ETF_0813

Samuel Lee, Editor

Portfolios 4Nothing New

Lessons From History 7Bad Times

News 8Flight to Quality

Fund Analysis 10iShares MSCI Mexico Capped

Fund Analysis 12iShares MSCI USA Quality Factor

Finance 14Factor Models: A Primer

Watchlist 18

Exchange-Traded Notes 31Still Risky

ETFInvestorAugust 2013 Vol. 7 No. 12

A rational approach to asset allocation

SM

Before we get to the main article, Id like to point out that the ETFInvestor tagline has changed. I think this new one better reflects my approach. To be rational means to be sane, in possession of all your faculties. My main job is to keep clear-headed, even when it hurts. Markets can be over- or undervalued for years at a time, and even a well-executed process can produce bad results over the short run. Often the rational thing to do is to suffer a bit now for a lot more gratification down the road.

To be rational also means to be guided by reason. It implies a willingness to consider evidence and, if the evidence warrants it, to change ones beliefs. When I examine a strategy or fund, I look for its underlying economic intuition, and whether the evidence speaks persuasively in favor of it, by which I mean a record of success over many decades and in many different countries. This approach is stringent by the standards of typical investment analysis, but its critical. An all-too-common mistake many investors make is extracting too much meaning from small data sets, which is why theyre often poleaxed when a star manager massively underperforms or a safe asset implodes.

Its no secret I like to dig through the finance litera-ture. The evidence therein is of much higher quality than what youd find almost anywhere else. I admit unless youre a bit strange, its not the most enter-taining reading. (As it happens I am a bit strange,

sparing you the pain.) But the scientific method and, by extension, the literature it spawns is one of the best ways to acquire genuine knowledge. The great thing about the scientific enterprise is that its always trying to poke holes in the received wisdom, cor-recting itself. I happen to relish this, because I enjoy finding the folly of crowds. It would be hypocritical of me to not turn that lens on myself sometimes, too. In this spirit, I try to be forthright with my readers about my limitations and screw-ups.

Rationality implies a lot of things. It implies cool-headedness and a willingness to consider evidence, to change ones mind, and to do so in a disciplined, scientific manner. This is what Ive always strived for in the newsletter. While the tagline has changed, the underlying philosophy hasnt.

Its far better to buy a wonderful company at a fair price than a fair company at a wonderful price. Warren Buffett

Buffett must have had those words in mind when he and private-equity firm 3G Capital offered to buy out H.J. Heinz Company for a hefty 20% premium to market value. At the time (not too long ago), con-sumer staples stocks were widely thought to be expensive. Eyebrows were raised, and some went as far as to say he and 3G overpaid. A few months later, he confounded expectations again by agreeing to buy out NV Energy at a 23% premium, again when the consensus was (and still is) that utilities were expensive. Buffett learned from Benjamin Graham that all assets have an intrinsic value, and the goal of the intelli- gent investor is to buy assets at a substantial discount

Time-Horizon Arbitrage

Continued on Next Page

2

to it, with a margin of safety. Its clear Buffetts esti-mates of Heinzs and NV Energys intrinsic values were substantially higher than the markets.

I think many observers were surprised because they associate Buffett with value investing, commonly defined as buying statistically cheap stocks. Rather, Buffett tries to buy assets at substantial discounts to intrinsic value, regardless of whether that value is crystallized in current assets or yet-to-be-realized future earnings. In my opinion, this is a better defini-tion of value investingcall it intrinsic value investing.

For the past few decades Buffett has favored the future earnings side of the intrinsic value calculus thanks to the influence of his partner, Charlie Munger. If you have to lump the duo into one of the commonly accepted styles of investing, Buffett and Munger are best described as quality or growth-at-a-reason-able-price investors.

Both Buffett and Munger declare their favorite hold-ing period is forever. This seems to contradict the fact that at a high enough price, even the most won-derful business in the world will produce less-than-wonderful returns. No doubt part of their hesitance to sell wonderful businesses at any price reflects a philo-sophical aversion to gin rummy investing. I think, though, the main reason they hold on is because they truly believe wonderful businesses are persistently undervalued by the market, even when common valua-tion metrics suggest otherwise. This suggests to me that much of Buffett and Mung-ers edge rests in the ability to engage in time- horizon arbitrage: buying assets with long-term value underappreciated by the market.

Of course, many managers claim they take the long view, bypassing Wall Streets quarterly earnings game. It sounds great in theory, but the nature of the invest-ment-management industry makes time- horizon arbitrage nearly impossible. Few managers can live through more than a few years of massive underperformance. Beyond 10 years? Forget it. Youve long since been fired.

This is a critical flaw of the investment-management industry, because the real value of most firms is not in their next 10 years of earnings, but the 20 years after that. The real time arbitrage is beyond what most investors can stomach.

Time and ValueIf we had a crystal ball that could reveal true intrinsic value, its virtually certain we would see many stocks are either substantially overvalued or undervalued relative to current market prices. It would be a lucky coincidence if a stock traded right at its fair value. The goal of the long-term investor is to come up with better estimates of intrinsic value than the market and buy stocks trading for below intrinsic value and sell stocks trading above it.

However, if you plug in reasonable-seeming numbers, you quickly discover that the majority of an invest-ments present value is often embodied in the cash flows many years out. After inflation, the U.S. stock market has returned about 7% and grown per-share earnings by 2% over the past century. Apply a 7% discount rate to an earnings stream growing by 2% per annum in perpetuity, and youll find that the earnings beyond the first five years account for almost 80% of intrinsic value. Earnings beyond 10 years account for more than 60%.

Even if you increase the discount rate to 10%, less-ening the value of future dollars, about half of the assets intrinsic value is determined by earnings gen-erated more than 10 years into the future. Imagine a professional investor coming up with intelli-gent predictions as to what a stock will be earning 10 and 20 years from now. Not only is this difficult to do, its nearly impossible to act on when you do it correctly, because a few years of underperforming your peers (no matter how stupidly theyre behav- ing) is a good way to lose all your clients. As a result, many analysts use higher discount rates to make the next few years of earnings matter more. By doing this, theyre largely betting on how earnings surprises will evolve over the next few years. The discounted-cash-flow framework becomes more a tool for longer-term speculation.

Time-Horizon Arbritrage Continued From Front Cover

Contact Samuel Lee [email protected] 312.244.7015.

3Morningstar ETFInvestor August 2013

The Value of MoatsBuffett inverts the solution, making far-flung future earnings the most important thing. He looks for firms with economic moats, sustainable competitive advantages that allow them to endure and thrive in spite of capitalisms tendency to creative destruction. The moat enables Buffett to do something that, on its face, seems insane: He uses the long-term U.S. Treasury rate to discount future cash flows (though I doubt hes actually plugging todays ultralow rates into his intrinsic value calculations). The only way to justify using such a low discount rate is to be abso-lutely certain that a firm will be around decades from now and thriving.

It turns out that a company with a genuine moat is so valuable that if you can identify one with certainty, you should be willing to pay seemingly silly prices to own it.

For a brief spell, Americans did just that. In the early 1970s, they were taken with the idea of high-quality, stable companies that could be bought and held forever, regardless of price. The Nifty Fifty had long histories of uninterrupted dividend growth and hefty market capitalizations. According to Jeremy Siegel, they had an average price/earnings ratio of 41.9 in 1972, more than double the S&P 500s 18.9.

Then they crashed, and their valuations fell to more pedestrian levels. For decades the Nifty Fifty became just another cautionary tale of the madness of crowds. In 1998, Siegel revisited the Nifty Fifty1. It turns out that buying and holding an equally weighted portfolio from the manias peak would have returned 12.5% annualized from 1972 to 1998, only a hair under the S&P 500s return. With hindsight, the lofty valuations didnt turn out to be so mad.

Siegel computed the warranted P/Es of the Nifty Fifty stocks if investors had perfect foresight. Philip Morris (now Altria) MO deserved a 68.5 P/E but traded at only 24. Coca-Cola KO deserved 82.3 but traded at only 46.4, and so on.

The biggest disappointments were technology firms. Xerox XRX, Polaroid, Eastman Kodak, Texas Instru-ments TXN, and Digital Equipment Corporation were all big losers. Without them the Nifty Fifty would have handily beaten the market.

Ironically, the Nifty Fifty phenomenon can be seen as a bout of temporary rationality brought on by mania. Yes, some stocks are so good that they deserve to be bought at what look like rich pricesprovided youre willing to own them forever. The real task is identi-fying those stocks in the first place and then ignoring all the noise. The former is nowhere near as hard as the latter. Taking the long view can be excruciatingly hard at times, and for that reason wide moats will almost always be undervalued.

Mungers genius was figuring this out before nearly everyone else. Buffetts genius was listening to him and following through, enduring the long, lonely periods when the market was telling him he was a fool.

1 Jeremy Siegel. Valuing Growth Stocks: Revisiting the Nifty Fifty. AAII Journal, 1998.

4

Most months, nothing really interesting happens to our positions. This has been one of those months. Sure, theres always some piece of economic or polit-ical news coming out that causes the needle to twitch. Such news is almost always irrelevant to the long-term investor. As I wrote in this months cover story (Time-Horizon Arbitrage), most of the markets intrinsic value is tied to cash flows more than 10 years out.

Ill be the first to admit I am not particularly skilled in reading the markets entrails to divine its near-

future returns; very few people are, and I assure you theyre not writing down their insights for mass consumption. Most prognosticators who claim to be able to make sense of it all are far better yarn- spinners than investors.

Fortunately, you dont need to make sense of it all to obtain satisfactory results. The things that matter are simple. Are we buying things at sensible prices? Are we keeping a lid on commissions, advisory fees, and taxes? Are we adequately diversified? (I enjoy delving into more-sophisticated topics, but theyre like the cherry on top of a rich dessert rather than the main course.) Its hard to mess up a simple, diversi-fied, low-cost, buy-and-hold portfolio.

From what Ive seen, far too many newsletters deviate from these common-sense principles. Why? Because investors want fast profits and excitement,

Nothing NewETFInvestor Portfolios | Samuel Lee

Since Trailing Returns % 1-Mo YTD 12-Mo Incep2

Income Portfolio 0.4 0.5 NA 2.930-Day T-Bill + 5% 0.4 3.3 NA 4.7

Performance

What is the ETF Income Portfolio? This portfolio targets a return of 5% in excess of the 30-day T-bill rate over a full business cycle, with the least risk possible. It favors high-yield opportunities with improving fundamentals. In addition, it will attempt to hedge against inflation, disinflation, recession and growth, a risk-parity approach. Tracking error to conventional benchmarks will be significant.

Who should invest in the ETF Income Portfolio? This portfolio is suitable for income-seeking investors who are comfortable deviating from the markets returns for long periods. It will not seek the highest yields possible at all times; at times it will shift to lower-yielding investments, or even cash, if valuations do not offer enough reward for the risk borne. It will be a relatively low turnover strategy.

1Current allocation reflects change in fund values since original recommendation. 2Portfolio Inception: Oct. 1, 2012. Data through August 5, 2013.

Sector Weighting % Portfolio Market Sector Weighting Weighting 1/2

r Basic Materials 4 7 -3t Consumer Cyclical 5 11 -6y Financial Services 26 18 8u Real Estate 6 4 2

i Communication Services 8 5 3o Energy 4 9 -5p Industrials 9 11 -2a Technology 3 12 -9

s Consumer Defensive 14 10 4d Health Care 8 10 -2f Utilities 13 3 10

Val Blnd Grth

Lrg

Med

Sm

Market Style %

1 511001 26501 11251 010

Val Blnd Grth

Lrg

Med

Sm

Portfolio Style %

1 511001 26501 11251 010

26 26 25

6 6 6

2 2 2

29 30 22

11 6 3

0 0 0

Income Portfolio ETF Snapshot Portfolio Allocation Returns (%)

Price $

Price $Price/Fair

ValueTTM

Yield % 1 Current

Allocation % Date First

RecommdAvg. Cost

Basis $Since

Recommd12-Mo Return

YTD Return

Financial Select Sector SPDR 20.49 1.01 1.51 5.11 6/14/13 19.73 3.85 39.56 25.88

iShares Gold Trust 12.75 3.95 2/8/13 16.27 -21.63 -19.73 -22.06

iShares MSCI EAFE Min Volatility 59.9 2.2 10.58 10/1/12 54.57 9.77 15.83 12.17

iShares MSCI Emerging Mkt Min Vol Index 57.68 1.76 9.76 10/1/12 57.15 0.93 5.41 -2.51

PIMCO 0-5 Year Hi Yld Corp Bond Idx ETF 104.28 4.76 14.06 10/1/12 101.7 2.54 8.65 4.12

PIMCO Total Return ETF 105.76 2.27 30.23 10/1/12 108.77 -2.77 2.1 -1.56

PowerShares S&P 500 Low Volatility 32.44 1.06 2.69 11.24 10/1/12 28.17 15.16 19.5 19.24

WisdomTree Emerging Markets Equity Inc 49.62 4.15 2.34 10/1/12 54.48 -8.92 -3.28 -9.94

Cash Holdings 12.71

Weighted Average 1.04 2.24


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and theyre all too willing to give up their skepticism because someone promises them the easy way out. What investors desire, entrepreneurs will provide in spades.

