Invest in Direct Energy

Transcription

1 Invest in Direct Energy (Forthcoming Journal of Investing) Peng Chen Joseph Pinsky February North Michigan Avenue, Suite 700, Chicago, IL ! (32) Peng Chen is Vice President, Direct of Research and Joseph Pinsky is a Consultant at Ibbotson Associates. The authors thank Tony Webber and Terry Gottberg from the Merit Energy Company for their valuable comments and suggestions. The authors also thanks John DeRango for helpful comments and encouragement.

2 Abstract When designing investment portfolios within a long-term strategic asset allocation context, the authors maintain that direct energy investments (diversified portfolio of producing oil and gas properties) should be evaluated as a separate, distinct asset class. These securities possess unique characteristics that are not directly available through traditional investment vehicles (such as stocks and bonds). Direct energy investment should be attractive to investors by virtue of their relatively low correlation with other traditional asset classes. They provide good diversification for traditional assets. In addition to their obvious appeal to investors building long-term diversified portfolios, they may also appeal to a broader audience as a hedge against inflation, which allows investors to maintain real purchasing power and protect against future nominal increases in the overall domestic price level. The authors demonstrate that direct energy investments offer potentially significant diversification benefits, establishing them as a viable asset class to be considered when constructing a long-term asset allocation policy. 2

3 . Introduction While financial assets (stocks and bonds) garner the most attention from investors, non-financial (real or tangible) assets actually constitute the majority of world wealth. In general non-financial assets tend to have intrinsic value, or value in use. Typically, this use is in some sort of manufacturing process or as a consumable. In the past few years, investors have been paying more attention to alternative asset classes for further diversification benefits of their stocks and bonds portfolio. This is partially due to the increase in the correlation between stock and bond. For the period of the correlation coefficient between stocks and U.S. long-term bonds is However, since 970 the correlation coefficient has increased. From the period , the correlation between stocks and U.S. long-term bonds is 0.23 and from the correlation is Several recent studies have investigated the benefits of including real assets (for example, Ankrim and Hensel [993]; Kaplan and Lummer [998]; and Georgiev [200]). Real assets include real estate, precious metals, commodities, and a variety of other items that have value independent of the monetary units in which they are denominated. By holding real economic assets directly in the portfolio, investors can hedge against inflation much more effectively than with stocks. Real assets also tend to diversify away some of the risk of equities, with which they have low correlations. All of these can be used to achieve the goals of diversification and inflation protection. In this study, we focus on direct energy, a hard asset, and analyze the impact of direct energy investments within the context of a diversified investment portfolio. For the purposes of this study, direct energy investments are defined as a diversified portfolio of producing oil and gas properties. From a practical standpoint, direct energy investments are usually structured as limited partnerships, whereby long-term investors participate directly in the cash flows generated by the producing properties. The primary objectives of this study are: ) To define a viable methodology for modeling the historical returns of a generic direct oil and gas investment in the 48 contiguous States in the U.S.; 2) Evaluate the behavior of direct energy investments relative to different macro-economic variables; 3) Utilize mean-variance analysis to evaluate the impact on risk and return of including direct energy investments in portfolios comprised of traditional financial assets. 2. Data and Synthetic Direct Energy Index Construction One of the challenges of studying the risk return characteristics of direct energy investments is the lack of reliable historical performance data, since most of direct energy investments are organized as private partnerships. Most of their operating information is not made public, nor is there a market where we can observe the return and price fluctuation of these investments regularly. One of the primary objectives of this study is to create a synthetic historical return data series that represents a viable proxy for direct energy investments. We developed a unique method to synthetically generate a return series for direct energy investments based on a discounted cashflow method. We created a synthetic direct energy total return series (defined for the purposes of this study as a portfolio comprised of 50% crude oil and 50% natural gas) based on the theoretical model, data and assumptions outlined below. The annual returns of a direct energy investment are derived from two components: income and capital gain (loss). The income portion is defined as the cashflow generated by the oil or natural 3

