U.S. oil and gas producers are among companies hit hardest by new restrictions on tax relief for interest payments, an analysis of the impact of the reforms has shown… […]
Since 2010, the United States has been in an oil-and-gas boom. In 2015, domestic production was at near-record levels, and we now produce more petroleum products than any other country in the world. President Trump said he plans to double down on the oil and gas industry, lifting regulations and drilling on federal land. Here is the state of the petroleum extraction industry that the new administration will inherit. […]
“[Certainty can] seem like a good idea, but actually lead us into trouble… The story here revolved primarily around the stochastic nature of product development… Succeeding in product development requires the discovery and exploitation of options where there is an asymmetry to the payoff function.” […]
There are dozens of different mathematical constructions that yield bell-shaped curves. The “Hubbert” or “Peak Oil” curve is actually a special case of a class of s-shaped functions called sigmoids. While most sigmoid functions begin and end at different values, Hubbert’s curve is constrained to begin and end at zero by the formula and boundary conditions imposed that represent a perfect mathematical translation of Hubbert’s worldview. The curve reflects a battle between two competing forces or trends – one for growth and one for contraction – where the balance shifts between the two along the way. […]
One can (correctly) argue that the foundations of the modern scientific inquiry are built on the foundations of rational skepticism. Contrary to some beliefs, science cannot “prove” anything. Rather, it is premised on the “refutation” of untruth. By eliminating all other possible explanations, the scientific method thereby accepts a theory as “truth”. All fields of inquiry which purport themselves to be scientific, but for which no theory is refutable, are not science.
So, what should we make of climate “consensus” promoters who deny the irrefutably of specific, unproven theories? Does this not contradict the basis of rational skepticism? For example, Jim Hoggan (a lawyer) and Brendan DeMelle (a writer) have this to say about those who question the “consensus truth” regarding ACC:
Unfortunately, a well-funded and highly organized public relations campaign is poisoning the climate change debate. Using tricks and stunts that unsavory PR firms invented for the tobacco lobby, energy-industry contrarians are trying to confuse the public, to forestall individual and political actions that might cut into exorbitant coal, oil and gas industry profits. DeSmogBlog is here to cry foul – to shine the light on techniques and tactics that reflect badly on the PR industry and are, ultimately, bad for the planet.
- 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).
In my last post, “US Employment: What’s Wrong With This Picture?“, I showed the workers of leaving the US Labor Force at an unprecedented rate. But who is leaving and why?
Obviously, workplace dynamics have changed dramatically (e.g., workers are retiring later in life and women have increasingly become a workforce with which to be reckoned). But how and by how much?
Specifically, what remained unclear was why the labor force, defined as the sum of employed and unemployed working age (25 – 54 y/o) adults, had undergone a secular increase from the 1940’s into the 2000’s and is now apparently reversing course. Is this due to a great dislocation of our perceptions and expectations? Perhaps there are other factors at play?
I can speculate all I want, but ultimately I need data to back my assertions. Fortunately, Quandl is making my data-life easier.
About a week or two ago, I found myself prompting Google and other search engines with questions like, “what is the best programming language?”, “how to choose a programming language?”, “how to interpret performance benchmarks?”, et ad nauseam. I even took a few cheap-o “what programming language are you?” type quizzes (I, in fact, created this “cheap-o” quiz).
Gimmicks aside, being a non-programmer, I neither have the luxury of being dictated languages to learn nor the opportunity to learn perhaps dozens of languages throughout my career. This lead me down a path in which I felt compelled to choose once, and choose right. In my visionquest to find the “right” one, and after weeks of research, I am no closer to nor am I any more certain about any of these answers. In failure, however, I discovered that I had framed the problem incorrectly. Instead of thinking about learning a programming language as a linear endeavor or as an exercise in academia, I should have remembered the wisdom passed down by my illustrious ancestor, the caveman: “every problem looks like a nail if all you’ve got is a hammer”. Even our great fore-bearers knew that it is better to adopt a synergistic array of tools, so why wasn’t this immediately obvious?
Welcome to my blog. I hope to be sharing my thoughts on just about everything on a fairly consistent basis. Check back soon for updates and other goodies.