This may be why the newsletter business is rife with oily salesmen and mountebanks, the dregs of the investment counsel industry. The barriers to entry are low. If someone tried to launch a mutual fund based on, say, astrology, theyd be laughed out of town, but apparently several scribblers have proved that some-one can base a newsletter on such a premise and make a living.

My goal is different. I dont promise high returns and little risk. However, I do promise to write a news-letter that Id want to read were I in your place, one that Id proudly hand to a family member. (Unfortu-

nately, my father enjoys speculating in stocks too much to buy index funds.) I also promise most of my wealth is tied to the strategies I publish.

One change I will be making in my personal accounts is to increase the portion of my wealth dedicated to the Income and Asset-Allocation Portfolios. While I already have a lot of money in them, my 401(k), dedi-cated to a version of the Global Momentum Strategy based on mutual funds, has swollen to the point where its now a bigger chunk of my portfolio than the ETF portfolios. By the time the print edition of this newsletter reaches your mailbox, I plan to own a port-folio split 2:1 between the Asset-Allocation and Income portfolios with a small slice dedicated to the Global Momentum Strategy, not counting my stake in Berkshire Hathaway BRK.B (which makes up

Performance

Asset Allocation Portfolio 0.5 5.4 NA 7.760/40 MSCI ACWI/Barclays Agg Bond 1.4 6.8 NA 8.8

Since Trailing Returns % 1-Mo YTD 12-Mo Incep2

Sector Weighting %

Portfolio Market Sector Weighting Weighting 1/2

What is the ETF Asset-Allocation Portfolio? This portfolio seeks to beat the 60/40 MSCI ACWI/Barclays US Aggregate benchmark over a full business cycle, with the least risk possible. Using analyst discretion, it will deviate from its benchmark allocations in order to exploit the best valuations and improving fun-damentals. Tracking error will be moderate.

Who should invest in the ETF Asset-Allocation Portfolio? This portfolio is suit-able for moderate-risk-seeking investors looking to maximize return and who are comfortable deviating from the markets returns for long periods. The portfolio is to have similar volatility to the 60% stock/40% bond index. It will be more aggres-sive and have higher turnover than the Income Portfolio, and use both fundamental and technical signals.

1Current allocation reflects change in fund values since original recommendation. 2Portfolio Inception: Oct. 1, 2012. Data through August 5, 2013.

Val Blnd Grth

Lrg

Med

Sm

Portfolio Style %

28 33 22

7 6 3

0 0 0

1 511001 26501 11251 010

Val Blnd Grth

Lrg

Med

Sm

Market Style %

26 26 25

6 6 6

2 2 2

1 511001 26501 11251 010

r Basic Materials 7 7 0t Consumer Cyclical 10 11 -1y Financial Services 22 18 4u Real Estate 4 4 0

i Communication Services 4 5 -1o Energy 6 9 -3p Industrials 12 11 1a Technology 5 12 -7

s Consumer Defensive 14 10 4d Health Care 9 10 -1f Utilities 8 3 5

Continued on Next Page

Asset-Allocation Portfolio ETF Snapshot Portfolio Allocation Returns (%)

Price $

Price $Price/Fair

ValueTTM

Yield % 1 Current

Allocation % Date First

RecommdAvg. Cost

Basis $Since

Recommd12-Mo Return

YTD Return

Financial Select Sector SPDR 20.49 1.01 1.51 5.09 6/14/13 19.69 4.06 39.56 25.88

iShares MSCI Emerging Mkt Min Vol Index 57.68 1.76 9.29 10/1/12 57.15 0.93 5.41 -2.51

PIMCO Total Return ETF 105.76 2.27 23.43 10/1/12 108.89 -2.87 2.1 -1.56

PowerShares S&P 500 Low Volatility 32.44 1.06 2.69 10.34 10/1/12 28.17 15.16 19.5 19.24

Vanguard Dividend Appreciation ETF 70.1 1.01 2.09 10.82 10/1/12 59.68 17.46 21.93 18.41

Vanguard FTSE Develoepd Mkts ETF 38.27 4.65 20.55 11/2/12 34.68 10.35 22.15 10.41

WisdomTree Japan Hedged Equity 45.56 1.07 4.89 2/8/13 40.47 12.58 44.81 25.99

Cash Holdings 0 15.58

Weighted Average 1.03 2.21

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6 Nothing New Continued From Previous Page

about 20% of my investable assets). This way, Im further aligning my interests with yours.

Revisiting a FavoriteI like Vanguard Dividend Appreciation VIG. Its cheap, well-managed, liquid and, most importantly, sensible. Over the long run, I expect it to beat the market on a risk-adjusted basis. (A high-up Vanguard executive once said he expects VIG to be the best-performing Vanguard fund over the next 100 years. I wouldnt go that far, but I dont think the sentiment is too far off the mark.) However, theres nothing magi-cal about VIGs methodology: It uses a rather blunt rule10 years of uninterrupted dividend increasesto pick its holdings. Such rules are robust, but they can miss nuance. VIG works because it buys quality stocks, which have economic moats that enable them to grow their earnings (and dividends) through thick and thin. There are many plausible ways to identify economic moats. I think iShares MSCI USA Quality Factor QUAL will probably do a better job because it mixes several reasonable signals and it weights its holdings by multiplying the quality scores by market weightings. VIG, on the other hand, focuses on one signal and weights holdings by market cap, diluting its exposure to the highest-quality stocks (though likely not by much, as such firms tend to be big).

When QUALs liquidity picks up, I will consider lightening up on VIG and adding QUAL. If youre com-fortable trading less-liquid ETFs, feel free to jump ahead of me. I wont be in a rush to dump VIG. QUALs main advantage is its somewhat purer exposure to quality stocks.

Current and Future PositioningMy last move in mid-June was a bit of lucky timing. I lightened up on emerging-markets stocks and put 5% of both the Income and Asset-Allocation portfolios in financials. This was a bit of momentum-driven investing, albeit with a strong valuation rationale. By my reckoning, banks and emerging markets have a similar expected return based on their valuations. However, banks are enjoying a strong tailwind thanks to ultraeasy monetary policy propping up refinanc- ing activity and housing prices; emerging markets are facing a headwind, largely originating from the hang-

over Chinas experiencing as a result of going hog wild on fixed investments. Given the choice between two equally cheap investments, Ill almost always pick the one with stronger positive momentum. I havent done much since, but the rising market is making me feel a bit uneasy. I thought U.S. stocks were on the pricey side when the S&P 500 was 1,400, but I held on because returns have the annoying tendency to persist. If the U.S. market continues its strong run, I may begin trimming my exposure to it even before any momentum signal says I should. However, any such moves will be gradual and slight.

Im having a hard time finding great bargains. I may raise more cash over the next few months, bringing my allocations in both portfolios to around 20%.

Ill also reiterate what you should sell: long-term bonds. They dont belong in your portfolio, unless you highly value the depression insurance they provide. I think inflation is far more likely than deflation over the next 20 to 30 years. Most central bankers have expressed a strong preference for slightly higher infla-tion than even mild deflation, for sensible reasons. I dont think that will change. I also suspect that theres far too much debt floating around for all lenders to be made whole.

Disclosure:I own the following funds and stocks in my personal portfolio: BOND, BRK.B, DEM, DODFX, DXJ, EEMV, EFAV, HYS, IAU, POAGX, SPLV, VASVX, VEA, VIG, XLF

2013 Morningstar, Inc. All rights reserved. Any opinions, recommendations, or informa-tion contained herein: (i) is for educational purposes only; (ii) is not guaranteed to be accurate, complete, or timely; (iii) has not been tailored to suit any particular persons portfolio or holdings; and (iv) should not be construed as investment advice of any kind. Neither Morningstar nor any of its agents shall have any liability with respect to such opinions, recommendations, or information. Morningstar has not given its consent to be deemed an expert under the federal Securities Act of 1933. Past performance is no guar-antee of future results. Before making any investment, consult with your financial advisor. Morningstar employees may have holdings in the stocks recommended.

Disclosure: Morningstar, Inc.s Investment Management division licenses indexes to finan-cial institutions as the tracking indexes for investable products, such as exchange-traded funds, sponsored by the financial institution. The license fee for such use is paid by the sponsoring financial institution based mainly on the total assets of the investable product. Please visit http://corporate.morningstar.com/us/documents/Indexes/Investable-Products-Linked-to-Morningstar-Indexes.pdf for a list of investable products that track or have tracked a Morningstar index. Neither Morningstar, Inc. nor its investment management division markets, sells, or makes any representations regarding the advisability of investing in any investable product that tracks a Morningstar index.

Investments in exchange-traded funds involve risk and may not always be profitable. ETFs are traded on national exchanges and are thus subject to certain market risks. For example, the market price of an ETF may be at, above or below its net asset value as its NAV will fluctuate due to changes in the market value of its underlying holdings whereas its market price will fluctuate in accordance with changes in the NAV plus the ETFs market supply and demand. Additionally, an ETFs performance may not be exactly that of the index it is intending to track due to imperfect matches between the ETFs underlying investments and those of the index because it incurs fees and expenses while the index does not. In transacting in ETFs, investors usually incur a brokerage fee/commission. Past performance is no guarantee of future results.


Lets talk about losing money. I think everyone should think about how much money they can lose and whether they can tolerate those losses. Unless youre in cash and cashlike investments, odds are you will, at some point, see a big chunk of your worth evapo-rate. In happy times, like now, investors tend to grossly underestimate the probability and severity of losses, and they overestimate their tolerance for pain.

The sharp lessons of history are a potent antidote to the dulling effects of a bull market.

From 1926 to 2013, the S&P 500 has drawn down more than 50% of its real value from its most recent peak on at least three occasions: 79% from August 1929 to May 1932, 51.9% from December 1972 to September 1974, and 54% from August 2000 to Feb-ruary 2009. (Only in May of this year did the S&P 500 surpass the after-inflation, total return peak it achieved during the tech bubble.) It seems that every 30 years or so, the stock market takes a beating. Despite the inevitability of bad times, most investors arent prepared for them because they form their expectations of risk on whats happened in the recent past. Thats like building a house on a floodplain based on the climate over the past few years, ignoring the once-a-decade floods that devastate the area. You dont want to be in a position where youre rush-ing to buy flood insurance as soon as the river starts swelling, or laying sandbags around your abode while the flood waters are already lapping at your door- step, because by then its too late.

I think most investors know equities can lose big, because theyve gone through the tech crash and the financial crisis. But I dont think most investors know how badly bonds can lose. Many bond investors havent experienced a single flood. According to

the financial media, the closest thing to a flood that counts happened in 1994, during which the 10-year Treasury went to about 8% from 5.7%. Over that time, an investor in a 10-year Treasury would have experienced a peak-to-trough loss of about 10%. Fortune ran an article titled The Great Bond Market Massacre. We recently had investors hyperven-tilating when the yield on the 10-year rate went to 2.6% from 1.6% in a month. Id call both paper cuts, to be honest.

A real bond-market massacre began in December 1940 and didnt stop until August 1952, a time over which intermediate-term U.S. Treasury bonds experienced a peak-to-trough loss of 37.8% after infla-tion and including reinvested coupons. The U.S. government paid back its accumulated wartime debts in debased dollars.

