Tag Archives: models

I Know It Was You, Fredo

If you want to read more about the Epsilon Theory perspective on polarized politics and the use of game theory to understand this dynamic, read “ Inherent Vice ”, “ 1914 Is the New Black ”, and “ The New TVA ”. Hollow Markets Whatever shocks emanate from polarized politics, their market impact today is significantly greater than even 10 years ago. That’s because we have evolved a profoundly non-robust liquidity provision system, where trading volumes look fine on the surface and appear to function perfectly well in ordinary times, but collapse utterly under duress. Even in the ordinary times, healthy trading volumes are more appearance than reality, as once you strip out all of the faux trades (HFT machines trading with other HFT machines for rebates, ETF arbitrage, etc.) and positioning trades (algo-driven rebalancing of systematic strategies and portfolio overlays), there’s precious little investment happening today. Here’s how I think we got into this difficult state of affairs. First, Dodd-Frank regulation makes it prohibitively expensive for bulge bracket bank trading desks to maintain a trading “inventory” of stocks and bonds and directional exposures of any sort for any length of time. Just as Amazon measures itself on the basis of how little inventory it has to maintain for how little a span of time, so do modern trading desks. There is soooo little risk-taking or prop desk trading at the big banks these days, which of course was an explicit goal of Dodd-Frank, but the unintended consequence is that a major trading counterparty and liquidity provider when markets get squirrelly has been taken out into the street and shot. Second, the deregulation and privatization of market exchanges, combined with modern networking technologies, has created an opportunity for technology companies to provide trading liquidity on a purely voluntary basis. To be clear, I’m not suggesting that liquidity was provided on an involuntary basis in the past or that the old-fashioned humans manning the old-fashioned order book at the old-fashioned exchanges were motivated by anything other than greed. As Don Barzini would say, “after all, we are not Communists”. But there is a massive and systemically vital difference between the business model and liquidity provision regime (to use a good political science word) of humans operating within a narrowly defined, publicly repeatable game with forced participation and of machines operating within a broadly defined, privately unrepeatable game with unforced participation. Whatever the root causes, modern market liquidity (like beauty) is only skin deep. And because liquidity is only skin deep, whenever a policy shock hits (say, the Swiss National Bank unpegs the Swiss franc from the euro) or whenever there’s a technology “glitch” (say, when a new Sungard program misfires and the VIX can’t be priced for 10 minutes) everything falls apart, particularly the models that we commonly use to calculate portfolio risk. For example, here’s a compilation of recent impossible market events across different asset classes and geographies (hat tip to the Barclays derivatives team) … impossible in the sense that, per the Central Tendency on which standard deviation risk modeling is based, these events shouldn’t occur together over a million years of market activity, much less the past 4 years. Source: Barclays, November 2015. So just to recap … these market dislocations DID occur, and yet we continue to use the risk models that say these dislocations cannot possibly occur. Huh? And before you say, “well, I’m a long term investor, not a trader, so these temporary market liquidity failures don’t really affect me”, ask yourself this: do you use a trader’s tools, like stop-loss orders? do you use a trader’s securities, like ETFs? If you answered yes to either question, then you can call yourself a long term investor all you like, but you’ve got more than a little trader in you. And a trader who doesn’t pay attention to the modern realities of market structure and liquidity provision is not long for this world. If you want to read more about the Epsilon Theory perspective on hollow markets and the use of game theory to understand this dynamic, read “ Season of the Glitch ”, “ Ghost in the Machine ”, and “ Hollow Men, Hollow Markets, Hollow World ”. Adaptive Investing and Aware Investing Okay, now for the big finish. What does one DO about this? How does one invest in a world of bimodal uncertainty and a market of skin-deep liquidity? Both of these investment goblins – Political Polarization and the Hollow Market – are so thoroughly problematic because our perceptions of both long-term investment outcomes and short-term trading outcomes are so thoroughly infected by The Central Tendency and a quasi-religious faith in econometric modeling. But while their problematic root