A Smart Approach to Smart Beta: Bridging the gap between active and passive with the Chaikin Power Gauge

RESEARCH PAPER Academic analysis of an investment approach A Smart Approach to Smart Beta: Bridging the gap between active and passive with the Chaikin Power Gauge Authors Marc Chaikin CEO, Chaikin Holdings Carlton Neel COO, Chaikin Investments Salvatore Bruno CIO, IndexIQ Smart beta investing, also known as strategic beta, builds on academic research to offer potential risk/return enhancements over traditional market capitalizationweighted indices. These strategies evolved from equal-weighted indices that were created to help address the shortcomings of traditional index construction, which by its very nature, unduly overexposes investors to the price movements of the largest stocks in an index, often at significantly elevated levels, relative to other holdings. The term now encompasses portfolios based on both single- and multi-factor inputs that can be applied through rules-based portfolio construction to create smart beta indices, though single-factor-based indices may also expose investors to unanticipated risks, as the popularity of an individual factor can result in extreme valuations for that particular factor. Multi-factor investment models have been around for decades, evolving from forecasting risk to predicting returns. The original roots of factor investing trace back to the fundamental valuation techniques developed by Benjamin Graham and David Dodd. The seminal Modern Portfolio Theory research by academics, such as Dr. Harry Markowitz, William Sharpe, and Eugene Fama and Kenneth French, was the first steps in decomposing stock risks into systematic factors i.e. the market, size, and value) and company specific-drivers. These theories were augmented by the research of Robert Haugen and Zhiwu Chen, Mark Carhart, and Richard Roll and Stephen Ross, who introduced the concept of multi-factor models by assuming that markets were actually inefficient. In this paper, we will show how the multi-factor Chaikin Power Gauge (CPG) bridges the gap between active and passive management by analyzing stocks based on four primary factors: Value, Growth, Technical, and Sentiment, including 2 sub-factors within these four categories. It accomplishes this by incorporating factors that many institutional investors typically evaluate when deciding to buy or sell a stock. Applying these rigorous ratings in a top-down, rules-based construction methodology results in the NASDAQ Chaikin indices that have delivered excess returns since their inception three years ago. The indices are equally-weighted groups of securities, reflective of the characteristics that active managers of different styles and time horizons favor, using a passive approach to rebalance each portfolio annually. 1

Building on Steady Research Advancements Active investors have been trying to beat the market for decades. The fathers of fundamental analysis, Benjamin Graham and David Dodd 1, developed the intellectual framework for what would later be known as value investing in their quest to buy assets on the cheap, spawning a generation of successful investors, including Warren Buffett, Bill Ruane, and Seth Klarman, who all adopted many of Graham and Dodd s valuation techniques. The subsequent pursuit to generate returns that outperformed a market index usually involved traditional fundamental approaches, utilizing either value or growth metrics, but rarely a combination of both, as pension fund consultants held investment managers to rigorous style purity tests. What was often overlooked, however, was the very nature of the index against which the investment manager was being measured and the inherent risk in the manager s approach. As academics, such as Dr. Harry Markowitz in the 195s, William F. Sharpe in the 196s, and Eugene Fama and Kenneth French in the 198s and 199s, began researching portfolio optimization and risk, the roots of Modern Portfolio Theory were planted. Markowitz s Modern Portfolio Theory and Sharpe s Capital Asset Pricing Model 2, 3, 4, which used Markowitz s optimization tool, both assumed an efficient market. Others, like Burton Malkiel 5, followed with his random walk theory. Fama and French 6, 7, followed by Chen, Carhart, Roll, and Ross 8, 9, then introduced the concept that quantitative factors, such as volatility, size, value, quality, and later momentum, could be used to construct stock portfolios that could generate above-average returns with lower risk. Both fundamental and quantitative analysts have been successful during the past 3 years in identifying stocks that could outperform the broader market. However, as the impact of management fees and risk on performance was weighed against pure index returns, the idea of constructing portfolios that essentially duplicated capitalization-weighted indices