What drives the market for exchange-traded notes?

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What drives the market for exchange-traded notes? David Rakowski , Sara Shirley PII: DOI: Reference:

S0378-4266(19)30276-6 https://doi.org/10.1016/j.jbankfin.2019.105702 JBF 105702

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Journal of Banking and Finance

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24 May 2017 14 November 2019

Please cite this article as: David Rakowski , ket for exchange-traded notes?, Journal of https://doi.org/10.1016/j.jbankfin.2019.105702

Sara Shirley , What drives the marBanking and Finance (2019), doi:

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What drives the market for exchange-traded notes? Journal of Banking and Finance, forthcoming David Rakowskia, Sara Shirleyb,* a

University of Texas at Arlington, College of Business, Arlington, TX 76019, USA Middle Tennessee State University, Jones College of Business, Murfreesboro, TN 37132, USA

b

Abstract Exchange-traded notes (ETNs) are a relatively new form of security design that appear similar to exchange-traded funds (ETFs), but with no underlying portfolio holdings. We identify those characteristics of ETNs that are distinct from ETFs, and we test which ETN characteristics are most associated with interest from investors. We find that some ETNs have return patterns that are not spanned by ETFs, while other ETNs employ different strategies to attract investors. We show that the market activity for ETNs is associated with the distinctiveness of ETN returns, improvements in tracking errors, access to leverage, and the extent of risk transformation that ETNs allow. ____________________________________________________ JEL classification: G100; G190; G230

Keywords: Exchange-traded notes (ETNs); Exchange-traded funds (ETFs); Financial innovation; Mean-variance spanning; Security design; Exchange-traded products (ETPs); Tracking error *

Corresponding author; Tel.: 615-898-2520; fax: 615-898-5596. E-mail address: [email protected] (D. Rakowski), [email protected] (S. Shirley)

Acknowledgements We are grateful for helpful comments from the editor (Geert Bekaert), anonymous reviewers, Vladimir Atanasov, Jamie John McNutt, Jason Greene, Mark Peterson, and Jeffrey Stark. Valuable suggestions from the faculty at the College of Charleston, Roger Williams University, Western Michigan University, Winthrop University, University of Arkansas at Little Rock, the University of Wisconsin-Eau Claire, Southern Illinois University Carbondale, and the University of Texas at Arlington and seminar participants at the 2015 Financial Management Association European Conference, are appreciated.

1. Introduction The exchange-traded products (ETPs) market has grown substantially since the late 2000s. In a September 8, 2017, speech, Michael S. Piwowar, commissioner on the US Securities and Exchange Commission, stated: “ETPs are among the most significant financial innovations in recent decades and have shaped financial markets as we know them today.” 1 Piwowar also noted that “although we have seen an increase in the level of research about the effects of ETPs … it is still a nascent field, and one in which we need more discussion and discovery.” ETPs have altered the nature of worldwide financial markets by combining the diversification and market-tracking ability of mutual funds with the liquidity and trading characteristics of market-listed stocks. Academic research on efficient markets in the 1960s and 1970s (Fama, 1965; Jensen, 1968; Malkiel and Fama, 1970) spurred interest in how portfolios could be traded quickly and at a low cost by less sophisticated retail investors. By the 1990s, the exchange-traded fund (ETF) structure evolved as a response by the asset management industry to growing investor demand for an index-tracking security that avoided the liquidity problems of open-end mutual funds, futures contracts, and structured products (Gastineau, 2001). Exchangetraded notes (ETNs) arose in the mid-2000s with a packaging similar to ETFs, but with prices determined by a different index-tracking mechanism. The objective of our study is to characterize the distinctiveness of the ETN portion of the ETP market and the factors that drive activity in the market for ETNs. An ETN is typically structured as an exchange-traded, unsecured debt security in which the principal is tied to a financial index. An ETN’s value, as determined by the underlying index, is referred to as the indicative value and functions similarly to the net asset value (NAV) of an

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The speech, given at the SEC-NYU Dialogue on Exchange-Traded Products, can be found at https://www.sec.gov/news/speech/speech-piwowar-2017-09-08.

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ETF. Several applied studies provide an introduction to the basic characteristics and features of ETNs (Haines, 2008; Geman et al., 2012; Alexander and Korovilas, 2013; Aroskar and Ogden, 2012; Wright et al., 2010). Unlike ETFs, ETNs typically have counterparty risk because no underlying securities are backing them and, thus, ETNs are vulnerable to the issuers’ creditworthiness. While the counterparty risk of the issuer may have a large conceptual impact on an ETN’s potential price, no evidence exists that counterparty risk plays a detectable role in determining the market prices of ETNs (Cserna et al., 2013). The unsecured structure of ETNs allows them to track indices for which holding the underlying securities may be difficult or impossible, while maintaining little to no tracking error, before fees. This structure offers advantages for ETNs, relative to ETFs, in tracking indices based on abstract mathematical values, such as the Chicago Board Options Exchange’s Volatility Index (VIX), or in implementing leveraged strategies. Investors therefore may find ETNs attractive either as a means to lower the costs of access to illiquid portfolios or as a way to obtain risk-return profiles that were not possible with existing securities. Our paper is the first academic study that draws primarily on ETN data to examine these issues. Our objective is twofold. First, we measure the extent of diversification benefits that ETNs provide relative to ETFs. Second, we evaluate whether diversification benefits or other factors explain activity in the ETN market. We meet these objectives by testing whether the returns of ETNs are spanned by, or are otherwise distinct from, existing ETFs and other benchmarks. We then associate the distinctiveness of ETNs’ returns to measures of market activity for ETNs. ETFs are used as benchmarks because of similarities in their broad investment objectives and because ETFs are often marketed as close ETN substitutes or simply referred to as

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a synonymous investment.2 Furthermore, both ETNs and ETFs can be traded throughout the day, have returns that are tied to an underlying index, can be sold short, have investor redemption features, and have fees associated with their management. We begin our analysis by examining the question of whether returns generated by ETNs are unique or if these returns are already spanned (Huberman and Kandel, 1987) by existing ETFs or broader benchmark indices. This portion of our analysis closely follows past studies on the diversification benefits of foreign securities and new security designs, such as Bekaert and Urias (1996), De Roon et al. (2001), Eun et al. (2008), and Cao et al. (2017). We find that approximately 19% of individual ETNs have returns that are not spanned by a matched ETF (at a 1% confidence level). Following Kan and Zhou (2012), we decompose the overall meanvariance spanning properties of ETNs into minimum variance and tangency portfolio components. We find that about 18% of ETNs offer improvements to an investor’s tangency portfolio at the 10% confidence level. In cross-sectional tests, we observe that improvements in tangency portfolios are significantly associated with select measures of ETN market activity. Therefore, some, but not all, measures of market activity are associated with the mean-variance spanning properties of ETNs. Because the typical retail investor is unlikely to conduct formal mean-variance spanning tests and because our spanning tests provide an incomplete characterization of market activity for ETNs, we next move to more specialized measures of how ETNs enhance the risk-return profiles that are available to investors. We consider differences in tracking errors, factor loadings, and leverage exposures, as well as the extent to which these variables further explain activity in the ETN market. We hypothesize that asset management firms employ two distinct strategies to capture market share, and thus fee revenue, from the issuance of ETNs. When ETNs have ETFs 2

See Appendix B for examples of ETNs being marketed as synonymous with ETFs.

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following the same index, the ETNs are potentially redundant products. Therefore, ETNs that offer a competitive advantage, such as lower fees, lower tracking errors, or leverage, should be driving market activity for these competing ETNs. For those ETNs not competing directly with individual ETFs, we expect that overall risk-return profiles are more important and, thus, more strongly associated with measures of ETN market activity. Following Svetina (2010) and Box et al. (2017), we classify competing ETNs as those with an ETF tracking the same underlying index and expanding ETNs as those without an ETF tracking the same index. Approximately 89% of ETNs are expanding and therefore track indices that are not covered by existing ETFs. There is little association between spanning properties and market activity for competing ETNs. For competing ETNs, we observe that market activity is associated with lower relative tracking errors. In contrast, because expanding ETNs do not have a direct ETF competitor, they may have less incentive to differentiate themselves on tracking error. Instead, improvements in an investor’s tangency portfolio are significantly associated with increased trading activity for expanding ETNs. Additionally, the expanding ETN market is associated with ETNs providing leveraged or inverse exposures, or both, and alternative factor loadings relative to available ETFs. Overall, our analysis suggests that ETNs offer a diverse collection of specialized benefits to investors over those provided by ETFs. These advantages may not be easy to discern when ETNs are seen as a homogenous group. Our analysis illustrates how a new security design may allow for a variety of potential portfolio improvements for investors. Viewed in isolation, each of these components provides only a partial explanation for patterns of activity in the ETN market. When viewed together, they reveal a complex set of motivations that drive activity in the market for ETNs.

