Tag Archives: economy

How To Bake A Highly Deficient Cake

What happens when you leave out a key ingredient in the recipe for baking a cake? We won’t keep you in suspense. What you get is a highly deficient cake, but how it is highly deficient can tell you quite a lot about what the omitted ingredient contributes to a competently executed cake! At Bristol Science Centre, Nerys and David illustrate what we can learn by baking four different cakes – one batch with all the ingredients the basic recipe calls for, then other batches where either the margarine, eggs or baking powder has been excluded from the recipe. The following video illustrates how the resulting cakes baked with a single missing ingredient differ from a proper cake baked with all the ingredients. The same principle applies to data analysis. For instance, if a set of economic data omits the contributions of one particular sector of the economy, and that sector turns out to contribute a large share to the performance of the overall economy, the analysis produced using such data that excludes the omitted sector’s contribution will be highly deficient, because the data itself is not adequately representative of the economy being analyzed. Much like what happens when you bake a cake without one ingredient and compare it with a cake baked with all of them, the deficiency becomes very evident when you compare the results of the deficient analysis with the results of analysis performed with data that does not omit the missing sector’s contributions. If a professional baker omitted an ingredient in a cake recipe, then their competence would certainly be at issue. If they weren’t aware that the ingredient was missing, it might all be chalked up to simple ignorance on their part – the kind of mistake that many of us all make from time to time, that we acknowledge, learn from and do not repeat. But if they were aware of the deficiency and then went on to claim that the results of their deficient recipe were just the same as a properly baked cake, then their integrity would certainly also be at issue. We wonder how many people would continue to buy the “cakes” of such a highly deficient professional baker!

Covered Call ETFs Sidestep Market Volatility

Many investors have now transitioned to a lower stock allocation during the midst of this early 2016 decline. In fact, it has likely created a new sense of reality that it may be time to transition to a structure of low volatility to wait out the storm. A conventional and highly touted method has been to own stocks with lower historical price fluctuations than their peers like the iShares MSCI USA Minimum Volatility ETF (NYSEARCA: USMV ). However, there is also another way for ETF investors to own a basket of stocks with built-in options to collect income and potentially reduce price volatility. Covered call ETFs are also often referred to as a “buy-write” options strategy. This process involves owning a group of publicly traded stocks and selling call options on the underlying securities to collect the premium. This can be done by sophisticated investors on individual positions or you can effectively own an ETF or two that will do it for you on a diversified basket of stocks. The end goal is to collect income from the options contracts, which will ultimately reduce the effectiveness of these ETFs during a sustained uptrend in the market. Nevertheless, they have shown far less relative drawdown than their fully loaded index peers during the last two recent corrections. The oldest and most established fund in this group is the PowerShares S&P 500 BuyWrite Portfolio (NYSEARCA: PBP ). This ETF debuted in 2007 and has accumulated $312 million in assets. As you can see on the chart below, PBP has been able to sidestep a great deal of the decline versus the broad-market SPDR S&P 500 ETF (NYSEARCA: SPY ). It was also able to accomplish that same feat in the summer 2015 swoon as well. It’s worth noting that over longer periods of time, the PBP performance story falls short of the stock-only SPY. This is primarily due to the drag of the options buy-write strategy on 3, 5, and 10-year time horizons. In addition, PDP charges a premium expense ratio of 0.75% for the implementation of its unique approach. The income from PBP is interesting because it often experiences big changes over time. Distributions are paid on a quarterly basis to shareholders and over the last 12-months the trailing yield is 5.40%. Some of those distributions have included short and long-term capital gains as well. Another worthy contender in this space is the Recon Capital NASDAQ 100 Covered Call ETF (NASDAQ: QYLD ). This ETF implements a similar strategy based on the NASDAQ-100 Index. The end result is a more concentrated mix of stocks with concentrations in technology and consumer discretionary sectors. This ETF has been able to achieve a similar pattern of reduced draw down relative to the PowerShares QQQ (NASDAQ: QQQ ) during periods of market stress. QYLD charges an expense ratio of 0.60% and income is distributed on a monthly basis to shareholders. This may be a more attractive feature for income investors who are searching for a more regular dividend stream . The trailing 12-month distributions indicate a yield of 10.49% based on the current share price of QYLD. These buy-write strategies have traditionally been a more obscure way to generate income while reducing draw down during sideways or falling markets. This likely means that they are going to be more of a tactical opportunity in the context of a diversified portfolio rather than a dedicated core position. Investors considering these funds should closely research the underlying mechanics of how the income is generated and compare against other potential low volatility alternatives as well. Disclosure: I am/we are long USMV. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article. Additional disclosure: David Fabian, FMD Capital Management, and/or clients may hold positions in the ETFs and mutual funds mentioned above. The commentary does not constitute individualized investment advice. The opinions offered herein are not personalized recommendations to buy, sell, or hold securities.

Are Momentum ETFs Delivering Momentum Returns?

