Tag Archives: politics

Optical Illusion / Optical Truth

A great deal of intelligence can be invested in ignorance when the need for illusion is deep. – Saul Bellow, “To Jerusalem and Back” (1976) It is difficult to get a man to understand something, when his salary depends on his not understanding it. – Upton Sinclair, “I, Candidate for Governor: And How I Got Licked” (1935) Knowledge kills action; action requires the veils of illusion. – Friedrich Nietzsche, “The Birth of Tragedy” (1872) To find out if she really loved me, I hooked her up to a lie detector. And just as I suspected, my machine was broken. – Jarod Kintz, “Love Quotes for the Ages. Specifically Ages 19-91” (2013) Edward Tufte is a personal and professional hero of mine. Professionally, he’s best known for his magisterial work in data visualization and data communication through such classics as The Visual Display of Quantitative Information (1983) and its follow-on volumes, but less well-known is his outstanding academic work in econometrics and statistical analysis. His 1974 book Data Analysis for Politics and Policy remains the single best book I’ve ever read in terms of teaching the power and pitfalls of statistical analysis. If you’re fluent in the language of econometrics (this is not a book for the uninitiated) and now you want to say something meaningful and true using that language, you should read this book (available for $2 in Kindle form on Tufte’s website ). Personally, Tufte is a hero to me for escaping the ivory tower, pioneering what we know today as self-publishing, making a lot of money in the process, and becoming an interesting sculptor and artist. That’s my dream. That one day when the Great Central Bank Wars of the 21st century are over, I will be allowed to return, Cincinnatus-like, to my Connecticut farm where I will write short stories and weld monumental sculptures in peace. That and beekeeping. But until that happy day, I am inspired in my war-fighting efforts by Tufte’s skepticism and truth-seeking. The former is summed up well in an anecdote Tufte found in a medical journal and cites in Data Analysis : One day when I was a junior medical student, a very important Boston surgeon visited the school and delivered a great treatise on a large number of patients who had undergone successful operations for vascular reconstruction. At the end of the lecture, a young student at the back of the room timidly asked, “Do you have any controls?” Well, the great surgeon drew himself up to his full height, hit the desk, and said, “Do you mean did I not operate on half of the patients?” The hall grew very quiet then. The voice at the back of the room very hesitantly replied, “Yes, that’s what I had in mind.” Then the visitor’s fist really came down as he thundered, “Of course not. That would have doomed half of them to their death.” God, it was quiet then, and one could scarcely hear the small voice ask, “Which half?” ‘Nuff said. The latter quality – truth-seeking – takes on many forms in Tufte’s work, but most noticeably in his constant admonitions to LOOK at the data for hints and clues on asking the right questions of the data. This is the flip-side of the coin for which Tufte is best known, that good/bad visual representations of data communicate useful/useless answers to questions that we have about the world. Or to put it another way, an information-rich data visualization is not only the most powerful way to communicate our answers as to how the world really works, but it is also the most powerful way to design our questions as to how the world really works. Here’s a quick example of what I mean, using a famous data set known as “Anscombe’s Quartet”. Anscombe’s Quartet I II III IV x y x y x y x y 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89 In this original example (developed by hand by Frank Anscombe in 1973; today there’s an app for generating all the Anscombe sets you could want) Roman numerals I – IV refer to four data sets of 11 (x,y) coordinates, in other words 11 points on a simple 2-dimensional area. If you were comparing these four sets of numbers using traditional statistical methods, you might well think that they were four separate data measurements of exactly the same phenomenon. After all, the mean of x is exactly the same in each set of measurements (9), the mean of y is the same in each set of measurements to two decimal places (7.50), the variance of x is exactly the same in each set (11), the variance of y is the same in each set to two decimal places (4.12), the correlation between x and y is the same in each set to three decimal places (0.816), and if you run a linear regression on each data set you get the same line plotted through the observations (y = 3.00 + 0.500x). But when you LOOK at these four data sets, they are totally alien to each other, with essentially no similarity in meaning or probable causal mechanism . Of the