4 gas production. It is simply the difference between the selling price of energy and the production cost. It can be calculated by equation (): I + = PDt ( SPt C ) () t, t t where, PD t = the production in period t, SP t = the spot price of direct energy in period t and C t = the cost of production in period t. A production decline curve was derived from domestic crude oil production data (excluding Alaska) obtained from the February 200 Basic Petroleum Data Book published by the American Petroleum Institute. The production decline curve experienced over the time period was deemed typical and the relative production over this 5-year period was utilized in the above calculation. Crude oil and natural gas spot prices are obtained from the Producer Price Index available from the Bureau of Labor Statistics. The per unit cost of direct energy is assumed to be the average historical expense to revenue ratio of the major U.S. energy producing companies 2. Revenue and expense data for the major U.S. energy-producing companies is obtained from the Energy Information Administration s Financial Reporting System database. The calculated average is based on operating figures over the time period; data prior to 977 was not available because the FASB rules on oil and gas disclosures did not become effective until after 977. The implicit assumption is that over a long period of time the average expense to revenue ratio will remain constant. That is, when energy prices are up, high cost (less efficient) producers will enter the market, thereby driving the average expense ratio up. When energy prices are down, high cost (less efficient) producers will exit the market, thereby driving the average expense ratio down. On the other hand, when energy production expenses (including costs of drilling, chemical materials, electricity, distribution, labor, etc.) increase, high cost producers will exit the market, driving the average expense ratio down. When energy production expenses decline, high cost producers will enter the market, driving the average expense ratio up. The historical average expense ratio is assumed to be the breakeven point, determining whether companies will remain in the market. In order to calculate annual capital gain figures, market prices for direct energy properties must be estimated. The market price of direct energy properties can be proxied by the present value of the discounted cash flows generated from the investment properties. Equations (2) and (3) calculate the market price of direct energy properties in periods t and : P t PD ( FP FC ) TV = ) T i, t i, t i, t t + i t T t i= ( + ri, ) ( + rt, t (2) 2 Major U.S. energy producing companies are defined as any U.S.-based company (or its parent company) that is publicly-traded and accounts for % or more of U.S. production or reserves of crude oil (including natural gas liquids) or natural gas, or % or more of U.S. refining capacity. Source: U.S. Department of Energy Financial Reporting System Form EIA-28. 4

5 P T PDi, ( FPi, FCi, ) TV = + t ( + r ) ( + r i= i t i, T T, ) (3) where, PD i,t = the projected production in period i, based on information at period t, FP i,t = the future price of direct energy in period i at period t, FC i,t = the cost of production in period i, based on information at period t, r i,t = the forward interest rate for period i at period t and TV t = estimated terminal value of the investment at period t. For every year from 970 through 2000, equations 2 and 3 are employed to price an assumed 5- year income stream from producing wells. Due to the unavailability of historical energy futures price data over this entire period, spot prices are substituted as a proxy for futures prices. Also, for the sake of simplicity, the cost of production term (i.e. FC i,t ) is assumed to be the constant C t described above. Rather than utilize forward interest rates as the discount factor for r i,t, a constant 0.0% was utilized. 3 It is assumed that this is a viable standard cost of capital for the industry over sufficiently long time periods. An investment time horizon (i.e. holding period of a producing well) of 5 years is assumed. The terminal value of a well is assumed to be 5 times the final year s cash flow (i.e. revenues less expenses). The total return of a direct energy investment is derived from equation (4): TR t, = P P t P t + I (4) The aforementioned process was utilized to create a series of annual total returns over the time period. The resultant series serves as the benchmark for direct energy used throughout this paper. 3. Performance of the Synthetic Direct Energy Index Compared with Other Investments Exhibit shows the growth of a $ investment made on January, 970 in various asset classes including direct energy. 4 Over the period of 970 to 2000, direct energy investments outperformed intermediate-term bonds, cash and inflation, but under-performed all of the equity investments. Direct energy investment was the leading performer in the late 70s and early 80s due to the significant increase in the energy prices. Since mid 80s, the growth of direct energy investments has been somewhat flat, while stocks grow rapidly. Direct energy did quite well in 999 and 2000, again due to the increase in the energy prices. 3 We also used US forward interest rate as the discount rate, there is no material difference between the results from using a 0% discount rate. 4 Exhibit assumes that all cash flows generated from each asset class are reinvested and that no taxes or transactions costs are paid. 5

