and their resulting tolerance for financial risk.
In this article, we provide results for 20 US-based oil companies for the period 1991-2002. We examine both domestic and foreign risk tolerances and explore the differences in financial risk-taking among E&P firms. In addition, we utilize a measure known as the risk tolerance ratio (RTR) as a means to examine the relative risk propensity of firms, thereby controlling for firm size. Finally, we provide some very interesting statistical results with regard to the relationship between risk-taking and performance over the 20-year study period.
The findings from this study have broad implications for E&P managers who are trying to best allocate corporate resources. First, a company’s E&P risk policy affects its business unit performance. Second, risk management models enable decision makers to implement their desired and appropriate risk tolerance level in all types of project analyses. Third, knowledge of competitors’ risk preferences may help managers make better decisions in a competitive setting. Lastly, quantifying a firm’s financial risk tolerance provides a mechanism for managers to communicate and act upon an explicit and coherent risk policy - a policy that can lead to improved performance.
Modeling financial risk tolerance in the E&P firm
Decision analysis and preference theory provide the foundations for measuring an individual’s financial risk propensity. Assessing risk tolerance is equivalent to estimating the individual’s utility function at a particular point in time. The utility function captures the decision maker’s preferences for uncertain financial outcomes and incorporates his or her attitude about financial risk. The technique utilized in this study draws on these same methodological approaches but extends the techniques in a way that enables their application in a firm setting, whereby we are attempting to elicit the managers’ risk preferences as agents for the firm.
While the vast majority of companies do not actually make explicit a corporate risk tolerance, estimating their implied risk tolerance through a systematic analysis of decision making under uncertainty is a practical approach to interpreting the firm’s risk taking behavior.
In essence, we utilize the E&P firms’ prior decisions regarding risky investments as a basis for inferring the risk attitude of the firm or business unit. Managers, as agents for the firm, make capital allocation decisions under conditions of risk and uncertainty and have knowledge concerning the magnitudes of potential outcomes and the probabilities associated with those outcomes. These decisions reflect the risk attitude of the firm and under certain conditions can be codified into a corporate risk tolerance measure.
In order to estimate the firm’s implied utility function, a common functional form of utility must be selected. One dominant form utilized in the areas of decision analysis and finance is the exponential utility function, and is of the form u(x) = -e-x/R, where R is the risk tolerance coefficient, x is the variable of interest (generally net present value), and e is the exponential constant. A value of R less than infinity and greater than 0 implies risk averse behavior. The exponential form of utility can satisfactorily treat a wide range of corporate risk preferences.
The risk tolerance measure, R, is an important parameter as it describes the willingness of the firm to take on risky investments. More formally, the risk tolerance measure, R, represents the sum of money such that the decision makers are indifferent as a company investment to a 50-50 chance of winning that sum and losing half of that sum. Risk tolerance is more complex than simply a sum of money that the firm is willing to put “at risk.”
The intuitive notion of risk involves both uncertainty and the magnitudes of the dollar values involved. The central issue associated with measuring corporate risk tolerance is one of assessing tradeoffs between potential upside gains versus downside losses under uncertainty. The decision maker’s attitude about the magnitude of capital being exposed to the chance of loss is an important component of this analysis.
Figure 1 provides additional meaning to the notion of risk tolerance in terms of decisions about risky investments. Consider, for example, that a decision maker is presented three lotteries with a 50-50 chance of winning a certain sum and losing half that sum. The decision to reject Lottery #3 which has an even chance of winning $30MM versus losing $15MM implies that the decision maker would view this investment as too risky. In other words, the decision maker is not willing to risk $15 million at an even chance of making twice that amount.
Conversely, the firm’s decision to accept Lottery #1 implies that the risk-return tradeoff associated with this lottery is acceptable, given the firm’s willingness to take financial risk. This iterative procedure is continued until we identify the lottery such that the firm is indifferent between a 50-50 chance of winning a certain sum versus losing half that sum. In our example, that sum is $25MM and represents the risk tolerance, R, of the firm.
Relating the risk preferences of the firm to decisions about risky investments, such as in Figure 1, provides a more meaningful interpretation of the firm’s risk propensity. In addition, it is reasonably easy to measure and serves as a sufficiently close approximation to the exponential risk tolerance, such that in this case, we are able to compute the firm’s risk tolerance as approximately $25 million.
The concept of a corporate risk tolerance is foundational to the empirical study and results presented in this article. The financial risk tolerances of firms included in this study are examined and the relationship between risk tolerance and performance is investigated.