four, linear regression and our typical summary statistical efforts make sense for only the upper left data set. For the other three, applying our standard toolkit makes absolutely no sense. But we’d never know that – we’d never know how to ask the right questions about our data – if we didn’t eyeball it first. Click to enlarge Okay, you might say, duly noted. From now on we will certainly look at a visual plot of our data before doing things like forcing a line through it and reporting summary statistics like r-squared and standard deviation as if they were trumpets of angels from on high. But how do you “see” multi-variate datasets? It’s one thing to imagine a line through a set of points on a plane, quite another to visualize a plane through a set of points in space, and impossible to imagine a cubic solid through a set of points in hyperspace. And how do you “see” embedded or invisible data dimensions, whether it’s an invisible market dimension like volatility or an invisible measurement dimension like time aggregation or an invisible statistical dimension like the underlying distribution of errors ? The fact is that looking at data is an art, not a science. There’s no single process, no single toolkit for success. It requires years of practice on top of an innate artist’s eye before you have a chance of being good at this, and it’s something that I’ve never seen a non-human intelligence accomplish successfully (I can’t tell you how happy I am to write that sentence). But just because it’s hard, just because it doesn’t come easily or naturally to people and machines alike … well, that doesn’t mean it’s not the most important thing in data-based truth-seeking. Why is it so important to SEE data relationships? Because we’re human beings. Because we are biologically evolved and culturally trained to process information in this manner. Because – and this is the Tufte-inspired market axiom that I can’t emphasize strongly enough – the only investable ideas are visible ideas . If you can’t physically see it in the data, then it will never move you strongly enough to overcome the pleasant fictions that dominate our workaday lives, what Faust’s Tempter, the demon Mephistopheles, calls the “masquerade” and “the dance of mind.” Our similarity to Faust (who was a really smart guy, a man of Science with a capital S) is not that the Devil may soon pay us a visit and tempt us with all manner of magical wonders, but that we have already succumbed to the blandishments of easy answers and magical thinking. I mean, don’t get me started on Part Two, Act 1 of Goethe’s magnum opus, where the Devil introduces massive quantities of paper money to encourage inflationary pressures under a false promise of recovery in the real economy. No, I’m not making this up. That is the actual, non-allegorical plot of one of the best, smartest books in human history, now almost 200 years old. So what I’m going to ask of you, dear reader, is to look at some pictures of market data, with the hope that seeing will indeed spark believing. Not as a temptation, but as a talisman against the same. Because when I tell you that the statistical correlation between the US dollar and the price of oil since Janet Yellen and Mario Draghi launched competitive monetary policies in mid-June of 2014 is -0.96 I can hear the yawns. I can also hear my own brain start to pose negative questions, because I’ve experienced way too many instances of statistical “evidence” that, like the Anscombe data sets, proved to be misleading at best. But when I show you what that correlation looks like … Click to enlarge © Bloomberg Finance L.P., for illustrative purposes only I can hear you lean forward in your seat. I can hear my own brain start to whir with positive questions and ideas about how to explore this data further. This is what a -96% correlation looks like. What you’re looking at in the green line is the Fed’s favored measure of what the US dollar buys around the world. It’s an index where the components are the exchange rates of all the US trading partners (hence a “broad dollar” index) and where the individual components are proportionally magnified/minimized by the size of that trading relationship (hence a “trade-weighted” index). That index is measured by the left hand vertical axis, starting with a value of about 102 on June 18, 2014 when Janet Yellen announced a tightening bias for US monetary policy and a renewed focus on the full employment half of the Fed’s dual mandate, peaking in late January and declining to a current value of about 119 as first Japan and Europe called off the negative rate dogs (making their currencies go up against the dollar) and then Yellen completely back-tracked on raising rates this year (making the dollar go down against all currencies). Monetary policy divergence with a hawkish Fed and a dovish rest-of-world makes the dollar go up. Monetary policy convergence with