ETF Trade Strategy. Portfolio & Derivatives Strategy. Leveraged ETFs failing to Deliver? Key points

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1 Portfolio & Derivatives Strategy North America Market Commentary 13 October 211 Phil Mackintosh ETF Trade Strategy Victor Lin Triple Trouble Key points Leveraged ETFs have been, by far, the most popular new category of ETF in the past 2 years. Despite a quadrupling of volatility leveraged long and short ETFs have attracted significant assets and even more liquidity. There are now a selection of triple ETFs to complement the wide range of double long and short ETFs. Back in 28, we highlighted the path dependency of these ETFs, by modelling the indices they track. In this report we review realized returns that have surprised many longer-term investors (despite our earlier report). We show that tracking error is not (generally) responsible for the underperformance of the ETFs But the huge increased in volatility has contributed significantly to longer term underperformance (vs beta x1 indices) We also show that leveraged ETFs will outperform in periods of momentum with lower volatility making them a dangerous short. Especially in the current high volatility, leveraged ETFs work better as extremely short-term trading tools than for long-term investments. Leveraged ETFs failing to Deliver? We first focused on the leveraged ETF products back in 28, in our Double Trouble report. This report goes into more depth on some of the surprising performance we ve seen thanks to the way leveraged index math works and highlighted the path dependency and volatility exposure of these leveraged ETFs. Despite volatility more than quadrupling in the past 2 years, the double ETFs have been the single most popular recent invention in the ETF market. So popular in fact, that Direxion have recently launched triple ETFs in November of 28. These have already attracted $1.8bn in assets. But in the press, leveraged ETFs continue to attract criticisms: Many long-term holders have underperformed 2x their underlying beta. A sign that few understand how leveraged indexes perform. In addition, the media seems to blame the leveraged products for MOC volatility. A factor we think is being over-exaggerated. We think neither criticism is fair and show why in this report. Long Term Performance Misses? Some double short indexes are actually down over the last 12 months, even though their underlying long benchmarks are also down, sometimes over 5%. Most often cited are the double short REIT, Oil and Financials ETFs; where short trades have actually exaggerated underperformance, instead of acting as a hedge or short view. (See Exhibit 1 Double short oil & gas [DUG] vs Long Energy [IYE]). Exhibit 1: IYE vs DUG Performance

2 Exhibit 2: Misconceptions Run High in Volatile Times (Spread b/w what S&P 5 2x funds return and what many mistake them to return using a one month holding period) 25% 2% 15% 1% 5% % -5% -1% -15% -2% 1/2/1998 1/2/2 1/2/22 1/2/24 1/2/26 1/2/28 Exhibit 3: 12 Month performance (vs Underlying) Tracking error is not the cause The media, and general investors, commonly blame this underperformance on tracking error however, this is rarely the biggest factor. Many investors mistake the leveraged ETFs to return a multiple of the underlying index regardless over their total holding period, whether it be days or months. In fact, these ETFs are only designed to double (or triple) the return of the underlying index each day (as are the underlying benchmarks). As a result, the best way to prove that a leveraged ETF is performing as mandated is to look at daily correlations vs the underlying index. As we show in Exhibit 4 below, correlations for SKF are almost perfectly positive or negative with a Beta of close to +/-2, despite it s long-run returns failing to hedge UYG. Over the course of just a month, this can cause significantly different performance than the expected 2x or 3x notional. But it s not always a drag on performance. Exhibit 2 shows the difference between 2x rolling month returns and what the ETF return was. We also highlight that this difference is especially large during volatile markets (like we ve experienced recently). Exhibit 4: Correlation (with Underlying) $22x2 $22x1%x2 = $44 $1x2 $1x1%x2 = $2 $2x1%x2 = $4 $2x2 Pair Statistics Correlation -.99 R-Squared.98 Beta -.99 Pair Statistics Correlation -.96 R-Squared.93 Beta