at a lower cost was embraced. As indexing became more popular, some of the inherent flaws in investing, based on traditional market capitalizationweighted indices, became evident. Approaches,such as chasing momentum or style concentration risk, led to the birth of the alternative index creation movement, currently referred to as strategic beta or smart beta. At its core, smart beta refers to strategies that combine single- or multi-factor research elements typically utilized by active managers, with a rules-based portfolio construction methodology typical of passive managers. Holdings are usually weighted by some criteria other than market capitalization, in an effort to optimize risk/return exposures. Figure 1: Smart beta ETF evolution First Stock Index Cap-Weighted ETFs Single-Factor ETFs Equal-Weighted Multi-Factor ETFs Custom-Weighted Multi-Factor ETFs 2

Smart beta indices evolved from the simple concept of equal-weighted indices or indices that were constructed using other single-factor weights, such as volatility or dividend yield, to indices based on multiple factors. In the late 199s, a new generation of academics, led by Robert Haugen 1, identified groups of factors he believed could predict future stock returns that went beyond the traditional three-, four-, or fivefactor models of Fama and French, as well as Roll and Ross. These multi-factor models have taken two distinct paths in index or portfolio construction: the sleeve approach or the integrated approach. The sleeve approach builds separate portfolios, typically based on four or five individual factors reflecting value, growth, quality, volatility, size, and price momentum, and then combines them. The integrated approach blends multiple factors into one model to create a single output that enables the resulting portfolio to be more responsive to the styles that are more attractive at any given point in time. The Chaikin Power Gauge described in this paper is an integrated approach. Figure 2: Strategic beta overview Combining factors that active managers have traditionally utilized with a rules-based quantitative approach that equally weights index constituents results in a more robust index construction solution. Traditional Passive Index Inputs Rules-Based Quantitative Approach Strategic Beta Solution Active Management Inputs Chaikin Power Gauge (CPG): A Comprehensive Multi-Factor Research Methodology Professional portfolio managers have a long history of utilizing data analytics to find factors that might identify stocks that are mispriced either cheap or expensive, relative to their peers. Even investors, such as Graham and Dodd, who are seen as more fundamental in their analysis, relied heavily on free cash flow as a factor in projecting long-term corporate performance and, by extension, a company s future stock returns. As the ability to analyze and process company-specific data became faster and easier with more powerful computers and robust data providers, investors and academics alike gravitated toward building quantitative models to gain an edge over capitalization-weighted market indices. Robert Haugen 11 and Nardin Baker conducted seminal research in this area, identifying several factors, such as price-to-sales ratio and price-to-cash-flow ratio, that have withstood the test of time. However, many of the quantitative models include an implicit value or growth bias and are often optimized for a specific type of market environment, commonly the type of climate most recently experienced. This may introduce unintended risks for long-term investors (e.g., momentum models work well in a trending market, but are of little value in a choppy, trendless market). As the construction of the CPG model took shape, it was obvious that this narrow nature of many existing quantitative models presented a major investment weakness. To develop more robust stock analysis, Marc Chaikin leveraged his 4 years of experience working with institutional investors and quantitative analysts to create a model that reflects how active portfolio managers make investment decisions. The resulting factor-based research is built on four distinct and complementary primary factors: Value, Growth, Technical, and Sentiment. This combination reflects the differing styles and time horizons of a variety of stock market participants. Factors and Weightings Each of the four primary factors in the CPG consists of five sub-factors that have been selected for their historically strong predictive value and relative lack of covariance with each other (Figure 3). The four primary factors have unique weights applied, with each stock receiving a score ranked on a relative basis from -99, representing the Power Gauge Rating. Sub-factor weights were derived using an iterative process that carefully analyzed the predictive power of each on a stand-alone basis, and then