everyone a dove makes the dollar go down. What you’re looking at in the magenta line is the upside-down price of West Texas Intermediate crude oil over the same time span, as measured by the right hand vertical axis. So on June 18, 2014 the spot price of WTI crude oil was over $100/barrel. That bottomed in the high $20s just as the trade-weighted broad dollar index peaked this year, and it’s been roaring back higher (lower in the inverse depiction) ever since. Now correlation may not imply causation, but as Ed Tufte is fond of saying, it’s a mighty big hint. I can SEE the consistent relationship between change in the dollar and change in oil prices, and that makes for a coherent, believable story about a causal relationship between monetary policy and oil prices. What is that causal narrative? It’s not just the mechanistic aspects of pricing, such that the inherent exchange value of things priced in dollars – whether it’s a barrel of oil or a Caterpillar earthmover – must by definition go down as the exchange value of the dollar itself goes up. More impactful, I think, is that for the past seven years investors have been well and truly trained to see every market outcome as the result of central bank policy, a training program administered by central bankers who now routinely and intentionally use forward guidance and placebo words to act on “the dance of mind” in classic Mephistophelean fashion. In effect, the causal relationship between monetary policy and oil prices is a self-fulfilling prophecy (or in the jargon du jour, a self-reinforcing behavioral equilibrium), a meta-example of what George Soros calls reflexivity and what a game theorist calls the Common Knowledge Game . The causal relationship of the dollar, i.e. monetary policy, to the price of oil is a reflection of the Narrative of Central Bank Omnipotence , nothing more and nothing less. And today that narrative is everything. Here’s something smart that I read about this relationship between oil prices and monetary policy back in November 2014 when oil was north of $70/barrel: I think that this monetary policy divergence is a very significant risk to markets, as there’s no direct martingale on how far monetary policy can diverge and how strong the dollar can get. As a result I think there’s a non-trivial chance that the price of oil could have a $30 or $40 handle at some point over the next 6 months, even though the global growth and supply/demand models would say that’s impossible. But I also think the likely duration of that heavily depressed price is pretty short. Why? Because the Fed and China will not take this lying down. They will respond to the stronger dollar and stronger yuan (China’s currency is effectively tied to the dollar) and they will prevail, which will push oil prices back close to what global growth says the price should be. The danger, of course, is that if they wait too long to respond (and they usually do), then the response will itself be highly damaging to global growth and market confidence and we’ll bounce back, but only after a near-recession in the US or a near-hard landing in China. Oh wait, I wrote that . Good stuff. But that was a voice in the wilderness in 2014, as the dominant narrative for the causal factors driving oil pricing was all OPEC all the time. So what about that, Ben? What about the steel cage death match within OPEC between Saudi Arabia and Iran and outside of OPEC between Saudi Arabia and US frackers? What about supply and demand? Where is that in your price chart of oil? Sorry, but I don’t see it in the data . Doesn’t mean it’s not really there. Doesn’t mean it’s not a statistically significant data relationship. What it means is that the relationship between oil supply and oil prices in a policy-controlled market is not an investable relationship. I’m sure it used to be, which is why so many people believe that it’s so important to follow and fret over. But today it’s an essentially useless exercise in data analytics. Not wrong, but useless … there’s a difference! Of course, crude oil isn’t the only place where fundamental supply and demand factors are invisible in the data and hence essentially useless as an investable attribute. Here’s the dollar and something near and dear to the hearts of anyone in Houston, the Alerian MLP index, with an astounding -94% correlation: Click to enlarge © Bloomberg Finance L.P., for illustrative purposes only Interestingly, the correlation between the Alerian MLP index and oil is noticeably less at -88%. Hard to believe that MLP investors should be paying more attention to Bank of Japan press conferences than to gas field depletion schedules, but I gotta call ’em like I see ’em. And here’s the dollar and the iShares MSCI Emerging Markets ETF ( EEM), the dominant emerging market ETF, with a -89% correlation: Click to enlarge © Bloomberg Finance L.P., for illustrative purposes only There’s only one