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2. Background 2.1. What are ETNs? ETNs are an unsecured debt security for which the interest, or return, that the investor earns is tied to an underlying index. Barclays Bank PLC, with its iPath ETNs, introduced ETNs into the US market in June 2006. While other issuers have entered the US ETN market, Barclays Bank PLC continues to be the largest issuer of ETNs in the US, amounting to approximately a third of the ETNs in our sample. Appendix Table A.1 provides details on the five largest ETN issuers in our sample. One important aspect of ETNs, in contrast to ETFs, is that they are unsecured. The counterparty risk of the issuer, however, is not incorporated into the market prices of ETNs (Cserna et al., 2013). Appendix Table A.1 reports the number of ETNs issued (column 1), average value of Bloomberg one-year default probabilities for each issuer (column 2), and Bloomberg five-year default probabilities (column 3). The counterparty risk of ETNs is not the focus of this study, but the risk is a factor that investors may consider when investing in ETNs. We utilize counterparty risk as a control variable to test the robustness of our findings in Section 5.3. From 2006 to 2012, the number of ETNs available in the US market increased substantially. As show in Table 1, the growth in the number of ETNs leveled off from 2013 to 2016.3 Even though the number of ETNs available has stabilized, a recent article on ETF.com states: “[I]n the early months of 2016, ETNs were a good chunk of the most popular launches. In fact, four of the top 10 launches so far this year are ETNs. ... Meanwhile, the other rollouts suggest that ETNs, [master limited partnerships] and smart-beta strategies are all at the top of

3

The observations in Table 1 include all securities that were available at any point during the year. Therefore, the values are higher than any year-end comparisons.

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investors’ minds.”4 The largest ETN in our sample as of year-end 2016 was the J.P. Morgan Alerian MLP Index ETN, ticker AMJ, with $3.86 billion in market capitalization.

[Insert Table 1 near here]

2.2. How ETNs differ from ETFs This paper examines if, and how, ETNs occupy a distinct market segment relative to ETFs. We follow in the path of several papers that explore the motivations of investors and fund sponsors who were responsible for the introduction of ETFs in the late 1990s. Understanding the similarities and differences between ETNs and ETFs is important, especially because the media and popular public often use the terms “ETN” and “ETF” interchangeably. Appendix B provides examples of cases in which ETFs and ETNs were presented synonymously. The risk and return for an ETN investor are primarily determined by the ETN’s underlying index. ETNs cover various objectives, with the majority of US-listed ETNs falling into the general classification of Domestic Equity and Commodity.5 Appendix Fig. A.1 provides a breakdown of ETNs and ETFs by broad objectives. Tang and Xu (2013) document the importance of leverage in determining investor demand for ETFs. One difference between ETNs and ETFs is the relatively large percentage of ETNs that have a leverage strategy associated with their return structure. Fig. 1 presents the proportion of ETNs and ETFs that follow various levered strategies. Almost a third of the ETNs in our sample have some form of leverage. Of those, 47 are double the index, 15 are triple the index, 14 are double-short the index, and 13 are

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See “2016’s Biggest ETF Launches to Date” at http://www.etf.com/sections/features-and-news/2016s-biggest-etflaunches-date?nopaging=1. 5 The objectives are based on the most recent Center for Research in Security Prices objective code assigned to the security.

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triple-short the index. We incorporate the leverage characteristic of ETNs in our analysis of the ETN market.

[Insert Fig. 1 near here]

The unsecured ETN structure has two potential benefits: the ability to more effectively track an underlying index and the ability to track indices that could otherwise be difficult or costly to track with a secured instrument, such as an ETF. Barclays Bank, referring to an advantage of iPath ETNs, states that “the ETN structure is designed to provide investors the opportunity to access previously expensive or difficult-to-reach market sectors or strategies through an exchange traded product.”6 We investigate the extent to which ETNs provide exposure to new asset classes in our analysis of the ETN market. The ETN structure also allows for lower tracking errors. Aroskar and Ogden (2012) take an introductory look at the tracking errors of ETNs. Appendix Table A.2 presents statistics on average tracking errors for the ETNs and ETFs in our sample, based on objectives. 7 ETN fees are determined by the issuer and stated in the prospectus at the time of issuance. Appendix Table A.3 presents supplemental statistics on ETNs characteristics and shows that, on average, ETN fees (0.86%) are higher than those of ETFs (0.52%). ETN fees are paid to the issuer and serve as one of the benefits to the issuer for creating these securities. An investor in ETNs can incur various additional costs. For example, an ETN may have a future execution cost, financing rate, or redemption fee, all of which reduce the return earned to the

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See iPath’s “Frequently Asked Questions” at http://www.ipathetn.com/contentStore.app?id=5593115. Surprisingly, our sample of ETNs has higher average tracking errors than ETFs, which is, in part, a result of the higher fees charged by ETNs and rounding considerations. For a detailed discussion and examples of ETN tracking error calculations, see Appendix C. 7

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investor. Panel B of Table A.3 shows that 44% of ETNs have some form of additional fee on top of their general expense ratio. As a debt security, ETNs are issued with a set maturity. Panel B of Appendix Table A.3 reports the average term of the ETNs in our sample. While the majority of ETNs have a 30-year term, some have shorter terms. One notable example is the iPath Standard & Poor’s (S&P) 500 VIX Short-Term Futures ETN (VXX), which issued $2.5 billion in principal on February 3, 2009, with a maturity date of January 30, 2019.8 Upon issuance of the ETN, issuers collect the principal and, as a result of the unsecured nature of the security, often have substantial freedom when using the funds raised. Investors may redeem ETNs in large predetermined quantities, and issuers may terminate some ETNs prior to maturity. Alexander and Korovilas (2013) discuss some of the risks and benefits surrounding these ETN features, particularly as they pertain to VIX-linked ETNs. Appendix Table A.4 provides supplemental statistics on the number of ETN and ETF closures by year.9

3. Data and descriptive statistics This section details our data, provides basic descriptive statistics for ETNs and ETFs, and explains our matching procedure. We start by comparing ETNs with all available ETFs. A matched sample is utilized for our formal tests.

3.1. Data We obtain daily observations for ETN and ETF trading activity from the Bloomberg database and the Center for Research in Security Prices Mutual Fund (CRSP MF) database. Our 8

See the VXX prospectus at https://www.sec.gov/Archives/edgar/data/312070/000119312512118832/d317408d424b3.htm. 9 Additional sources on ETN characteristics are Haines (2008), Wright et al. (2010), and Diavatopoulos et al. (2011).

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sample starts with Barclay Bank’s first issuance of two iPath ETNs in June 2006 and continues through year-end 2016. Only ETNs and ETFs traded on US markets and denominated in US dollars are included in the sample. To mitigate survivorship bias, both active and inactive securities are used. The CRSP MF database contains 295 ETNs and 2,152 ETFs that meet our initial criteria; the Bloomberg database, 297 ETNs and 2,406 ETFs. Merging the two databases, removing any actively managed securities, and retaining observations with the required data results in a sample of 275 ETNs and 1,877 ETFs. Daily data from Bloomberg are time series observations on volume, closing price, market returns, price premiums, shares outstanding, institutional ownership, and issuer credit risk. Bloomberg also provides cross-sectional data on expense ratios, leverage amount, and underlying index ticker. CRSP MF provides monthly total net assets, daily net asset value, daily indicative value returns, monthly returns, the management company, CRSP investment objective, first offer dates, and closing dates.10 The most recent investment objective is assigned to the security. Underlying index returns and ETN indicative value returns are manually collected from the issuers’ websites, when available. ETN issuers offer details pertaining to fees, additional investor costs, automatic redemptions, and the term of the ETN. When these data are not available, supplemental information is obtained from Bloomberg.

3.2. ETNs versus ETFs We begin by comparing our sample of ETNs with the full sample of ETFs. Table 2 reports average values of monthly observations of exchange-traded product characteristics and ttests for differences between ETNs and ETFs. Panel A covers our full sample period, and Panels B through D report on time-period subsamples. Across the time subsamples, the trends are 10

Due to extreme outliers, returns are winsorized at the 1% and 99% levels in test results reported below.

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consistent. As expected, the smaller size of the ETN market relative to the ETF market is clearly visible, with ETNs having significantly lower total net assets, fewer shares outstanding, and higher average market shares. ETNs, on average, have higher illiquidity measures (are less liquid) than ETFs. Also, as a more recent innovation, ETNs are significantly younger than ETFs. An important difference shown in Table 2 is that ETNs charge significantly higher expense ratios than ETFs. Therefore, we can preliminarily conclude that ETNs are not designed to compete with ETFs regarding investor fees. We also find that, for the full sample of ETNs and ETFs, ETNs have lower returns and higher volatility than ETFs. Table 2 compares our sample of 275 ETNs with the full sample of 1,877 ETFs. To incorporate a more appropriate comparison, we utilize a matched sample.

[Insert Table 2 near here]

3.3. Matching ETNs to ETFs Several problems arise with making a comparison of all ETNs with all ETFs. Most important, the number of securities and mean size of ETFs are far larger than those of ETNs. To mitigate the possibility of comparing the smaller sample of ETNs with the more diverse sample of ETFs, we utilize a matched sample based on returns. To take a conservative approach, we allow an ETN to match with any ETF, regardless of the given objective. This matching procedure eliminates concerns about imprecise objective codes or objective code misclassifications and allows us to investigate whether ETNs offer benefits beyond those of available ETFs. Each period, the ETF with the closest correlation of returns is retained as the match for each ETN. We do not use size in our matching procedure because ETNs are generally

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smaller than ETFs, resulting in an excessive number of matches to the smallest ETF. One additional criterion in the matching process is that the ETN must have at least 15 daily return observations in a given month and the matching ETF has to be active for at least 90% of the daily ETN observations in a given month. Matches and return correlations are recomputed monthly using daily returns, allowing for the closest available ETF each month to be retained as a match. In Section 5.1, we detail the robustness of alternative matching procedures. The final number of unique ETFs that appear in the matched sample is 1,263. Because our primary goal is to identify differences between ETN and ETF return patterns, any alternative matching procedure would produce matches with greater differences in returns and thereby strengthen our main results.

4. Methodology and results For our analysis of what drives the market for ETNs, we test whether the returns generated by ETNs expand the efficient frontier for an investor through the mean-variance spanning tests of Huberman and Kandel (1987), as modified, applied, and expanded by Ferson et al. (1993), Bekaert and Urias (1996), De Roon and Nijman (2001), and Kan and Zhou (2012). We then classify ETNs as expanding or competing, and we consider whether differences in tracking errors between ETNs and ETFs or various leverage and risk transformations provided by ETNs are associated with the market activity for ETNs.

4.1. Return spanning Testing whether ETN mean returns are significantly different from those of ETFs provides a starting point for explaining the potential benefits of ETNs for investors. Table 3

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details the differences in average ETN returns relative to ETFs for the full sample, matched sample, and various objective-based subsamples. Overall, ETNs tend to under-perform ETFs, although some subsamples have insignificant or positive return differences. For Panel B, only ETN months that meet the matching requirements are retained. This restriction causes slight differences in the average return values for the ETN observations in Panel A relative to Panel B. The lack of significant differences between ETNs and ETFs for the matched sample in Panel B confirms the effectiveness of our matching process, which is based on the best overall return match of ETN to ETF. The large negative returns within the mixed objective classification of ETNs are primarily due to extreme values tied to the VIX.

[Insert Table 3 near here]

The central question about ETN returns is whether a portfolio that includes ETNs leads to a more desirable efficient frontier relative to a portfolio composed only of ETFs. Mean-variance spanning tests were popularized in the work of Huberman and Kandel (1987). Modifications and improvements to the Huberman and Kandel procedure have been employed in several contexts similar to ours to examine if new security designs or objectives are spanned by existing products. These tests have been used to ascertain the distinctiveness of closed-end emerging market mutual funds (Bekaert and Urias, 1996), international stocks (De Roon et al., 2001), international smallcap stocks (Eun et al., 2008), and iShares ETFs (Cao et al., 2017), among others. We follow the approach of Huberman and Kandel (1987) with adaptations proposed by De Roon and Nijman (2001) and Kan and Zhou (2012) and apply mean-variance spanning tests to our sample of ETNs and matched ETF benchmarks.

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Kan and Zhou (2012) demonstrate that the original spanning test procedures of Huberman and Kandel (1987) lead to high rates of type I errors in finite samples. Therefore, we make multiple adjustments to our testing procedures to address concerns about false rejections of the null hypothesis of spanning. First, we conduct our tests at the individual ETN and matchedETF level, as preliminary analysis indicated that null hypothesis rejections were more common for index tests than for individual ETN-ETF tests. In the standard notation of the existing literature (Huberman and Kandel, 1987; De Roon and Nijman, 2001; Kan and Zhou, 2012), we have a sample of N test assets (ETNs) and K benchmarks (matched ETFs). We examine the cases of each ETN matched to one, two, five, and ten ETFs (i.e., K = 1, 2, 5, and 10). Second, we limit reported results to Lagrange multiplier tests, as these are more conservative than Likelihood ratio or Wald tests (Kan and Zhou, 2012). Third, we perform a bootstrapping procedure to calibrate critical values for test statistics. In the bootstrap procedure, we conduct 10,000 simulations of hypothesis tests for which the null hypothesis is true and returns are drawn from the empirical distribution of ETF and ETN returns over 68-month periods (68 months is the median sample value for the time series length, T). Appendix Table A.5 provides summary statistics on the actual and simulated tests, critical values, and rejection rates for our overall mean-variance spanning tests and the bootstrapping procedure. In untabulated robustness tests, we relax each of the four testing restrictions and confirm higher null hypothesis rejection rates. Our reported results may therefore be viewed as conservative lower bounds on the number of ETNs for which we can reject the null hypothesis of spanning between ETN returns and those of benchmark securities. We require at least 20 valid return observations for our spanning tests, resulting in a sample of 230 ETNs matched to single ETFs or combinations of multiple ETFs. Tests results are

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summarized across ETN classifications, with summary statistics of the test results reported in Table 4. Panel A of Table 4 reports test results for each ETN matched to a single ETF, and Panel B matches each ETN to ten ETFs. We report the number of tests for each ETN category and the percentage of tests for which we reject the null of spanning at the 1% and 10% confidence levels. Supplemental results for matches to two ETFs and five ETFs are reported in Appendix Table A.6.

[Insert Table 4 near here]

The results in column 2 of Table 4, Panel A, show that spanning can be rejected for 18.70% of ETNs in the overall sample (when 1% could be expected as a result of type I error). Considerable variation also is evident across ETN categories. Inverse ETNs (where 39.02% reject spanning at the 1% level) provide better diversification properties than levered ETNs (only a 14.58% rejection rate at the 1% level). Currency-focused ETNs provide the highest objectivebased rejection rate of spanning (80.00%). Robustness tests in Section 5.3 detail alternative spanning tests, most of which produce more significant rejections of spanning. Panel B of Table 4 (and Appendix Table A.6) indicates that matching each ETN to multiple ETFs leads to even higher rejection rates of the null hypothesis of spanning than one-toone tests. This counterintuitive finding is consistent with the results reported by Kan and Zhou (2012) and occurs because, as the number of required ETF matches (K) increases, the power of the tests decreases (as illustrated in Appendix Table A.5). Therefore, bootstrapped critical values decrease, leading to higher rates of null hypothesis rejections as the number of matched ETFs increases.

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While the mean-variance spanning tests reported in Table 4 give a broad indication of whether or not an investor’s opportunity set is improved by a portfolio that includes ETNs, Kan and Zhou (2012) point out that one does not know if this is due to a change in the global minimum variance portfolio (MVP) or a shift in the tangency portfolio. Investors are likely to place a much higher value on even a noisy shift in the tangency portfolio than to any improvement in the minimum variance portfolio. Kan and Zhou provide a step-down procedure to construct separate MVP and tangency tests. While the MVP component tends to be more important in determining the overall results of mean-variance spanning tests, the tangency test provides a more meaningful evaluation of economic significance. Summary statistics on our MVP and tangency tests are reported in columns 4 through 7 of Table 4. As suggested by Kan and Zhou (2012), we base our conclusions on a conservative significance level of 1% for MVP tests and a less restrictive 10% for tangency portfolio tests. For one-to-one tests reported in Panel A of Table 4, we observe null hypothesis rejections for 23.56% of ETNs at the 1% rate in MVP tests. The economically more important tangency tests give lower rates of 1.78% at the 1% level and 17.78% at the 10% level. As with our overall spanning tests, these rates do not decline as we match to multiple ETFs. Overall, a substantial, but far from complete, portion of our sample of ETNs appears able to deliver economically meaningful diversification benefits to investors. Table 4 also indicates considerable variation in diversification benefits across ETNs with different characteristics. Large proportions of levered (25.53%), fixed income (29.03%), and mixed (47.06%) investment objectives display tangency portfolio improvements. For currency, foreign equity, and inverse ETNs, we observe that most of the spanning benefits from ETNs are

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in improved minimum variance portfolios, not in better tangency portfolios. The patterns are preserved as we increase the number of ETFs in our tests, as reported in Panel B of Table 4.

4.2. Asset class exposure The mean-variance spanning tests in Section 4.1 lead to rejections of mean-variance spanning for only a subset of ETNs. In addition, the typical retail investor is unlikely to conduct such formal tests and may adopt more practical heuristics when selecting investments. We use Bloomberg to identify the underlying indices for the exchange-traded products and compute a competing indicator variable if the ETN tracks the same index as an ETF. We calculate competing each month to ensure that the ETN and ETF were both available to investors in the same period, allowing us to identify 31 competing ETNs out of the full sample of 275 ETNs. Section 5.3 describes several robustness tests of our method to identify competing and expanding ETNs. Table 5 reports descriptive statistics of our ETN sample partitioned by our expanding and competing classification. Of our 16,216 monthly ETN observations, 1,608 are drawn from competing ETNs. T-tests show that expanding ETNs are significantly older, have higher market share, and have lower expense ratios, standardized volume, and volatility compared with competing ETNs. The significant differences provide support for considering these two subsamples separately in subsequent analyses.