cause may be the same, their Epsilon Theory solutions are different. I call the former Adaptive Investing, and I call the latter Aware Investing. Adaptive Investing focuses on portfolio construction and the failure of The Central Tendency to predict long(ish)-term investment returns. Aware Investing focuses on portfolio trading and the failure of The Central Tendency to predict short(ish)-term investment returns. Each is a crucial concept. Each deserves its own book, much less its own Epsilon Theory note. But this note is going to focus on Adaptive Investing. Adaptive Investing tries to construct a portfolio that does as well when The Central Tendency fails as when it succeeds. Adaptive Investing expects historical correlations to shift dramatically as a matter of course, usually in a market-jarring way. But this is NOT a tail-risk portfolio or a sky-is-falling perspective. I really, really, really don’t believe in either. What it IS – and the stronger your internal Fredo the harder this concept will be to wrap your head around – is a profoundly agnostic investing approach that treats probabilities and models and predictions as secondary considerations. I’ll use two words to describe the Adaptive Investing perspective, one that’s a technical term and one that’s an analogy. The technical term is “convexity”. The analogy is “barbell”. In truth, both are metaphors. Both are Narratives. As such, they are applicable across almost every dimension of investing or portfolio allocation, and at almost every scale. Everyone knows what a barbell is. Convexity, on the other hand, is a daunting term. Let’s un-daunt it. The basic idea of convexity is that rather than have Portfolio A, where your returns go up and down with a market or a benchmark’s returns in a linear manner, you’d rather have Portfolio B, where there’s a pleasant upward curve to your returns if the market or benchmark does really well or really poorly. The convex Portfolio B performs pretty much the same as the linear Portfolio A during “meh” markets (maybe a tiny bit worse depending on how you’re funding the convexity benefits), but outperforms when markets are surprisingly good or surprisingly bad. A convex portfolio is essentially long some sort of optionality, such that a market surprising event pays off unusually well, which is why convexity is typically injected into a portfolio through the use of out-of-the-money options and other derivative securities. Another way of saying that you’re long optionality is to say that you’re long gamma. If that term is unfamiliar, check out the Epsilon Theory note “ Invisible Threads ”. All other things being equal, few people wouldn’t prefer Portfolio B to Portfolio A, particularly if you thought that markets are likely to be surprisingly good or surprisingly bad in the near future. But of course, all other things are never equal, and there are (at least) three big caveats you need to be aware of before you belly up to the portfolio management bar and order a big cool glass of convexity. Caveat 1: A convex portfolio based on optionality must be an actively managed portfolio, not a buy-and-hold portfolio. There’s no such thing as a permanent option … they all have a time limit, and the longer the time limit the more expensive the option. The clock works in your favor with a buy-and-hold portfolio (or it should), but the clock always works against you with a convex portfolio constructed by purchasing options. That means it needs to be actively traded, both in rolling forward the option if you get the timing wrong, as well as in exercising the option if you get the timing right. Doing this effectively over a long period of time is exactly as impossible difficult and expensive as it sounds. Caveat 2: A convex portfolio fights the Fed, at least on the left-hand part of the curve where you’re making money (or losing less money) as the market gets scorched. Yes, there are going to be more and more political shocks hitting markets over the next few years, and yes, those shocks are going to be exacerbated by the hollow market and its structurally non-robust liquidity provision. But in reaction to each of these market-wrenching policy and liquidity shocks, you can bet your bottom dollar that every central bank in the world will stop at nothing to support asset price levels and reduce market volatility. Make no mistake – if you’re long down-side protection optionality in your portfolio, you’re also long volatility. That puts you on the other side of the trade from the Fed and the ECB and the PBOC and every other central bank, and that’s not a particularly comfortable place to be. Certainly it’s not a comfortable (or profitable) place to be without a keen sense of timing, which is why, again, a convex portfolio expressed through options and derivatives needs to be actively managed and can’t be a passive buy-and-hold strategy. Caveat 3: Top-down