finalized a weighting system that reflected their relative contribution to the primary factor s predictive capability. Primary factor weights were derived in a similar iterative process, such that each reflects its relative predictive capability to the overall Power Gauge Rating. Value (35% model weighting) consists of five sub-factors that in and of themselves lend predictive value to future stock performance. By blending all five, their predictive value is enhanced. For example, both price-to-book and price-tosales ratios are widely acknowledged predictors of future stock performance, based on relative value considerations. Combining these with return on equity and free cash flow helps overcome the limitations of a pure value screen by helping identify and avoid potential value traps. 3

Figure 3: CPG primary factors and sub-factors Value factors (35%) LT debt to equity ratio Price-to-book value Return on equity Price-to-sales ratio Free cash flow Growth factors (2%) Earnings growth Earnings surprise Earnings trend Projected P/E ratio Earnings consistency Chaikin Power Gauge TM Value Growth Technical Sentiment Bullish Technical factors (15%) Price trend Price trend rate of change Chaikin Money Flow Relative strength vs. market Volume trend Sentiment factors (3%) Earnings estimate trend Short interest Insider activity Analyst ratings Industry relative strength The above Chaikin Power Gauge rating is for illustrative purposes only. Growth (2% model weighting) consists of five sub-factors that favor stocks with growing earnings of a consistent nature, with a tendency to report positive earnings surprises. Ultimately, earnings drive stock prices; no company can continually lose money and survive. Technical (15% model weighting) consists of five sub-factors that provide a score for the trading health of a stock, based on the premise that the old saying, the trend is your friend, is a reasonable fail-safe. As noted earlier, technical factors can give false signals if used alone, but when utilized in tandem with the CPG s fundamental factors, they provide confirmation for the rating. Sentiment (3% model weighting) consists of five subfactors that track opinions and actions of informed investment professionals and insiders. When Wall Street analysts raise or lower their earnings estimates or ratings for a given stock, there is usually a price impact. Changes are often triggered by positive or negative earnings surprises. Short sellers, in theory, conduct in-depth stock research, scrutinizing deteriorating fundamentals or aggressive accounting techniques. Insider activity, particularly insider buying, has a strong long-term predictive record, as corporate insiders often have the most informed window into a company s prospects over the next 12-18 months and tend to buy for the long term. It is important to note these factors and weightings were finalized in September 21 and remain the same today. We view this as a core strength of the model and its ability to perform well since launching in January 211. Stock Rating Output The Power Gauge Rating is calculated for each stock as a relative score from -99. A stock universe is rated and segmented into five categories, ranging from Bearish (stocks with the lowest rating) to Bullish (stocks with the highest rating). Based on a combination of research results prior to 21 and actual returns for the period after, categories ranked more highly have progressively outperformed as ratings improved, with Bullish stocks significantly outperforming Bearish stocks (see Figure 4). Hence, constructing indices and portfolios that favor Bullish-tilt CPG stocks and avoid those rated with a Bearish-tilt have the potential to generate positive alpha over the long term. Figure 4: Average annual returns based on Chaikin Power Gauge Ratings January 1, 1999 December 31, 216 (rebalanced annually) 15% Return 12 9 6 3 6.1 Russell 3 5.4 Bearish 7.1 Bearish 9.8 Neutral 11.3 Bullish 13.8 Bullish Source: Chaikin Analytics, as of 12/31/16. The above returns do not represent that of any fund or account. Return data is based on applying the Chaikin Power Gauge to the Russell 3 Index. The Russell 3 Index is a market capitalization-weighted equity index that seeks to be a benchmark to the entire U.S. stock market. The above categories are rating buckets based on output of the Chaikin Power Gauge. Performance calculated using the equal-weighted average of the securities within each rating bucket. Past performance is not a guarantee of future results. It is not possible to invest directly in an index. See appendix for the Chaikin Power Gauge rating methodology. The Chaikin Power Gauge was developed in 211. Performance prior to the development of the Chaikin Power Gauge is hypothetical. Performance results are based on criteria applied retroactively with the benefit of hindsight and knowledge of factors that may have positively affected its performance, and cannot account for all financial risk that may affect the actual performance. 4

