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Time To Throw Out The Rising-Rate Playbook?

Summary Relying on “tried-and-true” rising-rate playbook strategies in today’s markets may not be helpful because we’ve never been in this exact economic environment before. The “typical” conditions that have accompanied tightening cycles during the past 35 have mostly run their course in the present cycle, which is more than six years old. Unprecedented conditions call for active management because rules-based approaches that rely heavily on historical playbooks often break down at inflection points. Today’s markets are in uncharted economic territory, where ‘go-to’ strategies require prudent skepticism By Clint Harris, Senior Client Portfolio Manager We’ve seen daily references to what has worked – or hasn’t worked – when interest rates have risen in the past. Strategists, asset managers and pundits have dusted off numerous “tried-and-true” historical playbooks for investing in a rising-rate environment. But here’s the problem with applying those lessons to today’s markets: We’ve never been in this exact economic environment before, so relying too heavily on what’s worked in the past may not be particularly helpful. What makes today’s environment unique? In my view, the current torrent of rising-rate analyses should be taken with healthy dose of skepticism. Why? Here are three reasons. First, the “typical” conditions that have accompanied tightening cycles during the past 35 years include rebounding margins and improving credit conditions and sales growth. But these conditions have mostly run their course in the present bull market cycle, which began in March 2009. Today, margins are plateauing (Figure 1), credit conditions are worsening (Figure 2), and sales revisions are showing declines in growth. Figure 1 Source: FactSet Research Systems. Past performance is not a guarantee of future results. What happens when monetary policy tightens in the midst of these conditions? We don’t know because we’ve never seen it happen. It’s unusual for the Federal Reserve (Fed) to begin monetary tightening so late in the profit cycle. The Fed has rarely, if ever, started to raise rates when sales growth is disappointing, delinquency rates for commercial and industrial (C&I) loans are worsening (Figure 2), corporate margins are narrowing, and other major central banks are loosening monetary policy. Figure 2 Source: FactSet Research Systems. Past performance is not a guarantee of future results. Second, the federal funds target rate, currently between zero and 0.25%, 1 hasn’t seen these levels since the 1940s (Figure 3). The 10-year US Treasury bill, at 2.32% for the week ending Nov. 13, hasn’t been this low in 60 years. 1 We have simply not seen a tightening cycle from these levels in modern times. Figure 3 Source: US Treasury. Past performance is not a guarantee of future results. Third, many asset classifications in use today – for example, value stocks, growth stocks, core plus bond strategies, emerging market debt, master limited partnerships (MLP), floating rate securities and convertibles – either didn’t exist or didn’t have widely available proxies in the 1920s and 1930s. As a result, no one has been able to write a playbook that goes back far enough in history to be applicable to today’s environment. Even if we could, the economy is so different today that the interpretation may be misused. Where do we go from here? Unprecedented conditions call for active management. Why? Rules-based approaches that rely heavily on historical playbooks often break down at inflection points. This is particularly important today as I believe we face the most expected tightening cycle in history. Active management uses sound fundamental research to anticipate potential changes in conditions to position client assets accordingly. Our Invesco Dividend Value team has often remarked that results for our clients are made or broken at inflection points. It’s important to exercise a healthy degree of skepticism and a willingness to go against consensus when supported by bottom-up analysis. This is particularly important today as everyone seems to be using the same rising-rate playbook. Our team has successfully navigated numerous economic environments by maintaining a full market cycle perspective. We believe this experience becomes even more important as investors, who are less focused on the signs of a mature profit cycle, seek a revised rising-rate playbook for today’s environment. Learn more about Invesco Diversified Dividend Fund and Invesco Diversified Income Fund . Sources US Federal Reserve System, Nov. 16, 2015 Important information Profit margin measures the profitability of a company by dividing net income by revenues. A master limited partnership is a publicly traded limited partnership in which the limited partner provides capital and receives periodic income distributions from the MLP’s cash flow and the general partner manages the MLP’s affairs and receives compensation linked to its performance. Floating rates are interest rates that are allowed to move up and down with the rest of the market or with an