The United States isnt unique. The United Kingdom did the same thing several times over its long history. (It costs a lot of money to take over the world and maintain a global empire.) Its simply the nature of governments to inflate away their big debts when-ever possible. However, inflationary defaults happen in bursts. Econ-omies can enjoy decades of relative stability, which lulls investors into narrowing their perspective of what counts as possible or extraordinary. I think thats true of many investors today. A fast rise in interest rates isnt unprecedented.

So whats the point of talking about the unpleasant, scary history of financial markets? Its not to scare you into cash, which would all but doom the long-run investor to lose money at todays miserly yields. Its to argue that the only real protection involves accept- ing equity risk and managing it intelligently, buying when equities are cheap and lightening up on them when theyre not. In my opinion, theres no more reli-able way to maintain purchasing power.

Bad TimesLessons From History | Samuel Lee

8

Fund sponsors are herd animals. Over the past few months, theyve rushed to launch or announce plans for quality funds. While a spate of fund launches is often one of many signs that an asset class is be- coming dangerously expensive or crowded, I dont think this is the case with the quality funds. Yes, theres pandering going onwho doesnt love quality or dividend growth?but theres also little sign that a quality bubble exists. Quality funds havent gathered much in the way of assets, and quality stocks arent trading at the valuation premiums to the market theyve historically commanded. In fact, I think the gold-rush dynamics in this area are great for investors, who benefit from competition driving expense ratios to rock-bottom levels. IShares is lead-ing the way with a quality fund that charges an annual fee of only 0.15%. Thats within spitting dis-tance of plain-vanilla passive funds. IShares Launches Tech-Heavy, Quality Factor ETFOn July 18, iShares launched IShares MSCI USA Quality Factor QUAL. QUAL is the newest member of iShares suite of factor-based ETFs created in conjunction with the Arizona State Retirement System, the first of which began trading on April 18. Other funds in the suite focus on momentum, size, and value factors. Like the other factor-based ETFs, the new quality ETF tracks an MSCI index and aims to exploit a specific factor--quality--that academic and industry research has shown to generate outperformance over the broad market.

QUAL, which charges 0.15%, holds 124 companies. It tracks an index of U.S. large- and mid-cap stocks that are chosen from the MSCI USA Index with high returns on equity, stable year-over-year earnings

growth, and low financial leverage. (QUAL is analyzed in depth on Page 12.) WisdomTree Rolls Out Small-Cap and Emerging-Markets Dividend Growth FundsOn July 25, WisdomTree launched WisdomTree U.S. SmallCap Dividend Growth DGRS. The firm followed up on Aug. 1 with WisdomTree Emerging Markets Dividend Growth DGRE. Like WisdomTree U.S. Dividend Growth DGRW, which began trading in May, the funds first screen for dividend-paying stocks with a coverage ratio of at least 1. Then the firms are assigned a growth score based on analysts projected earnings growth and a quality score based on three-year average return on assets and three-year average return on equity. The growth and quality scores are equal weighted. The firms with the highest scores are then weighted based on their share of projected aggregate dividends. DGRS index contains the companies in the bottom 25% of the market cap of the broader WisdomTree Dividend Index after the 300 largest companies have been removed. Then, its index takes the top 50% of companies with the best combined rank of those same fundamental metrics listed above. DGREs index begins with the WisdomTree Emerging Markets Dividend Index and applies the same sel-ection criteria and is based on the top 50% of eligible companies ranked by their composite scores. DGRS and DGRE charge annual fees of 0.38% and 0.63%, respectively. WisdomTrees international dividend-growth funds come a few months after Northern Trust, under the FlexShares brand, launched its international Quality Dividend series of funds. FlexShares International Quality Dividend IQDF comes in two alternative flavors: Defensive IQDE, targeting lower-beta stocks; and Dynamic IQDY, targeting higher-beta stocks. All three funds charge an annual fee of 0.47%.

Flight to QualityNews | Robert Goldsborough and Samuel Lee


EGShares Launches Emerging-Markets Divi-dend Growth FundOn July 1, EGShares launched EGShares Emerging Markets Dividend Growth ETF EMDG, which tracks the FTSE Emerging All Cap ex-Taiwan Diversi-fied Capped Dividend Growth 50 Index.

The fund selects stocks that paid dividends in each of the past five years, grew dividends by at least 6% annualized over the period, kept dividend payout ratios below the average for the Industry Classifica-tion Benchmark plus an additional 30%, have a trailing dividend yield at least half of the parent indexs, and have a minimum of one year of positive trailing earnings.

It caps the number of stocks from any given country or sector at eight, and it seems to exclude Taiwan.The numerous screening criteria suggest that any back-tested results should be met with even more skep-ticism than usual. EMDGs hefty 0.85% expense ratio also warrants skepticism.

Van Eck Files for International MSCI Quality ETFsAlmost as if to ride the coattails of iShares launch of QUAL, on July 24, Van Eck filed with the SEC for permission to create four passively managed ETFs that would hold foreign companies and track MSCI indexes that include only companies with high

quality scores based on certain fundamental characteristics.

All proposed funds would track indexes that rank possible constituents based on return on equity, year-over-year earnings growth, and earnings leverage.The proposed Market Vectors MSCI Emerging Mar- kets Quality ETF would hold companies scoring high on those variables from emerging-markets coun-tries, including Brazil, China, India, Russia, and Taiwan. The proposed Market Vectors MSCI Emerg-ing Markets Quality Dividend ETF would hold companies meeting those same criteria but also with a dividend yield at least 30% higher than its parent indexs yield and that has been positive for the past

five years. The parent index of both proposed funds indexes is the MSCI Emerging Markets Index.

Meanwhile, the proposed Market Vectors MSCI International Quality ETF would follow the same criteria as the proposed emerging-markets quality ETF but would include companies from both developed-markets and emerging-markets countries and would exclude U.S. companies. Finally, the proposed Market Vectors MSCI International Quality Divi-dend ETF would follow the same criteria as the proposed emerging-markets quality dividend ETF but also would include companies from developed- and emerging-markets countries (and again would exclude U.S. companies). The parent index of both proposed funds indexes is the MSCI ACWI ex USA Index.

Van Eck did not provide ticker symbols or expense ratios for the proposed ETFs.

ETFs Launched In July Ticker Exp. Ratio %

First Trust Morningstar Mgd Futs Strat FMF 0.95

LocalShares Nashville Area ETF NASH 0.49

KraneShares CSI China Internet ETF KWEB 0.68

VelocityShares Equal Risk Wt Lg Cp ETF ERW 0.65

WisdomTree Emerging Mkts Dividend Gr DGRE 0.63

WisdomTree US SmallCap Div Growth DGRS 0.38

KraneShares CSI China Five Yr Plan ETF KFYP 0.68

AdvisorShares Athena Intl Bear ETF HDGI 1.50

iShares MSCI USA Quality Factor QUAL 0.15

Guggenheim BulletShares 2022 Corp Bd ETF BSCM 0.24

Guggenheim BulletShares 2021 Corp Bd ETF BSCL 0.24

iSharesBond 2016 Corporate Term IBDA 0.10

iSharesBond 2018 Corporate Term IBDB 0.10

iSharesBond 2020 Corporate Term IBDC 0.10

iSharesBond 2023 Corporate Term IBDD 0.10

SPDR Russell 2000 ETF TWOK 0.18

EGShares Emerging Markets Div Gr ETF EMDG 0.85

10

iShares MSCI Mexico Capped EWWFund Analysis | Patricia Oey and Samuel Lee

I normally dont look at single-country funds, espe-cially ones for small, emerging-markets countries. Theyre expensive, specialized, and hard to analyze. Im making an exception for iShares MSCI Mexico Capped EWW not because I think you should go out and buy it, but because it shows that great eco-nomic growth is not necessary for great stock returns. According to the OECD, Mexicos GDP grew 2.8% annualized from 1988 to 2012, a far cry from Chinas sustained 10% growth over much of that period. Yet the MSCI Mexico Index grew by about 20% annu-alized, beating the broader MSCI Emerging Markets Index by about 8 percentage points and the MSCI EM Latin America Index by 2 percentage points.

What explains this phenomenal run? Much of it cer-tainly has to do with valuations. Unfortunately, I dont have historical valuation data, so I dont know how big of a contributor it was.

But another factor was the remarkable performance of a single firm, Amrica Mvil AMOV (and its previous incarnations), which under Carlos Slims stewardship has grown into Latin Americas biggest telecom firm. Slim got his start in the industry by purchasing Mexicos fixed-line telephone monopoly at a cut-rate price when the government decided to get out of the phone bus-iness. Fueled by monopoly profits, Slim expanded his empire into wireless communications and other Latin American countries. Amrica Mvil is so dominant, at times it has ac- counted for more than 25% of EWWs assets, threat-ening EWWs status as a Registered Investment Company under IRS regulations. BlackRock changed EWWs index to a capped version to avoid this problem.

Amrica Mvils ability to entrench itself behind reg-ulations and earn monopolylike profits suggests that Mexicos regulators historically havent had much power, or were captured by the very firms they were tasked to regulate. This may have contributed to Mexican firms ability to generate substantial returns on capital over such a long period of time.

Assets ($Mil) Expense Ratio % Tax-Cost Ratio % Turnover % Inception

2,220 0.53 0.28 10.0 03/12/96

Performance data from 03/31/199607/31/2013.

1000

780

560

340

120

Total Return % p EWW p MSCI Mexico GR USD p MSCI EM GR USD

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

America Movil, S.A.B. de C.V. 17.5

Fomento Economico Mexicano SAB de CV Units 8.7

Wal - Mart de Mexico, S.A.B. de C.V. Class V WMMVF 6.1

Cemex, S.A.B. de C.V. CXMSF 5.5

Grupo Televisa, S.A. 5.0

Grupo Mexico, S.A.B. de C.V. 4.7

Grupo Financiero Banorte SAB de CV 4.2

Alfa, S.A.B. de C.V. ALFFF 3.6

Grupo Modelo, S.A.B. de C.V. GPMCF 3.2

Kimberly - Clark de Mexico S.A.B. de C.V. KCDMF 3.0

Industrias Peoles, S. A.B. de C. V. 2.9

Top Holdings Wtg (%)

Data through Aug. 6, 2013.

EWW -8.12% 7.17% 10.40% 9.19% 13.01%

MSCI EM GR -7.67% 2.30% 1.33% 1.44% 6.63%

MSCI Mexico GR -7.61% 5.99% 10.20% 8.94% 13.81%

Returns 3-Mo 1-Year 3-Year 5-Year SI

Wide 0.8

Narrow 5.6

No Moat 0.0

Moat Rating

Yield 1.0

P/E 15.8

P/B 2.5

Fundamental

P/FV

Coverage 6.7

Analyst Fair Value


However, were left to explain why foreign share-holders benefited. If regulators cant protect their own citizens, why would they protect foreign share-holders? Big Russian and Chinese companies, for example, have also enjoyed privileged perches, but insiders have done excellent jobs extracting all the benefits for themselves.

I think it has to do with the fact that the U.S. considers Mexico well within its sphere of influence. Because of Mexicos tight integration with the U.S. market and close proximity, the U.S. has both ample incentive and the means to protect its interests. This may explain why American firms have had great success setting up publicly traded subsidiaries in Mexico. It also explains why Mexico has allowed Wal-Mart WMT to become the largest private employer in the country, a state of affairs most emerging-markets would probably reject. (Wal-Mart has had a devilishly hard time entering India, despite the countrys ami-cable relations with the U.S.)

So there you have it. Low valuations, weak regula-tors, and an outside hegemons aegis all worked together to produce phenomenal results for foreign shareholders, despite low growth. Below is my colleague Patty Oeys view on Mexicos fundamentals.

Fundamental ViewIn the wake of the devaluation of the peso and the economic crisis of the mid-1990s, Mexicos economy has benefited from both sound macroeconomic policy and growing exports. The benefits have shown up in the form of steady job growth and mild inflation. More recently, Mexicos relatively attractive sovereign debt yields and stable macroeconomic fundamentals have attracted strong investment inflows into Mexican equities and debt, which in turn has sup-ported the Mexican peso.