[Insert Table 5 near here]

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In addition to classifying competing and expanding ETNs, identification of the underlying index allows for an investigation of the distribution of indices tracked by ETNs. Appendix Table A.7 lists all indices tracked by competing ETNs, as well the number of ETNs and ETFs tracked by each index. Of the sample ETNs, only one index is followed by more than five ETNs. The relatively low number of ETNs following the same index provides additional evidence of the uniqueness of these securities, not only from ETFs, but also within the ETN population. In the ETN sample, 54 ETNs track the same index in a paired manner (i.e., 27 ETNs are paired with an inverse version of the same security and thus are tracking the same index and are issued by the same issuer on the same date). This phenomenon provides a unique opportunity to the issuers of the ETNs to hedge their ETN exposures. ETN issuers have a great deal of flexibility when using the cash raised from ETNs. Paired ETNs provide issuers with access to capital and allow issuers to profit from fees while having little to no risk if hedged with an ETN pair. Paired ETNs are also attractive to investors because they allow access to the short position of a paired ETN without having to partake in a short position. Appendix Table A.8 lists all the paired ETNs in our sample, along with data on underlying indices, issuers, leverage, and offer dates. Classifying our ETNs into competing and expanding groups allows for an initial univariate comparison of demand for access to particular indices. Prior to our study, the only published research, to our knowledge, that examines competition between ETNs and ETFs, in terms of indices tracked, is the applied study of Geman et al. (2012), which considers only the precious metals sector. The high proportion of expanding ETNs (about 89% of the sample) indicates that the majority of ETNs are providing investors with a unique asset class exposure.

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Table 6 summarizes the overall distribution of spanning test results for competing and expanding ETNs. Our sample has 31 competing ETNs, of which 30 have sufficient monthly observations for our spanning tests. These competing ETNs track the same index as an ETF, but the two products may have different leverage or inverse characteristics. In general, the expanding ETNs entail more frequent rejections (at the 1% level) of spanning than competing ETNs (19.50% compared with about 13.33%), in one-to-one matches, as indicated by column 2 of Table 6, Panels A and B. Competing ETNs offer higher rates (36.67% compared with 14.87%) of tangency portfolio improvements (column 7 of Table 6, Panels A and B). This result is largely driven by inverse, levered, and mixed investment objectives. The inverse and leverage characteristics provide some distinguishing characteristics for these competing ETNs relative to ETFs. Unfortunately, the small number of competing ETNs in many objectives makes it difficult to determine how spanning is associated with the expanding-competing classification in narrow objectives. Results for ETN matches to ten ETFs are presented in Panels C and D. Supplemental results for ETN matches to two ETFs and five ETFs are presented in Appendix Table A.9 and provide mixed results regarding mean-variance spanning differences between expanding and competing ETNs.

[Insert Table 6 near here]

4.3. ETN market activity We directly test if activity in the market for ETNs is associated with the spanning test properties of ETNs. We characterize the market for ETNs with five variables: Volume, Size, Illiquidity, Market Share, and Net Issuance. Liquidity is an important consideration in the

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evolution of a market. Falkenstein (1996) finds investor preferences for liquid securities within the mutual fund space. Broman and Shum (2018) find that liquid ETFs are more attractive to investors. Given the importance of liquidity to investors, the relative trading activity of a given ETN serves as a proxy for investors’ interest in the ETN market. Regarding liquidity, Amihud (2002) states that “it is doubtful that there is one single measure that captures all aspects” (p. 35). Therefore, we focus on two variables to proxy for the trading activity of an ETN: the standardized volume of an ETN and Amihud’s illiquidity measure (Amihud, 2002). Market capitalization, market share, and net issuance serve as additional measures of market activity for ETNs. Sherrill and Stark (2018) find that larger-sized mutual funds and ETFs are less likely to be liquidated, and Falkenstein (1996) reports investor preferences for larger securities within the mutual fund industry, supporting the use of market capitalization to capture an ETN’s success in establishing itself. Our use of market share to capture the importance of innovation in ETN security design is motivated by Khorana et al. (2005), who show that mutual fund innovation is associated with mutual fund industry size relative to the country’s primary market, by Khorana and Servaes (2012), who look at the movers of market share in the mutual fund industry, and by Li and Qiu (2014), who model changes in market share prompted by innovation. Share creation and redemption of ETFs is often considered a proxy for investor demand. Clifford et al. (2014) investigate ETF flows and, in doing so, highlight that investor demand for ETFs results in share creation. Gastineau (2001) discusses how ETF-authorized participants create additional ETF shares as a result of investor demand. As mentioned in Section 2.2, share creation and redemption for ETNs is not as fluid as that of ETFs. Subsequent issuance of ETN shares is dependent on the ETN issuer and therefore may capture issuers’ motivations in

19

conjunction with the market activity of ETNs. While recognizing any constraints that coincide with ETN creation and redemption, our final proxy for activity in the ETN market is ETN net issuance. When looking at the market for ETNs, the incentives for sponsors to issue ETNs may also be considered. While a full analysis of the motivations of financial institutions to issue ETNs is beyond the scope of this paper, Appendix Table A.1 provides supplemental descriptive characteristics of the largest ETN issuers. ETN issuers profit from the fees collected from the ETNs and from the flexibility of how to use the cash obtained from ETN issuance. 11 To capture investor demand, issuers can issue new shares of existing ETNs or create new ETNs that meet demand. Issuers also face potentially unhedged exposure from their ETN commitments, which we show to be concentrated in ETNs that are illiquid and often levered. We model Size, Volume, Illiquidity, Market Share, and Net Issuance as dependent variables. Size is the natural logarithm of the market capitalization of an ETN, found by multiplying shares outstanding by the closing price. Volume is the standardized volume of an ETN and is measured as an ETN’s daily volume as a percentage of shares outstanding. Illiquidity is as defined in Amihud (2002):

𝑖

𝑖 𝑖

∑

|

|

where i is equal to a given ETN, t is a given month, and d is a given day. days that the ETN has valid data in a given month,

11

is the number of

is the daily return of the ETN, and

ETNs are unsecured and, therefore, issuing firms are often allotted substantial flexibility in the uses of the cash raised from ETN issuances, prior to maturity of the security.

20

𝑂

is the dollar trading volume for the ETN that day. 12 Because Amihud’s measure

captures illiquidity, the relationships for this variable should be the opposite of those found for our other market measures. Market Share is the average size of the ETN over the month relative to the size of the entire ETN market in that month. Net Issuance is the average number of shares outstanding during the month minus the average number of shares outstanding over the prior month, scaled by the number of shares outstanding over the prior month. To directly test if investors’ actions are associated with the spanning test properties of ETNs, we use the minimum variance and tangency portfolio test statistics from the spanning tests and include them as explanatory variables in a cross-sectional regression. Our crosssectional regression model is

𝐸𝑇𝑁 𝑀𝑎𝑟𝑘𝑒

𝛽 + 𝛽 𝑀𝑖𝑛𝑖𝑚 𝑚 𝑎𝑟𝑖𝑎𝑛𝑐𝑒 + 𝛽 𝑇𝑎𝑛𝑔𝑒𝑛𝑐

+∑

𝛽 𝑜𝑛 𝑟𝑜

+ 𝑒 (2)

where i is equal to a given ETN. Dependent variables are the full sample period average values of our two measures of ETN market size (Size and Market Share), our two measures of ETN trading activity (Volume and Illiquidity), and Net Issuance. Our variables of interest are the one-to-one minimum variance (Minimum Variance) and tangency portfolio (Tangency) test statistics from Section 4.1. Control variables are those ETN characteristics that are likely to influence the activity in the market for ETNs, based on past studies: the standardized premium or discount of the ETN (Premium) found by taking the closing market price minus the indicative value and then dividing by the indicative value, the annual investor fee charged (Expense), the age of the ETN (Age), and the standard

12

Following Amihud (2002), the value from Eq. (1) is multiplied by 106.

21

deviation of the ETN’s daily returns calculated over the current month (Volatility). Control variables are standardized to have a mean of zero and standard deviation of one for ease of interpreting results. For our cross-sectional analysis, we require at least 20 months of valid observations for a cross-sectional observation, yielding 200 expanding ETN observations and 30 competing ETN observations. Estimated coefficients from the cross-sectional regressions are presented in Table 7. Supplemental results for matches to two, five, and ten ETFs are presented in Appendix Table A.10.