portfolio risk adjustments like convexity injection through index options or risk premia derivatives are *always* going to disappoint bottom-up stock-picking investors. I’ve written a lot about this phenomenon, from one of the first Epsilon Theory notes, “ The Tao of Portfolio Management ”, to the more recent “ Season of the Glitch ”, so I won’t repeat all that here. The basic idea is that it’s a classic logical fallacy to infer characteristics of the whole (in this case the portfolio) from characteristics of the component pieces (in this case the individual securities selected via a bottom-up process), and vice versa. What that means in more or less plain English is that risk-managing individual positions in an effort to achieve a risk-managed overall portfolio is inherently an exercise in frustration and almost always ends in unanticipated underperformance for stock pickers. Okay, Ben, those are three big problems with implementing convexity in a portfolio. I thought you said this was a good thing. You’ll notice that each of these three caveats pertain most directly to the largest population of investors in the world – non-institutional investors who create an equity-heavy buy-and-hold portfolio by applying a bottom-up, fundamental, stock-picking perspective. The caveats don’t apply nearly so much to institutional allocators who apply a systematic, top-down perspective to a portfolio that’s typically too large to engage in anything so time-consuming as direct stock-picking. They have no problem employing a staff to manage these portfolio overlays (or hiring external managers who do), and they’re not terrified by the mere notion of negative carry, derivatives, and leverage. These institutional allocators may not be large in numbers, but they are enormous in terms of AUM. I spend a lot of time meeting with these allocators, and I can tell you this – implementing convexity into a portfolio in one way or another is the single most common topic of conversation I’ve had over the past year. Every single one of these allocators is thinking in terms of portfolio convexity, even if most are still in the exploration phase, and you’re going to be hearing more and more about this concept in the coming months. So that’s all well and good for the CIO of a forward thinking multi-billion dollar pension fund, but what if it’s a non-starter to have a conversation about the pros and cons of a long gamma portfolio overlay with your client or your investment committee? What if you’re a stock picker at heart and you’d have to change your investment stripes (something no one should ever do!) and reconceive your entire portfolio to adopt a top-down convexity approach using derivatives and risk premia and the like? This is where the barbell comes in. The basic concepts of Adaptive Investing can be described as placing modest portfolio “weights” or exposures on either side of an investment dimension. This is in sharp contrast to what Johnny Ola has convinced most of us to do, which is to place lots and lots of portfolio weight right in the middle of the bar, with normally distributed tails on either end of the massive weight in the center (i.e., a whopping 5% allocation to “alternatives”). What are these investment dimensions? They are the Big Questions of investing in a world of massive debt maintenace (and are actually very similar to the Big Questions of the 1930s), questions like … will central banks succeed in preventing a global deflationary equilibrium? … is there still a viable growth story in China and in Emerging Markets more broadly, or was it all just a mirage built on post-war US monetary policy? … is there a self-sustaining economic recovery in the US? Here’s an example of what I’m talking about, a barbell portfolio around the Biggest of the Big Questions in the Golden Age of the Central Banker: will extraordinarily accommodative monetary policy everywhere in the world spur inflationary expectations and growth-supporting economic behaviors? Like all barbell dimensions, there’s really no middle ground on this. In 2016, either the market will be surprised by resurgent global growth / inflation, or the market will be surprised by anemic growth / deflation despite extraordinary monetary policy accommodation. I want to “be there” in my portfolio with modest exposures positioned to succeed in each potential outcome, as opposed to having a big exposure somewhere in the middle that I have to drag in one direction or another when I end up being “surprised” just like the rest of the market. Specifically, what might those positions look like? Everyone will have a different answer, but here’s mine: • If deflation and low global growth carry the day, then I want to be in yield-oriented securities where the cash flows are tied to real economic activity in geographies with real growth prospects, and where company management is really distributing