We consider a popular momentum ETF and illustrate that its historical performance is almost entirely attributable to passive exposures to simple non-momentum factors , such as Market and Sectors. Investors may thus be able to achieve and even surpass the performance of popular momentum ETFs with transparent, passive, and potentially lower-cost portfolios of simpler funds. Attributing the Performance of Momentum ETFs to Simpler Factors We analyzed the iShares MSCI USA Momentum Factor ETF (NYSEARCA: MTUM ) using the AlphaBetaWorks Statistical Equity Risk Model – a proven tool for forecasting portfolio risk and performance . We estimated monthly positions from regulatory filings, retrieved positions’ factor ( systematic ) exposures , and aggregated these. This produced a series of monthly portfolio exposures to simple investable risk factors such as Market, Sector, and Size. The factor exposures at the end of Month 1 and factor returns during Month 2 are used to calculate factor returns during Month 2 and any residual (security-selection, idiosyncratic , stock-specific) returns un-attributable to factors. There are only two ways for a fund to deviate from a passive portfolio: residual returns un-attributable to factors and factor timing returns due to variation in factor exposures over time. We define and measure both components below. iShares MSCI USA Momentum Factor ETF – MTUM: Performance Attribution We used the iShares MSCI USA Momentum Factor ETF as an example of a practical implementation of a theoretical momentum portfolio. MTUM is a $1.1bil ETF that seeks to track an index of U.S. large- and mid-cap stocks with high momentum. The fund’s turnover, around 100% annually, is about half that of the theoretical momentum factor. iShares MSCI USA Momentum Factor ETF – MTUM: Factor Exposures The following factors are responsible for most of the historical returns and variance of MTUM: MSCI USA Momentum Factor ETF – MTUM: Significant Historical Factor Exposures Click to enlarge Source: abwinsights.com Latest Mean Min. Max. Market 88.44 84.12 65.46 96.03 Health 23.73 30.28 23.73 34.94 Consumer 74.02 32.53 13.10 74.06 Industrial 1.69 9.71 1.13 24.51 Size -10.47 -1.04 -11.09 7.67 Oil Price -2.90 -2.45 -4.94 -0.04 Technology 17.72 16.56 1.50 32.29 Value -4.86 -2.13 -8.00 5.20 Energy 0.00 1.86 0.00 4.12 Bond Index 6.51 1.08 -22.90 23.64 iShares MSCI USA Momentum Factor : Active Return To replicate MTUM with simple non-momentum factors, one can use a passive portfolio of these simple non-momentum factors with MTUM’s mean exposures as weights. This portfolio defined the Passive Return in the following chart. Active return, or αβReturn, is the performance in excess of this passive replicating portfolio. It is the active return due to residual stock performance and factor timing: MSCI USA Momentum Factor ETF – MTUM: Cumulative Passive and Active Returns Click to enlarge Source: abwinsights.com MTUM’s performance closely tracks the passive replicating portfolio. Pearson’s correlation between Total Return and Passive Return is 0.96. Consequently, 93% of the variance of month returns is attributable to passive factor exposures, primarily to Market and Sector factors. Once passive exposures to simpler factors have been removed, MTUM’s active return is negligible. Since MTUM’s launch, the cumulative return difference from such passive replicating portfolio has been approximately 1%: 2013 2014 2015 Total Total Return 16.73 14.62 8.50 45.18 Passive Return 16.06 16.48 4.55 41.34 αβReturn 1.11 -2.46 2.54 1.12 αReturn 3.91 0.05 0.29 4.27 βReturn -2.71 -2.52 2.23 -3.05 This active return can be further decomposed into security selection (αReturn) and factor timing (βReturn). These active return components generated low volatility, around 1% annually, mostly offsetting each other as illustrated below: iShares MSCI USA Momentum Factor ETF – MTUM: Active Return from Security Selection AlphaBetaWorks’ measure of residual security selection performance is αReturn – performance relative to a factor portfolio that matches the funds’ historical factor exposures. αReturn is the return a fund would have generated if markets had been flat. MTUM has generated approximately 4% cumulative αReturn, primarily in 2013, compared to roughly 1.5% decline for the average U.S. equity ETF: MSCI USA Momentum Factor ETF – MTUM: Cumulative Active Return from Security Selection Click to enlarge Source: abwinsights.com iShares MSCI USA Momentum Factor ETF – MTUM: Active Return from Factor Timing AlphaBetaWorks’ measure of factor timing performance is βReturn – performance due to variation in factor exposures. βReturn is the fund’s outperformance relative to a portfolio with the same mean, but constant, factor exposures as the fund. MTUM generates approximately -3% cumulative βReturn, compared to a roughly 1% decline for the average U.S. equity ETF: MSCI USA Momentum Factor ETF – MTUM: Cumulative Active Return from Factor Timing Click to enlarge Source: abwinsights.com These low active returns are consistent with our earlier findings that many “smart beta” funds are merely high-beta and offer no value over portfolios of conventional dumb-beta funds. It is thus vital to test any new resident of the Factor Zoo to determine whether they are merely exotic breeds of its more boring residents. Conclusion Theoretical, or academic, momentum portfolios are not directly investable. A popular momentum ETF, MSCI USA Momentum Factor , did not deviate significantly from a passive portfolio of simpler non-momentum factors. Investors may be able to achieve and surpass the performance of the popular momentum ETFs with transparent, passive, and potentially lower-cost portfolios of simpler index funds and ETFs. The information herein is not represented or warranted to be accurate, correct, complete or timely. Past performance is no guarantee of future results. Copyright © 2012-2016, ALphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved. Content may not be republished without express written consent. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.