Still, the country has problems. Mexicos president Enrique Pena Nieto has laid out a reform program

to tackle them, including overregulation, underinvest-ment in the state-owned oil monopoly Pemex, and an unhealthy dependence on oil revenue to fund the government. If some of these plans come to frui- tion, it will be positive for Mexicos economy. Ironi-cally, whats good for Mexico isnt necessarily good for shareholders. The presidents planned reforms may expose cosseted firms to the harsh forces of competition.

EWW holds a number of them. Amrica Mvil is one of the worlds most profitable telecom companies, largely because of its dominant 70% share in Mexi-cos wireless market. Over the years, Amrica Mvil has used its ample free cash flows from its Mexico operations to fund international expansion. It is now the largest wireless provider in the region, with about 200 million customers. A number of EWWs other large constituents have enjoyed a similar path to success, starting with a highly profitable stran- glehold on the Mexican market, which ultimately funded international expansion. These companies include retailer Fomento Economic Mexicano FMX, bottler Coca-Cola FEMSA KOF, and building mate-rials company CEMEX CX.

A second irony is that due to its relative stability and higher yields, Mexico has recently enjoyed strong foreign fund inflows. This puts it in danger of hot money flowing out, possibly triggered by a wind-down in quantitative easing by central bankers in the devel-oped world or a sudden spike in market volatility.

12

iShares MSCI USA Quality Factor QUALFund Analysis | Samuel Lee

Quality is one of those slippery investment terms. Who doesnt love high-quality companies, of course run by able managers and bought at low prices? I admit Ive added to the confusion by using the word inconsistently or imprecisely myself. At its worst, quality simply becomes indistinguishable from good. The most logical and useful definition I know of is Warren Buffetts concept of the economic moat, a dur-able competitive advantage that allows a firm to reap above-average returns on its capital even when faced with aggressive competitors. Quality stocks have wide moats; theyre insulated from the ravages of creative destruction. A quality strategy, then, should capture the essence of Buffetts moat-invest-ing philosophy, and indeed thats what many profes-sional investors profess to do: buy great companies at reasonable prices, also known as growth-at-a-reasonable-price, or GARP, investing.

Does iShares MSCI USA Quality Factor QUAL cap-ture some of Buffetts essence? I dare say it does. Now, Im not claiming youre going to get Buffett-like results by owning this fund. However, its clear that MSCI took its inspiration from all the right places when it devised the index.

In the early 1980s, Buffett began advertising his acquisition criteria (the emphasis is mine):

We prefer:1 large purchases (at least $5 million of aftertax earnings),2 demonstrated consistent earning power (future projections are of little interest to us, nor are turnaround situations),3 businesses earning good returns on equity while employing little or no debt,4 management in place (we cant supply it),5 simple businesses (if theres lots of technology, we wont understand it),6 an offering price (we dont want to waste our time or that of the seller by talking, even prelimina- rily, about a transaction when price is unknown).

Compare that with the MSCI Quality Index, which selects stocks with:1 high return on equity (good returns on equity),

Assets ($Mil) Expense Ratio % Tax-Cost Ratio % Turnover % Inception

110 0.15 07/16/13

Performance data from 06/30/0307/31/13.

140

108

76

44

12

Total Return % p QUAL p MSCI USA GR

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Apple Inc AAPL 5.3

Google, Inc. Class A XOM 4.9

Exxon Mobil Corporation GOOG 4.9

Chevron Corp CVX 4.9

International Business Machines Corp MSFT 4.6

Microsoft Corporation IBM 4.5

Oracle Corporation ORCL 2.9

Home Depot, Inc. HD 2.7

McDonalds Corporation MCD 2.5

Qualcomm, Inc. QCOM 2.4

Top Holdings Wtg (%)

MSCI USA Qlty GR 4.48% 19.55% 20.53% 10.24% 8.61%

MSCI USA GR 6.05% 27.02% 20.62% 8.05% 7.98%

Hypothetical Returns 3-Mo 1-Year 3-Year 5-Year 10-Year

Wide 53.2

Narrow 37.5

No Moat 5.4

Moat Rating

Yield

P/E 15.5

P/B 4.1

Fundamental

P/FV 1.0

Coverage 96.1

Analyst Fair Value

Data through Aug. 6, 2013.


2 low debt/equity ratios (little or low debt),3 low variability in their year-over-year per-share earnings growth over the trailing five years (consistent earnings power).

All the quantitative criteria Buffett describes are there, though MSCI doesnt exclude technology firms. The indexs selection rules are simple, common-sense interpretations of Buffetts criteria.

This by itself doesnt mean the index can capture Buffetts magic. (It cant.) Buffett takes into consider-ation valuation, managerial quality, and his assess-ment of the industrys future dynamics, among other things. He doesnt make many purchases, and when he does he keeps them, whereas this index buys more than a hundred companies and can churn them during its semiannual rebalances. The best that can be said is that if Buffett buys the needle, the index buys the haystack. However, that need not invalidate the strategy, because its buying the haystacks where Buffett tends to find needles.

We dont have much live performance data for QUAL, which was launched in July. So we have to resort to MSCIs hypothetical return data, which begins in December 1981. The starting date is a little suspi-ciouswhy 1981, when MSCI has the data to go back further? Probably because quality stocks took a savage beating during the 1970s. They achieved lofty price/earnings multiples during the Nifty Fifty craze and became relatively cheap by 1980. According to a study on quality strategies conducted by MFS, quality stocks (defined using somewhat different criteria) underperformed the broad market by more than 20% from 1975 to 1980.1

Even with the 70s bust excluded, the quality index beat the broad-market MSCI USA Index by only 1.7% annualized. So whats the excitement about?

For one, the boost is about as consistent as you could ask for in a nonfraudulent, non-data-mined return series. Exhibit 1 shows the cumulative total return of the quality index divided by the cumulative total return of the MSCI USA Index. When the line slopes up, quality is outperforming; when it declines, quality is underperforming. It seems that as long as quality stocks dont trade at extreme valuations, they enjoy a persistent performance edge over a full business cycle.

In addition, after taking into account the indexs tilts to value, size, and momentum factors, its annual-ized excess returns rise to 3.1%. Adding the fund to a conventional equity portfolio may result in decent diversification benefits. GMO argues that U.S. quality stocks arent trading at the valuation premiums theyve historically com-manded, making them relatively attractive. Indeed, QUALs aggregate forward price/earnings, price/sales, and dividend yield arent much different from the S&P 500s, even though QUALs holdings have experi-enced faster growth in earnings, sales, cash flow, and book value. As of June 30, GMO expects U.S. quality stocks to earn 3.7% annualized after inflation during the next seven years, much better than the negative returns it expects the aggregate market to experience. Finally, out of curiosity, I checked whether the MSCI Quality Index could explain away the historical excess returns of GMO Quality GQETX and Jensen Quality Growth JENSX, Silver- and Gold-rated funds, respectively. Lo and behold, it could. In fact, once you control for the funds substantial loadings to the quality factor, they actually underperfor-med, suggesting that both funds historical returns can be passably mimicked by MSCIs mechanical quality strategy.

1 Katrina Mead, Jonathan Sage, and Mark Citro. Power Couple: Quality and Value Are Strong Drivers of Long-Term Equity Returns. MFS, 2013.

Data through July 31, 2013.

1.8

1.6

1.4

1.2

1.0

Exhibit 1: Relative Cumulative Returns of MSCI Quality and MSCI USA

1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

14

The linear factor model attempts to answer a simple question: Can a return stream be broken down into explainable and unexplainable parts?

Most studies looking at mutual fund manager perfor-mance use factor models to disentangle the roles of skill and luck and risk in producing a given manag-ers return stream. Roughly speaking, a linear factor model creates the best-fitting custom benchmark for each manager. It provides additional information, such as the consti-tution of the benchmark, how nicely the benchmark fits the managers returns, by how much the manager beat the benchmark, and how likely these findings are attributable to chance.

My goal for this primer is to provide a practical under-standing of factor models. I try to keep the math as simple as possible, but some familiarity with sta-tistics helps a lot. The Case of Fidelity MagellanExhibit 1 is a scatter plot of the monthly returns over cash of Fidelity Magellan FMAGX (vertical or y-axis) and the U.S. stock market (horizontal or x-axis) from May 2003 to March 2013.

The graph shows a strong linear relationship between the market and Magellan: When the market is up, Magellan is up about the same amount; when the market is down, the fund is down the same amount.

The data suggest there is a strong fundamental rela-tionship between this fund and the market. And indeed there is: Magellan is a well-diversified basket of U.S. equity stocks and therefore is exposed to the same broad macroeconomic risks as the U.S. stock market. A Brief Statistical DetourThe factor models were interested in use linear regression, a statistical method that fits a line through two sets of data, a dependent variable that we want to explain, and one or more independent or explan-atory variables (which, naturally, do the explaining). Fidelity Magellan is our dependent variable, and the stock market is our explanatory variable. We could use the fund as the explanatory variable and the market as the dependent variable, but that would be getting our causation backward: Changes to Magellan dont move the markets; markets move Magellan. So we always begin with a fundamental story before setting up the scaffolding around the numbers.

The fitted regression line through the scatter plot provides an estimate of the true relationship between the market and this fund.

Recall from high school geometry that the equation for a line follows the form

y = mx + b, where m is the slope term and b is the y-intercept term, where the line crosses the y-axis. By the conventions of financial statistics, the intercept term b is denoted , Greek for alpha, and the slope term m is denoted by , Greek for beta, and the terms are rearranged such that y = + x,

Factor Models: A PrimerFinance | Samuel Lee

Exhibit 1: Magellans Returns Are Linearly Related to the Market (05/200303/2013)

20

15

10

5

0

-5

-10

-15

-20

-25 -20 -15 -10 -5 5 10

Market-Rf Return

Mag

ella

n-Rf

Ret

urn


where y is the funds excess return (fund return minus the risk-free or cash rate), and x is the markets excess return (market return minus risk-free). In order to make y and x more explicit, theyll henceforth be renamed R-Rf and Mkt-Rf, respectively, where R is the monthly fund return, Rf is the risk-free return, and Mkt is the S&P 500s return: R-Rf = + *(Mkt-Rf )

The linear regression procedure finds the values of and that produce the best-fitting line. (A technical note for the curious: The line is fitted to minimize the total sum of the squares of the vertical distances between the data points and the line.) In this case, the procedure estimates the following: R-Rf = -0.35 +1.16(Mkt-Rf)

This equation is straightforward to interpret; for each percentage-point change in the U.S. stock markets monthly excess return, Fidelity Magellans monthly excess return is predicted to move in the same direc-tion by 1.16 percentage points, minus 0.35 percen-tage points. For example, the equation predicts that if the market is up 10% one month, the fund will be up 10%*1.16 0.35% = 11.25%.

The equation tells you two things. First, Magellans return pattern can be replicated by simply leveraging the S&P 500 by 1.16. Second, Magellan underper-formed that simple leveraged strategy by 0.35 per-centage points a month, or 4.2 percentage points a year (0.35%*12).

In finance jargon, the funds beta to the market was 1.16, and its annual alpha was negative 4.2%. Sound familiar? Thats because this is what experts mean when they talk about beta and alpha.

Of course, Magellan couldve been unlucky. Recall that the linear regression is an estimate of the true rel-ationship between this fund and the market. The true relationship could actually be y = 0.50 + 1.20x, for example, and the period we looked at captured some

extreme data points that skewed our estimates of alpha and beta. Of course, no one knows the true relationship; statistical methods can provide only informed guesses as to what it actually is.

The statistical uncertainty of the alpha and beta terms is quantified by the p-value, which indicates the percent chance of obtaining as extreme of a value if the alpha or beta were zero. The regression output looks like this:

where the intercept is the monthly unexplained return, or alpha, and Mkt-Rf is the market factor. The intercepts p-value shows that theres only a 1% chance an outcome this bad or worse couldve oc- curred due to luck, assuming the funds true alpha is zero (that is, the funds managers had no skill). The t-statistic is another way of expressing the p-value (and favored over the p-value; however, its interpreta-tion isnt as intuitive, so Im glossing over it). Finally, the R2 is a measure of fit. An R2 of 100% indicates that the model perfectly fits the data. A value of 0% indicates its completely unrelated to it. The regressions R2 is very high, indicating it does a good job explaining FMAGXs monthly returns.

