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Fund Managers Have Some Valid Reasons To Avoid Momentum

Momentum, relative, absolute or dual, is essentially a timing strategy that is used for the purpose of achieving better risk-adjusted returns in the longer-term as compared to passive allocation strategies or even buying and holding. Below is a backtest of a dual momentum strategy with two assets, S&P 500 Total Return and cash, and a 12-month timing period, since 1989. Click to enlarge It is clear that risk-adjusted returns of this dual momentum strategy are superior when compared to those of an equal weight portfolio (50% in S&P 500 Total Return and 50% in cash) or to those of a passive investment in S&P 500. Specifically, the annualized return of the dual momentum strategy (blue line) outperforms a passive investment in S&P 500 total return (yellow line) by 160 basis points and drawdown is lower by a factor of 3. The above results illustrate the potential of timing models, especially when combined with relative momentum. However, this is a trivial example and most investors prefer a certain degree of diversification. In addition, the improved risk-adjusted performance of the above trivial strategy can be attributed to trend-following, which can be achieved by a wide variety of simpler strategies, for example moving average crossovers. Below I list three reasons why investors neglect momentum: Reason #1: Momentum strategies require a transition from passive to active management This transition is not trivial and actually requires that a fund manager is also a trader. Going from passive allocation to timing models requires different systems and operating structure. In an era of constant bashing of active management, some fund managers decide that the transition is risky for their business. Reason #2: With momentum strategies there is possible loss of investment discipline Timing models require trading discipline. The most difficult task of trend-followers is adhering to strategy rules. This is in contrast to passive allocation schemes that offer inherent discipline because they only require rebalancing. Loss of discipline can cause friction in a fund management firm due to different opinions of managers about whether or not to adhere to strategy rules and signals. Those of us who have actually used timing strategies can understand the impact of loss of discipline and the friction in can create. In reality, using timing strategies without a mechanism to enforce discipline slowly leads to random decisions and losses. Most fund managers know the risks involved but researchers do not have actual experience with the dangers involved in transitioning from passive to active management. Managing the savings of people is a job that requires high level of professionalism and respect for the customer. Those who wonder why momentum is neglected should try to answer the following question: If you were given today $1B to manage, would you choose a passive allocation scheme or a timing method? Most fund managers choose the passive allocation scheme because they understand the risks of trading timing models. This decision is not because they do not understand momentum. Actually, momentum is a trivial timing strategy. Reason #3: Momentum suffers from data-snooping bias This is a very serious objection against using momentum and also other technical strategies despite the convincing backtests offered by some researchers even if they include robustness and out-of-sample tests. Note that if a strategy is optimized, robustness tests are unlikely to fail. Also, note that out-of-sample tests make sense only in the case of a single independent hypothesis. As soon as one mixes and matches assets to produce a desired result based on backtested performance on already used data, out-of-sample tests lose their significance. It is known that if one tries many strategies on historical data, a few of them may outperform in out-of-sample testing by luck alone. Let us look at some examples of dual momentum strategies below. The first strategy is for SPDR S&P 500 Trust ETF (NYSEARCA: SPY ) and the iShares 20+ Year Treasury Bond (NYSEARCA: TLT ) and with 12 months timing period. Below are the backtest results: Click to enlarge It may be seen that the dual momentum strategy (blue line) underperforms the equal weight portfolio in SPY and TLT. The annualized return of dual momentum is 300 basis points lower and maximum drawdown is higher by nearly 9%. Next, EEM is added in an effort to provide exposure to emerging markets. However, as soon that is done, data-snooping is introduced. Below are the results: Click to enlarge It may be seen that although the dual momentum strategy outperforms equal weight, there is a correction in equity (blue line) in 2015. The return for 2015 was -8.5%. However, this is not the main problem with this attempt to improve the asset mix in an effort to obtain superior performance. Actually, the outperformance was possible due to conditions in emerging markets (NYSEARCA: EEM ) that may never occur again, or better said, the risks of never occurring again are high. Specifically, in 2005 EEM was up more than 55% and in 2009 the return was close to 72%. However, last year emerging markets crashed. Therefore, a fund manager employing this strategy in 2015 paid the price of data-snooping bias. But why EEM and not QQQ? Below is the backtest for SPY, QQQ and TLT dual momentum with a 12-month timing period: Click to enlarge In this case, the equal weight portfolio generated 360 more basis points of annualized return with just 7% more drawdown and it outperformed dual momentum. One may find many backtests where dual momentum works well and many where it does not. This is actually the point, and the risk involved. If your research shows a specific asset mix where dual momentum worked well, I do not care about any out-of-sample and robustness tests unless you can prove that there was no data-snooping involved. Since providing such proof is highly unlikely, I can understand why most fund managers neglect momentum. Besides, momentum becomes a crowded trade when its signals align with strong uptrends and are influenced by passive investment decisions. In the era of Big Data and machine learning, it is difficult to know which strategy represents a unique, independent hypothesis, or it is the result of data-snooping and p-hacking. Thus, many fund managers hesitate in adopting popular strategies that are based on trivial rules and fully disclosed in books, articles and blogs. They may be wrong but I do not blame them for their decision in adhering to passive allocation. Momentum is part of technical analysis and many traders know that this type of analysis has contributed to a massive wealth-redistribution in recent history. Note: Charts created with Portfolio Visualizer. Original article

