Drilling for Value, Pt. 4: The Economics of Petroleum Exploration and Production

Note: this post has been heavily redacted since its original data of publication in order to expand on the fundamentals of petroleum geology and the upstream business elsewhere. 

Summary

  • Economic models use assumptions which simplify the effects of accounting, taxes, regulations, and other minutiae in order to glean insights into the drivers of market behavior and value.
  • The effects depletion and commoditization, relatively low cash costs, and often prohibitive resource replacement costs drive the endemically cyclical petroleum investment cycle
  • Petroleum economics are strongly levered to petroleum prices and other extrinsic factors.
  • Maintaining a sufficiently low cost of supply is the primary operational lever capable of creating long-term investment value in the upstream business.
  • Timings of costs are a key consideration for evaluating investment decisions — known discount rates simplify decisions regarding timing preferences.

Figure 1: Pecos, Texas Oilfield
February-22-Hogue-1937-Pecos-AOGHS
Source: Alexander Hogue. Pecos, Texas Oilfield. 1937

The Economics of the Upstream Petroleum Industry
The economics of the petroleum extraction is overwhelmingly colored by the economic factors of depletion and commoditization. Due to the fact that production depletes limited natural resources, the upstream industry must constantly explore for and develop additional resources. Given that the capital investments required to replace depleted resources are usually quite significant in relation to operating costs, resource replacement is a primary driver of costs. Commoditization describes the lack of differentiation in upstream business models and their end products. As a direct result of commoditization, the value propositions of upstream businesses are strongly levered to external market conditions (i.e., namely prices). Taken together, high replacement costs and supplier susceptibility to external market conditions have resulted in endemically cyclical petroleum supplies and prices.

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On Market Efficiency: Market Fair Value Estimates and the True Cost of Capital

In the world of investing and corporate finance, the Efficient Market Hypothesis (EMH) casts a long shadow. EMH states that a sufficiently liquid market reflects the “correct” price at all times. Since efficient markets factor in all known and relevant information at all times, it is therefore practically futile to attempt to predict the future direction of market prices. In other words, a blindfolded monkey throwing darts at the Wall Street Journal has about the same chance as beating the market averages as any professional investor. At one extreme, the founder of Vanguard Investments Jack Bogle revolutionized the mutual fund industry around cheap indexing, which he posited as the solution to efficient markets. At the other, Warren Buffet’s seminal essay, The Super-Investors of Graham and Doddes-ville, defends the notion that right-headed investors can carve out a significant edge [1. The Super-Investors of Graham and Doddes-ville]. In the middle, you have the greater majority of investors who will likely cede that both extremes contain some amount of the truth. Even 2013 Nobel Laureate Eugene Fama, of the University of Chicago Booth School of Business, who is credited with developing EMH, has stated that “[asset prices] are typically right and wrong about half the time” [2. The Super-Brainy Quote]. Being able to determine when they are right and when they are wrong is the holy grail to traders and investors alike. In order to investigate how correctly assets prices reflect all known information, we must develop an intuition and methodology for estimating the fair value of an asset. As we will discuss, just because a methodology is descriptive does not mean it is predictive (i.e., correlation does not imply causation).
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Yongye (ticker: YONG): A unique risk-arbit​rage opportunit​y

A lone Chinese female investor, Xingmei Zhong, d.b.a. Full Alliance International Ltd., finalized its plan to buyout all outstanding shares of YONG for $6.69 per share in cash. The deal is expected to close at the end of the first fiscal quarter of 2014 (i.e., between October and January). The buyout price reflects a 40% premium to YONG’s market price ($4.79) as of the date of the announcement on 12-Oct-2012.

At $6.25 per share, the buyout represent a 7.04% premium to market price. Investor’s looking for a relatively low-risk return on investment can engage in a risk-arbitrage trade. Investors can buy YONG now and will likely realize the differential between market and buyout price within 3 to 6 months. At the present, one could realize a 29.18% annualized return if the deal executes in 3 months; 14.20% if the deal executes in 6 months.

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Analyzing RenTec’s Top Holdings using SEC Form 13-F Filings (Precis)

In this post, I present a case that alpha can be gleaned from publicly available SEC Form 13-F data. Traditionally, pundits looked at commonalities in institutional top-holdings by dollar amount. Research suggests that these aggregated top holdings among many institutions can be indicative of their “best ideas” (1). That may be all good and well, but common sense indicates that the best leads should come from a good institution’s top holdings per unit of capacity. For my institution, I use RenTec because:
a.) they are quantitative and therefore it may be easier to find commonalities in their holdings; and,
b.) they have consistently delivered exceptional returns.
I believe that their “best ideas” should be those positions in which the position size is largest relative to capacity because a moderately-sized holding for a small float stock is much more indicative of expected risk-reward than a relatively much larger position in a relatively much larger float stock. Additionally, focusing on a single institution (rather than many) allows us to ask the all-important “why” by determining if there are any commonalities in their top holdings. Understanding the “why” might us allow us to move beyond “piggybacking” off of quarterly 13-F data, and understand what drives the decisions of the best in the industry. I argue that if we can deconstruct some of the decision-making criteria, we can use this for finding our own unique source of alpha.

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