Multifactor ModelsThe single-factor model I used to examine Fidelity Magellans return is a version of the capital asset-pricing model, or CAPM, which predicts that the only way to obtain higher returns is to increase exposure to market beta. One way to test this claim is to sort stocks by beta and see whether higher beta is associated with higher returns. Since the 1960s, researchers have known that its not.

In the 1970s and 1980s, researchers found that fun-damentally cheap stocks beat expensive stocks (value beats growth), and that small-cap stocks beat

Coefficients T-stat P-value

-0.35 -2.55 0.01

Mkt-RF 1.16 36.54 0.00

R2 0.92

16

large-cap stocks (small beats large), even when market exposure is controlled for. Running with these findings, Eugene Fama and Kenneth French aug-mented CAPM with two factors capturing the excess returns of value and small-cap stocks, producing the now famous Fama-French model.

They constructed their value factor by simulating the return history of a long-short strategy that every year bought low book/price (value) stocks and short-sold high B/P (growth) stocks. Similarly, their size factor was simply the returns from buying small-cap stocks and short-selling large-cap stocks each year.

They didnt add value and size arbitrarily. They found that the lower the B/P or smaller the market cap, the higher a stocks returns were, and this relation-ship was smooth. On the other hand, beta didnt seem connected to a stocks returns in any meaningful manner. This was, in their eyes, convincing evidence that the stock market priced size and value as risk factors, meaning they were associated with higher returns because they were somehow riskier.

Around the same time Fama and French created their famous namesake model, Narasimhan Jegadeesh and Sheridan Titman found that stocks with relatively high recent returns beat stocks with relatively low returns. Soon, Mark Carhart extended the Fama-French model with a momentum factor, constructed by simulating the returns of a monthly strategy that bought the best-performing stocks by trailing 12-month returns, excluding the most recent month, and short-selling the worst-performing stocks.

The Fama-French-Carhart model has been a mainstay of academic and practitioner research since.It looks like this:

R-Rf = + *(Mkt-Rf) + *HML + *SMB + *UMD, where R is the return of the asset, Rf is the risk-free rate, is the unexplained return, Mkt is the U.S. markets return, HML (high-minus-low) is the value-

factor-mimicking portfolios return, SMB (small- minus-big) is the size-factor-mimicking portfolios return, and UMD (up-minus-down) is the momentum-factor-mimicking portfolios return. This equation simply says that an assets return above cash can be described as a linear combination of exposures to market, value, size, and momentum factors, plus an unexplained alpha. The or beta coefficients in front of each term show how sen- sitive the asset is to each factor, holding all other factors constant. Notice that the equation looks very much like the simple line equation we countered earlier. Thats because theyre nearly the same thing, except this time the line is fitted to four different explanatory variables instead of one.

The multiple linear regression procedure attempts to find the alpha and beta coefficient values that best explain the assets returns.

The table below shows the results of a Carhart regression of Fidelity Magellans monthly returns. The interpretation is similar to the single-factor regression we looked at earlier. Take HML, the value factor.

Its coefficient, or loading, of negative 0.20 means that for each percentage point value stocks beat growth stocks in a month, Magellans monthly return is pre-dicted to fall by 0.20 percentage points, all other factors held constant. Magellan has a negative loading to the value factor; that is, its behaved like a growth fund.

Coefficients T-stat P-value

-0.32 -2.40 0.02

Mkt-RF 1.18 31.38 0.00

SMB -0.03 -0.43 0.67

HML -0.20 -3.28 0.00

UMD -0.04 -1.28 0.20

R2 0.92


Notice that the p-values of the SMB and UMD fac-tors are 0.67 and 0.20, respectively, and the loadings are close to zero. This says that theres a 67% (20%) chance of obtaining as extreme of a value or greater if the true SMB (UMD) loading were zero. By convention a p-value of 5% or lower is considered

statistically significant.

Overall, the results suggest that Magellan had high market beta, a pronounced growth tilt, a large- to mid-cap size tilt, and little exposure to momentum stocks. Unfortunately, it underperformed its regres-sion-based benchmark by 3.85% per year, and this outcome was statistically significant at the 2% level.

The Use and Abuse of Factor ModelsWhile factor models are considered quite robust, they have limitations. First, they often need a lot of histor-ical data to detect statistically significant evidence of skill (or lack thereof). A manager who beats his regression-based benchmark over a decade may still not be deemed to have surpassed the hurdle of stat-istical significance. Such is the limitation of data. This is why finance researchers often claim that its hard to be certain a manager is skilled, even with many years of performance data to consider.

Second, even if you find a strategy with economi- cally and statistically significant alpha, its unlikely to persist. So you cant run around with a Carhart regression, check every single mutual fund out there, buy the highest-alpha funds, and go on to enjoy outsized returns. In other words, past performance doesnt predict the future (aside from a short-lived

hot hands effect).

Finally, the factor model cant capture all the impor-tant nuances of a strategy or asset. All models are wrong to some degree. The question is whether they say something useful.

With all those caveats out the way, factor models can be tremendously useful. They can tell you whether an asset or fund is offering something uniquealpharather than repackaging known factor exposures that

you can obtain with low-cost index funds. Factor models can explicate a managers process and give you more confidence that hes doing what he says he does. And perhaps most importantly, they instill humility in youbeating a factor-based benchmark is often fiendishly difficult.

Summaryp An assets periodic returns can be broken down into components attributable to factors, traits that explain or predict returns. p Finance researchers broadly agree that stock returns can largely be explained by market, value, size, and momentum factor exposures. (Bond returns can also be broken down into factor exposures, though linear factor models dont work as well with them.)

p A common way to figure out an assets factor expo-sures is to perform a multiple linear regression of an assets periodic returns (usually monthly) against the returns of long-short factor-mimicking portfolios. Doing so creates a regression-based benchmark that tries to explain the returns of the asset. p Unexplainable excess return, or alpha, is often interpreted as evidence of skill or some kind of addi-tional risk not captured by the factor model. p Because linear regressions are statistical in nature, their outputs shouldnt be taken as gospel. Even the measures of statistical uncertainty that accompany them, such as the t-stat or p-value, should be taken with a grain of salt.

NAV Return % Trailing NAV Rtn % Ann. Tax Current Total Daily Exp. Est.Star Cost Yield Market Assets Vol. Ratio Holding Inception Rating YTD 1Mo 3Mo 1Yr 3Yr Ratio % % Price $ ($Mil) (Thou) % Costs Date

Nuts and BoltsHistorical Performance

ETF Watchlist Domestic Equity

18

i Income Portfolio Holding g Asset-Allocation Portfolio Holding e Both Income and Asset-Allocation Holding

Large BlendGuggenheim Russell Top 50 Mega Cap XLG QQQQ 16.8 4.5 5.0 18.6 16.6 0.78 2.1 119.12 542 24 0.20 0.28 05-04-05Guggenheim S&P 500 Equal Weight RSP QQQQQ 22.3 5.4 7.1 32.5 18.7 0.57 1.4 64.72 4,713 765 0.40 0.46 04-24-03iShares Core S&P 500 ETF IVV QQQQ 19.6 5.1 6.1 24.9 17.6 0.44 1.9 169.55 44,704 5,397 0.08 0.06 05-15-00iShares Core S&P Total US Stock Mkt ETF ITOT QQQQ 19.9 5.2 6.3 25.8 17.8 0.41 1.8 77.15 810 150 0.13 0.11 01-20-04iShares Dow Jones U.S. Index IYY QQQQ 19.9 5.3 6.2 26.0 17.8 0.41 1.8 85.17 753 42 0.20 0.22 06-12-00iShares Russell 1000 Index IWB QQQQ 19.9 5.3 6.2 26.1 17.9 0.43 1.9 93.87 7,978 568 0.15 0.13 05-15-00iShares Russell 3000 Index IWV QQQQ 20.1 5.5 6.5 26.6 17.9 0.41 1.8 100.78 4,661 280 0.20 0.20 05-22-00iShares S&P 100 Index OEF QQQQ 18.2 5.0 5.6 21.4 17.1 0.46 2.0 75.73 4,037 717 0.20 0.24 10-23-00Market Vectors Wide Moat ETF MOAT NR 17.9 7.2 9.3 31.8 0.5 26.22 283 81 0.49 04-24-12Schwab U.S. Broad Market ETF SCHB QQQQ 20.3 5.4 6.5 26.7 18.2 0.70 1.9 40.98 2,194 471 0.06 0.01 11-03-09Schwab U.S. Dividend Equity ETF SCHD NR 22.2 4.3 5.1 23.8 2.6 34.20 1,162 296 0.17 10-20-11Schwab U.S. Large-Cap ETF SCHX QQQQ 19.8 5.2 6.1 25.5 17.8 0.72 1.9 40.23 1,791 402 0.08 0.08 11-03-09SPDR S&P 500 SPY QQQQ 19.5 5.1 6.1 24.8 17.6 0.69 2.0 168.71 154,099 134,235 0.09 0.14 01-22-93g Vanguard Dividend Appreciation ETF VIG QQQQQ 18.3 5.3 5.0 23.5 16.8 0.36 2.1 69.78 16,993 1,207 0.10 04-21-06Vanguard Large Cap ETF VV QQQQ 19.8 5.3 6.1 25.6 17.8 0.33 1.9 77.34 4,223 195 0.10 0.16 01-27-04Vanguard Mega Cap ETF MGC QQQQ 19.3 5.2 6.1 24.3 17.7 0.35 2.1 57.69 599 33 0.12 0.13 12-17-07Vanguard Russell 1000 Index ETF VONE NR 19.9 5.3 6.2 26.1 1.8 77.58 225 27 0.12 09-20-10Vanguard Russell 3000 Index ETF VTHR NR 20.2 5.5 6.5 26.7 1.7 78.00 78 3 0.15 09-20-10Vanguard S&P 500 ETF VOO NR 19.6 5.1 6.1 25.0 2.0 77.23 10,647 1,939 0.05 0.03 09-07-10Vanguard Total Stock Market ETF VTI QQQQ 20.4 5.5 6.6 26.8 18.2 0.33 1.9 87.42 33,455 2,539 0.05 0.01 05-24-01

Large ValueiShares High Dividend Equity HDV NR 18.6 3.2 0.8 15.6 3.1 68.59 3,245 312 0.40 0.44 03-29-11iShares MSCI USA Minimum Volatility USMV NR 17.4 3.6 0.8 17.1 2.1 33.77 2,325 2,264 0.15 10-18-11iShares Russell 1000 Value Index IWD QQQ 22.0 5.4 7.1 30.4 17.8 0.49 2.0 87.83 19,223 1,460 0.20 0.25 05-22-00iShares Russell 3000 Value Index IWW QQQ 21.9 5.5 7.2 30.6 17.6 0.48 2.0 114.98 540 40 0.25 0.28 07-24-00iShares S&P 500 Value Index IVE QQQ 21.5 5.1 6.7 30.0 17.1 0.50 2.1 79.74 6,440 679 0.18 0.22 05-22-00PowerShares FTSE RAFI US 1000 PRF QQQQQ 22.9 5.5 7.1 32.4 18.3 0.72 1.8 76.11 2,194 176 0.39 0.37 12-19-05e PowerShares S&P 500 Low Volatility SPLV NR 18.8 4.2 1.2 18.6 2.7 32.35 4,743 1,624 0.25 05-05-11Schwab U.S. Large-Cap Value ETF SCHV QQQQ 21.0 5.0 5.6 26.6 17.7 0.97 2.4 38.07 713 114 0.13 0.15 12-11-09SPDR Dow Jones Industrial Average DIA QQQQ 19.8 4.1 5.1 22.1 16.9 0.85 2.2 154.84 13,207 6,148 0.17 0.19 01-13-98SPDR S&P Dividend SDY QQQQQ 22.0 5.6 4.8 27.8 16.8 1.06 2.6 70.13 12,704 1,041 0.35 0.34 11-08-05Vanguard High Dividend Yield Indx ETF VYM QQQQQ 21.1 4.3 5.0 23.6 19.5 0.50 2.9 58.89 6,603 689 0.10 0.05 11-10-06Vanguard Mega Cap Value Index ETF MGV QQQ 22.3 5.1 7.1 28.3 17.3 0.44 2.4 51.61 639 58 0.12 0.09 12-17-07Vanguard Russell 1000 Value Index ETF VONV NR 22.0 5.4 7.1 30.5 2.1 76.86 169 25 0.15 09-20-10Vanguard S&P 500 Value Index ETF VOOV NR 21.5 5.1 6.7 30.1 2.0 76.67 115 10 0.15 09-07-10Vanguard Value ETF VTV QQQ 22.6 5.2 6.9 29.3 17.5 0.43 2.3 71.25 11,360 940 0.10 0.20 01-26-04WisdomTree Dividend ex-Financials DTN QQQQQ 18.2 3.8 2.7 21.9 19.6 1.35 3.6 64.59 1,188 73 0.38 0.36 06-16-06WisdomTree Equity Income DHS QQQQ 18.2 3.1 0.8 17.4 19.3 1.38 3.5 53.13 749 71 0.38 0.31 06-16-06WisdomTree LargeCap Dividend DLN QQQQ 17.7 4.1 3.7 20.1 18.4 1.06 2.9 62.29 1,724 129 0.28 0.27 06-16-06WisdomTree Total Dividend DTD QQQQ 18.4 4.5 4.1 21.7 18.4 1.09 2.9 62.85 358 33 0.28 0.32 06-16-06