[Insert Table 7 near here]

Despite the low power provided by the small sample size in the cross-sectional regressions, the results in Table 7 reveal a couple of interesting associations between ETN spanning test characteristics and market activity for ETNs. First, only tangency test statistics are associated with market activity, with no significant coefficient estimates for any of the minimum variance portfolio test statistics. The significance of some of the tangency test results relative to the minimum variance tests provides, to our knowledge, the first independent empirical validation of the Kan and Zhou (2012) stepdown procedure in the context of real-world investor actions. It also justifies our adoption of the Kan and Zhou (2012) spanning test procedure in Section 4.1. Second, we observe that trading activity, captured by standardized volume, is associated with improved tangency portfolios for expanding ETNs, as shown in column 2 of Table 7. Competing ETNs exhibit no associations between liquidity measures and tangency portfolio improvements. Instead, improvements in the tangency portfolio are associated with

22

larger size and increased market share for ETNs that directly compete with ETFs. Supplemental results for matches to two ETFs, five ETFs, and ten ETFs are presented in Appendix Table A.10. When spanning tests are conducted between ETNs and portfolios of multiple ETFs, the associations with market activity measures weaken considerably. Overall, the combination of our spanning test results with our competing and expanding classification indicates that ETNs provide a diverse range of benefits to investors, with some ETNs successfully expanding the mean variance frontier and other ETNs presumably delivering different benefits to attract investors. The mean-variance spanning characteristics are associated with only a small number of our ETN market activity proxies, and in a different manner for competing and expanding ETNs. Because the spanning tests provide only a partial explanation for variation in market activity for ETNs, we consider alternative measures of diversification and test to see if these additional measures further explain market activity for ETNs.

4.4. Leverage Traditional portfolio theory (Markowitz, 1952) posits that investors require a certain level of return for a given level of risk and that the structure and, therefore, the diversification benefits of the security’s returns over time are important. While different asset class exposures may be in part what is driving the market for ETNs, market activity could be associated with the variations in risk that ETNs allow. Because ETNs have the potential to implement levered, inverse, and risk-enhancing (or hedged) strategies more effectively than ETFs, such transformations of risk could spur market activity from investors. These differences in risk are also easily identifiable as they are commonly associated with the ETN’s name, factsheet, and prospectus. Thus, we examine how an ETN’s risk profile is associated with activity in the ETN market. 23

We begin by studying how the use of leverage relates to the market for ETNs. Fig. 1 motivated this analysis by showing the relatively large proportion of ETNs that have a leveragebased strategy tied to their return structure. We expand on this by testing whether various leverage and inverse strategies relate to the market for ETNs. Our multivariate analysis uses Volume, Size, Illiquidity, Market Share, and Net Issuance, as defined in Section 4.3, as our dependent variables. The variables of interest are indicator variables for various ETN leverage and inverse strategies. An ETN is classified into one of six categories based on its leverage objective: Long, Long 200, Long 300, Short 100, Short 200, and Short 300. All classifications, excluding the long position, are included as our primary independent variables. A positive coefficient for our variables of interest suggests that the market for ETNs is at least partially driven by ETNs with the associated leverage characteristics. As with our prior analysis, the meaning and hypothesized coefficients are negative and significant for Illiquidity and positive for our other market measures. Our ETN controls are as defined in Eq. (2). All independent variables are lagged one month relative to the dependent variable. We include objective and time fixed effects, with the former based on the two-digit CRSP objective code and the latter set at the annual level. Following Petersen (2009), we cluster errors at the ETN level and control variables are standardized to have a mean of zero and standard deviation of one for ease of interpreting results. Our regression model is

𝐸𝑇𝑁 𝑀𝑎𝑟𝑘𝑒 𝛽 𝑆𝑕𝑜𝑟 300

𝛽 𝑜𝑛𝑔 200 +∑

𝛽 𝑜𝑛 𝑟𝑜

+ 𝛽 𝑜𝑛𝑔 300 + 𝐹𝐸 + 𝑒

+ 𝛽 𝑆𝑕𝑜𝑟 (3)

where i is equal to a given ETN and t is a given month.

24

00

+ 𝛽 𝑆𝑕𝑜𝑟 200

+

Columns 1 through 5 of Table 8 report estimates from Eq. (3) for ETNs that have direct ETF competition, and columns 6 through 10 report estimates for expanding ETNs. The results provide little support that leverage positions drive the market for competing ETNs. Our significant coefficients for competing ETNs suggest that leveraged, competing ETNs have less market activity. For expanding ETNs, leverage positions, particularly double-long, triple-long, and triple-short, provide evidence that, for an ETN with no direct ETF competition, the market is in part being driven by the attractiveness of the various leverage positions provided by the ETN. Our results in column 9, using Market Share as our proxy for the ETN market, show marginally significant coefficients that are of the opposite direction. We expand our analysis of the differences in risk and the impact of leverage by incorporating leverage-based subsamples and more precise measures of risk exposure in Sections 4.5 and 4.6.

[Insert Table 8 near here]

4.5. Tracking errors One benefit of the unsecured ETN structure is the ability to effectively track an underlying index. Tracking error represents how closely a security’s market returns replicate those of its underlying index. The tracking error of an ETN may be an important aspect for investors given that a possible advantage of ETNs over ETFs is the ETN’s potentially lower tracking error.13 We hypothesize that investor activity is stronger for those ETNs that have significantly lower tracking errors than ETFs. This should be particularly relevant for ETNs that directly compete with an ETF. We calculate tracking errors for ETNs and ETFs as the tracking

13

iPaths’s webpage “Differences between ETNs and ETFs” highlights ETN’s tracking errors as being “minimal to none.” See http://www.ipathetn.com/US/16/en/static/education-fundamentals.app.

25

error (TE) of daily returns relative to the underlying index calculated over the month. Our measure, Tracking Error, is calculated as in Cremers and Petajisto (2009):

𝑇𝑟𝑎𝑐𝑘𝑖𝑛𝑔 𝐸𝑟𝑟𝑜𝑟

𝑆 𝑒 [ 𝑒 𝑟𝑛

𝑒 𝑟𝑛

]

(4)

where i is equal to a given ETN, t is a given month, and d is a given day. Aroskar and Ogden (2012) and Diavatopoulos et al. (2011) carry out applied analyses of ETN tracking errors, but they do not consider differences in tracking errors.14 We compare ETN tracking errors with those of matched ETFs to test how differences in tracking errors are associated with activity in the ETN market. An additional discussion of ETN tracking errors, their calculation in this study, and issues in computing ETN tracking errors are provided in Appendix C and Table A.2. To test the impact of differences in tracking errors on the market for ETNs, we utilize our competing and expanding subsamples. Our multivariate analysis uses Volume, Size, Illiquidity, Market Share, and Net Issuance, as defined in Section 4.3, as our dependent variables. Our independent variable of interest, TE Difference, is calculated as the ETN tracking error over a particular month subtracted from the ETF tracking error over that same month. A positive and significant coefficient suggests that ETNs for which the matched ETF has a notably higher tracking error have significantly more activity. Our ETN control variables are as defined in Table 7. Objective and time fixed effects are included as in Eq. (3). Following Petersen (2009), we cluster errors at the ETN level and variables are standardized for ease of interpreting results. Our regression model is 14

Differences between our tracking errors and those of Aroskar and Ogden (2012) are predominately a result of our utilization of a substantially larger sample of ETNs and a longer sample period.

26

𝐸𝑇𝑁 𝑀𝑎𝑟𝑘𝑒

𝛽 𝑇𝐸 𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒

+∑

𝛽 𝑜𝑛 𝑟𝑜

+ 𝐹𝐸 + 𝑒

(5)

where i is equal to a given ETN and t is a given month. Table 9 presents the results of our estimation of Eq. (5), with columns 1 through 5 considering competing ETNs and columns 6 through 10 examining expanding ETNs. Our variable of interest, TE Difference, is not significant for the most of the expanding subsample, suggesting that differences in tracking errors are not spurring market activity for these ETNs. The one exception is with Market Share, which provides marginal support for our hypothesis that these ETNs with lower relative tracking errors obtain higher market share. The coefficient for the difference in tracking errors is significant for all variations of the model with competing ETNs. This is consistent with our hypothesis that the competing ETN market activity is driven by ETNs that offer investors lower tracking errors than the competing ETFs.

[Insert Table 9 near here]

To ensure that our results are not solely a function of ETN leverage, we run Eq. (5) with subsamples of ETNs based on leverage. We distinguish any ETNs that track double or triple their underlying index and classify them as leverage ETNs. The results for the no-leverage subsample are in Panel B. The results for the leverage subsample are in Panel C. With the no-leverage subsample, we continue to find that differences in tracking errors are significantly related to the market for competing ETNs. The results are not significant with the leverage subsample. The results in Panel B and Panel C confirm that our conclusion is not driven by ETN leverage.