Chaikin Power Gauge TM Bullish Case Study ABC Research is an example of a stock with a Bullish CPG Rating. The Bullish rating has been persistent for more than 12 months. Value Growth Bullish Value Bullish + Low long-term debt to equity, high return on equity, low price-to-sales ratio, and strong cash flow - High price-to-book value Growth Bullish + strong earnings growth rate over the past three to five years, upward yearly earnings trend, relatively low price-to-earnings ratio, and consistent earnings over the past five years Technical Sentiment Technical Bullish + Attractive price strength vs. the market, strong Chaikin Money Flow persistency, strength vs. long-term price trend, and an increasing volume trend Sentiment Bullish + Upward earnings revisions, optimistic analyst opinions, and strong industry group performance - High short interest ratio and low levels of insider purchasing Chaikin Power Gauge TM Value Growth Technical Sentiment Bearish Bearish Case Study XYZ, Inc. is an example of a stock with a Bearish CPG Rating, with three of the four primary factors either Bearish or Bearish. The Growth factor, however, is bullish, illustrating the efficacy of an integrated approach to a multi-factor ranking model. Value Bearish - Poor financial metrics, relatively low revenue per share, and high long-term debt levels Growth Bullish + High earnings growth over the past three to five years, upward yearly earnings trend, relatively low price-to earnings ratio, and consistent earnings over the past five years - Worse-than-expected earnings in most recent quarter Technical Bearish - Relative weakness vs. the market, weak Chaikin Money Flow persistency, and weakness vs. long-term price trend Sentiment Bearish - Downward earnings revisions, high short interest ratio, and low levels of insider purchasing The above is for illustrative purposes only. The Chaikin Power Gauge Rating is illustrative of an individual security being run through the Chaikin Power Gauge. Applying CPG to an Index CPG can be used to identify the most promising constituents in any index, resulting in a new index with fewer constituents that seeks to outperform its benchmark parent. This methodology applies ratings, output to create an equally-weighted index of Bullish-tilt CPG stocks, reconstituted annually. Currently, there are three NASDAQ Chaikin indices: NASDAQ Chaikin Power US Small Cap Index (NQUSCHK), which typically holds 2-35 stocks selected from the NASDAQ US 15 Index. NASDAQ Chaikin Power US Large Cap Index (NQULCHK), which typically holds 45-65 stocks selected from the NASDAQ US 3 Index. NASDAQ Chaikin Power US Dividend Achievers Index (NQDACHK), which typically holds 5-65 stocks selected from the NASDAQ US Broad Dividend Achievers Index. Equal weighting helps eliminate concentration risks associated with capitalization-weighted indices and their inherent bias towards larger-cap growth or momentum stocks. Equalweighted indices, even in the large-cap segment, seek to take advantage of the potential for value stocks and relatively smallercapitalization stocks to outperform the market over time. The majority of the sub-factors underlying the CPG model is longer term in nature, thereby supporting an annual index reconstitution. For example, the price-to-book ratio and free cash flow sub-factors underlying the Value factor, as well as the earnings consistency and earnings surprise sub-factors underlying the Growth factor, tend to have a longer-term focus, relative to the sub-factors underlying the Technical factor, which receive a lower weight. 5

These Value and Growth sub-factors, as well as select Sentiment sub-factors, such as insider activity, receive a higher combined weight within the model. Due to this methodology, the NASDAQ Chaikin indices lend themselves better to a longer-term investment mindset. In fact, as shown in Figure 5, in the past three NASDAQ Chaikin Power US Small Cap Index s annual rebalances, 76% or more of its stocks rated Bullish or Bullish at the start of the period retained a Bullish-tilt rating at the end of the period, with few stocks turning Bearish. Therefore, having the index rebalance on an annual basis seems an appropriate frequency, as it allows the selected constituents a necessary time frame to reach the true investment potential indicated by their strong ratings, while not exposing the model to unnecessary and costly turnover. Figure 5: Bullish rated stocks have tended to maintain their high rankings Number of Securities 25 2 15 1 5 4/1/214 3/31/215 start 15 19 235 19 (81%) end Stock Selection Criteria 4/1/215 3/31/216 start end start Bullish Neutral Bearish 4/1/216 3/31/217 end To be eligible for inclusion in one of the three NASDAQ Chaikin indices, a stock must be part of the respective NASDAQ parent index. Additionally, it must have a three-month average daily dollar trading volume greater than one million dollars, as of the index reconstitution date. All eligible securities are evaluated using the multi-factor CPG model. There are no sector constraints placed on any of the NASDAQ Chaikin indices. Further requirements for specific inclusion in each index are described below. NASDAQ Chaikin Power US Small Cap Index CPG Rating must be greater than 9 (i.e., rated higher than 9% of securities in the parent index). Alternatively, a security will be included in the Index if its CPG Rating is at least 7 AND it ranks in the lowest quintile (bottom 2% of the Universe), based on its price-to-sales value. 3 37 223 17 (76%) 16 16 249 192 (77%) Source: Chaikin Analytics as of 4/1/17. The above is illustrative of securities, Chaikin Power Gauge Ratings at the beginning and end of each period. NASDAQ Chaikin Power US Large Cap Index CPG Rating greater than 86, or it ranks in the lowest quintile, based on its price-to-sales ratio with a CPG Rating greater than 72. NASDAQ Chaikin Power US Dividend Achievers Index Evaluates all securities in the parent index using CPG and the Chaikin Shareholder Yield Model. Must have a Shareholder Yield Rank of at least 5 and a price-to-sales ratio less than 2 and a CPG Rating greater than 28 (the requirement of a CPG Rating greater than a Bearish 28 or lower rank eliminates securities that rank poorly in the CPG model, thus excluding stocks that are most likely to underperform the market). Historical Performance Data As shown in Figure 6, the NASDAQ Chaikin indices have offered strong historical track records of generating consistent excess returns over their respective parent indices. Figure 6: The Chaikin Power Gauge has provided consistent excess returns across multiple asset classes Annualized Return Annualized Return Annualized Return 3% 2 1 3% 21.8 18.11 28.75 23.64 + 767 + 553 13.29 8.57 1.11 11.89 + 178 13.7 15.85 + 215 12.28 8.47 + 381 +472 7.22 12.1 One-year Three-year Since Inception NASDAQ US 15 Index NASDAQ Chaikin Power US Small Cap Index 2 1 3% + 479 1.67 11.87 + 12 One-year Three-year Since Inception NASDAQ US 3 Index NASDAQ Chaikin Power US Large Cap Index 2 1 9.1 12.52 + 342 One-year Three-year Since Inception NASDAQ US Broad Dividend Achievers Index NASDAQ Chaikin Power US Dividend Achievers Index Source: Morningstar, as of 5/31/17. The NASDAQ Chaikin Power indices were incepted on April 1, 214. Past performance is not a guarantee of future results. The NASDAQ US 15 Index contains up to the 15 largest securities in the NASDAQ US Small Cap Index. The NASDAQ US 3 Index includes up to the 3 largest securities in the NASDAQ US Large Cap Index. The NASDAQ US Broad Dividend Achievers Index is comprised of U.S. accepted securities with at least ten consecutive years of increasing annual regular dividend payments. It is not possible to invest directly in an index. 6

Figure 7: Bullish CPG stocks, and performance through various market cycles Bull markets Russell 3 Index Bullish CPG stocks Cumulative Return 45% 3 15 13 392 1/9/22 1/9/27 + 26,2 77 25 3/1/29 4/22/21 +12,8 37 45 + 8 7/6/21 4/28/211 16 164 1/4/211 5/2/215 + 5,8 32 53 2/12/216 3/31/217 + 2,1 Bear markets Russell 3 Index Bullish CPG stocks Cumulative Return % -2-4 -6 1/1/27 3/9/29-56 -57-1 4/23/21 7/2/21-16 -16 + 4/29/211 1/3/211-17 -25-8 5/21/215 8/25/215-12 -7 +5 11/3/215 2/11/216-14 -18-4 Source: Chaikin Analytics, as of 4/1/17. The Russell 3 Index is a market capitalization-weighted equity index that seeks to be a benchmark to the entire U.S. stock market. Bullish CPG stocks are stocks rated Bullish based on the Chaikin Power Gauge. Past performance is not a guarantee of future results. It is not possible to invest directly in an index. See appendix for the Chaikin Power Gauge Rating methodology. The Chaikin Power Gauge was developed in 211. Performance prior to the development of the Chaikin Power Gauge is hypothetical. Performance results are based on criteria applied retroactively with the benefit of hindsight and knowledge of factors that may have positively affected its performance, and cannot account for all financial risk that may affect the actual performance. Stress Analysis We also explored what investors might expect in difficult markets when they may believe the model might not work as well. Our research concluded that the CPG output tended to fare very well in markets exhibiting security differentiation and less correlation. Even the bear market of 21-22, sparked by the Internet technology bubble burst, saw a number of stock market segments that performed relatively well. Consequently, model rankings identified attractive securities to hold, even in the difficult overall market environment. In contrast, the financial