Large GrowthGuggenheim S&P 500 Pure Growth RPG QQQQQ 24.7 5.9 8.6 33.0 21.9 0.22 0.7 61.43 558 72 0.35 0.36 03-01-06iShares Russell 1000 Growth Index IWF QQQQ 17.6 5.3 5.2 21.4 17.8 0.33 1.6 76.47 19,970 1,579 0.20 0.20 05-22-00iShares Russell 3000 Growth Index IWZ QQQQ 18.2 5.5 5.7 22.4 17.9 0.31 1.5 62.87 417 19 0.25 0.21 07-24-00iShares Russell Top 200 Growth Index IWY QQQQ 15.9 4.8 4.2 17.9 17.3 0.38 1.8 39.87 383 50 0.20 09-22-09iShares S&P 500 Growth Index IVW QQQQ 17.6 5.1 5.5 20.3 18.0 0.37 1.7 88.45 7,614 406 0.18 0.21 05-22-00PowerShares QQQ QQQ QQQQQ 16.9 6.3 7.4 18.5 19.4 0.37 1.3 75.77 37,002 29,970 0.20 0.20 03-10-99Schwab U.S. Large-Cap Growth ETF SCHG QQQQ 18.4 5.4 6.6 24.2 17.7 0.41 1.3 40.30 817 128 0.13 0.11 12-11-09SPDR S&P 500 Growth ETF SPYG QQQQ 17.6 5.0 5.5 20.2 18.3 0.53 1.6 76.51 283 13 0.20 09-25-00Vanguard Growth ETF VUG QQQQ 16.5 5.3 5.0 21.5 18.0 0.22 1.4 82.51 11,121 573 0.10 1.02 01-26-04Vanguard Mega Cap Growth Index ETF MGK QQQQ 15.6 5.3 4.6 19.7 17.8 0.24 1.6 63.71 1,057 90 0.12 1.18 12-17-07Vanguard Russell 1000 Growth Index ETF VONG NR 17.6 5.3 5.2 21.5 1.6 78.52 151 10 0.15 09-20-10Vanguard S&P 500 Growth Index ETF VOOG NR 17.6 5.1 5.5 20.3 1.6 79.25 150 10 0.15 09-07-10

Mid-Cap BlendiShares Core S&P Mid-Cap ETF IJH QQQQ 21.6 6.2 6.6 32.9 19.0 0.33 1.4 123.02 17,379 795 0.17 0.08 05-22-00iShares Russell Midcap Index IWR QQQQ 22.0 5.8 6.7 32.2 18.8 0.39 1.6 137.00 8,238 223 0.20 0.16 07-17-01Schwab U.S. Mid-Cap ETF SCHM NR 22.8 6.3 7.2 34.8 1.6 33.97 603 187 0.13 0.07 01-13-11

19

5Yr Historical Growth % Price/ Price/ Covrg Average Assets Worst 3 Yr 3 Yr Price/ Cash Price/ Price/ Fair Rate Style Mrkt Cap # of in Top Turnover 3Mo Standard Sharpe Ticker Earnings Sales Cash Flow ROE Earn Flow Book Sales Value % Box ($Mil) Holdgs 10 % % Return % Deviation Ratio

Valuation Portfolio Style RiskFundamentals


19

Lg BlndXLG 11.6 4.0 8.6 21.4 16.2 10.3 2.5 2.0 1.0 100 4 173,460 52 37.8 7 -23.5 12.4 1.3RSP 6.3 4.5 3.7 16.8 18.6 10.0 2.3 1.3 1.0 92 4 17,475 501 2.4 20 -36.2 15.4 1.2IVV 6.8 2.9 6.6 19.7 17.2 10.3 2.4 1.5 1.0 97 4 61,482 503 18.0 4 -29.6 13.3 1.3ITOT 6.9 3.2 6.8 18.9 17.5 10.4 2.4 1.5 1.0 90 4 42,960 1,502 15.9 5 -30.4 13.6 1.3IYY 5.7 3.1 6.8 18.8 17.3 10.4 2.4 1.5 1.0 90 4 41,915 1,245 15.3 7 -31.1 13.8 1.3IWB 5.7 3.5 6.7 18.9 17.2 10.4 2.4 1.5 1.0 91 4 45,993 1,008 15.7 5 -30.9 13.7 1.3IWV 4.6 -22.5 6.7 18.1 17.3 10.4 2.4 1.5 1.0 85 4 34,564 2,985 14.5 5 -31.3 14.0 1.2OEF 6.0 0.1 6.3 21.1 16.4 10.0 2.4 1.7 1.0 100 4 128,532 101 28.9 5 -26.2 12.8 1.3MOAT 6.9 12.4 3.4 18.7 17.3 11.8 2.4 1.7 0.9 100 7 35,000 20 51.5 0 SCHB 5.3 3.0 6.9 18.3 17.3 10.4 2.4 1.5 1.0 86 4 36,128 1,946 14.6 5 -15.0 13.9 1.3SCHD 6.3 2.0 5.4 26.9 16.6 11.0 3.5 1.6 1.0 97 1 74,859 101 41.4 17 SCHX 5.8 3.2 6.8 19.2 17.2 10.4 2.4 1.6 1.0 94 4 50,998 764 16.4 4 -14.3 13.5 1.3SPY 6.8 2.9 6.6 19.7 17.2 10.3 2.4 1.5 1.0 98 4 61,471 501 18.0 4 -29.6 13.3 1.3g VIG 8.0 7.5 6.6 25.6 16.0 10.9 3.1 1.3 1.0 94 4 47,884 149 39.4 15 -21.9 11.5 1.4VV 6.8 3.2 6.6 19.6 16.7 9.9 2.3 1.5 1.0 95 4 52,164 644 17.4 8 -30.4 13.6 1.3MGC 6.1 2.4 6.5 20.4 16.4 9.7 2.3 1.5 1.0 99 4 75,559 292 21.0 19 -28.8 13.2 1.3VONE 6.0 4.4 6.4 19.2 16.8 9.8 2.3 1.4 1.0 92 4 43,654 996 16.3 36 VTHR 5.6 3.9 6.6 18.5 16.8 9.8 2.3 1.4 1.0 86 4 33,027 2,964 15.0 20 VOO 7.9 2.9 6.4 19.8 16.6 9.7 2.3 1.5 1.0 98 4 58,058 508 18.4 3 VTI 5.2 3.0 6.8 18.5 16.8 9.8 2.3 1.4 1.0 86 4 32,637 3,250 15.0 3 -31.1 14.1 1.3

Lg ValHDV 0.4 2.4 2.9 19.9 18.6 9.1 2.8 2.0 1.0 98 1 83,434 77 57.3 43 USMV 5.9 7.0 4.2 22.2 17.2 10.8 2.9 1.7 1.0 94 4 34,655 134 15.5 30 IWD 2.5 -0.1 3.3 12.7 14.8 8.3 1.7 1.3 1.0 92 1 48,576 649 27.2 16 -28.8 13.9 1.3IWW 2.5 -9.0 3.3 12.2 14.9 8.3 1.7 1.2 1.0 85 1 36,028 1,981 25.0 16 -29.1 14.1 1.2IVE 1.7 -0.2 2.2 15.4 15.9 8.6 1.9 1.2 1.0 97 1 55,709 358 25.9 35 -30.1 14.3 1.2PRF 1.1 0.0 2.6 15.6 16.0 8.4 1.9 1.0 1.0 91 1 40,490 1,020 18.5 13 -31.4 14.1 1.3e SPLV 5.1 2.4 2.1 18.6 19.3 9.2 2.4 1.5 1.1 99 1 23,440 100 12.6 17 SCHV 1.2 -1.6 3.0 18.1 15.7 9.1 2.0 1.4 1.0 95 1 56,292 356 27.4 8 -13.4 12.4 1.4DIA 9.3 7.0 5.4 28.1 15.7 10.4 3.0 1.4 1.0 100 1 130,491 31 54.0 6 -22.8 11.9 1.4SDY 6.1 4.6 4.8 21.6 19.5 11.2 2.9 1.3 1.0 84 4 20,083 84 20.6 94 -25.5 11.1 1.5VYM 3.3 2.7 3.6 19.7 15.5 8.7 2.3 1.5 1.0 95 1 69,067 397 41.3 11 -24.7 10.8 1.7MGV 0.0 -2.3 2.4 14.7 15.0 7.8 1.7 1.3 1.0 99 1 77,969 164 35.0 17 -25.5 13.2 1.3VONV 2.1 1.3 3.2 12.5 15.1 8.0 1.6 1.2 1.0 91 1 41,337 701 26.9 29 VOOV 3.3 -0.1 2.2 15.6 15.3 8.1 1.8 1.1 1.0 98 1 52,076 359 26.0 20 VTV 0.6 -0.8 3.6 14.3 15.0 7.9 1.7 1.2 1.0 94 1 50,596 417 28.8 22 -27.2 13.5 1.3DTN -1.5 0.7 0.3 17.8 17.7 8.4 2.5 1.3 1.0 99 1 29,216 84 17.9 34 -26.5 10.4 1.8DHS -0.2 -0.5 1.6 16.7 18.5 8.2 2.4 1.7 1.0 91 1 47,105 351 42.4 28 -30.6 9.6 1.9DLN 5.8 3.9 4.1 20.4 16.8 9.3 2.5 1.5 1.0 99 1 77,849 299 26.7 14 -25.7 10.6 1.7DTD 4.6 3.8 4.0 19.3 16.9 9.2 2.4 1.5 1.0 91 1 49,321 930 22.6 13 -26.6 10.9 1.6

Lg GrwRPG 24.5 10.5 23.0 23.1 18.8 12.2 3.2 1.9 1.1 92 7 20,963 111 19.6 35 -35.5 16.1 1.3IWF 12.0 10.0 11.2 25.4 20.4 12.8 4.5 1.9 1.0 91 7 43,444 612 21.1 17 -32.9 13.9 1.3IWZ 9.4 -33.1 11.2 24.2 20.6 12.9 4.4 1.9 1.0 85 7 33,008 1,708 19.5 18 -33.4 14.3 1.2IWY 12.1 10.7 11.8 27.8 19.1 12.3 4.4 2.0 1.0 100 7 90,211 123 31.3 16 -12.3 13.1 1.3IVW 13.9 9.8 12.6 24.0 18.7 12.4 3.4 2.3 1.0 98 7 67,603 296 24.7 28 -29.2 12.8 1.4QQQ 20.0 14.7 15.8 21.2 17.4 12.4 3.6 2.4 1.0 97 7 73,033 101 48.8 9 -38.6 15.5 1.2SCHG 14.1 10.9 11.9 20.3 18.9 11.9 3.2 1.8 1.0 93 7 45,991 410 24.5 8 -15.5 15.3 1.1SPYG 13.9 9.8 12.6 24.0 18.7 12.4 3.4 2.3 1.0 98 7 67,605 296 24.7 21 -33.0 13.3 1.3VUG 15.3 10.4 11.2 25.1 18.9 12.4 3.8 1.8 1.0 94 7 45,355 417 22.9 21 -33.3 14.3 1.2MGK 15.6 11.3 12.5 26.5 18.3 12.2 3.8 1.8 1.0 98 7 67,209 179 28.0 16 -31.7 13.9 1.3VONG 13.7 10.4 10.9 26.7 19.0 12.1 4.3 1.9 1.0 93 7 46,780 579 24.3 25 VOOG 14.3 9.7 12.4 23.9 18.1 11.7 3.2 2.2 1.0 98 7 64,408 293 25.0 22