27

4.6. Measuring the differences in risk Koski and Pontiff (1999) examine mutual fund risk factors when testing the impact of derivatives on mutual funds. Evans (2010) utilizes the differences in factor loadings from the Carhart (1997) four-factor model to test differences in risk between incubated and non-incubated mutual funds. We implement a similar method to capture the transformation of risk provided by ETNs relative to ETFs. Four variables represent differences in the monthly factor loadings between an ETN and matched ETF based on a four-factor model (Carhart, 1997): Beta Difference, SMB Difference, HML Difference, and MOM Difference. We use daily raw returns, for the given security, as the dependent variable to calculate monthly factor loadings for each ETN and ETF.15 When running the factor regressions, lagged daily factors are included to account for any errors that may result from the influence of nonsynchronous trading that occurs with daily returns. When testing the differences in factor loadings, we retain the absolute value of the differences for simplicity and clarity in testing for transformations in risk. The average values of these differences for our expanding and competing subsamples are presented in Table 5. To evaluate the hypothesis that ETN market activity is associated with the distinct risk profile provided by the ETN, we incorporate a variation of Eq. (3) and Eq. (5) that utilizes the differences in risk measures. Our variables of interest are Beta Difference, SMB Difference, HML Difference, and MOM Difference, which capture the absolute differences in factor loadings between ETNs and ETFs. Our ETN controls are as defined in Eq. (2). Objective and time fixed

15

Factor variables are obtained from Kenneth R. French’s website: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

28

effects are included. Following Petersen (2009), we cluster errors at the ETN level and variables are standardized. Our regression model is

𝐸𝑇𝑁 𝑀𝑎𝑟𝑘𝑒 𝛽 𝐵𝑒 𝑎 𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒

+ 𝛽 𝑆𝑀𝐵 𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒

𝛽 𝑀𝑂𝑀 𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒

+∑

𝛽 𝑜𝑛 𝑟𝑜

+ 𝛽 𝐻𝑀 + 𝐹𝐸 + 𝑒

𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒

+

(6)

where i is equal to a given ETN and t is a given month. Table 10 reports the test results of whether the ETN market is related to the differences in risk characteristics that can be obtained through ETNs. Similar to our findings for leverage in Section 4.4, the results for competing ETNs provide little support that the competing ETN market is explained by the transformation of risk provided by the ETNs. As hypothesized, expanding ETNs have significant coefficient estimates for Beta Difference, HML Difference, and MOM Difference in relation to the ETN’s volume, size, and illiquidity, suggesting that the market for ETNs is associated with these differences in risk exposure. The SMB Difference is significant for volume and market share, but the signs are opposite, preventing us from making a clear distinction on the relationship.

[Insert Table 10 near here]

The enhanced leverage of ETNs may explain why factor loadings are significantly different for ETNs relative to ETFs. In Panel B, we find some loss of significance with the differences in factor loadings tests with the no-leverage subsamples. The significance remains 29

for all variables except volume and market share for the no-leverage expanding ETN subsample. In column 6, the significance related to Volume for expanding ETNs appears to be primarily driven by leverage ETNs. Similarly, the significance for the competing ETNs is primarily driven by the leverage ETNs, with more significance present in Panel C. Overall, the results in Panels B and C suggest that leverage does partially explain our results for ETN factor loading differences. For our size and illiquidity ETN market activity proxies, the differences in risk between expanding ETNs and ETFs are not driven by leverage.

5. Robustness We discuss robustness tests to our matching procedure. We then present several alternative measures of market activity. Last, we detail additional robustness considerations including variations to our regression and spanning tests.

5.1. Matching procedure Our results use a matching procedure that yields the best possible ETF match every month regardless of ETF objective. This procedure is the most conservative, returning the lowest number of null hypothesis rejections compared with other testing procedures. This matching procedure also allows us to test whether ETNs offer benefits above and beyond those offered by any available ETF. Lastly, matching solely on returns removes any concerns of objective classification errors for ETNs or ETFs. While investment objectives are subject to classification errors, they provide a screening mechanism for investors. We rerun our matching procedure restricting each matched ETF to be within the same CRSP objective code as each ETN. We find that objective-based matching

30

results in similar rejections of mean-variance spanning. For example, Table A.11 reports several additional variations of the spanning tests for ETNs matched to one ETF. Before the bootstrapping procedure (reported in Panel A of Appendix Table A.5), we obtained a spanning rejection rate of 64.78% with non-objective-based matches and a 71.79% rejection rate with the objective-based matched sample, as shown in Panel A of Table A.11. The bootstrapped spanning rejection rates rises from 17.54% (Panel B of Table A.11) with objective-based one-to-one matches to 18.70% with non-objective-based one-to-one matches (Panel A of Table 4). We also consider matching based on a wider range of variables. Panels C and D of Appendix Table A.11 show that matching over longer time frequencies results in higher spanning rejection rates, as does the use of higher frequency (i.e., daily) returns. The problem with matching on other characteristics, such as size, is that most ETFs are larger than ETNs, resulting in the smallest-cap ETF being matched to a disproportionate number of ETNs. Therefore, we choose to report results for the most conservative matching procedure, by using the method that would deliver the closest possible ETF match based on return variation.

5.2. Alternative measures of market activity We consider three alternative measures of market activity: Premium, Dollar Flow, and Percentage Flow. The standardized premium (Premium) or discount of an ETN is included as a control variable in our analysis. This variable is measured by taking the difference between the market closing price and the indicative value of the ETN and then dividing by the indicative value of the ETN. The motivation for considering the standardized premium is that ETNs may be more in demand and therefore sell at a premium, which can indicate investor demand. Premiums also capture a wide range of additional motivations that go beyond simple investor market

31

activity. For this reason, Premium is not included in our primary analysis. Flow into and out of ETFs and mutual funds is commonly used in the investment’s literature. Section 2.2 discussed some of the complications that coincide with ETN share creation and share redemption, which also pertain to ETN flow. We formally test net issuance because it is a direct measure of the market for ETNs. ETN flow is a related, indirect measure of investor market activity. We repeat all tests using both dollar and percentage changes in ETN capitalization, after discounting by prior period return, yielding a measure comparable to fund flow. With Premium, Dollar Flow, and Percentage Flow as our dependent variables, Table A.12 confirms our findings for competing ETNs, with negative and significant results. With the expanding ETN subsample, significance is found only in column 4, with the Long 200 indicator variable. Table A.13 presents the results from Eq. (5) with our alternative market measures. Panel B reports a positive and significant coefficient for our premium ETN market activity proxy, which is consistent with our conclusions from Section 4.5. The ETN flow measures are not significantly related to TE Difference for the competing ETN sample. Table A.14 tests the differences in factor loadings [Eq. (6), Section 4.6]. For both the competing and expanding ETN sample, we observe mostly insignificant coefficients. This suggests that differences in factor loadings do not explain these proxies for the market for ETNs. Overall, ETN premiums provide similar, but often weaker, conclusions to those presented earlier, while the use of ETN flows provides at best a noisy confirmation our analysis. In Section 4.5 (Table 9), we report that differences in tracking errors are associated with activity for competing ETNs. Differences in the ETPs’ expense ratios also could be related to the market for ETNs. We replicate the tests of Eq. (5), replacing TE Difference with differences in expense ratios (Expense Difference), and the results are presented in Table A.15. We find that the

32

difference is significant only at the 10% level for our size measure associated with activity for competing ETNs. For expanding ETNs, volume has a marginally negative association and size is positively associated. We conclude that tracking error differences influences activity for competing ETNs, not relative expenses.

5.3. Additional robustness considerations Because of the conservatism in our spanning test procedures, the results presented in Sections 4.1 and 4.2 represent a lower bound on the number of ETNs for which we can reject the null hypothesis of spanning. In these sections, we focus solely on the Lagrange multiplier test, as it is the most conservative (Kan and Zhou, 2012). In the spanning test results reported in Sections 4.1 and 4.2, we employ a bootstrap procedure to calibrate test statistics for our finite sample size. The bootstrap approach represents the strongest adjustment in reducing the number of null hypothesis rejections reported in Sections 4.1 and 4.2. Appendix Table A.5 presents summary statistics on the actual, simulated, and bootstrapped tests statistics and rejection rates for our overall mean-variance spanning tests. For example, the full sample spanning rejection rate of 18.70% for one-to-one matches reported in Panel A of Table 4 (and Panel C of Table A.5) rises to 64.78% without the bootstrap procedure (Panel A of Table A.5). Other portions of our analysis exhibit similar increases for non-bootstrapped tests and remain untabulated (i.e., ETN subsamples, one-to-many matches, and step-down tests). The spanning tests reported in Sections 4.1 and 4.2 focus on security-to-security tests (i.e., matched ETN and ETFs). Untabulated tests of ETNs matched instead to the CRSP valueweighted index, CRSP equally weighted index, and S&P 500 index yield null hypothesis

33

rejection rates for over 99% of ETNs for all three indices in non-bootstrapped tests (the bootstrapping procedure does not directly extend to index comparisons). The identification of competing and expanding ETNs is a central aspect of our study. We explain, in Section 4.2, the motivation for our decision to utilize a conservative measure when competing ETNs are identified each month. For robustness, we also compute a full sample period cross-sectional indicator variable that equals one if an ETN ever has an ETF with the same underlying index ticker. All reported conclusions remain valid with this cross-sectional definition. A separate issue is near misses in underlying index matches in which ETNs could track a very similar, but not identical, underlying index as an ETF. Therefore, we rerun our matching procedure and allow for competing ETNs to be determined based on similarities between the underlying index returns. This method identifies competing ETNs if the underlying index returns are identical to an ETF for at least 90% of the observations over a particular month, allowing for looser matches and thus identifying 44 competing ETNs, in contrast to the 31 identified with the index ticker. Our primary conclusions in Section 4.4 and Section 4.5 remain valid with this procedure. Overall, various alternative methods to classify ETNs as competing yield similar results to the more conservative measure for which we report results. We explore several variations to our regression modeling approach. We believe the most important additional control variable to consider is the credit risk of the issuer. The average default risk of ETN issuers is presented with the supplemental information on ETN issuers in Table A.1. Issuers’ credit risk is not included in reported results because these data do not cover our full sample of ETNs. In addition to missing data, certain issuers, such as the Swedish Export Credit Corporation, are not publicly traded corporations and do not have comparable default probabilities available in Bloomberg. When we include the Bloomberg five-year default

