- Equity investments into upstream oil and gas companies are largely levered commodity price plays; long-term total returns barely offset the carry costs of taking a long position in oil futures.
- There are multitudes of ways by which experts seek to forecast future commodities prices; most don’t work.
- The failure of forecasting should not be surprising if the Efficient Market Hypothesis is even partly correct.
- Even barring market efficiency, behavioral models provide ample reason for the widespread inaccuracy of forecasts.
- The idea that commodities prices — including oil — follow a random walk is both overwhelmingly supported by evidence and practical.
Figure 1: Black Gold
Source: Andy Thomas. Black Gold
Evidence overwhelmingly supports the notion that investments into upstream oil and gas producers are basically levered commodity price plays. This, and the fact that commodities producers are price-takers, indicates that petroleum economics are overly levered to commodities prices. It should follow, therefore, that an ability to accurately predict petroleum prices could result in advantageous market timing — i.e. investments in the right petroleum producing assets during the right times in the cycle. As a result of this ostensible potential for riches, prognosticators have devised multitudes of ways to forecast oil prices. Unfortunately, most of these efforts fall short of useful — no known forecasting approach, not even futures strip prices, significantly outperforms the assumption that price evolutions are random walks using out-of-sample data. This failure is not surprising, however, if we are to believe even a watered-down form of the Efficient Market Hypothesis (EMH).
A capitalization weighted index of upstream oil and gas companies exhibits long and short-term price fluctuations that are almost in lock-step with crude oil spot prices. This, and the fact that commodities producers are price-takers, indicates that equity investments into these types of companies are essentially levered commodities plays which have only managed to offset the carrying cost of a long position in crude oil futures. Figure (1), below, handily demonstrates this case.
Figure 2: Upstream Total Return Index vs. WTI Crude Oil PricesSource: Portfolio123
Note to Figure 2: Custom indices are constructed according to a modified capitalization weighted indexing methodology. Upstream Total Return Index includes all U.S. publicly traded companies in the Portfolio123 database which are assigned primary GICS Codes 10102010 (Integrated Oil & Gas) and 10102020 (Oil & Gas Exploration & Production). Dividends explain nearly half of the returns of holding a capitalization weighted index of oil and gas producers. A price return index which excludes dividends would have increased about 58% of the rate of the total return index.
The realization that investing in this space is essentially a casino — unless one can exercise foresight regarding the future direction of commodities prices — leads many to rely on their own or third-party commodity price forecasts. The rationale behind this is pretty straightforward: if one has a probabilistic edge in forecasting commodities prices, this can lead to investments into the right types of assets during the right times of the cycle. For example, a rational investor armed with foresight would allocate to defensive assets with high margins, long-lived expectancies, and good access to infrastructure preceding downturns, even if those assets command premium valuations. Likewise, our hypothetical sage would re-allocate to distressed assets preceding commodity price recoveries, even if those assets are marginally profitable in the present market environment. The net effect of this timing strategy should result in the efficient compounding of capital.
Conventional Econometric Approaches to Forecasting
However, overwhelming evidence indicates that academic and econometric approaches to commodity price forecasts are not very good. Experts are likely to get things wrong, especially the big moves. The most likely explanation for this lack of prescience is that experts are expertly forecasting the past (i.e., telling themselves a story through models which over-fit historical data). Over-fitting is a problem which extends well beyond the realm of commodities prices.
To quote a 2015 post on the fundamentals of the refining business:
With light-sweet oil prices currently hovering around the $45-50/bbl handle, just as recently as last June, BofA analysts had expressed confidence that Brent would continue to trade around $100/bbl1. Last September, EIA expressed with 95% confidence that NYMEX WTI would stay above $80/bbl through January 20152. Last October, Goldman Sachs analysts predicted that WTI would stay above $90 and Brent above $100/bbl for the following three months3.
Rather than babble on ad nauseam, it is instructive to examine a case in which asset price researchers believed they had hit pay-dirt. IMF’s June 2014 paper, The future of oil: Geology versus technology, presented a strikingly accurate econometric model which revised previous work in the field with an over-riding assumption: geological constraints impose an upward trend on oil prices, i.e., depletion increases the technical difficulty involved with the extraction of petroleum resources. The model forecasted that oil prices would reach $150 by 2020 due overwhelmingly to geological constraints. While it remains to be seen whether the forecast will work out over the long-run, the authors dismissed the possibility of positive supply shocks and had even dismissed the idea that any supply shock could exert a long-term and meaningful effect on price.