6 Exhibit 2 further presents the performance summary of these six asset classes over the entire period as well as in high and low inflation environments. The period from 970 to 98 is defined as the high inflation and increasing interest rates environment; while 982 to 2000 is defined as the low inflation and decreasing interest rates environment. In general, higherreturning asset classes have higher risk. The average compounded return of direct energy investment is 2.06 percent, slightly lower than that of US large-cap equities. However, direct energy investment has been more volatile than US large-cap stocks. It has a standard deviation of 2.53 percent, compared to 6.28% for US large cap over the 970 to 2000 period. This indicates that investors probably should not invest 00% into direct energy. During high inflationary periods, direct energy provides much higher return with substantially lower risk, while stocks and bonds do poorly over the high inflationary periods. On the other hand, direct energy exhibits lower return and higher standard deviation in the low inflationary environments, while equities and bonds performs significantly above the average of the entire period. Correlation Coefficients between Direct Energy and Other Asset Classes The correlation coefficient measures the degree to which two asset classes returns change with respect to each other. Correlation coefficients can range between positive one (+) and negative one (-). The lower the correlation coefficient of an asset class with the other asset classes in a portfolio, the more diversification benefit can be gained by including the additional asset class in the portfolio. Most traditional asset class correlation coefficients range between -0.2 and 0.9. Exhibit 3 shows the correlation coefficients of annual returns between the direct energy series and the other asset classes over the period. The direct energy series has negative correlation coefficients with most traditional equity and fixed income asset classes, ranging from 0.05 (Small Cap Stocks) to 0.42 (International Stocks). This illustrates the added benefits from including direct energy in diversified portfolios. It offers diversification benefits by hedging the risk associated with traditional asset classes. Many studies have documented that broad market equity indexes tend to be negatively correlated with inflation. 5 The correlation coefficient between the direct energy series and inflation was 0.58 for the 970 to 2000 period. Analysis of direct energy correlation coefficients with other traditional asset classes reveals that there is a tremendous diversification opportunity available in direct energy investments. Additionally, because direct energy is relatively highly correlated with inflation, these investments can provide a powerful inflation hedge in a portfolio. 4. Role of Direct Energy in Strategic Asset Allocation We have employed mean-variance optimization techniques to analyze the effects on portfolios by the inclusion of direct energy investments. Mean variance optimization provides the set of portfolios that maximize return at any level of risk. This set of portfolios is known as the efficient frontier and portfolios contained in this set are said to be mean-variance efficient. Given a set of asset classes or securities, it is not possible to improve the risk-return relationship of a portfolio beyond the efficient frontier. However, adding asset classes to the asset mix may improve the entire set of portfolios and result in a more favorable efficient frontier. It is possible to determine whether there are benefits to including an additional asset class by first examining the efficient 5 The first empirical works in this area appear to be Jaffe and Mandelker [976], Bodie [976], and Nelson [976]. 6

7 frontier of a basic set of asset classes and then observing how the additional asset class effects the frontier. For this study, large cap stocks, small cap stocks, international stocks, U.S. long-term bonds, and U.S. treasury bills were selected as the basic set of asset classes. Portfolios developed with the basic set of asset classes will henceforth be referred to as the Base Case. Efficient frontiers are constructed using historical data from Once the Base Case efficient frontiers are developed, direct energy is added to the portfolio mix to determine if there is an improvement in the efficient frontier. The inputs used to conduct the mean-variance analysis are presented in Exhibit 4. Historical Efficient Frontier Analysis We developed the Base Case efficient frontier with the historical data and selected three portfolios. The portfolios were chosen based on their risk and have standard deviations of 8%, 2% and 6%. These portfolios are labeled A, B, and C, and represent three distinct investment risk tolerances (low risk, medium risk, and high risk, respectively). For comparison purposes, we selected six portfolios from the Base Case + Direct Energy frontier. Three of the portfolios, A-, B-, and C-, have the same standard deviation, or equivalent risk, as the Base Case portfolios. The other three portfolios, A-2, B-2, and C-2, have the same expected return. The following table outlines the portfolios on each frontier: Base Case Portfolio Base Case + Direct Energy Equivalent Risk as Base Case Equivalent Return as Base Case A A- A-2 B B- B-2 C C- C-2 Exhibit 5 displays the historical Base Case efficient frontier with low, medium and high risk portfolios selected. Also plotted on the graph are Base Case + Direct Energy portfolios that have equivalent risk or return to the Base Case. Exhibit 6 is a tabular display of the characteristics of the portfolios from Exhibit 5. Direct Energy plays an important role in the efficient portfolios. The allocations to Direct Energy range from about 0% in the minimum variance portfolio to about 35% for moderate-risk portfolios. Compare to efficient portfolios without direct energy investment, the allocations to direct energy come from both the equities and fixed income. This indicates that direct energy provide diversification benefits for both equities and fixed income. Exhibit 8 shows the detailed allocations along the efficient frontier. The allocation to direct energy reaches its maximum for a moderate risk portfolio. Benefits of Direct Energy in Diversified Portfolios An additional display of the role that direct energy plays in improving portfolio risk and return characteristics can be found in Exhibit 8. This figure displays the benefit of incrementally adding small amounts of direct energy to a three-asset-class portfolio (large cap stocks, long-term bonds and direct energy). The first portfolio is made up of 60% large cap stocks, 40% long-term bonds and 0% to Direct Energy. In each subsequent portfolio % is subtracted from the stock and bond asset class and added to direct energy. By adding small amounts of the portfolio to direct energy a more realistic portrayal of how a portfolio might benefit from direct energy allocations is 7