3 Exhibit 5: Volatility Drag Increases Exponentially For a Constant Level of Expected Return 25% 2% 15% 1% 5% % -5% -1% Realized Return Volatility Drag Expected Return -15% % 2% 4% 6% 8% 1% 12% 14% 16% 18% 2% Volatility Dynamic Rebalancing in Practice Imagine 2 investors buy a double-long ETF, one day apart.but the market moves 1% on the first day: 1. The first investor buys when the ETF (and the underlying) are both $1 2. The double long ETF will use his $1, and double it (using futures or a swap) creating $2 exposure to the market 3. The 1% return will generate $2 profit ($2 x 1%) in the ETF. This matches the 2% return (1% x 2) that the ETF was designed to achieve. Consequently, the ETF should close at $12, equal to its net asset value. 4. The assets in the ETF at the end of day 1 are $22 ($1+$1+$2) 5. On Day 2, Investor 2 wants to go double long. Ignoring overnight risk, this will cost $12 for the ETF 6. Investor 2 expects to have $24 ($12 x 2) in the market. 7. However, as we noted in step 4, the underlying ETF assets are just $22. To overcome this problem leveraged ETFs need to trade MOC of Day 1 to also leverage their unrealized gains. What about UltraShort? Using the same approach, a leveraged short ETF would need to reduce its unrealized losses (or buyback some of its short positions). A Hidden Investment Strategy Confounding Compounding Problem To fully understand the long-term performance of these ETFs it is important to understand the underlying index mathematics which we included in the appendix of our Double Trouble report. Most importantly, indexes use geometric returns where the return from each day compounds on the prior days portfolio. (As a quick example, consider these daily returns [+2% and -1%]. The simple average (or arithmetic mean) would be 5%, whereas the geometric mean, and what the investor actually realizes, is 4%. The difference between the compounded return and simple average return can be considered the volatility drag. A common approximation relating the geometric mean with the arithmetic mean is: Geometric mean Arithmetic mean (.5 x volatility^2) How does this relate back to leveraged ETFs? The index math doubles, or triples the compounding effect as we discuss below. ETFs Reset Daily: Magnifies Compounding Problem When you buy an investment on credit, as the investment goes up, your leverage actually reduces. (Leverage = Assets/(assets-borrowings), so as assets increase, leverage falls). However it would be very difficult to explain to a 3x ETF buyer that today s leverage is actually 2.7x, because the market is up recently. To ensure every new investor gets the advertised leverage ratio, the ETFs (and the indices) reset their leverage at the end of each day. This ensures a constant leverage ratio. We discuss the mechanics of this in the sidebar. In summary: The ETFs must be double the previous nights close each day. After making a gain they need to increase their exposure. After making a loss they need to decrease their exposure (& repay borrowings). This means the ETFs need to rebalance each night. This trade is larger on volatile trade days. Both (long and short) ETFs buying on up days, and selling on down days (for example, on a down day, the short ETF makes gains and the long ETF has losses so the short ETF has to short more to add to exposure while the long ETF sells to reduce exposure) This re-investment process is similar to negative gamma experienced by Options market makers. As the ETF buys high, and sells low, in a volatile market, this reduces performance. However in a trending market, it compounds gains on gains exaggerating the momentum, and exposure. Exhibit 6: Leverage & Compounding $1 Double Long +1% $12 $ % Bought $2 more stock at $11 Average price is now >$1 $1 = $11 x (1-9.9%) $98.18 = $12 x (1-9.9%) 3

4 Exhibit 7: UCO (daily reset) vs DXO (monthly reset) DXO (Monthly reset) UCO (Daily Reset) Oil Future (front month) 11/25/8 12/15/8 1/4/9 1/24/9 2/13/9 Exhibit 9: Daily vs Monthly Reset, 2x Long Crude x monthly (DXO NAV) 2x daily DBOLIX Index 6/17/8 8/17/8 1/17/8 12/17/8 2/17/9 Exhibit 11: DXO Underlying Diverges from UCO Underlying DJAIGCL (UCO Underlying) DBOLIX (DXO Underlying) WTI Crude Front Mo. Future 1/2/8 3/2/8 5/2/8 7/2/8 9/2/8 11/2/8 1/2/9 Does Reset Frequency Matter? Almost all leveraged ETFs currently listed have their leverage reset daily in the rebalancing process described in this report. However, some ETFs such as the PowerShares DXO (2x long crude) are reset monthly. Rather than receiving twice the daily return of the underlying index, investors receive twice