In only four short months after the paper was published, the market awoke to the reality that a fusion of market forces had unlocked vast resource potential. By February 2016, WTI spot prices had bottomed near the $30 handle. Clearly, the premise that supply shocks are irrelevant was proven false. And it may also be too soon to really know what the geological constraints actually are. Indeed, the North American unconventional resource revolution which came into full bloom over the last decade is beginning to work itself over the United Kingdom and Europe as I write this. Geology appears to be a global phenomenon after all. Drill or Drop? is an independent blog which provides ongoing updates of this phenomenon. In addition, it is widely believed that the South American and the Siberian landmasses contain vastly greater quantities of untapped and unknown petroleum resources.
This is just one of many anecdotes in which economists and academics correctly forecasted the past (i.e., over-fitted the data so that it looked good), but failed to see what was going on right underneath their own noses4.
For brave souls who dare venture into the depths of commodity hell, the ECB’s Economic Bulletin on Forecasting the price of oil provides a solid overview of academic literature on sophisticated approaches, such as risk-adjusted futures, statistical regressions, and structural models.
Figure 3: Monthly WTI Oil Price Expectations based on the Futures and Risk-Adjusted Futures Term Structures
Source: Baumeister and Kilian. Forty Years of Oil Price Fluctuations: Why the Price of Oil May Still Surprise Us. Journal of Economic Perspectives, Vol. 30, No. 1. Winter 2016
Alternative Methods for Forecasting
Given the probable lack of utility of most econometric models for forecasting commodities prices, many practitioners opt to rely on commodities, investment, business, and market cycles analysis. The idea behind this approach is fundamentally sound: the drivers of supply and demand are not reducible to linear regressions on a finite number of variables, such as manufacturing capacity utilization — there are vastly more factors at play. Rather than bootstrapping a time-series using explanatory variables, cycles analysis seeks to roughly approximate the market’s present appetite for investment, capacity, risk, and credit in relation to historical levels. Profitable investing does not usually require investors to have a precise time-series which pegs expected value to a number. It does, on the other hand, require investors to accurately conclude that the bias is significantly up, down, or neither. “It is better to be roughly right than precisely wrong” (misattributed to John Maynard Keynes). Oaktree Capital’s Howard Marks might call this method “taking the temperature of the market”.
However, there is little in the way of consensus on how to interpret any given cycle. Some approaches attempt to analyze cycles using price only, some are price-blind, and others utilize some ratio of commodities prices. Aside from variable selection, there are multitudes of confounding factors: that cycles can be of varied lengths and can be embedded within other types cycles invariably leads to the superposition and convolution of factors. But some generalities may exist. According to a June 2014 paper from the Bank for International Settlements, “business cycles tend to last from one to eight years, and financial cycles around 15 to 20 years.” Even though financial cycles are increasingly globalized, business cycles can still be intensely local in nature. Furthermore, there may exist a preponderance of commodities super-cycles which, for oil, “have historically lasted an average of 27 years, with a minimum period of 21 years and a maximum of 32 years”.
The lengthy nature of cycles combined with the lack of data means that many perceived historical patterns will be spurious. There is also the problem of articulation within a broader analytical framework. Multi-variate models for market cycles ultimately fall prey to the same fallacies as complex econometric models as soon as factors become ranked, weighted, and parameterized. Unlike within the fields of physical science in which constants are “constant”, markets are functions of non-stationary and non-linear processes. Even if one could identify a correct model for the anthropomorphic underpinnings of market behavior, there is the insurmountable problem associated with the accurate measurement of non-stationary, non-independent, and non-linear variables. Yet, any modicum of study on market cycles reveals a common central theme: commodities prices are cyclical. ‘Nuff said.
The Problem with Efficient Markets
The failure of conventional models should not come as a huge surprise to those that subscribe to even the weakest form of the EMH. Even if markets used to be relatively more inefficient, the proliferation of capital now means that the financial industry pays the best and attracts the best. This has changed the game. For example, over any sufficiently long time-frame, the overwhelming majority of active money managers under-perform even their self-selected benchmarks. If professionals who went to the best schools and had the best professors cannot do better than average, what does that portend for lay-folk, such as I claim to be? Moreover, if the marketplace reflects the capital-weighted consensus of all known information at any given point in time, there is still great fundamental uncertainty regarding geology and future technological advances with which to contend.