Allocation Strategy During The Corporate Debt Hangover

Are corporations in great shape? Three consecutive quarters of declines in earnings suggest that they are not. Worse yet, record high leverage coupled with close-to-record low interest coverage indicate stress within corporate balance sheets. Beginning with the “profit recession,” it has become fashionable to describe the deterioration as a function of the price collapse in oil and gas. However, that assessment fails the sniff test on three different levels. One, six of the ten S&P 500 economic segments share in the year-over-year earnings contraction, not the energy sector alone. Second, if one excludes energy as an outlier on the negative side, one would be obliged to throw away super-sized contributors like healthcare on the positive side of the ledger. In doing so, the profit picture still appears weak. A third reason that it is foolish to dismiss energy earnings? Analysts made the same mistakes prior to the economic downturns in 2001 and 2008. It was short-sighted to toss the technology sector in the dot-com collapse. It was irrational to exclude financials in the banking crisis. It follows that it would be just as insular to ignore the influential energy segment when evaluating corporate profitability today. Perhaps more troubling is the erroneous belief that corporations have improved their balance sheets since the Great Recession. In truth, U.S. companies have doubled their total debt levels since 2007, while simultaneously finding it more difficult to pay interest expenses on outstanding obligations. According to Investopedia , the interest coverage ratio determines the ease or difficulty by which a company can service its existing debt. The ratio is calculated by dividing a company’s earnings before interest and taxes (EBITA) by the company’s interest expenses for the same period. The higher the ratio, the less burdened by borrowing costs a company is; the lower the ratio, the more onerous the debt expense is for a company. Now take a look at the charts below. Total leverage by U.S. “investment grade” (IG) corporations has catapulted through the proverbial roof. Leverage does not matter as long as companies can service the debt, right? Unfortunately, investment grade interest coverage is back to levels not seen since 2009. If one shifts to corporations on the world stage, the picture becomes more nebulous. Consider the net debt-to-earnings (EBITA) at global companies. This measure looks at the number of years, theoretically speaking, that a company would require to pay obligations back. And right now, according to Standard & Poor’s, net debt-to-EBITA in 2015 at 3.0 was the highest since 2003. That’s not all. Analysts typically regard a ratio below three as “safe.” With the average global company straddling the fence between safe and not-so-safe, what does that tell investors about the financial health of the world’s corporations? Why should anyone focus on all the debt talk surrounding the world’s corporations? Don’t they always find a way to right their respective ships? Well, for one thing, if a company has money left over after it services its debt obligations, it cannot necessarily expand its business in productive ways, including research, development, human resources acquisition, marketing and so forth. We’ve already seen the most recent reading of the Institute For Supply Management (ISM) Non-Manufacturing Index hit its lowest level since March of 2014 (55.3). That’s not encouraging, even if it shows expansion in the services arena. In a similar vein, it is highly discouraging to witness carnage in the capital goods arena. It would seem that companies are unwilling and/or do not have the discretionary dollars to invest in tangible assets to produce goods or services such as office buildings, equipment and machinery. Maybe debt is taking a nasty toll after all. (See the chart below.) So how might one invest in an environment where corporate and government debts have skyrocketed, asset prices have hit extremes and the Federal Reserve is committed to raising borrowing costs? Former PIMCO “guru” Mohamed El-Arian has finally decided that 25%-30% in cash is the best way to survive what he anticipates will be better buying opportunities down the pathway. For my clients at Pacific Park Financial, Inc., we began making the tactical allocation shift in June of 2015 – seven months ago. We downshifted from 70% growth (e.g., large-cap, smaller-cap, foreign, etc.) to roughly 50% growth (high-quality, low volatility large-cap stocks). We moved from 30% income (e.g., short, long, investment grade, higher-yielding, etc.) to approximately 20%-25% investment grade income. With cash or cash equivalents approximating 25% – safer harbors such as the SPDR Nuveen Barclays Short-Term Municipal Bond ETF (NYSEARCA: SHM ) as well as money market vehicles – we reduced volatility while awaiting better buying opportunities. While I expect the corrective activity that began in May of 2015 to continue, my clients understand that I seek to reduce risk, not eliminate it. It follows that current stock exposure at 45%-50% does not represent a mindset of “shorting” or being out of equities completely. For the most part, we have been out of foreign positions and smaller U.S. companies for quite some time. Nevertheless, we maintain an allocation to equity ETFs via funds like the iShares MSCI USA Quality Factor ETF (NYSEARCA: QUAL ) and iShares USA Minimum Volatility ETF (NYSEARCA: USMV ). The bond story is remarkably similar. Rather than pursue cross-over corporates or high-yield or even long-term investment grade corporates, we have stayed near the middle of the curve with funds like: (1) the SPDR Nuveen Barclays Municipal Bond ETF (NYSEARCA: TFI ), (2) the Vanguard Total Bond Market ETF (NYSEARCA: BND ), (3) the iShares 7-10 Year Treasury Bond ETF ( IEF) and (4) the iShares 3-7 Year Treasury Bond ETF (NYSEARCA: IEI ). There are those who crave a bit more potential than cash or T-bills. For those folks, rather than “shorting,” we employ multi-asset stock hedging. We’ve picked up some of the assets in the FTSE Multi-Asset Stock Hedge Index , including the yen, gold, and zero-coupon treasuries. Make no mistake about it, however. The cash that had been raised in 2015 has multiple purposes. It provides a measure of comfort when stock volatility surpasses norms. In addition, cash offers one the ability to acquire “buy low” value propositions. Even now, there are folks with excess cash who might want to examine a dividend aristocrat like Aflac (NYSE: AFL ). With a trailing P/E of 10, a forward P/E of 9, a dividend yield of 2.9% and a price from mid-2014, you may decide the rewards are worthy of the risk. Disclosure: Gary Gordon, MS, CFP is the president of Pacific Park Financial, Inc., a Registered Investment Adviser with the SEC. Gary Gordon, Pacific Park Financial, Inc., and/or its clients may hold positions in the ETFs, mutual funds, and/or any investment asset mentioned above. The commentary does not constitute individualized investment advice. The opinions offered herein are not personalized recommendations to buy, sell or hold securities. At times, issuers of exchange-traded products compensate Pacific Park Financial, Inc. or its subsidiaries for advertising at the ETF Expert web site. ETF Expert content is created independently of any advertising relationships