the monthly return. Does this significantly affect returns? To compare, we calculate a theoretical 2x leveraged daily reset version of DXO. In general, the two have similar performance when volatility is on the lower side. However, high volatility levels widen the performance differential between the daily and monthly reset series as it magnifies the effect of leverage and the short gamma exposure described earlier. Exhibit 8: Daily and Monthly Reset Differences Magnified in High Volatility Spread V o la tility 6/17/8 8/17/8 1/17/8 12/17/8 2/17/9 Cumulative Spread We show (below, Exhibit 2) an exaggerated example between daily and weekly leverage resets to illustrate how extremely volatility can affect their performance. While it s unlikely such a scenario would occur, it does show how high volatility and different reset periods can significantly affect returns. Exhibit 1 : Daily vs Weekly Reset Example 2 x 2x ETF Rolling Weekly 2 x Weekly 2x ETF Index Return Return (daily) Return Return (weekly) Day % -5% % -5% 5 Day % -32% % -74% 26 Day % -6% % -78% 22 Day % -33% % -98% 2 Day % 192% % % 1 Underlying Matters Most! So does the reset frequency explain the difference in performance between the monthly reset DXO and the daily reset ProShares UCO that many investors have noted recently? It does partly contribute, but in this particular case, it s the benchmarks that are more responsible for the difference than the reset period. As with all ETFs the underlying benchmark can be very important. Especially for futures based ETNs, used in many crude oil ETFs, the roll treatment and calculation can, in fact, be quite different. While both DXO and UCO track crude futures based indices, DXO tracks the Deutsche Bank Optimum Yield Crude Oil index while UCO tracks the Dow Jones-AIG Crude Oil sub index. The two had been tracking closely, but have recently diverged the primary reason being that the indices use different rules which may result in the selection of different expiration contracts or rolling futures at different times. Realized Volatility 4

5 Portfolio & Derivatives Strategy Exhibit 12: Historic performance of 2x Long and Short Funds using real market returns and volatility 5. Bull Bear 1/1/97 1/1/99 1/1/1 1/1/3 1/1/5 1/1/7 Bull Ultra Bull Ultra Bear S&P 5 Bear 1/1/9 Path Dependency is Misunderstood Short Term Trading Tool: Ride Momentum! Especially at the moment, it feels like both long and short ETFs underperform but that s not always the case. Because of the dynamic rebalancing of the ETF (discussed above) there is a significant degree of path dependency of returns. Those who perfectly time the market can ride momentum and substantially increase gains (as Exhibit 12 shows during trending markets, the leveraged ETFs give outsized exposure to the overall trend). But long-term holders can easily find: Gains increase exposure to a market correction. Note how in the 199 s bull market, and UltraBull ETF pulled back sharply each time a small correction occurred. Losses reduce exposure to a market correction. Note from Exhibit 12, that a double long ETF bought in 21 would have underperformed so much in the bear market, that it would not have recovered to par even with the underlying stock market hitting new highs. Range-bound markets decay. Range bound markets, like those highlighted in 24, are not suited top leveraged ETFs. Performance will show a gradual decay thanks to the borrow costs, and negative stock timing. Consequently, these products are generally better suited to active traders. Long Term Holders note: Good in Momentum, Bad when Volatility Rises, or Markets Turn By simulating specific market regimes, we can more clearly illustrate the risks & benefits of holding a leveraged ETFs. Exhibit 13: Good in Trending Markets ULTRA-SHORT $33 Share Price $28 $23 $18 $13 -SPY SPYx-2 UltraShort $ Exhibit 14: Good in a Trending Market ULTRA LONG $43 Share Price $38 $33 $28 $23 $18 $13 $8 SPY SPYx2 Ultra D Exhibit 15: Bad thru Market Cycles ULTRA-SHORT $2 $18 -SPY $16 SPYx-2 $14 UltraShort $12 $1 $8 $6 $4 $2 $- Share Price Days Exhibit 16: Bad thru Market Cycles Share Price $2 $18 $16 $14 $12 $1 $8 $6 $4 SPY SPYx2 Ultra ULTRA LONG Days Exh 17: Range Bound & Volatile Market Share Price $15 $125 $1 $75 ULTRA-SHORT SPYx-2 -SPY UltraShort $ This shows that Leveraged ETFs: Work well in a trending market as momentum and compounding can combine to produce significant outperformance (Exhibit 13 & 14). Compounding leaves them adversely exposed to shifts in market cycles (Exh 15 & 16) Suffer in range bound markets, especially when volatility is high. 5