those cash flows to shareholders directly. • If inflation and resurgent growth carry the day, then I want to be in growth-oriented securities linked to commodities. • And yes, there are companies that can thrive in both environments. Now of course you’ll get push-back to the notion of a barbell portfolio from your client or investment committee (maybe the investment committee inside your own head), most likely in the form of some variation on these three natural questions: Q: Wouldn’t you be be better off predicting the winning side of any of these Big Questions and putting all your weight there? A: Yes, if I had a valid econometric model that could predict whether central banks will fail or succeed at spurring inflationary expectations in the hearts and minds of global investors, then I would definitely put all my portfolio weight on that answer. But I don’t have that model, and neither do you, and neither does the Fed or anyone else. So let’s not pretend that we do. Q: But if one side of your portfolio barbell ends up being right, that must mean that the other side is wrong. Wouldn’t we be just as well off putting all the weight somewhere in the middle like we usually do? A: No, that’s not how these politically-polarized investment dimensions play out, with one side clearly winning and one side clearly losing. The underlying dynamics of the Big Questions in investing today are governed by the multi-year spiraling back-and-forth of multiple equilibria games like Chicken, not The Central Tendency (read “ Inherent Vice ” for some examples). Not only is it far more capital efficient to use a barbell approach, but both sides will do relatively better than the middle. That is, in fact, the entire point of using an allocation approach that creates optionality and effective convexity in a portfolio without forcing the top-down imposition of option and derivative overlays. Q: But how do we know that you’ve identified the right positions to take on either side of these Big Questions? A: Well, that’s what you hire me for: to identify the right investments to execute our portfolio strategy effectively. But if we’re not comfortable with selecting specific assets and companies, then we might consider a trend-following strategy. Trend-following is profoundly agnostic. Unlike almost any other strategy you can imagine, trend-following doesn’t embody an opinion on whether something is cheap or expensive, overlooked or underappreciated, poised to grow or doomed to failure. All it knows is whether something is working or not, and it is as happy to be short something as it is to be long something, maybe that same thing under different circumstances. As such, a pure trend-following strategy will automatically move on its own accord from weighting one end of a barbell to the other, spending as little time as possible in the middle, depending on which side is working better. That is an incredibly powerful tool for this investment perspective. A barbell portfolio captures the essence or underlying meaning of portfolio convexity without requiring top-down portfolio overlays that are either impractical or impossible for many investors. The investments described here have a positive carry, meaning that the clock works in your favor, meaning that – unlike convex strategies that are actively trading options and volatility – these strategies fit well in a buy-and-hold, non-Fed fighting, stock-picking portfolio. I think it’s a novel way of rethinking the powerful notions of convexity and uncertainty so that they fit the real world of most investors, and whether these ideas are implemented or not I’m certain that it’s a healthy exercise for all of us to question the conceptual dominance of The Central Tendency. You know, Michael Corleone has a great line after he wised up to Fredo’s betrayal and the true designs of Johnny Ola and Hyman Roth: “I don’t feel I have to wipe everybody out … just my enemies.” It’s the same with our portfolios. We don’t have to completely reinvent our investment process to incorporate the valuable notion of convexity into our portfolios. We don’t have to sell out of everything and start fresh in order to adopt an Adaptive Investing perspective. Our investment enemies live inside our own heads. They are the ideas and concepts that we have allowed to hold too great a sway over our internal Fredo, and they can be put in their proper place with a fresh perspective and a questioning mind. Econometric modeling and The Central Tendency don’t need to be eliminated; they need to be demoted from a position of unwarranted trust to a position of respectful but arms-length business relationship. After all, let’s remember the secret of Hyman Roth’s success: he always made money for his partners. I’m happy to be partners with modeling because I think it’s a concept that can make me a lot of money. But I’m never going to trust my portfolio to it.