34

probability as an additional control variable in Eqs. (3), (5), and (6), when available for issuers, the reported conclusions remain. Rakowski et al. (2017) find that open-end mutual funds trade in certain segments of the ETN market on a limited scale for hedging purposes and dividend-capture strategies. Table A.16 considers how the proportion of the shares held by institutional investors (Inst Own) relates to the market for ETNs. The first five columns of Table A.16 include our full sample and test what ETN characteristics, in general, are associated with our measures of the market for ETNs. Columns 6–10 test how the proportion of the shares held by institutional investors relates to the market for ETNs. We find that institutional ownership results are mixed. The positive association between institutional ownership and the market activity for ETNs (negative for illiquidity) suggests that higher institutional ownership is associated with more market activity for ETNs. Institutional ownership is negatively related to net issuance, suggesting that institutional ownership is associated with lower levels of share creation. Our institutional ownership data begin in 2010 and do not cover all ETNs. The data limitations result in a loss of 3,637 ETNmonth observations (about 23% of total observations). Because institutional ownership displays relatively little variation across our four measures of market activity and reduces our sample, we do not include Inst Own in our primary analyses. Including institutional ownership as a variable for the analyses presented in Tables 8, 9, and 10 does not substantially change our conclusions. A minor reduction in significance is evident for the expanding ETN subsample. Another unreported robustness test is to examine the 2006–2009 and 2010–2016 subperiods separately because of the 2007–2009 financial crisis and changes in tax laws that could have been pertinent to ETNs. However, the development of the ETN market over time makes it difficult to interpret the nature of time-period subsamples. For example, the majority of

35

our observations, and almost all of the competing ETN observations, occur after 2009, suggesting that many of our competing ETN results are driven by the post–financial crisis portion of our sample period.

6. Conclusion Our analysis shows that the market for a new financial security, the exchange-traded note, is influenced by the extent to which ETNs provide returns that are not spanned by existing ETFs, as well as ETNs’ diversification benefits, innovative asset classes, and risk transformations relative to existing securities. This association is especially apparent for the trading activity of expanding ETNs, which do not track the same index as an existing ETF. For ETNs that compete directly with ETFs, we observe a different strategy. These ETNs offer investors different advantages in terms of lower tracking error. Our results provide a partial illustration for how changes in security design allow financial institutions to introduce products with new characteristics that drive market activity. We identify several ways in which ETNs are distinct from ETFs and how these distinctions translate into ETN market activity. Our findings are able to only partially explain what drives the market for ETNs, and future research can further understanding of this market. Many ETNs offer diversification benefits to investors both in the context of traditional meanvariance spanning and by tracking new indices that are not available through ETFs. The distinctiveness of ETN returns, relative to ETFs, suggests that further development of the ETN industry may differ from the historical patterns seen for ETFs. A large proportion of ETFs compete directly with established open-end index funds, yet ETFs have witnessed tremendous growth over the past two decades. Their growth has been limited to when they offer

36

improvements in tax exposure (Poterba and Shoven, 2002; Agapova, 2011), liquidity (Boehmer and Boehmer, 2003), expenses (Elton et al., 2002), or tracking error (Marshall et al., 2013; Petajisto, 2017). Our results suggest that only about 11% of ETNs compete directly with ETFs. The remaining 89% of ETNs may therefore experience growth trajectories that differ from our results with ETFs. With ETNs being unsecured debt instruments, this future expansion could present unforeseen risks for both investors and issuers if, or when, the ETN market grows to the point where issuers’ credit risks become associated with ETN performance. Our work provides several findings that help foster understanding of how the market currently treats ETNs and how this is different from ETFs. Our analysis of trading activity and market share identify some of the ways that investor interest is fulfilled with ETNs. Many of these associations illustrate the response of financial intermediaries to provide investment products serving investor needs in ways that could not have been easily addressed with existing securities. The distinct return and risk patterns of ETNs help explain the growth that ETNs witnessed in the decade following their introduction. We identify several properties of ETNs that are associated with the market for ETNs. However, our models explain only a portion of activity in the market for ETNs. We leave it to future research to investigate the extent to which non-return-based measures impact ETN market activity. Some potential avenues for further exploration include fund families’ unique marketing strategies for ETNs and clientele-driven demand from select groups of investors for the return patterns documented in our work.

37

18.0% 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% Short x3

Short x2

Short x1 ETN

Long X1.25

Long x2

Long x3

ETF

Fig. 1. Proportions of levered ETNs and ETFs. This figure illustrates the total proportions of exchange-traded notes (ETNs) and exchange-traded funds (ETFs), by leverage type, in our raw data. Not plotted are the ETNs and ETFs with no leverage (Long x 1).

38

Table 1. Number of ETNs and ETFs by year. This table displays the number of exchange-traded notes (ETNs) and exchange-traded funds (ETFs) in our full sample, by year. Year

Number of ETNs

Number of ETFs

Total

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

4 24 81 90 127 202 214 211 205 209 194

359 602 725 798 922 1,076 1,165 1,209 1,301 1,462 1,507

363 626 806 888 1,049 1,278 1,379 1,420 1,506 1,671 1,701

39

Table 2. Descriptive statistics of the full sample of ETNs and ETFs. This table provides descriptive statistics of our sample of 275 exchange-traded notes (ETNs) and 1,877 exchange-traded funds (ETFs). Panel A displays the mean values for the full sample period. Panels B–D provide statistics for time subsamples. Statistics are provided on the average number of shares outstanding in millions, standardized volume (as a percent of shares outstanding), total net assets (TNA) in millions, age in years, annual expense ratios, premiums of market prices relative to indicative values [relative to net asset values (NAVs) for ETFs], average returns to the underlying indicative value (NAV for ETFs), market share, Amihud’s illiquidity measure, the monthly tracking error of daily returns relative to the underlying index, and volatility calculated as the standard deviation of daily returns over a given month. Significant differences between ETNs and ETFs are indicated by ***, **, and * at the 1%, 5%, and 10% level, respectively. Variable

ETNs

ETFs

Difference

3.611 0.043 110.010 3.300 0.821%

21.603 0.044 1238.740 4.736 0.515%

-17.993*** -0.001 -1128.730*** -1.436*** 0.306%***

0.019 -0.006% 0.408% 0.769% 1.934 0.016

-0.062 0.018% 0.322% 0.105% 0.225 0.012

0.080*** -0.024%*** 0.086%*** 0.663%*** 1.710*** 0.003***

3.734 0.042 105.415 0.999

18.182 0.079 933.315 3.074

-14.448*** -0.036*** -827.901 -2.075***

0.735% 0.000 -0.002% 0.368% 2.311% 0.988

0.506% -0.098 0.003% 0.474% 0.178% 0.177

0.229%*** 0.098*** -0.006%** -0.105%*** 2.133%*** 0.810***

0.020

0.017

0.003***

3.126 0.040 94.407 2.500

20.291 0.040 1117.340 4.417

-17.165*** -0.001 -1022.933*** -1.917***

0.830% 0.017 -0.005% 0.395% 0.609%

0.523% -0.065 0.032% 0.308% 0.098%

0.308%*** 0.082*** -0.037%* 0.087%*** 0.511%***

Panel A: Descriptive statistics for full sample Shares Outstanding (millions) Volume Standardized TNA ($ millions) Age (years) Expense Ratio Premium Standardized Returns Tracking Error Market Share Illiquidity Volatility Panel B: Descriptive statistics for 2006–2009 Shares Outstanding (millions) Volume Standardized TNA ($ millions) Age (years) Expense Ratio Premium Standardized Returns Tracking Error Market Share Illiquidity Volatility Panel C: Descriptive statistics for 2010–2013 Shares Outstanding (millions) Volume Standardized TNA ($ millions) Age (years) Expense Ratio Premium Standardized Returns Tracking Error Market Share

40

Illiquidity Volatility Panel D: Descriptive statistics for 2014–2016 Shares Outstanding (millions) Volume Standardized TNA ($ millions) Age (years) Expense Ratio Premium Standardized Returns Tracking Error Market Share Illiquidity Volatility

41

0.565 0.015

0.159 0.012

0.405*** 0.003***

4.139 0.047 129.125 4.860 0.833% 0.026

24.681 0.029 1515.860 5.924 0.512% -0.040

-20.541*** 0.017*** -1386.735*** -1.064*** 0.322%*** 0.066***

-0.008% 0.431% 0.531% 3.779 0.015

0.011% 0.267% 0.075% 0.316 0.010

-0.019%*** 0.164%*** 0.455%*** 3.463*** 0.004***

Table 3. Average ETN and ETF returns. Panel A presents the average annualized returns for our full sample of 275 exchange-traded notes (ETNs) and 1,877 exchange-traded funds (ETFs). Panel B compares the average annualized returns for our 275 ETNs with the matched sample totaling 1,263 ETFs. For Panel B, only ETN months when an ETF match is made are retained. Overall averages are presented (“All”) as well as by objective-based subsamples. Significant differences between mean ETN and ETF observations are indicated by ***, **, and * at the 1%, 5%, and 10% level, respectively. Objective Panel A: Full sample