8 provided. The figure displays the effects of including small amounts of direct energy on portfolio risk and return. 5. Conclusion Direct energy offers investors an attractive option for portfolio diversification. Given the asset classes evaluated in this study, adding direct energy in a portfolio can potentially increase returns and/or reduce risk. Furthermore, allocations to direct energy should help risk-averse investors further diversify their portfolios without impacting their expected return. Using Sharpe ratio analysis, portfolios including direct energy have been shown to offer better performance than those without direct energy do. Direct energy alone has not presented an opportunity for extraordinary returns, nor does it eliminate portfolio risk. However, as part of a diversified portfolio, its low correlation coefficients with other asset classes and positive correlation with inflation offer some protection against adverse market and inflation movements. Finally, we would like to emphasis that investors who invest in direct energy should have a relatively long investment horizon (at least 5-7 years), since direct energy investments are illiquid and could experience poor performance over shorter horizons. 8

9 References Ankrim, E., and Hensel, C. Commodities in Asset Allocation: A Real-Asset Alternative to Real Estate, Financial Analyst Journal, May-June 993, pp Basic Petroleum Data Book, American Petroleum Institute, February 200. Georgiev, Georgi Benefits of Commodity Investment, Journal of Alternative Investments, Summer 200, pp Lummer, Scott, and Kaplan, Paul GSCI Collateralized Futures as a Hedging and Diversification Tool for Institutional Portfolios: An Update, Journal of Investing, Winter

10 Exhibit - Growth of $ Investment, $45.90 $43.06 $35.5 $34. $ $7.40 Small Cap Stocks Large Cap Stocks International Stocks Direct Energy U.S. Long-term Bonds U.S. Treasury Bills

11 Exhibit 2 Historical Return and Risk High Inflation and Increasing Interest Rates (970 98) Compound Annual Return Standard Deviation Sharpe Ratio Low Inflation and Decreasing Interest Rates ( ) Compound Annual Return Standard Deviation Sharpe Ratio Asset Class Asset Class Large Cap Stocks Large Cap Stocks Small Cap Stocks Small Cap Stocks International Stocks International Stocks Direct Energy Direct Energy U.S. Long-term Bonds U.S. Long-term Bonds U.S. Treasury Bills U.S. Treasury Bills Inflation Inflation Entire Period ( ) Compound Annual Return Standard Deviation Sharpe Ratio Asset Class Large Cap Stocks Small Cap Stocks International Stocks Direct Energy U.S. Long-term Bonds U.S. Treasury Bills Inflation Note: Statistics reported are based on annual returns.

12 Exhibit 3 - Correlation Coefficients Between Direct Energy and Other Asset Classes, Asset Class Correlation Coefficient Large Cap Stocks Small Cap Stocks International Stocks Public Energy Stocks 0.53 Commodities 0.36 Long Term Bonds Cash Equivalents 0.34 Inflation

13 Exhibit 4 Historical Return, Risk, and Correlation. Asset Class Benchmark Arithmetic Mean (%) Standard Deviation (%) Correlations Direct S&P 500 Energy CRSP Deciles 6-8 MSCI EAFE U.S. LT Gvt U.S. 30 Day Tbill Direct Energy Synthetic Large Cap Stocks S&P Small Cap CRSP Stocks Deciles Internation MSCI EAFE al Stocks U.S. Longterm Bonds U.S. LT Gvt U.S. U.S. 30 Treasury Day Tbill Bills

14 Exhibit 5 Historical Efficient Frontiers Base Case and Base Case + Direct Energy 8 Historical Return (%) A-2 B-2 A- A C-2 B- B C- C Base Cse Base Case + Direct Energy Historical Standard Deviation (%) Exhibit 6 Historical Portfolios Base Case and Base Case + Direct Energy Portfolio Portfolio Portfolio Asset Class A* A- A-2 B* B- B-2 C* C- C-2 Direct Energy Large Cap Stocks Small Cap Stocks International Stocks U.S. Long-term Bonds U.S. Treasury Bills Expected Return Standard Deviation Sharpe Ratio * - Base Case Portfolio - Base Case + Direct Energy Portfolio with Equivalent Risk as Base Case - Base Case + Direct Energy Portfolio with Equivalent Return as Base Case 4

15 Exhibit 7 -- Historical Frontier Area Graph % All oc ati on (%) 80% 60% Tbill US LT GVT MSCI EAFE CRSP 6-8 S&P 500 Direct Energy 40% 20% 0% Portfolio Standard Deviation (%) 5

16 Exhibit 8 Diversification Benefits of Direct Energy using Annual Data Portfolio Allocations Large Cap Stocks /Long Term Bonds/Direct Energy Compound Annual Return Standard Deviation Sharpe Ratio 60 / 40 / / 39 / / 38 / / 37 / / 36 / / 35 / / 30 /