Another term of importance from preference analysis is the valuation measure known as the certainty equivalent. The certainty equivalent is defined as that certain value for an uncertain event which a decision maker is just willing to accept in lieu of the gamble represented by the event. It is, in essence, the “cash value” that a firm attributes to a decision alternative which involves uncertain outcomes. The certainty equivalent (Cx) is equal to the expected value less a risk discount, known as the risk premium. In the case of the exponential utility function, the firm’s maximum buying price or minimum selling price for a given risky investment represents its certainty equivalent for that investment. For discrete probability distributions, the formula for the certainty equivalent is:
where pi is the probability of outcome i, xi is the value of outcome i, and ln is the natural log. In effect, if we know the characteristics of the risky investment alternative, as defined by pi and xi, and we know the amount of money the firm was willing to pay to participate in that risky investment, Cx, then we can infer the firm’s risk tolerance, R. In the earlier example of risk tolerance described in Figure 1, the certainty equivalent of lottery 1 is some positive number, the certainty equivalent for lottery 3 is negative, while the certainty equivalent of lottery 2 is 0, since the decision maker was indifferent.
Measuring E&P risk tolerance
Consider the simple case where the firm chooses to invest in one domestic project and one foreign project with probability and outcome information for each project as shown in Figure 2a. Assume these investment opportunities have estimates of probabilities of success and failure that are probabilistically independent. Then the probability distribution of outcomes associated with investing in both can be combined into a composite lottery as shown in Figure 2b.
Now suppose that the firm’s allocation for exploration expense (buying price) for each investment category, domestic and foreign, is known. We interpret these allocations as the certainty equivalent, Cx, for each individual lottery, that is, their buying price to participate in these investments.
For example, let’s assume that the Cx values are $2.2 million and $1.0 million for the domestic and international projects, respectively. The Cx value for the domestic investment implies, for example, that the firm is indifferent between a certain value of $2.2 million and an uncertain gamble as characterized by the properties of the domestic investment. Assuming the exponential form of utility, a firm that engages in n independent lotteries has a certainty equivalent for the portfolio of lotteries equal to the sum of the certainty equivalents for each individual lottery.
The certainty equivalent, then, for the composite lottery in Figure 1b is equal to $3.2 million. We can now compute the implied risk tolerance, R, for the composite lottery from the closed form expression in equation (1). In this example, the firm’s risk tolerance value, R, $40 million.
Similar to the approach outlined above, we infer the firm’s risk tolerance by reconstructing the set of risky alternatives that were selected for capital allocations by the firm. Instead of the discrete framework, however, we reconstruct the firm’s opportunity set in a mean-variance framework in order to estimate the risk tolerance, R.
We use publicly available data in this study, primarily from each company’s 10-K filings as required by the SEC. These data do not provide detailed information on individual investment opportunities, but do provide a basis for reconstructing typical projects that were selected by the firm, and the number of projects that were selected. Data categories utilized in our analysis include domestic and international budget allocations, leasehold costs, exploratory and development costs, success rates, reserve additions, total wells, and productive wells.
We apply this methodology to the 50 largest (by E&P assets) US-based oil companies from the period 1983-2002. Table 2 shows the implied risk tolerance levels for 20 firms in this sample for the period 1991-2002. Note that these risk tolerance levels relate to the E&P business unit only. In the case of “NA” results for risk tolerance, insufficient data was reported for that year in order to make a statistically significant computation.
| ------- Risk tolerance, R ------ | ||
| Activity region | Domestic | Foreign |
| Mean value | 15.4 | 26.0 |
| Standard deviation | 17.0 | 31.7 |
| Size (n) | 447 | 252 |
| Note: Analysis indicates significant difference in risk tolerance for domestic ($15.4 million) versus foreign ($26.0 million) E&P activities. Mean and standard deviation shown in $ millions. | ||
Differences in risk-taking by region
In this study of E&P risk-taking we discriminate between domestic and foreign E&P activities. As a result, we are able to explore the differences in risk taking by regions. Table 2 shows the differences in implied risk tolerance for domestic versus foreign activities for the 50 firms included in this analysis.
On the basis of this analysis, there appear to exist systematic and statistically significant differences in observed risk tolerances between domestic and foreign activities. Note that the mean risk tolerance for domestic activities for all firms and all years is $15.4 million while the mean risk tolerance for foreign activities is $26.0 million.
This difference is statistically significant. We observe even larger differences in the case of certain firms. For example, over the entire study period the Chevron Corporation (and Chevron/Texaco) exhibits an average E&P risk tolerance for domestic activities of $5 million. For foreign activities, it exhibits an average risk tolerance of $29 million.