question that matters about Emerging Markets as an asset class, and it’s the subject of one of my first (and most popular) Epsilon Theory notes, ” It Was Barzini All Along “: are Emerging Market growth rates a function of something (anything!) particular to Emerging Markets, or are they simply a derivative function of Developed Market central bank liquidity measures and monetary policy? Certainly this chart suggests a rather definitive answer to that question! And finally, here’s the dollar and the US Manufacturing PMI survey of real-world corporate purchasing managers, probably the most respected measure of US manufacturing sector health. This data relationship clocks in at a -92% correlation. I mean … this is nuts. Click to enlarge © Bloomberg Finance L.P., for illustrative purposes only Here’s what I wrote last summer about the inexorable spread of monetary policy contagion. Monetary policy divergence manifests itself first in currencies, because currencies aren’t an asset class at all, but a political construction that represents and symbolizes monetary policy. Then the divergence manifests itself in those asset classes, like commodities, that have no internal dynamics or cash flows and are thus only slightly removed in their construction and meaning from however they’re priced in this currency or that. From there the divergence spreads like a cancer (or like a cure for cancer, depending on your perspective) into commodity-sensitive real-world companies and national economies. Eventually – and this is the Big Point – the divergence spreads into everything, everywhere. I think this is still the only story that matters for markets. The good Lord giveth and the good Lord taketh away. Right now the good Lord’s name is Janet Yellen, and she’s in a giving mood. It won’t last. It never does. But it does give us time to prepare our portfolios for a return to competitive monetary policy actions , and it gives us insight into what to look for as catalysts for that taketh away part of the equation. Most importantly, though, I hope that this exercise in truth-seeking inoculates you from the Big Narrative Lie coming soon to a status quo media megaphone near you, that this resurgence in risk assets is caused by a resurgence in fundamental real-world economic factors. I know you want to believe this is true. I do, too! It’s unpleasant personally and bad for business in 2016 to accept the reality that we are mired in a policy-controlled market, just as it was unpleasant personally and bad for business in 1854 to accept the reality that cholera is transmitted through fecal contamination of drinking water. But when you SEE John Snow’s dot map of death you can’t ignore the Broad Street water pump smack-dab in the middle of disease outcomes. When you SEE a Bloomberg correlation map of prices you can’t ignore the trade-weighted broad dollar index smack-dab in the middle of market outcomes. Or at least you can’t ignore it completely. It took another 20 years and a lot more cholera deaths before Snow’s ideas were widely accepted. It took the development of a new intellectual foundation: germ theory. I figure it will take another 20 years and the further development of game theory before we get widespread acceptance of the ideas I’m talking about in Epsilon Theory . That’s okay. The bees can wait.

Short EDF/ Long Engie: Why Investors Should Prefer Commodities Over Politics

Summary Engie’s share price has under-performed EDF despite its much stronger positioning in terms of strategy and financials. EDF is exposed to materially higher political risk than Engie and the Areva deal is does not mean an end to it. There is now increased execution risk for EDF, while recent short term headwinds should reverse for Engie. EDF (OTC: ECIFF ) and Engie (OTCPK: GDFZY ) are both major French energy companies with global operations. A short EDF / long Engie trade is based on political risk, balance sheet strength, growth prospects and corporate strategy. Some key points are: chosen (Engie) vs. forced acquisitions ; commodities exposure with reasonable hedge (Engie) vs. capped tariffs ; balance sheet strength (Engie) vs financial strain ; earnings growth (Engie) vs. flat earnings . EDF has outperformed Engie by 300bps year to date, even when including the period during which one of the arguably worst news flow for EDF has come into the market. Engie’s under-performance is largely due to commodity weakness. I expect relative performance to revert strongly. Key catalysts Potential catalysts for a reversal of performance are plentiful: · Any indication of a turn in commodities prices. Failing that, improvement in the LNG to oil spread would have the same result, even though it will take longer to feed into the share price. · News flow on a re-start of the Belgian nuclear reactors Tihange and Doel should remove an overhang for Engie and lead to out-performance. · Over the summer, there