Evaluating Enterprise Risk For Alternative Investments

Enterprise risk includes all of the factors that can affect an enterprise: market factors, reputation, regulation, compliance, operations, and legal risk are among the most prominent. Enterprise risk analysis , which uses stress tests and scenario assessments to estimate investment risks across asset classes, has become increasing popular with institutional investors, who understand the impact enterprise risk can have on their portfolios. But while enterprise risk analysis works well with traditional asset classes, using enterprise risk analysis on alternatives – such as hedge funds, private equity, and real estate – presents challenges. This, at least, is the view of BNY Mellon (NYSE: BK ) and affiliate HedgeMark, as articulated in their January 2016 white paper Considering the Alternatives: A Practical Look at Enterprise Risk Analysis and Alternative Investments . The paper explores the impact of incorporating alternative investments into enterprise risk analysis and looks at how different approaches to data management can impact the resulting conclusions. Evaluating Risk Across the Portfolio “With a sharper focus on risk by regulators and other stakeholders, many institutional investors seek a fuller picture of how risk operates across investments within an entire portfolio,” said Frances Barney, head of Consulting-Americas for Global Risk Solutions at BNY Mellon, in a recent announcement. “Data is getting more and more critical and investors need to be informed and comfortable with the assumptions of their risk assessment, otherwise, they can come out of it with a false sense of security about their portfolio.” The paper’s key findings and insights into best practices include: A “granular approach” to risk evaluation is preferable, with position-level information for all asset classes “the gold standard.” This kind of position-level transparency, liquidity, and control may be available in dedicated managed accounts and liquid alts, as well as traditional hedge funds. Information accuracy is obviously important, and that’s why the paper argues for single-vendor sourcing of investment data. Using a single vendor promises uniform data, whereas drawing data from multiple sources increases the likelihood of errors. Different approaches to data management can lead to different conclusions about risk. Having a different approach for each asset class can be problematic, which is why many firms are establishing a Chief Risk Officer position to evaluate risks across all asset classes. Consistency is especially important in light of the regulatory environment. Some regulators already require reports on stress testing and scenario analysis, through Form PF for U.S. investment advisers to hedge funds; and pursuant to Solvency II for insurance companies, and UCITS for European investment funds. “We’ve learned the most crucial component is the veracity of the underlying data, which becomes even more important and difficult to manage as more opaque assets are held in the portfolio,” said Ms. Barney. Jason Seagraves contributed to this article.