It is important to note here that this observed difference in risk tolerance is somewhat of an artifact of the study approach. Consider that the characteristics of foreign activities generally include larger capital outlays and higher risks than domestic activities. In fact, one might argue that the opportunity set for foreign activities is broader and more variable. If so, then just by the nature of a firm’s higher emphasis on these opportunities a higher implied risk tolerance for foreign activities can be expected.
Statistical analysis also shows that there exists a significant positive relationship between the size of the firm (or E&P business unit) and corporate risk tolerance, R. A regression analysis shows a significant positive correlation between the financial risk tolerance of firms and their size as measured by the Standardized Measure of Discounted Future Net Cash Flows (SMCF)1.
Since we know that larger firms, on average, will have a higher absolute risk tolerance than smaller firms, it may be more important to evaluate relative risk tolerance rather than absolute risk tolerance. In order to accomplish this we need to utilize a measure of risk propensity that controls for firm size.
For example, consider a simple drilling opportunity where the chance of success is 40% and the success payoff is $50 million while the failure payoff is -$10 million. Now assume that we have two firms, one with a $500 million capital budget and one with a $50 million capital budget and that both these firms agree to accept this project. On an absolute risk tolerance basis we might say that these firms are equivalent but on a relative risk-taking basis the smaller firm is much more aggressive in its risk-taking. We investigate this important difference by utilizing the risk tolerance ratio (RTR) measure.
The RTR measure is constructed to control for firm size. For any firm i, the RTRi value is equal to RTi /RTi’, where RTi is the observed risk tolerance for firm i in period t and RTi’ represents the predicted risk tolerance of firm i as a function of size (SMCF) for that same period. An example of the risk tolerance ratio calculation is shown in Table 3.
| ------------- Risk Tolerance Ratio (RTR) Example ------------- | |||
| Firm 1 | Firm 2 | ||
| Firm 3 | |||
| SMCFi (wealth) | $1000 MM | $100 MM | $10 MM |
| RTI’ (predicted) | $100 MM | $15 MM | $2 MM |
| RTi (actual) | $50 MM | $20 MM | $2 MM |
| RTRi (RTi/RTi’) | 0.50 | 1.33 | 1.0 |
| Note: This represents a stylized example of the RTR computation. The RTR value is the ratio of actual risk tolerance to predicted risk tolerance, given firm size. The larger the RTR value the more aggressive the firm in its financial risk taking, given the size of the E&P firm. | |||
The RTR measure describes the firm’s relative risk propensity as compared to other firms in the industry during the period of investigation. An RTR value greater than 1.0 implies a stronger propensity to take risk than firms of equivalent size. An RTR value less than 1.0 implies a weaker propensity to take risk than firms of equivalent size. Table 4 presents the RTR values for the same 20 firms we examined in Table 1.
As an example, compare the relative risk propensities of Chevron (RTR = 0.65) and Conoco (RTR = 2.93) for the year 2000. The RTR measure implies that Chevron, given the size of its E&P unit, was less willing to take risk than firms of its equivalent size. Conversely, Conoco with an RTR value of 2.93 could be characterized as an aggressive risk taker compared to similar-sized firms in the industry.
Competitor analysis and performance effects
Firms might be interested in examining a particular peer group of firms and their willingness to take financial risks. This could have broad implications on a number of dimensions including competitive bidding issues as well as potential partnership decisions among firms.
Figure 3 shows a competitive analysis that compares a select group of firms and how they stack up in terms of risk-taking during the study period. This log-log plot shows the size of the firm (as measured by SMCF) on the X axis and financial risk tolerance, R, of the firm on the Y axis for multiple periods. The bold line included in this graph is the industry regression line as noted earlier in this article.
We see, for example, that Chevron falls consistently below the regression line (low risk propensity) while Shell Oil lies consistently above the regression line (high risk propensity). Interestingly, Conoco and Phillips are generally high risk-takers while Chevron and Texaco consistently fall below the line. This may also have implications in terms of recent merger activities within the industry, as well as propositions about future merger and acquisition activities. The important implication here is that firms can get a sense of relative risk taking by firms in their peer group which may, in certain cases, influence competitive actions.
In addition to understanding the competition, managers are also concerned with establishing the appropriate level of risk taking for their firms. Managers in the E&P sector are confronted daily with important decisions characterized by a high degree of risk and uncertainty. Developing a coherent risk policy for the firm and acting on that policy can be an important consideration.
Equally important is understanding the impact of a particular risk policy. So managers are confronted with two important issues: (1) setting and articulating a risk policy; and (2) evaluating the effect of that policy on business unit performance. This study investigates that relationship, if any, between the degree of corporate risk taking and E&P performance.
In this study E&P performance is measured in terms of return on exploration and production assets (ROA). For purposes of return calculation, income is defined as earnings before interest but after taxes. We utilize an accounting-based measure rather than a market-based measure (such as stock price) because we are investigating the E&P business unit of the firm.