will be abundant news flow and uncertainty with regards to EDF’s regulated retail tariffs. · EDF will soon give an update on its investment decision with regards to the nuclear new build project at Hinkley Point in the UK. There is a high chance for a negative reaction either way: Either, EDF will proceed – then there may be concerns over financing, the company’s ability to source enough partners and long term cash generation and profitability of the project. Or, it does not go ahead. That would lead to a very negative reaction on the loss of a long term opportunity. Another possibility still, would be further delay, again likely to lead to EDF underperforming. · Engie management has communicated it is in acquisition mode. Judging by the company’s track record, a growth acquisition would likely lead to outperformance. EDF EDF is the dominant French power utility. It controls the country’s nuclear power plant base of 63 GW and has an 85% supply market share. It also runs 9GW of nuclear capacity in the UK and is engaged in the UK’s nuclear new build programme, by far the largest in Europe. In France, there is limited competition, with the most important competitors being Energie Direct (an independent supplier with very little vertical integration) and Engie (a global integrated energy company and the dominant national gas supplier). The French government owns 85% of EDF. Key bear points: · Political intervention. There is consistent intervention by the French government into electricity markets, regulation as well as the company’s strategy. The CEO is government appointed. The company is seen as a public good as well as political vehicle by the government. It is questionable to what degree minority shareholders are relevant for the crucial decisions by the government. This has been illustrated very strongly by the government orchestrated acquisition of Areva’s (OTCPK: ARVCY ) nuclear reactor business by EDF. The French government has decided, not surprisingly, to execute its plan A, EDF acquiring Areva’s entire nuclear reactor business. EDF’s offer stands at Eur 2.7bn, or 0.74x book. EDF will acquire a majority and Areva retain a minority stake in the business. The potential for the business to be structured in a joint venture, but with the same majority EDF/minority shareholding structure. Areva itself will become a front end and nuclear fuels company. The take out multiple is expensive for a business with large unknown liabilities. The most important liability is the Finnish project, but there are others, too. As I have previously argued, reactor construction and export is outside of EDF’s core competencies. EDF will further strain its balance sheet. In addition to the straight outlay for the deal, it will have to take on provisions. The benefit for EDF, streamlining of its own reactor build and maintenance will be marginal when compared to the risk and financial strain. · A very large part of the company’s revenues are conditioned on government regulated tariffs that are not reflective of EDF’s true cost base. There is little prospect for tariffs reaching a level that reflects costs any time soon. Rather, the prospect for any significant tariff increase has been pushed away further than ever. EDF is currently asking the government for a 2.5% increase to regulated tariffs over three years. It is the time of the year where the negotiations for the annual tariff increase begin, to be confirmed in August. This increase is particularly sensitive, because it comes after the Energy Minister froze tariffs, and now because of the Areva deal. There are suggestions that a stronger tariff increase might be a reward for EDF’s offer for Areva’s reactor business. The government will tread very carefully, in order to avoid any negative interpretation on the Areva re-capitalisation vs consumer vs taxpayer interests. Also, the new formula fixed by the government gives some framework for tariff increases. The government had just devised that formula last year, in order to reduce tariffs, and asked EDF to reduce costs. A u-turn would be difficult. Investors should keep in mind that nuclear energy is seen as a public good, the profits of which are not due alone to EDF shareholders. That opens the way for redistribution · EDF is very strained on capex and the balance sheet. Net debt exceeds 4.4x Ebitda. EDF needs to finance over Eur 60bn capex over the next five years, out of annual operating cash flow of Eur 12bn. The company has reported negative free cash flow before dividends for 2014. All of that is before the significant impact of nuclear new build capex yet to come. The company is engaged in the very sizeable nuclear new build programme in the UK. · The French government has just approved a bill that foresees the reduction of nuclear in the country’s power mix from 75% to 50%. That will likely mean early closures of EDF plant, even if only over a longer