index. The federal funds target rate is the interest rate at which banks and other depository institutions lend money to each other, usually on an overnight basis. An inflection point is an event that results in a significant positive or negative change in the progress of a company, industry, sector, economy or geopolitical situation. Credit conditions denote the availability of loans, or credit. About risk A value style of investing is subject to the risk that the valuations never improve or that the returns will trail other styles of investing or the overall stock markets. The Fund is subject to certain other risks. Please see the current prospectus for more information regarding the risks associated with an investment in the Fund. Before investing, carefully read the prospectus and/or summary prospectus and carefully consider the investment objectives, risks, charges and expenses. For this and more complete information about the products, visit invesco.com/fundprospectus for a prospectus/summary prospectus. The information provided is for educational purposes only and does not constitute a recommendation of the suitability of any investment strategy for a particular investor. Invesco does not provide tax advice. The tax information contained herein is general and is not exhaustive by nature. Federal and state tax laws are complex and constantly changing. Investors should always consult their own legal or tax professional for information concerning their individual situation. The opinions expressed are those of the authors, are based on current market conditions and are subject to change without notice. These opinions may differ from those of other Invesco investment professionals. NOT FDIC INSURED MAY LOSE VALUE NO BANK GUARANTEE All data provided by Invesco unless otherwise noted. Invesco Distributors, Inc. is the US distributor for Invesco Ltd.’s retail products and collective trust funds. Invesco Advisers, Inc. and other affiliated investment advisers mentioned provide investment advisory services and do not sell securities. Invesco Unit Investment Trusts are distributed by the sponsor, Invesco Capital Markets, Inc., and broker-dealers including Invesco Distributors, Inc. PowerShares® is a registered trademark of Invesco PowerShares Capital Management LLC (Invesco PowerShares). Each entity is an indirect, wholly owned subsidiary of Invesco Ltd. ©2015 Invesco Ltd. All rights reserved. Time to throw out the rising-rate playbook? by Invesco Blog

A (Partial) Solution For Narrative Risk: Probit Modeling

The search for objective analysis in the cause of making informed investment decisions is the Holy Grail of finance. Unfortunately, narrative risk continually threatens to derail us on our crucial quest for perspective. Everyone loves a good story, and it’s no different when it comes to finance and economics. The problem: there’s an excess of interesting narratives that too often are bereft of useful information. Genuine insight that’s earned by way of a clear-eyed review of the numbers, in other words, is the exception in a world that’s overflowing with story lines that appeal to emotion rather than intellect. Unfortunately, we’re bombarded with distraction. The self-proclaimed seer on TV who spins a good yarn about what’s really driving prices or the business cycle can draw a crowd by dispensing entertaining narratives about gloom and glory. Bigger is always better from a media vantage, even if the facts don’t easily fit the narrative. Meanwhile, a sober reading of the numbers is a yawn if you’re trying to maximize eyeballs pinned to the tube. Making reasonable decisions and producing compelling media content, in short, are often at cross purposes. What’s the solution? There are many paths to quantitative enlightenment, including the powerful analytics that arise from probit and logit regressions. There are no silver bullets here, but the ability to translate raw data into specific probability estimates for a particular condition offers a valuable resource for cutting through the noise. The flexibility and power that probit and logit models bring to the table suggest that running the numbers through these filters should be on everyone’s short list of analytical tools. As a simple review of how these models work, let’s run through the basics using R code as the quantitative lingua franca, although you could easily do the same in Python or even Excel. But first a warning: the illustration below is a toy example and not a very useful one as presented in terms of making real-world decisions. But the general outline is valid and so the process will offer a flavor of how to deploy this modeling framework. ( Here’s the R code to replicate the data discussed below.) Let’s say that you think that the VIX index, which tracks the implied volatility of the US stock market (S&P 500), offers useful information for monitoring and measuring equity risk. After eyeballing a chart of the two indexes (as shown below) you decide that a VIX reading above 20 is a warning sign for the market. But how threatening is a 20-plus reading? One way to quantify the danger is by analyzing the S&P 500 in context with