6 Exhibit 18: Assuming a Mean, Median Returns Decrease as Leverage and Volatility Increases 1% % -1% -2% -3% -4% -5% -6% -7% -8% -9% 1x 2 times 1x 2x short 2x 3x short 3x 1% Volatility 1% Volatility 2% Volatility 4% Volatility 6% Volatility Exhibit 19: Assuming a Mean, Magnitude of Beta For Shorts Drops Off As Leverage and Volatility Increases Volatility Makes Long Term Returns All Skewed Up! The impact of volatility can also be seen by modeling the long term distribution of leveraged ETF returns. Under the assumption that daily returns are independent and identically distributed, compounding these returns creates a lognormal distribution. A typical lognormal distribution is positively skewed where the median value is less than the mean. The higher the volatility of returns, the more pronounced the skew and the less likelihood that an investor will achieve at least the mean return. In Exhibit 2, we visually depict the impact of 1%, 2%, and 4% volatility on a Zero-mean lognormal distribution. This highlights that although the average remains = zero, the median becomes increasingly negative as volatility increases. This means a higher proportion of observations will underperform (however it also means there are some extreme winners ). With little volatility, investors would expect to outperform the mean almost half of the time. As volatility increases, this percentage decreases. Thus, the more volatility, the more skewed the distribution, and the less likelihood investors will be on a path that achieves at least the expected return. Exhibit 2: Distribution Becomes More Skewed As Volatility Increases % Volatility 1% Volatility 2% Volatility 4% Volatility 6% Volatility short 2x short 3x Return (%) Exhibit 21: Leveraged ETF 2x Index Spread Magnified in High Volatility Times Spread (%) 1% 5% % -5% -1% -15% Long Spread Short Spread Volatility -2% 12/9/98 12/9/ 12/9/2 12/9/4 12/9/6 12/9/ Volatility (%) And Distorts Concept from Reality Another way to look at this is to consider the difference between a whole month leveraged return (using a margin account) vs holding the ETF for a month. We can see from Exhibit 21 that the difference a margin account and an ETF return differs most in high volatility regimes. The fact that realized volatility has been running (well) above 4% for most of the last 9 months is exceptional and is adversely impacting these ETFs. In such a high volatility environment, derivatives such as futures, swaps, and options may be better suited for investors looking for leveraged exposure over a longer time horizon. In lower volatility regimes, the differences between leveraged returns should be less significant. 6

7 Zero Sum Game? So you ve worked out the releveraging math and you re willing to trade every day to rebalance a $1 portfolio back to 2 equal $5 legs (double long & double short). Does an arbitrage exist from them somehow by buying (or shorting) both. Theoretically holding both should yield: cash returns - carry costs. In fact, this relationship has held true (except for the period last year where there were short sell restrictions which severely hampered the efficiency and arbitrage of inverse ETFs), as we show below. Ultrashorts + Ultralongs = ~, Except During Short Sell Restricted Period 8% 6% 4% 2% % -2% -4% -6% -8% SSO + SDS UYG + SKF Short sell restrictions -1% 1/2/28 4/2/28 7/2/28 1/2/28 1/2/ SSO + SDS UYG + SKF 1/2/28 5/2/28 9/2/28 1/2/29 Exhibit 22: 3x Leveraged ETFs Name Ticker Assets Direxionshares Financial Bull 3X Shares FAS $ 45,775,5 Direxionshares Large Cap Bull 3X Shares BGU $ 362,461,6 Direxion Financial Bear 3X Shares FAZ $ 248,422,7 Direxionshares Small Cap Bull 3X Shares TNA $ 225,949,5 Direxionshares Large Cap Bear 3X Shares BGZ $ 185,635,9 Direxionshares Small Cap Bear 3X Shares TZA $ 133,244,6 Direxionshares Energy Bull 3X Shares ERX $ 92,65,45 Direxion Energy Bear 3X Shares ERY $ 27,339,64 Direxionshares Emerging Markets Bear 3X Shares EDZ $ 18,437,5 Direxionshares Emerging Markets Bull 3X Shares EDC $ 16,294,95 Direxionshares Technology Bull 3X Shares TYH $ 15,546,96 Direxionshares Developed Markets Bear 3X Shares DPK $ 9,425, Direxionshares Technology Bear 3X Shares TYP $ 6,343, Direxionshares Developed Markets Bull 3X Shares DZK $ 2,88,26 Triple Leveraged ETFs Take Off With the popularity of the double leveraged ETFs, Direxion launched 8 triple leveraged ETFs in November and another 6 in December of 