The risk-taking measure we utilize is the previously defined risk tolerance ratio (RTR). We distinguish among firm risk propensities by using four categories of risk tolerance. Those categories are defined along the dimension of RTR and are shown in Table 5, which also summarizes the statistical information relating to return on assets for the number of firms within each of these risk tolerance categories over the study period.
| ----------------- Return on E&P assets (ROA) ---------------- | ||||
| RTR Category: RTR: | High RTR > 2.5 | Moderate 2.5 ≥ RTR >1.5 | Average 1.5 ≥ RTR >0.5 | Low RTR ≤ 0.5 |
| Mean return | 8.2% | 4.9% | 4.7% | 2.5% |
| Standard deviation | 9.5% | 8.5% | 7.8% | 14.0% |
| Size (n) | 84 | 119 | 201 | 107 |
Based on this analysis, there is a statistically significant difference in E&P performance as it relates to risk taking categories. With regard to achieving higher return on assets, statistical tests indicate there exists an optimal level of financial risk tolerance. E&P firms categorized with High Risk Tolerance levels (RTR > 2.50) demonstrate significantly higher returns (8.2%) than those firms that exhibit less tolerance for risk-taking.
Firms that are willing to implement risk policies characterized by RTR values greater than 2.5 are more likely to achieve superior returns. These results are statistically significant at the 0.05 confidence level.
For those firms in the remaining risk tolerance categories (Low, Average, and Moderate) this would suggest that they have exercised highly risk averse behavior, and in so doing have reduced their opportunity for higher returns. The resulting effect is the selection of a sub-optimal risk tolerance level and less than superior asset returns. Note that firms do move from category to category over time so the results here are cross-sectional in nature. Rather than suggesting any one firm’s policies are optimal these study results provide some evidence that a particular risk tolerance category may be appropriate in terms of improving overall E&P performance.
Conclusions and implications
This study utilizes a measure of corporate risk taking that conforms more closely to the manner in which managers and decision makers conceptualize the notion of risk. In addition, the risk tolerance ratio is used to quantify and analyze the relative risk propensities of E&P firms who vary significantly in size. The important implication is that we are able to make stronger inferences about causal relationships between risk taking and performance over time, thus understanding better the effects of risk-taking actions by E&P firms.
The study results indicate that those firms that behave in a highly risk averse manner generate less than superior asset returns. The firms in this study who reveal a relatively high risk propensity, compared to their competitors, generate significantly higher asset returns. This finding has profound implications with regard to how E&P managers should set risk policies for their firms. Those firms who can identify their appropriate risk tolerance level, and make allocation decisions based on that risk tolerance, may demonstrate significantly higher returns than those firms implementing lower and perhaps inappropriate risk tolerance levels.
Understanding corporate risk tolerance and its impact on E&P firms’ investment decisions has implications with regard to competitor analysis on a number of dimensions. Decisions regarding competitive bidding, optimal share decision, partnership selections, and even merger and acquisition candidates can be influenced by firms’ risk propensities. Tracking and understanding competitors’ risk taking actions can provide valuable insights that may influence a firm’s competitive behavior. It is one of many important dimensions that E&P firms should consider in terms of their strategic decision making and capital allocations under uncertainty.
The author
Dr. Michael R. Walls is professor and chair in the Engineering and Technology Management Program, Division of Economics and Business, at the Colorado School of Mines in Golden, Colo. He specializes in the areas of strategic decision-making, business strategy, and risk management, particularly as it relates to the oil and gas industry. As a consultant, Dr. Walls has assisted numerous oil and gas firms in developing strategies, allocating resources, and managing risks. He holds a BS degree in geology from Western Kentucky University, an MBA in finance, and a PhD in management from the University of Texas at Austin.
EDITOR’S NOTE: This article summarizes a study of the upstream energy sector from 1983 to 2002 and examines the financial risk tolerance of E&P firms with particular attention to the impact of financial risk-taking on performance. The author, professor and chairman of the Engineering and Technology Management Program in the Division of Economics and Business at the Colorado School of Mines, has revised the original more academic version of his paper especially for Oil & Gas Financial Journal readers.
1SMCF represents a reasonably consistent measure of the value of the firm’s oil and gas reserves through a calculation where future production and development costs and income taxes are subtracted from future cash inflows from production. The result is then discounted by 10% for the timing of estimated cash flows to produce the standardized measure of discounted future net worth. The SMCF measure is the same whether full cost or successful efforts accounting is used and is a required computation for 10-K filings. In effect, this measure represents the “wealth” of the oil and gas exploration business unit.