Portfolio Construction Techniques: A Brief Review

Summary The mean-variance optimization suggested by Henry Markowitz represents a path-breaking work, the beginning of the so-called Modern Portfolio Theory. This theory has been criticized by some researchers for issues linked to parameter uncertainty. Two main approaches to the problem may be identified: a non-Bayesian and a Bayesian approach. Smart Beta strategies are virtually placed between pure alpha strategies and beta strategies and emphasize capturing investment factors in a transparent way. The article does not determine which strategy is the best, since I believe that the success of an investment technique cannot be determined a priori. Introduction How to allocate capital across different asset classes is a key decision that all investors are required to make. It is widely accepted that holding one or few assets is not advisable, as the proverb “Don’t put all your eggs in one basket” suggests. Hence, practitioners recommend their clients to build portfolios of assets in order to benefit from the effects of diversification. An investor’s portfolio is defined as his/her collection of investment assets. Generally, investors make two types of decisions in constructing portfolios. The first one is called asset allocation, namely the choice among different asset classes. The second one is defined security selection, namely the choice of which particular securities to hold within each asset class. Moreover, portfolio construction could follow two kinds of approaches, namely a top-down or a bottom-up approach. The former consists in facing the asset allocation and security selection choices exactly in this order. The latter inverts the flow of actions, starting from security selection. No matter the kind of approach, investors do need a precise rule to follow when building a portfolio. In fact, the choice of asset classes and/or of securities has to be done rationally. The range of existing strategies is considerably wide. Indeed, one may allocate his/her own capital by splitting it equally among assets, optimizing several functions and/or applying some constraints. Every day in the asset management industry, there are plenty of strategies that are proposed to investors all over the world. The aim of this article is to provide the reader with a comprehensive summary of those. Static and Dynamic Optimization Techniques To begin with, it is worth distinguishing the existing portfolio optimization techniques by the nature of their optimization process. In particular, static and dynamic processes are considered. In the former case, the structure of a portfolio is chosen once for all at the beginning of the period. In the latter case, the structure of the portfolio is continuously adjusted (for a detailed survey on this literature, see Mossin (1968), Samuelson (1969), Merton (1969, 1971), Campbell et al (2003), Campbell & Viceira (2002). Maillard (2011) reports that for highly risk-averse investors, the difference between the two is moderate, whereas it is larger for investors who are less risk averse. Markowitz Mean-Variance Optimization Within the static models, it is common knowledge that the mean-variance optimization suggested by Henry Markowitz represents a path-breaking work, the beginning of so-called Modern Portfolio Theory (MPT). In fact, Markowitz ( 1952 , 1959 ) presents a revolutionary framework based on the mean and variance of a portfolio of “N” assets. In particular, he claims that if investors care only about mean and variance, they would hold the same portfolio of risky assets, combined with cash holdings, whose proportion depends on their risk aversion. Despite of its wide success, this theory has been criticized by some researchers for issues linked to parameter uncertainty. In fact, the true model parameters are unknown and have to be estimated from the data, resulting in several estimation error problems. The subsequent literature has focused on improving the mean-variance framework in several ways. However, two main approaches to the problem may be identified, namely a non-Bayesian and a Bayesian approach. Two Approaches As far as the former is concerned, it is worth reporting several studies. For instance, Goldfarb & Iyengar (2003) and Garlappi et al. (2007) provide robust formulations to contrast the sensitivity of the optimal portfolio to statistical and modelling errors in the estimates of the relevant parameters. In addition, Lee (1977) and Kraus & Litzenberger (1976) present alternative portfolio theories that include more moments such as skewness; Fama (1965) and Elton & Gruber (1974) are more accurate in describing the distribution of return, while Best & Grauer (1992), Chan et al. (1999) and Ledoit & Wolf (2004a, 2004b) focus on methods that aim to reduce the estimation error of the covariance matrix. Other approaches involve the application of some constraints. MacKinlay & Pastor (2000) impose constraints on moments of assets returns, Jagannathan & Ma (2003) adopt short-sale constraints, Chekhlov et al (2000) drawdown constraints, Jorion (2002) tracking-error constraints, while Chopra (1993) and Frost & Savarino (1988) propose constrained portfolio weights. On the other hand, the Bayesian approach plays a prominent role in the literature. It is based on Stein (1955) , who proved the inadmissibility of the sample mean as an estimator for multivariate portfolio problems. In fact, he advises to apply the Bayesian shrinkage estimator that minimizes the errors in the return