term horizon. Engie (GSZFP) Engie is a global integrated energy company. It is dominant in the French gas downstream market. It has a globally diversified portfolio of power generation assets and is one of the largest global LNG operators. The company owns a large energy services business. Key bull points: · The recent share price under-performance reflects pressure on global LNG margins as a result of weak pricing. That is now a consensus view. Meanwhile, Engie benefits from an LNG/oil price hedge that shelters its margins to a degree. Despite short term commodity headwinds, the company’s vertical integration gives it a very good hedge and margin protection. Its positioning across the energy value chain is second to none. · The company has the best positioned power generation portfolio in Europe, if not globally. It is the largest global IPP with a well-diversified portfolio. Its generation business is amongst the most profitable in the European sector. It benefits from the growth trend in renewables through well diversified assets. It globally has over 10GW of power generation assets of various kinds under construction. It proactively captures the renewables trend with a deep and diversified pipeline across the mature technologies. · Engie is largely deprived of political risk, contrary to EDF. Its regulated gas tariffs are governed by a transparent formula and are not high up on the current political agenda. It comes from a background as a fully private company and has never seen the same kind of intervention as EDF has. Even though there is now a state participation of 30% that is unlikely to change. The state has been almost completely hands off. There was no suggestion of Engie having to participate in the Areva restructuring, for example. · Engie’s balance sheet is amongst the strongest in the sector. Net debt stands at 2.4x Ebitda. Capex of Eur 6bn is well covered by operational cash flow of Eur 8bn. Along with portfolio rotation, the company will have Eur 6-7bn of growth capex pa available. Given its very strong asset base, opportunities with synergies and/or acquisitions with a fast impact are plentiful. · Engie’s energy services business captures the most important new growth trends in the energy sector: Energy efficiency, new capacity build and optimisation. The company’s market leadership is getting built out further and gains further speed. As an additional benefit, the business is characterised by high and stable margins, which compensates for current volatility in the commodity exposed businesses. EDF trades on a P/E of 9.6x, Engie on 13.7x 2016E. Engie’s P/E is in line with the sector, whereas I believe it merits a premium. Further, the difference reflects stronger earnings growth: Engie’s EPS are likely to growth 6% pa compound 2015-17, whereas EDF’s are flat over the same period. EDF has historically traded on a discount to the sector. Engie trades on a 2016E EV/Ebitda of 16.2x, EDF on. Engie’s 5.7% yield is much better underpinned than that of EDF of 5.8%. Editor’s Note: This article covers one or more stocks trading at less than $1 per share and/or with less than a $100 million market cap. Please be aware of the risks associated with these stocks. Disclosure: The author has no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. (More…) The author wrote this article themselves, and it expresses their own opinions. The author is not receiving compensation for it (other than from Seeking Alpha). The author has no business relationship with any company whose stock is mentioned in this article.

A Word Of Caution About New Purchases In The Utility Sector

My first purchase of an electric utility stock was 400 shares of Duke Energy (NYSE: DUK ) around 1978. Somewhere along the way, I sold those shares for reasons that I no longer recall. I am not expressing a word of caution about the long term benefits that have flowed through buying and holding quality utility stocks and reinvesting the dividends. The ten year annualized total return numbers for a number of electric utility stocks are superior to the 7.83% annualized total return of the S&P 500 ETF (NYSEARCA: SPY ) through 2/13/15. Some examples include the ten year annualized performance numbers of American Electric Power (NYSE: AEP ), Dominion Resources (NYSE: D ), Edison International (NYSE: EIX ), NextEra Energy (NYSE: NEE ) and Wisconsin Energy (NYSE: WEC ) Ten Year Annualized Total Returns Through 2/13/15 Computed by Morningstar: AEP +8.09% D + 9.93% EIX + 8.72% NEE +12.15% WEC +13.03% Several other well known “utility” stocks have come close to matching the S & P 500 ten year annualized total return without the same decree of drama. AT&T +7.69% SCANA +6.86% Southern Co +6.75% Verizon Communications +7.54% The question that I am addressing now is whether new buys can be justified based on current yields and valuations. I started to look at this question a few days ago when