MC BlndIJH 8.3 5.2 9.4 13.6 20.4 11.4 2.3 1.2 47 5 4,265 403 7.2 9 -36.5 16.5 1.1IWR 6.0 6.4 5.7 15.1 18.2 10.8 2.4 1.3 1.1 73 5 8,809 810 4.1 13 -38.7 15.6 1.2SCHM 4.2 4.6 9.0 13.6 18.5 10.0 2.3 1.2 50 5 4,925 585 4.3 19




SPDR S&P MidCap 400 MDY QQQQ 21.5 6.2 6.5 32.6 18.8 0.40 1.1 224.11 14,322 1,953 0.25 0.31 05-04-95Vanguard Extended Market Index ETF VXF QQQQ 23.8 7.0 8.9 35.6 19.6 0.29 1.3 74.80 2,551 209 0.10 0.10 12-27-01Vanguard Mid-Cap ETF VO QQQ 22.1 5.7 6.3 32.1 18.5 0.29 1.2 100.58 5,562 248 0.10 0.01 01-26-04Vanguard S&P Mid-Cap 400 Index ETF IVOO NR 21.6 6.2 6.6 32.8 0.9 82.83 195 15 0.15 09-07-10WisdomTree MidCap Earnings EZM QQQQQ 24.6 7.2 10.2 40.4 21.0 0.53 1.5 77.31 286 32 0.38 02-23-07

Mid-Cap ValueiShares Dow Jones Select Dividend Index DVY QQQQ 20.0 5.5 4.1 22.1 18.3 0.76 3.2 67.54 12,768 979 0.40 0.28 11-03-03iShares Russell Midcap Value Index IWS QQQQ 22.1 5.3 5.8 33.4 18.5 0.50 1.9 60.78 5,193 483 0.25 0.24 07-17-01iShares S&P MidCap 400 Value Index IJJ QQQQ 23.2 6.3 7.3 35.1 18.4 0.41 1.7 107.81 3,271 153 0.25 0.21 07-24-00Vanguard Mid-Cap Value ETF VOE QQQQ 23.3 5.5 6.1 33.5 18.1 0.46 1.6 72.54 1,798 130 0.10 0.05 08-17-06Vanguard S&P Mid-Cap 400 Value Index ETF IVOV NR 23.1 6.3 7.3 35.1 1.3 81.13 28 5 0.20 09-07-10WisdomTree MidCap Dividend DON QQQQ 22.4 6.2 5.5 30.6 18.9 1.10 3.1 69.43 746 78 0.38 06-16-06

Mid-Cap GrowthGuggenheim S&P Midcap 400 Pure Growth RFG QQQQQ 19.2 6.6 6.8 30.5 21.3 0.15 0.9 108.31 639 30 0.35 03-01-06iShares Russell Midcap Growth Index IWP QQQ 21.7 6.2 7.6 30.5 19.1 0.24 1.1 76.14 3,956 287 0.25 0.16 07-17-01iShares S&P MidCap 400 Growth Index IJK QQQQ 20.0 6.1 5.8 30.5 19.5 0.22 1.0 136.79 3,973 166 0.25 0.18 07-24-00Vanguard Mid-Cap Growth ETF VOT QQQ 20.5 5.8 6.6 30.3 18.7 0.09 0.6 82.71 1,664 75 0.10 1.30 08-17-06Vanguard S&P Mid-Cap 400 Growth Idx ETF IVOG NR 20.0 6.1 5.8 30.5 0.6 84.55 185 16 0.20 09-07-10

Small-Cap BlendiShares Core S&P Small-Cap ETF IJR QQQQ 24.2 6.8 11.4 34.8 20.4 0.34 1.3 96.50 11,667 1,003 0.17 0.03 05-22-00iShares Russell 2000 Index IWM QQQ 24.0 7.0 10.7 34.8 18.7 0.46 1.7 103.66 25,449 37,473 0.20 0.03 05-22-00PowerShares FTSE RAFI US 1500 Small-Mid PRFZ QQQQ 25.5 6.9 12.3 39.6 19.1 0.46 1.5 86.89 691 38 0.39 0.22 09-20-06Schwab U.S. Small-Cap ETF SCHA QQQQ 24.5 7.0 10.6 36.2 20.1 0.53 1.6 47.12 1,461 309 0.13 0.02 11-03-09Vanguard Russell 2000 Index ETF VTWO NR 24.0 7.0 10.7 34.7 1.3 83.03 258 52 0.15 09-20-10Vanguard S&P Small-Cap 600 Index ETF VIOO NR 24.1 6.8 11.3 34.6 1.2 87.11 83 7 0.15 09-07-10Vanguard Small Cap ETF VB QQQQ 23.7 6.7 9.5 35.2 19.9 0.35 1.5 100.07 7,130 381 0.10 1.02 01-26-04

Small-Cap ValueiShares Russell 2000 Value Index IWN QQ 21.6 6.4 9.1 33.9 16.9 0.60 2.2 90.98 5,760 936 0.25 0.15 07-24-00iShares S&P SmallCap 600 Value Index IJS QQQ 24.0 6.4 11.0 37.1 19.6 0.37 1.4 99.57 2,659 146 0.25 0.19 07-24-00Vanguard Russell 2000 Value Index ETF VTWV NR 21.6 6.4 9.1 33.9 1.6 79.29 47 5 0.20 09-20-10Vanguard S&P Small-Cap 600 Value Idx ETF VIOV NR 24.0 6.4 11.0 37.1 1.1 85.64 30 4 0.20 09-07-10Vanguard Small Cap Value ETF VBR QQQ 23.1 6.7 8.7 35.2 18.1 0.55 2.1 89.43 3,346 138 0.10 2.90 01-26-04WisdomTree SmallCap Dividend DES QQQQ 24.9 7.5 10.4 35.9 18.7 1.32 3.2 62.66 814 102 0.38 06-16-06

Small-Cap GrowthiShares Russell 2000 Growth Index IWO QQQ 26.4 7.6 12.3 35.5 20.4 0.27 1.2 120.00 5,709 1,180 0.25 0.13 07-24-00iShares S&P SmallCap 600 Growth IJT QQQQ 24.2 7.2 11.6 32.3 21.1 0.27 1.1 104.00 2,350 190 0.25 0.04 07-24-00Vanguard Russell 2000 Growth Index ETF VTWG NR 26.3 7.6 12.4 35.3 0.6 87.58 70 7 0.20 09-20-10Vanguard S&P Small-Cap 600 Gr Idx ETF VIOG NR 24.1 7.3 11.6 32.1 0.9 89.13 22 2 0.20 09-07-10Vanguard Small Cap Growth ETF VBK QQQ 23.4 6.7 10.4 34.3 21.2 0.12 0.9 109.93 3,115 137 0.10 1.62 01-26-04

CommunicationsiShares Dow Jones US Telecom IYZ QQQ 16.7 7.8 5.2 20.8 14.4 0.63 2.7 27.93 488 502 0.46 0.43 05-22-00iShares S&P Global Telecommunications IXP QQQ 10.0 3.2 -2.0 7.4 10.0 1.04 4.1 61.21 480 42 0.48 0.71 11-12-01Vanguard Telecom Services ETF VOX QQQQ 17.5 4.3 2.5 20.0 16.0 0.47 3.0 82.26 551 75 0.14 09-23-04

ConsumerConsumer Discret Select Sector SPDR XLY QQQQ 25.9 5.2 9.1 38.4 25.6 0.51 1.4 59.37 7,024 5,927 0.18 0.26 12-16-98Consumer Staples Select Sector SPDR XLP QQQQ 19.8 4.2 1.5 19.4 18.7 0.93 2.7 41.39 6,761 11,170 0.18 0.23 12-16-98iShares Dow Jones US Consumer Goods IYK QQQQ 21.4 4.2 3.7 26.2 18.9 0.45 1.9 90.44 470 23 0.46 06-12-00iShares Dow Jones US Consumer Services IYC QQQQ 25.2 5.7 8.5 34.7 25.1 0.28 1.2 108.34 444 30 0.46 06-12-00iShares Dow Jones US Home Construction ITB QQ 5.3 -0.5 -8.3 37.9 24.7 0.12 0.5 22.28 1,906 6,830 0.46 0.48 05-01-06iShares S&P Global Cons Discretionary RXI QQ 23.1 6.1 8.6 37.9 20.5 0.36 1.4 75.08 259 37 0.48 09-12-06iShares S&P Global Consumer Staples KXI QQQ 14.1 3.8 -1.6 17.3 16.4 0.57 2.4 83.01 596 29 0.48 09-12-06SPDR S&P Homebuilders XHB QQ 13.4 2.2 -0.6 44.0 27.6 0.38 0.5 30.09 2,251 7,865 0.35 0.32 01-31-06SPDR S&P Retail XRT QQQQ 31.5 6.7 11.6 40.4 30.5 0.43 1.3 81.68 1,181 3,076 0.35 0.23 06-19-06Vanguard Consumer Discretionary ETF VCR QQQQ 26.4 5.5 9.7 40.7 25.7 0.19 1.2 96.03 1,018 115 0.14 0.13 01-26-04Vanguard Consumer Staples ETF VDC QQQQ 21.1 4.5 2.6 21.2 19.2 0.38 2.4 106.62 1,546 72 0.14 0.14 01-26-04

20


5Yr Historical Growth % Price/ Price/ Covrg Average Assets Worst 3 Yr 3 Yr Price/ Cash Price/ Price/ Fair Rate Style Mrkt Cap # of in Top Turnover 3Mo Standard Sharpe Ticker Earnings Sales Cash Flow ROE Earn Flow Book Sales Value % Box ($Mil) Holdgs 10 % % Return % Deviation Ratio

Valuation Portfolio Style RiskFundamentals


MDY 8.6 5.2 9.5 13.5 19.3 10.8 2.2 1.2 48 5 4,033 400 7.3 14 -36.6 16.5 1.1VXF -3.0 -21.9 8.8 11.9 18.3 10.2 2.2 1.1 36 9 2,826 3,052 4.8 12 -38.3 17.5 1.1VO 11.6 6.5 6.8 15.6 18.5 10.7 2.4 1.3 1.1 79 5 8,680 368 5.9 17 -38.3 15.9 1.2IVOO 9.0 5.4 9.6 14.8 19.7 10.6 2.2 1.2 49 5 4,117 402 7.7 13 EZM 14.7 5.4 9.2 16.9 13.0 8.3 2.0 0.9 38 5 3,413 610 7.8 39 -34.7 17.2 1.2

MC ValDVY 6.3 -0.7 1.5 20.3 16.6 8.2 2.3 1.3 1.1 80 2 13,355 103 21.0 13 -30.1 9.9 1.7IWS 3.6 5.0 2.5 9.6 14.2 8.0 1.6 1.1 1.0 71 2 8,106 521 7.5 23 -36.7 15.0 1.2IJJ 1.3 4.1 3.4 10.8 18.9 9.3 1.8 0.9 43 2 3,666 295 9.5 38 -34.7 16.8 1.1VOE 3.9 5.2 9.8 12.2 15.4 7.9 1.7 1.0 1.0 70 2 6,864 255 8.8 33 -34.7 15.1 1.2IVOV 2.0 4.3 3.6 10.9 17.9 8.8 1.7 0.9 44 2 3,495 298 9.3 31 DON 2.1 3.7 3.0 14.3 17.9 8.0 2.2 1.1 62 2 5,081 366 12.3 33 -32.6 13.2 1.4