crisis of 28 and early 29 saw little performance differentiation in each rating. Although Bullish CPG stocks did no worse than the market with all assets so highly correlated, there was little incremental gain. However, as demonstrated in Figure 7, which shows the period directly leading up to the crisis and immediately after, it is clear that the model was beneficial both going into and coming out of the financial crisis. The conclusion is that many solid stocks were sold, regardless of fundamental soundness during the financial panic. Desperate sellers sold what they could, not necessarily what they wanted to, in an effort to raise cash. As rationality returned to the market post-crisis, Bullish CPG stocks demonstrated significant outperformance, supporting the model s ability to identify attractive stocks during this period of market stress. Conclusion The Chaikin Power Gauge stock selection model applies a robust investment process, developed with decades of academic and real-world insights. By employing a top-down, rules-based methodology that utilizes multi-factor research to create equally-weighted indices that are passively rebalanced on an annual basis, these strategies seek to capture the best of active and passive portfolio management. The result has been a series of three carefully constructed indices that have delivered attractive long-term performance through a range of market cycles. This offers investors a compelling solution to pursue more efficient risk/return performance in select U.S. stock market segments than a strictly passive approach. 7

Multi-Boutique Investments Long-Term Perspective Thought Leadership 1. Benjamin Graham & David Dodd, Security Analysis, 1934. 2. Harry M. Markowitz, Portfolio Selection, Efficient Diversification of Investments. (New York: John Wiley and Sons, Inc., 1959). 3. Harry M. Markowitz, Portfolio Selection, The Journal of Finance XII (March 1952), 4. William F. Sharpe, Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk, The Journal of Finance, September 1964. 5. Burton Malkiel, A Random Walk Down Wall Street, 1973. 6. Eugene F. Fama, Kenneth R. French, The Cross-Section of Expected Stock Returns, The Journal of Finance, June 1992. 7. Eugene F. Fama, Kenneth R. French, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics, 33 (1993). 8. Stephen A. Ross, The Arbitrage Theory of Capital Asset Pricing, Journal of Economic Theory, 13 (1976) (3) 341-489. 9. Nai-Fu Chen, Richard Roll, and Stephen A. Ross, Economic Forces and the Stock Market, The Journal of Business, 1986, Vol 59, No. 3. 1. Robert A. Haugen, The Inefficient Stock Market, 1999. 11. Robert A. Haugen, The Inefficient Stock Market: What Pays Off and Why (Second Edition), Pearson, 21. Chaikin Power Gauge Rating Methodology Factors are combined with a proprietary weighting system into a score which is ranked across the Russell 3. This ranking is turned into a raw rating by dividing the universe into seven equal parts, or septiles, and assigning them into simple, color-coded ratings. Bearish Bearish Neutral Bullish Bullish The Chaikin Power Gauge Rating ranges from VERY BEARISH (likely to underperform) to VERY BULLISH (likely to outperform). For more information 888-474-7725 IQetfs.com Chaikin, Chaikin Analytics, and Chaikin Power Gauge are registered trademarks or service marks of Chaikin Analytics LLC and Chaikin Investments LLC and are used under license. Nasdaq, NASDAQ Chaikin Power US Dividend Achievers Index, NASDAQ Chaikin Power US Large Cap Index, and NASDAQ Chaikin Power U.S. Small Cap Index are registered trademarks of Nasdaq, Inc. (which with its affiliates is referred to as the Corporations ) and are licensed for use by IndexIQ. The Product(s) have not been passed on by the Corporations as to their legality or suitability. The Product(s) are not issued, endorsed, sold, or promoted by the Corporations. THE CORPORATIONS MAKE NO WARRANTIES AND BEAR NO LIABILITY WITH RESPECT TO THE PRODUCT(S). MainStay Investments is a registered service mark and name under which New York Life Investment Management LLC does business. MainStay Investments, an indirect subsidiary of New York Life Insurance Company, New York, NY 11, provides investment advisory products and services. IndexIQ is an indirect wholly owned subsidiary of New York Life Investment Management Holdings LLC. ALPS Distributors, Inc. (ALPS) is the principal underwriter of the ETFs. NYLIFE Distributors LLC is a distributor of the ETFs and the principal underwriter of the IQ Hedge Multi-Strategy Plus Fund. NYLIFE Distributors LLC is located at 3 Hudson Street, Jersey City, NJ 732. ALPS Distributors, Inc. is not affiliated with NYLIFE Distributors LLC. NYLIFE Distributors LLC is a Member FINRA/SIPC. Not FDIC/NCUA Insured Not a Deposit May Lose Value No Bank Guarantee Not Insured by Any Government Agency 173939 ME42-17 ME38h-8/17