expectations, rather than trying to minimize the errors in each asset class return expectation separately. In following studies, this approach has been implemented in multiple ways. Barry (1974) and Bawa et al (1979) use either a non-informative diffuse prior or a predictive distribution obtained by integrating over the unknown parameter. Then, Jobson & Korkie (1980), Jorion (1985, 1986) and Frost & Savarino (1986) use empirical Bayes estimators, which shrink estimated returns closer to a common value and move the portfolio weights closer to the global minimum-variance portfolio. Finally, Pastor (2000), and Pastor & Stambaugh (2000) use the equilibrium implications of an asset-pricing model to establish a prior. Simpler Models To attempt portfolio construction throughout optimization is not the only alternative, though. In fact, alongside the wide range of portfolio optimization techniques, it is also worth considering other rules that require no estimation of parameters and no optimization at all. DeMiguel at al (2005) define them as ” simple asset-allocation rules “. For instance, one could just allocate all the wealth in a single asset, i.e., the market portfolio . Alternatively, investors may adopt the 1/N rule, dividing their wealth according to an equal-weighting scheme. At this point, the reader may wonder why one should consider this kind of rules. In fact, techniques that require no optimization should not be optimal according to any measure. However, as far as the naïve 1/N is concerned, some researchers have reported some interesting results. For instance, Benartzi & Thaler (2001) and Liang & Weisbenner (2002) show that more than a third of direct contribution plan participants allocate their assets equally among investment options, obtaining good returns. Moreover, Huberman & Jiang (2006) find similar results. Similarly, DeMiguel et al (2009) evaluate 14 models across seven empirical datasets, finding that none is consistently better than the 1/N rule in terms of Sharpe ratio, certainty-equivalent return or turnover. However, Tu & Zhou (2011) challenge DeMiguel et al. (2009) combining sophisticated optimization approaches with the naïve 1/N technique. Their findings confirm that the combined rules have a significant impact in improving the sophisticated strategies and in outperforming the simple 1/N rule. Moreover, other naïve rules are reported by Chow et al. (2013), such as the 1/σ and the 1/β, included in the so-called low-volatility investing methods. In particular, they report that low-volatility investing provides higher returns at lower risk than traditional cap-weighted indexing, at the cost of underperformance in upward-trending environments. Smart Beta Strategies Finally, it is worth mentioning a special group of strategies that are extremely popular among asset management firms, known as Smart Beta strategies. Smart Beta strategies are virtually placed between pure alpha strategies and beta strategies, and emphasise capturing investment factors in a transparent way, such as value, size, quality and momentum. Examples of these strategies are risk parity, minimum volatility, maximum diversification and many others. Apart from the wide range of these kinds of techniques, it is critical to highlight why they are so diffuse among practitioners. Their enormous success is due to several interesting advantages, including the flexibility to access tailored market exposures, improved control of portfolio exposures and the potential to achieve improved return/risk trade-offs. Final Remarks This article aims to be a summary of the most notorious techniques considered in the existing literature, but the list is far from being complete. Moreover, the article does not analyze which strategy is the best, since I believe that the success of an investment technique depends on several factors, including the time frame considered, the kind of assets, the geography of the examined portfolio, the client’s preferences, and it surely must rely on a quantitative application using real or simulated data.

Portland General: Utility With Some Promise

Summary Short-term, headwinds exist related to heavy capital expenditures and poor weather forecasts. Long-term, spending should be down and income up, freeing up cash flow for shareholder returns. Two natural gas-fired plant openings, one in 2016 and one in 2020, will be key to company success. Portland General Electric Company (NYSE: POR ) is an electric utility that operates wholly within the state of Oregon, providing power to nearly 50% of Oregonians with over 3,400MW of available energy generation. Primarily serving residential customers, the company’s bottom line has been bolstered by domestic migration to the Northwest. From 2010-2014, the Portland metropolitan area added over one hundred thousand new residents – an annual growth rate of 5.2%. This strong local population growth has helped bolster earnings results and shareholder returns, with investors reaping 100% in total return over the past five years, roughly double the return of utilities indexes. Does Portland General have more room to run or has the utility run its course? Future Is Natural Gas, Profit Is With Hydro * Portland General September 2015 Investor Presentation Portland General has a diverse portfolio of power generation. Including purchased power, 36% of power was created from renewable sources and an additional 25% generated from cleaner-burning natural gas. This is going to change drastically over the next few years, however. Given Oregon’s