making a comment here at SA about the impact of rising rates on REIT and utility stocks. Both of those industry sectors have attracted a large number of investors searching for yield. For many investors, REITs and utility stocks are viewed as “bond substitutes”. I started an analysis simply be looking at the current yield of the Utilities Select Sector SPDR ETF (NYSEARCA: XLU ), a low cost sector fund that owns primarily electric utility stocks. Yield: As of 2/12/15, the sponsor calculated the dividend yield at 3.28%. The attractiveness of that yield will depend on an investor’s view about the direction of interest rates. Notwithstanding the abundance of contradictory evidence, the Bond Ghouls have been predicting that a Japan Scenario will envelope the U.S. until the end of days, a slight exaggeration, based on the pricing of a thirty year treasury bond at a record low 2.25% yield recently. If an investor believes that deflation will alternate with periods of abnormally low inflation for the next 30 years, then the pricing of several long term sovereign bonds may at least appear to be rational rather than delusional. The current yields of a U.S. electric utility stock, with modest earnings and dividend growth, may even look good compared to those yields and the dire future predicted by those sovereign bond yields (U.S., Germany, Switzerland, Netherlands, Japan, etc.) The 30 year German government bond closed last Friday at a .92% yield. German Government Bonds – Bloomberg The average annual inflation rate in Germany between 1950-2015 was 2.46%. Just assume for a moment that the future will be similar to the past, with some hot and low inflation numbers and possibly a brief period of slight deflation. The .92% 30 year German government bond would produce a 1.54% negative annualized real rate of return before taxes. The average annual U.S. inflation rate between 1914-2015 was 3.32%. When the 30 year treasury hit a 2.25% earlier this year, and assuming the historical average annual rate of inflation, the total annualized return before taxes would be -1.07%. The first item for investors to consider is why are so many predicting the Japan Scenario given the recent U.S. economic numbers and the non-existence of a single annual deflation number since 1955 other than the understandable -.4% reported for 2009. Consumer Price Index, 1913- | Federal Reserve Bank of Minneapolis When looking a long term charts, it is hard to see the underlying support for what the Bond Ghouls are saying about the future. (click to enlarge) (click to enlarge) (click to enlarge) The DSR ratio highlights that U.S. households have more disposable income after debt service payments to pay down debt, to spend, or to save. I view this chart as bullish long term for stocks but not far bonds. I have been making the same point here at SA for over three years now without convincing a single bear of my point. An example of banging my head against the wall was a series of comments to this SA article published in February 2013: Sorry Bulls, But This Is Still A Secular Bear Market-Seeking Alpha It is interesting to go back and read some of those comments from other investors. Yes, I am referring to the bears here who were predicting a bear market starting in 2012 just before the S & P 500 took off on a 700 points move up, not down by the way. (click to enlarge) (click to enlarge) (click to enlarge) (click to enlarge) (click to enlarge) Links to some other relevant charts: Financial Stress-St. Louis Fed Household Financial Obligations as a percent of Disposable Personal Income-St. Louis Fed Mortgage Debt Service Payments as a Percent of Disposable Personal Income-St. Louis Fed Charge-Off Rate On All Loans, All Commercial Banks-St. Louis Fed Retail Sales: Total (Excluding Food Services)-St. Louis Fed E-Commerce Retail Sales-St. Louis Fed Light Weight Vehicle Sales: Autos & Light Trucks- St. Louis Fed Corporate Net Cash Flow with IVA -St. Louis Fed ISM Non-manufacturing-St. Louis Fed And what are the current economic statistics (not the ones generated through reality creations) that support the long term Japan Scenario prediction that underlies current intermediate and long term bond prices? I will just drag and drop here my recent discussions of this data. Is that dire long term U.S. inflation and growth forecasts embedded in those historically abnormal yields justified by the 5% real Gross Domestic Product growth in the 3rd quarter perhaps slowing to 3% in the 2014 4th quarter with personal consumption expenditures accelerating; the lowest readings on record in the debt service payments to disposable income ratio (DSR) ; the decline in the unemployment rate to 5.7% with 257,000 jobs added in January with a 12 cent rise in average hourly earnings and a 147,000 upward revision for the prior two months; a decline in the 4-week moving of initial unemployment claims to the historical lows over