the VIX by way of a probit model. The first step is creating a data set of binary signals that reflect your definition of “risk.” The sky’s the limit for customizing this definition, but in the interests of keeping this short review from becoming a consulting project let’s use one simple metric: rolling one-year return for the S&P 500. The research agenda is estimating the probability of a negative one-year return based on the current VIX reading. (Yes, this is a bit naïve not to mention superfluous, but it’s good enough to profile probit modeling.) With our research map in hand, it’s a simple matter of getting the data in shape. The first step is creating a set of binary signals to indicate the state of the market that we’re trying to model. Remember, a probit model is designed to estimate probabilities for one of two states, which is considerably easier and therefore more practical in the real world vs. trying to model a spectrum of conditions. In keeping with our simplistic example. any negative one-year return for the S&P is labeled as “1” and a positive return as “0”. The next step is instructing the probit model to estimate the probability that the S&P is in negative territory by way of analyzing the historical relationship between the VIX and the signal data as defined above. Right about now you’re probably complaining that we already know the state of S&P one-year return by looking at real-time market data without referring to the VIX. Agreed, and so creating a probit model to tell us what’s already obvious is a redundant exercise. True, at least in this case, but the point of all this is to outline a basic probit procedure. Keep in mind that a genuine effort in this corner would probably focus on modeling a state that’s unclear in real time, such as the start of a recession or some other aspect of market risk that’s not readily available. As for our toy example with the S&P, here’s the result of the probit model estimates for the probability that the S&P’s trailing return is below zero. Visual inspection suggests there’s some meat on this bone. The rising probability that eventually reached 100% in late-2008, for instance, tells us that there’s a relatively robust relationship between the S&P and the VIX. Well, of course there is! We already knew that. The probit model is simply quantifying the relationship per our specifications. The question is how or if such a model should be adjusted. Is modeling trailing 6-month return preferable to 1-year performance? Should we raise or lower the 20-plus VIX trigger? What about adding in additional variables – the 10-year Treasury yield, for instance. There’s a wide array of possibilities here, which is a benefit and a curse. A benefit because probit modeling (and its close cousin logit modeling) can be customized in an endless variety to extract estimates of a particular state from raw data. But that’s a curse if you’re unsure of how to proceed. In other words, doing preliminary research to map out a reasonable strategy is essential before you dive into the numbers. But with a bit of advance planning, deploying a probit model can offer deep insight into market and macro analysis. There are no guarantees, of course-probit models can lead us astray in some cases, particularly when we’re sloppy with assumptions about relevant variables. But compared with listening to someone’s interpretations of what the latest market moves suggest, probit modeling offers objective context without the baggage of behavioral biases. It’s not a complete solution to narrative risk, but it’s a good start.

As Producers Get Out, You Should Get In: Why I’m Long XLE

Summary WTI crude in the mid-30s is close to the cash operating cost of many high-cost oil producers. As oil trades in the mid-30s, production will be shut in and supply will fall, in theory creating a floor in the price of oil. Continued low oil prices will likely create an underinvestment in oil production, and could create risk to the upside in future oil prices. Investors should consider buying XLE, as it will likely be able to weather the storm, and avoid XOP, as it contains much smaller producers that may not survive. Investors should avoid USO as it is subject to the decay associated with negative roll yield in WTI futures contracts. On December 15th, West Texas Intermediate Crude traded through $35 a barrel. This is close to the variable operating cash cost of many high-cost producers operating out of North America. At these prices, producers potentially stop pumping crude from their wells because it is more expensive to pull it out of the ground than the oil is worth. North American rig counts have already fallen precipitously; at these levels, they are likely set to fall even more. As rig counts fall, supply lessens from this area, and investment in future productive capacity also likely falls. This may set the oil market up for much higher prices in the future, repeating past energy cycles. Investors should consider buying the Energy Select Sector SPDR ETF (NYSEARCA: XLE ), which contains the larger players in the energy sector, to capitalize on this potential for higher oil prices in the