progressive nature, it wasn’t a surprise to see Oregon residents campaign for clean power. Management quickly bowed to customer and political pressure, leading to plans for the elimination of all coal-fired generation in Oregon. Under the Boardman 2020 plan, Portland General will close its 518MW Boardman coal asset by 2020, instead building a natural gas facility on the site. This will be a costly project, but doing so will save the company $470M in required upgrades to meet emissions guidelines had the plant remained open until 2040 as previously guided. The risk here is that the new plant is delayed and is not completed by the time Boardman is scheduled to be mothballed. Portland General relies heavily on the Boardman plant to produce electricity as coal-fired generation is in many cases the cheapest and most reliable asset the company has. Coal represents 16.5% of available resource capacity but generated 28% of the load in 2015 and is run at capacity nearly constantly. The company’s peak power load in 2014 was 3866MW which was already above currently available company-owned power generation and the shortfall from the Boardman plant closure could force Portland General to increase purchased power during peak times. While these costs will inevitably be passed along to the consumer because of Portland General’s clauses with the Public Utility Commission of Oregon, higher prices could still cause a slack in energy demand and bad press is never good for the bottom line. The company’s Carty Generating Station, slated to be completed in 2017, will help cover future shortfalls built is imperative for investors to track how the new Boardman facility’s construction is proceeding over the coming years. This risk is noted in the company’s 10-K: “Beyond 2018, PGE may need additional resources in order to meet the 2020 and 2025 RPS requirements and to replace energy from Boardman, which is scheduled to cease coal-fired operations in 2020. Additional post-2018 actions may also be needed to offset expiring power purchase agreements and to back-up variable energy resources, such as wind generation facilities. These actions are expected to be identified in a future IRP. PGE expects to file its next IRP with the OPUC in 2016.” – Portland General, 2014 Form 10-K From a profitability standpoint, the key to the company’s energy costs however is hydroelectricity. Hydroelectric generation can be the lowest cost source of generation for Portland General if conditions are right. The state of the Deschutes and Clackamas Rivers (tributaries of the Columbia River) is key. Both of these rivers’ headwaters are fed by the Cascades, a mountain range spanning from Canada to Northern California. In general, the greater the snowfall, the better the power generation is for hydroelectric when the spring thaw comes. Unfortunately for Portland General shareholders and highlighted in a recent prior SeekingAlpha article by Tristan Brown , weather models show lower than average snowfall likely for Oregon, along with a more mild winter in regards to temperature. This presents a double whammy for Portland General in the form of higher energy costs and lower revenue in the winter months during which customers typically draw around 10-15% more electricity than in the summer months. Past Operating Results (click to enlarge) Operating results have been steady and rather uneventful over the past five years (my own estimates used for the back half of 2015). Of note however is depreciation/amortization costs have been increasing dramatically due to the large capital investments the company has been making over the past five years, developing relatively more expensive wind/solar farms and the costs associated with the Carty Generating Station. Overall, this is steady-as-she-goes results that utility investors like to see. (click to enlarge) Frequent readers of my utilities research know that I look for solid coverage of capital expenditures and dividends from operating cash flow for mature utilities. Starting in 2013, Portland General reversed course and begun stepping up the leverage as capital expenditures rose for the natural gas plants at the Carty Generation Station and the old Boardman location. To fund this, Portland General issued $865M in long-term debt in 2013/2014 and also issued $67M worth of common stock in 2013 to cover the cash flow gaps. While this picture looks currently worrisome, it should moderate over time. Capital expenditures are expected to fall from the $600-650M range in 2015 to $289M in 2019, back to levels we saw in 2011/2012 when cash flow was positive. Unfortunately, Portland General won’t see much recovery in the form of increased rates because of offsetting factors, based on the overall breakdown of the 2016 rate case filing: (click to enlarge) Conclusion Portland General saw a little bit more renewed interest after the 7% dividend increase in 2015, well in excess of 2% annual growth from 2009-2014. In regards to operating income, however, 2016 looks unclear given the poor weather outlook. Earnings per share are likely to be flat to down in 2015/2016, so I would not expect a repeat of that hefty 2014 dividend increase. Before entering a position, I would like to see the valuation come down along with more visibility on completion of the two big natural gas facilities (early 2016 should give excellent insight into schedule on Carty Generation Station). Overall, however, shares are quite fairly valued given the long-term prospects of the region. Being long won’t hurt you.