the past four decades; the long term forecasts of benign inflation; a temporary decline in inflation caused by a precipitous drop in a commodity’s price, the consistent and long term movement in the ISM PMI indexes in expansion territory; capacity utilization returning to its long term average where business investment has traditionally increased by 8% , or perhaps some other “negative” data set. That kind of data has to be negative rather than positive, right? Even the government’s annual inflation numbers for 2014 showed a + 3.4% increase in food prices; a 2.4% increase in medical service costs, a 2.9% increase in shelter expenses, and a 4.8% increase in medical commodities. The BLS called the rise in food prices “a substantial increase” over the 1.1% rate for 2013. While I am not predicting here a return to $80+ crude, the price may have already bottomed and the disinflationary impact created by the 50%+ decline is consequently a temporary abnormality that will self correct with supply and demand moving back into balance. Consumer Price Index Summary While it is too early to know whether intermediate and long term rates have started to turn back up, the recent movement is certainly cautionary and resembles the lift off in interest rates that started in May 2013, when the ten year was at a 1.68% yield, and culminated in a rate spike to 3.04% for that note by year end. 7 to 30 Year Treasury Yields 2/2/15 to 2/13/15 Daily Treasury Yield Curve Rates When looking at that table, it is important to keep in mind that a ten year treasury yield of 2.00% is abnormally low by historical standards since 1962: (click to enlarge) 10-Year Treasury Constant Maturity Rate – FRED – St. Louis Fed And this brings me to my word of caution about utility stocks. A 3% dividend yield is not too hot using history as a guideline. To be justified, the investor will have to buy into most of the Bond Ghouls Japan Scenario unfolding in the U.S. rather than a gradual return to something close to normal inflation and GDP growth. Valuation: For me, valuation is the kicker. What S & P sector currently has the highest P.E.G. ratio? Back in the late 1990s, I would have said technology stocks without looking to verify the answer. I said utility stocks now and I took the time to verify that response. An investor can download the current E.P.S. estimates for the S & P 500 and the various sectors from S & P in the XLS format. I can not link the document here, but anyone interested can find it using the exact google search phrase “XLS S & P Dow Jones Indices”. It should be the first result. As of 2/12/15, the estimated forward 5 year estimated P.E.G. for the utility sector is a stunningly high 3.65, and this sector has traditionally been one of the slowest growing sectors. Technology is at a 1.27 P.E.G. The P/E based on estimated 2015 earnings is 17.12. The data given by the sponsor of XLU immediately set off alarm bells when I looked at it recently. In addition to the vulnerability of stock prices due to a rising interest rate environment, the sponsor calculated the forward P/E at 17.14, which is normally a non-GAAP ex-items number, a 7.23 multiple to cash flow, and a projected 3 to 5 year estimated E.P.S. growth rate of only 4.86%, or a similar P.E.G. to one calculated by S & P and mentioned above. The Vanguard Utilities ETF (NYSEARCA: VPU ) has another set of data that is even more concerning than the XLU valuation information: As of 1/31/15, this fund owned 78 stocks, with a P/E of 20.8 times and a 2% growth rate. Portfolio & Management Taking into consideration the possible or even probable rise in rates, the low starting yields for utility stock purchases now, the high P/E and abnormally high P.E.G. ratio, I am just saying be careful out there. I will be discussing in my next blog a reduction in my position in the Duff & Phelps Global Utility Income Fund Inc. (NYSE: DPG ), a closed end fund that has performed well for me since my purchases. I may not start writing that blog until Monday after taking the time to write this one in my usual stream of consciousness writing mode. CEFConnect Page for DPG According to Morningstar, the Utilities Select Sector ETF ( XLU ) had a 2014 total return based on price of 28.73%, much better than SPY, and was up YTD 2.33% through 1/31/15. The tide has turned with the recent rise in rates since the end of last month. The total return for XLU is now at -4.34% YTD through 2/12/15. Just as a reminder, I only have cash accounts and consequently do not short stocks. I do not borrow money to buy anything. I have never bought an option or a futures contract. I am not paid anything to write these SA Instablogs or SA articles or any of my almost 2000 blogs written since early October 2008, mostly very long ones, published at Stocks, Bonds & Politics . I do not own any of those short ETFs. I am currently substantially underweighted in the Utility sector.