28. Rather than shy away amidst record volatility, traders have embraced these ETFs, which have already attracted $1.8bn in assets. Exhibit 23: BGU (3x Long Russell 1) vs SSO (2x Long S&P 5) Exhibit 24: Daily Return Distribution of S&P 5, 2x, & 3x S&P 5 2x 3x Functionally, triple leveraged ETFs work the same way as double leveraged ETFs with the obvious difference being that leverage, and consequently volatility, is increased. As leverage increases, return distributions become less peaked and fatter tailed, meaning a higher frequency of large moves. Additionally, as previously discussed, a 3x ETF will effectively increase volatility by at least a factor of 3, leading to a more heavily skewed long-run lognormal distribution. 7

8 Exhibit 25: S&P Intraday Volatility High at Open/Close 3-min Intraday Price Movement 3.% 2.5% 2.% 1.5% 1.%.5%.% 9:3 1:3 11:3 12:3 Average daily amount of rebalancing required for leveraged ETFs: ~ $1.4 billion total assets in leveraged ETFs: ~ $2 billion assuming an average magnitude intraday move in the S&P of: 3.5% average amount needed to trade each day to readjust to market moves: ~ $3 billion Total amount of trading in the last 3 min: ~$7 billion Amount of EOD Trading from Leveraged ETFs: $3bn/$7bn ~ 4% Exhibit 27: High Rate of End of Day Reversals Move Prior to 3 PM Continues in Last Hour? No 38% 13:3 Yes 62% 14:3 15:3 Jul-8 Aug-8 Sep-8 Oct-8 Nov-8 Dec-8 Jan-9 Feb-9 Exhibit 28: Financials Exhibit Higher Frequency Same Direction Moves Energy Financials Real Estate Opposite 44% 32% 28% Same 56% 68% 72% Are Leveraged ETFs Increasing Close Vol? As we highlighted in our report, Intraday Rollercoaster Rides: Market Moves at Day s End, in late 28 closes became very volatile and difficult to trade. There was some media speculation that leveraged ETFs were a big contributor. We think this was overplayed: 1. Leveraged ETFs are less than 2% of end-of-day trading As we showed earlier, leveraged ETFs need to trade into the close to realign their exposure appropriately for the following day s open. We estimate that this rebalancing accounts for less than $1.5 billion (see sidebar), while around $7 billion trades in the last half-hour based on our calculations, leveraged ETFs are only a very small percentage of that ($1.5 billion / $7 billion = 2%). Exhibit 26: Closing Volumes Likely Dwarf Leveraged ETF Rebalancing 2: 8% 7% 6% 5% 4% 3% 2% 1% % 2:3 9:3 3: ( ) 1: 3:3 1:3 4: 11: 11:3 12: Jul-8 Aug-8 Sep-8 Oct-8 Nov-8 Dec-8 Jan-9 Feb-9 12:3 1: 1:3 Average Daily V olume Curves Over Time (S& P 5) c26-27 t-8 Nov-8 Jul-8 Dec-8 Aug-8 Jan-9 Feb-9 Sep-8 Oct-8 Nov-8 Dec-8 Jan-9 Feb-9 2: 2. Leveraged ETFs should ADD to the day s moves As we also showed earlier, leveraged ETFs (both long and short) are short gamma, meaning they must buy when the market goes up and sell when the market goes down. Thus, their activity into the close should confirm the trend from the rest of the day. However, we find that in the most volatile months (Oct & Nov 28), the market reversed the intraday trend in the last hour 4% of the time. The opposite direction to the leveraged ETF trades. 3. Momentum is already a strong factor According to our Quant research teams analysis, momentum has been one of the best performing factors in the past 12 months. This would partially explain some momentum into close. We also highlight that the leveraged ETFs trade should occur MOC, to eliminate basis risk for the ETF performance. Despite this, we observe market rallies and sell-offs have often occurred an hour (or more) before the end of the day. Effect More Apparent at Sector Level? Although the magnitude of ETF trading on broad market indices is small, we acknowledge that the rebalancing may have more influence on less liquid stocks or at the sector level. We examined financials, energy, and real estate sectors, that all have significant leveraged ETFs assets. Since last September, the direction of the last hour confirmed the prior day s move much more frequently in financials and real estate, and the frequency of same direction moves was higher in financials and real estate than in prior years. This would seem to suggest that the leveraged ETFs may be partly contributing to end of day sector moves. 2:3 3: 3:3 9:3 4: 1: 1:3 11: 11:3 12: 12:3 1: 8