MC GrwRFG 27.8 9.3 26.0 16.4 18.8 13.5 2.9 1.6 48 8 4,164 94 21.9 56 -36.1 17.0 1.2IWP 11.8 8.7 9.7 20.3 23.9 14.2 4.6 1.8 1.1 75 8 9,495 490 7.1 25 -40.7 16.5 1.1IJK 19.5 7.8 19.2 16.3 22.2 14.2 3.3 2.0 51 8 4,967 227 13.9 46 -38.4 16.6 1.2VOT 14.5 6.6 5.4 18.8 22.3 13.4 3.7 1.7 1.1 75 8 7,848 239 10.0 38 -42.0 17.2 1.1IVOG 20.2 7.9 19.2 18.7 21.8 13.1 3.2 1.9 53 8 4,839 227 14.1 26

SC BlndIJR 7.0 4.6 7.6 11.7 20.8 11.7 2.1 1.2 12 6 1,382 602 5.5 12 -34.3 17.4 1.2IWM -9.8 -61.0 6.6 8.0 19.3 10.9 2.1 1.1 11 6 1,252 1,981 2.6 19 -35.6 18.6 1.0PRFZ -7.2 -60.3 0.4 9.0 18.2 8.9 1.8 0.8 13 6 1,141 1,480 2.4 30 -37.7 19.0 1.0SCHA 0.7 -42.8 7.0 10.2 18.9 10.2 2.1 1.1 20 6 1,785 1,773 2.3 12 -21.5 18.3 1.1VTWO 0.3 0.0 9.2 9.6 17.9 9.7 2.0 1.1 13 6 1,209 1,974 3.0 35 VIOO 7.0 4.6 8.6 11.9 19.8 10.7 2.0 1.1 14 6 1,271 601 5.3 12 VB -0.6 2.1 8.9 11.8 18.3 9.8 2.0 1.1 28 6 2,294 1,443 3.1 14 -37.0 18.1 1.1

SC ValIWN -2.2 -29.4 3.2 5.9 16.0 8.3 1.4 0.9 10 3 1,094 1,355 4.2 29 -32.5 17.6 1.0IJS 2.0 3.5 6.5 8.3 19.0 9.4 1.7 0.8 12 3 1,250 445 8.4 44 -33.0 17.9 1.1VTWV 1.0 -0.9 4.8 6.2 15.5 7.8 1.4 0.9 10 3 1,042 1,419 4.6 40 VIOV 2.6 3.5 7.2 8.9 17.6 8.4 1.6 0.8 13 6 1,127 445 8.6 29 VBR 0.3 0.2 6.7 8.9 15.1 7.7 1.5 0.9 23 3 1,701 1,009 8.1 25 -33.6 17.1 1.1DES 2.5 2.2 5.3 11.5 18.5 8.7 2.0 1.0 13 3 1,226 634 12.1 49 -28.8 15.0 1.2

SC GrwIWO -16.2 -70.8 11.7 10.1 24.3 14.1 3.9 1.5 12 9 1,430 1,129 4.9 32 -38.9 19.7 1.0IJT 15.3 7.6 9.5 15.4 22.9 15.4 2.8 1.9 12 9 1,533 354 10.6 47 -35.5 17.1 1.2VTWG 2.0 1.6 14.9 13.2 21.1 11.9 3.5 1.3 15 9 1,413 1,124 5.7 51 VIOG 15.0 7.6 11.1 15.0 22.4 14.5 2.7 1.8 14 9 1,433 352 10.1 45 VBK 7.0 4.7 10.0 12.9 21.5 12.4 3.0 1.3 24 9 1,850 953 8.1 37 -40.5 19.2 1.1

CommIYZ -25.9 3.5 -6.0 -1.7 29.7 4.8 2.1 0.9 1.0 68 2 6,580 26 61.1 40 -30.4 15.2 1.0IXP -17.3 1.1 -0.5 13.9 19.2 4.7 1.9 1.3 66 1 75,532 48 71.8 7 -25.1 12.2 0.8VOX -18.9 2.0 -1.5 -1.5 26.5 4.5 2.2 0.9 1.0 72 1 17,000 35 71.0 28 -26.6 13.0 1.2

ConsmrXLY 2.1 7.5 12.0 23.1 19.0 12.7 3.7 1.4 1.1 97 7 39,673 84 45.8 5 -32.5 14.0 1.7XLP 4.6 8.2 6.0 27.8 19.8 14.0 3.9 1.1 1.0 99 4 69,718 41 64.9 12 -17.6 9.3 1.9IYK -5.1 3.7 8.5 28.8 19.3 14.2 3.8 1.4 1.0 89 4 40,489 120 53.4 7 -20.0 10.1 1.8IYC 14.5 9.5 9.6 21.1 18.6 12.1 3.5 1.0 1.1 90 7 34,580 183 39.1 9 -30.5 12.9 1.8ITB 78.3 2.7 67.1 14.0 18.4 15.9 2.5 1.2 63 5 5,058 33 60.7 17 -43.1 25.6 1.0RXI 7.7 4.4 4.8 19.7 17.0 11.3 2.5 1.0 64 7 36,134 181 30.8 8 -34.1 15.8 1.3KXI -8.8 5.6 7.0 24.6 18.9 12.5 3.4 1.1 1.0 72 7 67,657 114 43.9 6 -17.8 10.8 1.5XHB 26.1 3.5 15.3 15.8 19.4 17.4 3.0 1.2 40 8 4,219 36 35.5 46 -39.6 23.0 1.2XRT 17.0 5.1 11.3 16.9 18.6 11.4 3.0 0.5 55 8 4,482 98 12.2 39 -42.7 17.6 1.6VCR 4.2 6.9 11.7 22.9 17.3 11.3 3.3 1.2 1.0 82 7 21,300 370 33.6 6 -35.9 15.2 1.6VDC 2.7 6.5 6.9 26.6 19.7 13.3 3.6 1.0 1.0 92 4 53,331 110 61.8 7 -15.3 9.2 2.0

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EnergyEnergy Select Sector SPDR XLE QQQQQ 16.5 5.1 5.7 20.7 17.2 0.57 1.7 82.42 8,197 10,741 0.18 12-16-98iShares Dow Jones US Energy IYE QQQQQ 15.9 5.2 5.5 19.3 16.7 0.35 1.6 46.94 1,357 536 0.46 0.24 06-12-00iShares Dow Jones US Oil & Gas Ex Index IEO QQQQ 18.6 4.8 6.1 26.2 15.2 0.17 0.9 75.05 360 86 0.46 0.45 05-01-06iShares S&P Global Energy IXC QQQQ 7.1 5.4 2.1 11.0 10.6 0.57 2.5 40.29 974 122 0.48 0.60 11-12-01JPMorgan Alerian MLP Index ETN AMJ QQQQQ 22.8 -0.6 0.3 20.5 16.9 1.83 4.6 46.26 5,744 820 0.85 5.22 04-02-09Market Vectors Oil Services ETF OIH NR 16.1 4.8 4.8 15.9 0.9 44.84 1,478 3,305 0.35 12-20-11SPDR S&P Oil & Gas Exploration & Prod XOP QQQQ 16.1 7.1 9.0 23.4 16.6 0.36 1.3 62.35 854 3,429 0.35 0.22 06-19-06Vanguard Energy ETF VDE QQQQQ 15.6 5.2 5.6 19.2 16.4 0.24 1.7 118.31 2,278 96 0.14 0.11 09-23-04

FinancialFinancial Select Sector SPDR XLF Q 25.8 5.4 9.9 42.1 13.5 0.55 1.5 20.49 16,732 49,792 0.18 12-16-98iShares Dow Jones US Financial Sector IYF QQ 23.9 4.8 8.1 37.5 14.1 0.37 1.4 74.98 1,226 405 0.46 0.49 05-22-00iShares Dow Jones US Financial Services IYG Q 29.0 6.6 14.0 48.9 14.0 0.25 1.0 76.23 582 103 0.46 06-12-00iShares S&P Global Financials IXG QQ 15.3 6.1 2.5 35.8 8.3 0.61 2.2 51.42 306 59 0.48 11-12-01SPDR S&P Bank ETF KBE Q 32.3 9.0 18.7 47.1 10.8 0.59 1.7 31.24 2,541 1,605 0.35 0.43 11-08-05SPDR S&P Regional Banking ETF KRE QQQ 32.5 8.7 18.9 39.3 17.8 0.62 1.6 36.80 2,200 3,588 0.35 0.41 06-19-06Vanguard Financials ETF VFH QQ 24.0 5.2 8.0 37.9 13.8 0.44 1.9 41.94 1,521 266 0.14 0.09 01-26-04

Health CareHealth Care Select Sector SPDR XLV QQQ 28.9 7.3 8.3 35.4 23.8 0.66 1.6 51.02 7,380 8,433 0.18 12-16-98iShares Dow Jones US Healthcare IYH QQQ 29.7 7.6 8.4 36.2 24.3 0.33 1.3 107.71 1,545 241 0.46 0.50 06-12-00iShares Nasdaq Biotechnology IBB QQQQ 44.3 13.9 14.9 48.4 35.2 0.07 0.3 197.83 3,738 782 0.48 0.28 02-05-01iShares S&P Global Healthcare IXJ QQQ 23.8 5.5 4.2 31.0 21.7 0.49 1.8 78.79 814 70 0.48 0.75 11-13-01SPDR S&P Biotech XBI QQQ 39.2 17.3 15.7 36.4 30.9 0.04 0.2 122.57 992 332 0.35 0.12 01-31-06SPDR S&P Pharmaceuticals XPH QQQQQ 36.6 8.1 15.6 34.1 26.0 0.44 1.5 76.21 510 88 0.35 06-19-06Vanguard Health Care ETF VHT QQQ 29.7 7.7 9.0 36.4 24.5 0.25 1.3 92.99 1,944 177 0.14 0.11 01-26-04

IndustrialsIndustrial Select Sector SPDR XLI QQQ 20.2 5.8 9.5 29.0 16.7 0.71 2.0 45.16 5,679 10,701 0.18 12-16-98iShares Dow Jones US Industrial IYJ QQQ 20.7 6.0 8.7 31.1 18.1 0.34 1.4 87.91 1,183 251 0.46 0.44 06-12-00iShares S&P Global Industrials EXI Q 14.9 5.6 5.2 26.2 12.8 0.49 1.9 62.33 246 37 0.48 09-12-06Vanguard Industrials ETF VIS QQQ 21.5 6.1 9.7 32.7 17.8 0.26 1.7 86.62 1,014 124 0.14 0.08 09-23-04

Natural ResourcesFlexShares Mstar Gbl Upstrm Nat Res ETF GUNR NR -8.3 2.7 -6.1 -2.1 1.1 32.35 2,447 342 0.48 09-16-11iShares Dow Jones US Basic Materials IYM QQQ 3.5 5.8 2.2 13.9 7.9 0.45 2.2 71.02 511 118 0.46 0.40 06-12-00iShares North American Natural Resources IGE QQQQ 7.5 5.8 3.9 12.8 8.8 0.34 1.7 40.58 2,005 367 0.48 0.53 10-22-01iShares S&P Global Materials MXI QQ -8.3 4.8 -3.5 3.7 1.0 0.55 2.6 56.03 383 34 0.48 0.64 09-12-06Market Vectors Agribusiness ETF MOO QQQ -5.8 -2.7 -8.2 2.2 7.6 0.36 2.0 49.81 4,656 368 0.54 08-31-07Materials Select Sector SPDR XLB QQQQ 9.0 5.6 3.1 19.0 11.2 0.83 2.4 40.48 2,831 6,136 0.18 12-16-98SPDR S&P Metals & Mining XME Q -20.4 7.4 -3.7 -9.2 -9.5 0.39 1.7 35.67 572 3,207 0.35 0.20 06-19-06Vanguard Materials ETF VAW QQQQ 8.7 5.7 3.2 20.6 13.2 0.31 1.7 91.52 791 49 0.14 0.11 01-26-04

Real EstateSchwab US REIT ETF SCHH NR 6.4 0.8 -7.0 6.3 2.2 32.31 579 153 0.10 01-13-11SPDR Dow Jones REIT RWR QQ 6.3 0.7 -7.0 6.2 14.4 1.13 3.0 76.50 2,239 236 0.2

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ETF_0813