Even if one does not presume that markets are efficient, research in behavioral economics provides ample rationale as to why experts are likely to get things wrong. In spite of their advanced degrees and high IQs, experts are still susceptible to cognitive dissonance which affects humanity as a whole. Experts may in fact be particularly predisposed to cognitive biases which lead them to either misjudge and/or overstate confidence in their own judgments. For example, the well-documented bandwagon effect leads affiliated individuals to think and feel and act the same way due to the safety in numbers principle (i.e., how can everyone be wrong?). For those who follow a given market regularly, it is very difficult not to become infected by the latest vogue.
Bandwagon effects tend to distort and exacerbate commodities and investment cycles and, in extreme cases, result in speculative bubbles. For example, in bull markets, increasing prices increases buying interest for no other reason than that enough people believing in a fallacy can make it true (i.e., “the trend is your friend”). In fact, the presence of momentum in most financial markets is probably the most problematic phenomenon to those subscribe to the notion of market efficiency because it suggest that there is a free lunch to be had — unrelated to any kind of risk premia — by simply buying winners and selling losers. Attempts to rationalize irrational behavior is clearly an exercise in futility5.
Therefore, it might follow that in order to be a good forecaster, one must be willing to hold unpopular views. But even if one could accurately forecast economic conditions according to some model, that model would invariably fail to predict random events which result in events such as unanticipated disruptions. Event-driven risks foil the efforts of even the best prognosticators.
Or, in more eloquent words:
It isn’t surprising that experts aren’t good at predicting prices. Global oil markets are a function of countless variables — geopolitics, economics, technology, geology — each with its own inherent uncertainty. And even if you get those estimates right, you never know when a war in the Middle East or an oil boom in North Dakota will suddenly turn the whole formula on its head.
– Ben Casselman. The Conventional Wisdom On Oil Is Always Wrong. FiveThirtyEight. 18 December 2015
Even good economic forecasts may not be very useful to investors of upstream companies or their managements. From a capital allocation perspective, economic forecasts may be meaningful to select the most advantageous sectors or products to invest in. However, if constrained to investments in assets or securities within the upstream petroleum sector, macroeconomic factors are mostly extraneous since neither managements nor investors exert meaningful influence over them.
The Practitioner’s Dilemma
Against this back-drop of all-encompassing futility, there is the reality that the business cycle must go on. Given the difficulty of forecasting prices, and probable lack of incentive for doing so, it could said that any such effort is a waste of time and energy. However, companies, regulatory bodies, and individuals must have some expectancy of the future in order to make capital budgeting decisions.
For example, it is standard practice for integrated petroleum companies to evaluate upstream investments using a “most conservative” case for oil prices. These planning assumptions are likely indicative of a given company’s resilience to downturns, and may also be inversely indicative of gearing to commodity price upside.
Table 1: Commodity Price Projections
|Company||Price Projection for Investment Decisions|
|Royal Dutch Shell||$45*|
Any further effort spent forecasting is, for all the reasons cited above, likely to suffer from its own sort of diminishing returns.
Footnotes [ + ]
|1.||↑||Crude oil benchmark backwardation to face pressure moving forward: BofA ML|
|2.||↑||EIA, Short-term Energy Outlook, September 2014|
|3.||↑||U.S. Oil Output May Slow If WTI Drops Below $90: Goldman|
|4.||↑||Forecasting the end of the petroleum-age may also be consistent with the IMF’s not-so-implicit agenda of imposing a global tax regime on carbon. The justification seems altruistic enough: anthropogenic climate change is the greatest impediment to a future of sustainable prosperity. However, it is perhaps too convenient that a global carbon tax is being promoted as the panacea for all the woes of society. Will it really solve any core problems such as growing irrelevance of labor capital, over-population, or unworkable behemoths of bureaucracy? Moreover, the premise of a global tax is square at odds with classically liberal economic ideals. Please forgive me for waxing political.|
|5.||↑||It is perhaps indicative of underlying fragility that our present financial system is built on models which assume humans behave like rational agents (i.e., “econs”) according to some idealized utility function.|
|6.||↑||The most common type of semi-martingale used in financial modeling is probably geometric Brownian Motion (GBM) in which past price changes may contain only probabilistic information regarding future prices changes. Marginal improvements over GBM might be had by adding terms for jump stochasticity and mean reversion.|
|7.||↑||Hamilton and Wu (2014) propose one method for extracting the risk premium for futures price data. However, sophisticated methods such as this which seek to decode this information rely on many degrees freedom and are therefore heavily exposed to over-fitting risk.|
|8.||↑||Fischer Black, Myron Scholes. The Pricing of Options and Corporate Liabilities. The Journal of Political Econony, Volume 81 Issue 3 (May-Jun 1973), 637-654|