9 Use EDGE to test out ETF tracking Search Leveraged ETFs with EDGE With over 8 ETFs listed in the US alone, traders have plenty of options for getting exposure to different industries and even different asset classes now. To find the right ETF for your portfolio: visit our website at select Trade Ideas > ETFs from the menu bar enter desired keywords in the search box (e.g. leveraged ) Plot correlations and returns with EDGE 1. Enter tickers 2. Select chart type 3. Input date range Pair characteristics 9

10 Credit Suisse Portfolio & Derivatives Strategy USA Phil Mackintosh Edward K. Tom Victor Lin Glenn DeSouza Sveinn Palsson Ana Avramovic Europe Stanislas Bourgois stanislas.bourgois@credit-suisse.com Colin Goldin colin.goldin@credit-suisse.com Laurent Boldrini laurent.boldrini@credit-suisse.com Marwan Abboud marwan.abboud@credit-suisse.com Raymond Hing raymond.hing@credit-suisse.com Sikander Samar sikandar.samar@credit-suisse.com Asia Murat Atamer murat.atamer@credit-suisse.com preliminary and subject to our formal written confirmation. Market Commentary Disclaimer Please follow the attached hyperlink to an important disclosure: Structured securities, derivatives and options are complex instruments that are not suitable for every investor, may involve a high degree of risk, and may be appropriate investments only for sophisticated investors who are capable of understanding and assuming the risks involved. Supporting documentation for any claims, comparisons, recommendations, statistics or other technical data will be supplied upon request. Any trade information is preliminary and not intended as an official transaction confirmation. Use the following links to read the Options Clearing Corporation's disclosure document: Because of the importance of tax considerations to many option transactions, the investor considering options should consult with his/her tax advisor as to how taxes affect the outcome of contemplated options transactions. This material has been prepared by individual traders or sales personnel of Credit Suisse and its affiliates ('CS') and not by the CS research department. It is not investment research or a research recommendation, as it does not constitute substantive research or analysis. It is provided for informational purposes, is intended for your use only and does not constitute an invitation or offer to subscribe for or purchase any of the products or services mentioned. The information provided is not intended to provide a sufficient basis on which to make an investment decision. It is intended only to provide observations and views of individual traders or sales personnel, which may be different from, or inconsistent with, the observations and views of CS research department analysts, other CS traders or sales personnel, or the proprietary positions of CS. Observations and views expressed herein may be changed by the trader or sales personnel at any time without notice. Trade report information is CS may, from time to time, participate or invest in transactions with issuers of securities that participate in the markets referred to herein, perform services for or solicit business from such issuers, and/or have a position or effect transactions in the securities or derivatives thereof. The most recent CS research on any company mentioned is at Backtested, hypothetical or simulated performance results have inherent limitations. Simulated results are achieved by the retroactive application of a backtested model itself designed with the benefit of hindsight. The backtesting of performance differs from the actual account performance because the investment strategy may be adjusted at any time, for any reason and can continue to be changed until desired or better performance results are achieved. Alternative modeling techniques or assumptions might produce significantly different results and prove to be more appropriate. Past hypothetical backtest results are neither an indicator nor a guarantee of future returns. Actual results will vary from the analysis. Past performance should not be taken as an indication or guarantee of future performance, and no representation or warranty, expressed or implied is made regarding future performance. The information set forth above has been obtained from or based upon sources believed by the trader or sales personnel to be reliable, but each of the trader or sales personnel and CS does not represent or warrant its accuracy or completeness and is not responsible for losses or damages arising out of errors, omissions or changes in market factors. This material does not purport to contain all of the information that an interested party may desire and, in fact, provides only a limited view of a particular market. 1