future. Investors should avoid buying the SPDR S&P Oil and Gas Exploration and Production ETF (NYSEARCA: XOP ); however, as many of these producers are smaller and may not be able to ride out the storm. Investors should also avoid the United States Oil ETF (NYSEARCA: USO ), as contango will eat away at profits over time. Cash Operating Costs of the Marginal Shale Producer Below is a chart of the estimated cash operating cost of oil production for oil producers globally. “Cash cost” is the variable operating cost of pulling oil out of the ground. These figures are roughly a year old, but are probably still relevant. Note that Canadian oil sands, U.K. producers, and U.S. producers are on the upper end, within a $25-40 barrel cash cost range. As oil prices dip into these levels, independent producers will begin to shut-in production as it stops making economic sense to continue producing. This, in theory, should create a floor on the price of oil, as the price-determining marginal supply from these producers diminishes. Note that as the price dips as low as $30, almost 30% of producers are operating at levels that don’t make sense to continue. (click to enlarge) Source: Morgan Stanley and Business Insider Falling North American Rig Count Rig counts have fallen dramatically since last year as oil has collapsed and oil producers have cut back CAPEX in the face of a deteriorating credit market in the oil and gas sector. New rigs that would be too expensive to operate at low oil prices are not coming online, and old rigs being phased out are not getting replaced. Per Baker Hughes, North American rig counts have collapsed from a high of 2,300 rigs to 883 today, or a decline of 61% in one year. North American Rig Count through Time (click to enlarge) Source: Baker Hughes and Bloomberg Given that many producers have cash costs of oil in the $30-40 range, rig count is likely to decline further with oil breaching $35 a barrel, in my opinion. As rig count falls, the industry as a whole sets itself up for stronger oil in the future. The effect is twofold; supply of oil falls initially, stabilizing prices, but then the ensuing underinvestment in oil infrastructure creates a situation where oil prices could increase dramatically as underinvested producers are less able to quickly increase production in response to higher oil. We could see a repeat of the underinvestment of the late 1990s that led to the boom in oil prices in the mid-2000s. Buy XLE, Avoid XOP and USO Investors should consider XLE, as it contains very large producers such as Exxon Mobil (NYSE: XOM ), Chevron (NYSE: CVX ), and Schlumberger (NYSE: SLB ) that have the ability to weather the storm of lower oil prices for a long time. Investors should avoid XOP, as it contains a higher concentration of smaller capitalization companies that may not be able to survive mid-30s oil for a long time. I could imagine a situation where oil remains in the mid-30s, and XOP continues to tank, as smaller producers come under increasing financial pressure. See below for the breakdown of top holdings of XLE and XOP; note that some of the largest concentrations in XOP are in stocks with market caps of less than 4BN: Source: Bloomberg Investors should also avoid USO, as it is long oil futures contracts, and is therefore subject to the negative roll yields associated with contango. Oil contracts trade for future delivery at specified points in time. Currently, the market is in contango, meaning that contracts further into the future are more expensive than contracts expiring closer to the present. Contango in WTI Crude (click to enlarge) Source: Bloomberg USO owns short-dated contracts, and as those contracts expire, it sells them and buys contracts further into the future. With today’s prices, for example, USO would sell the Jan. 16 expiries at 37.11 and buy the Feb. 16 expiries at 38.27, creating a 37.11/38.27-1= -3.03% yield in just one month. A rough annualization of that yield means that USO is currently losing 36.4% annually! It is better to own the producers themselves who sell their production forward in the futures market than to own a fund exposed to the cost of maintaining a long position in futures, as the price performance between XLE and USO over the past five years has shown. Shorting $1 of USO and buying $1 of XLE, price performance over past five years, excluding dividends: Source: Bloomberg Conclusion Oil in the mid-30s is approaching the cash operating costs of many North American oil producers. As oil falls, they will shut in production, theoretically creating a floor in the price of oil in this range. Underinvestment in oil production in the future due to low oil prices today may also one day contribute to strong future oil prices. Investors looking to take advantage of this potential floor should look to buy XLE, as it contains some of the largest oil-producing companies in the world, and should be able to weather the supply glut in oil, and avoid XOP, as it has smaller producers that may not be able to survive lower oil. Investors should avoid USO as it is subject to negative roll yields associated with contango in WTI futures markets.