ETN returns

ETF returns

Difference

-1.48% -3.27% -0.99% 8.98% 4.01% 13.43% -27.10% 4.26%

4.52% 1.33% 2.21% 6.41% 2.57% 2.86% -5.26% 1.33%

-6.00%*** -4.60%* -3.20% 2.57% 1.45% 10.57%* -21.84%*** 2.93%

All Commodity Currency Domestic equity

-1.99% -3.45% -1.23% 8.81%

-2.98% -5.25% 0.63% 8.61%

0.99% 1.80% -1.85% 0.19%

Fixed income Foreign equity Mixed Other

4.11% 12.45% -31.35% 4.07%

3.43% 4.35% -61.77% 3.95%

0.67% 8.11% 30.42%*** 0.13%

All Commodity Currency Domestic equity Fixed income Foreign equity Mixed Other Panel B: Matched sample

42

Table 4. Mean-variance spanning tests. This table presents results for Lagrange multiplier tests of the null hypothesis that the efficient frontier of a benchmark exchange-traded fund (ETF) spans the frontier of an expanded portfolio that includes exchange-traded notes (ETNs). Column 1 provides the number of ETNETF tests that were conducted. Columns 2 and 3 report the percentage of overall mean-variance spanning tests that were significant. Columns 4 and 5 report the percentage of minimum variance portfolio tests that were significant. Columns 6 and 7 report the percentage of tangency portfolio tests that were significant. Critical values for test statistics are calibrated from bootstrapping simulations with returns drawn from the empirical distribution and replicated 10,000 times over 68-month periods, with test results for significance at the 1% level reported in columns 2, 4, and 6 and at the 10% level reported in columns 3, 5, and 7. # of tests (1)

Overall spanning α = 1% α = 10% (2) (3)

Minimum variance α = 1% α = 10% (4) (5)

Tangency α = 1% α = 10% (6) (7)

230 141 48 41 96 10 58 31 4 19 12

18.70% 14.18% 14.58% 39.02% 25.00% 80.00% 5.17% 6.45% 25.00% 21.05% 8.33%

23.91% 17.73% 22.92% 46.34% 28.13% 80.00% 13.79% 12.90% 25.00% 31.58% 8.33%

23.56% 16.06% 23.40% 48.78% 27.37% 80.00% 17.54% 9.68% 33.33% 23.53% 8.33%

32.44% 26.28% 31.91% 53.66% 34.74% 80.00% 29.82% 22.58% 33.33% 35.29% 8.33%

1.78% 1.46% 4.26% 0.00% 0.00% 0.00% 3.51% 3.23% 0.00% 5.88% 0.00%

17.78% 14.60% 25.53% 19.51% 8.42% 10.00% 24.56% 29.03% 0.00% 47.06% 0.00%

All Unlevered, long Levered Inverse Commodities Currency

230 141 48 41 96 10

37.83% 24.82% 56.25% 60.98% 34.38% 80.00%

47.39% 34.04% 64.58% 73.17% 43.75% 80.00%

38.12% 24.44% 55.32% 63.41% 35.48% 80.00%

46.19% 31.85% 63.83% 73.17% 43.01% 80.00%

8.97% 6.67% 8.51% 17.07% 5.38% 10.00%

23.32% 19.26% 25.53% 34.15% 13.98% 30.00%

Domestic equity Fixed income Foreign equity

58 31 4

41.38% 41.94% 50.00%

51.72% 51.61% 75.00%

41.07% 41.94% 50.00%

51.79% 51.61% 50.00%

12.50% 6.45% 25.00%

32.14% 32.26% 25.00%

ETN Type Panel A: Each ETN matched to one ETF (K = 1) All Unlevered, long Levered Inverse Commodities Currency Domestic equity Fixed income Foreign equity Mixed Other Panel B: Each ETN matched to ten ETFs (K = 10)

43

Mixed

19

31.58%

47.37%

29.41%

41.18%

23.53%

35.29%

Other

12

8.33%

8.33%

8.33%

8.33%

0.00%

8.33%

44

Table 5. Descriptive statistics for expanding and competing ETNs. This table provides descriptive statistics of our sample of exchange-traded notes (ETNs), partitioned into expanding and competing subsamples. Competing ETNs are those that track the same index as an exchange-traded fund (ETF). Expanding ETNs are those that track an index that cannot be matched with a corresponding ETF. Statistics are provided on the average number of shares outstanding in millions, standardized volume (as a percent of shares outstanding), total net assets (TNA) in millions, age in years, annual expense ratios, premiums of market prices relative to indicative values, average returns to the underlying indicative value, monthly tracking error of daily returns relative to the underlying index, the volatility calculated as the standard deviation of daily returns over a given month, market share, Amihud’s illiquidity measure, the difference found by subtracting the ETN’s tracking error from that of the matched ETF, and the absolute difference in factor coefficients [Beta, SMB (small minus big), HML (high minus low), and MOM (momentum)] between ETNs and matched ETFs. Significant differences between ETNs and ETFs are indicated by ***, **, and * at the 1%, 5%, and 10% level, respectively. Variable

Expanding ETNs

Competing ETNs

Difference

Returns Volatility Market Share Illiquidity

3.786 0.038 112.121 3.367 0.807% 0.019 -0.011% 0.015 0.788% 1.955

2.489 0.078 105.756 3.110 0.943% 0.028 0.020% 0.018 0.648% 1.810

1.296*** -0.040*** 6.365 0.257*** -0.136%*** -0.009* -0.031%*** -0.003*** 0.141%*** 0.145

TE Difference Beta Difference

-0.159% 0.666

-0.527% 1.606

0.368%*** -0.940***

SMB Difference HML Difference

0.691 0.776

0.636 0.624

0.055*** 0.152***

MOM Difference

0.700

0.522

0.178***

Shares Outstanding (millions) Volume Standardized TNA ($ millions) Age (years) Expense Ratio Premium Standardized

45

Table 6. Spanning tests for competing and expanding ETNs. This table presents results for Lagrange multiplier tests of the null hypothesis that the efficient frontier of a benchmark exchange-traded fund (ETF) spans the frontier of an expanded portfolio that includes exchange-traded notes (ETNs). Column 1 provides the number of ETN-ETF tests that were conducted. Columns 2 and 3 report the percentage of overall mean-variance spanning tests that were significant. Columns 4 and 5 present the percentage of minimum variance portfolio tests that were significant. Columns 6 and 7 show the percentage of tangency portfolio tests that were significant. Critical values for test statistics are calibrated from bootstrapping simulations with returns drawn from the empirical distribution and replicated 10,000 times over 68-month periods. Expanding ETNs are those that track an index that cannot be matched with a corresponding ETF. Competing ETNs are those that track the same index as an ETF. N/A = not applicable. # of tests (1)

Overall spanning α = 1% α = 10% (2) (3)

Minimum variance α = 1% α = 10% (4) (5)

Tangency α = 1% α = 10% (6) (7)

All Unlevered, long Levered Inverse

200 136 35 29

19.50% 14.71% 17.14% 44.83%

24.50% 17.65% 28.57% 51.72%

24.10% 16.67% 29.41% 51.72%

33.85% 27.27% 41.18% 55.17%

1.54% 1.52% 2.94% 0.00%

14.87% 14.39% 20.59% 10.34%

Commodities Currency Domestic equity Fixed income Foreign equity Mixed Other

95 10 38 29 2 14 12

25.26% 80.00% 5.26% 6.90% 0.00% 14.29% 8.33%

28.42% 80.00% 15.79% 13.79% 0.00% 21.43% 8.33%

27.66% 80.00% 18.92% 10.34% 0.00% 16.67% 8.33%

35.11% 80.00% 37.84% 24.14% 0.00% 25.00% 8.33%

0.00% 0.00% 2.70% 3.45% 0.00% 8.33% 0.00%

8.51% 10.00% 18.92% 31.03% 0.00% 33.33% 0.00%

All Unlevered, long Levered Inverse Commodities Currency

30 5 13 12 1 0

13.33% 0.00% 7.69% 25.00% 0.00% N/A

20.00% 20.00% 7.69% 33.33% 0.00% N/A

20.00% 0.00% 7.69% 41.67% 0.00% N/A

23.33% 0.00% 7.69% 50.00% 0.00% N/A

3.33% 0.00% 7.69% 0.00% 0.00% N/A

36.67% 20.00% 38.46% 41.67% 0.00% N/A

Domestic equity Fixed income Foreign equity

20 2 2

5.00% 0.00% 50.00%

10.00% 0.00% 50.00%

15.00% 0.00% 50.00%

5.00% 0.00% 0.00%

35.00% 0.00% 0.00%

15.00% 0.00% 50.00%

ETN Type Panel A: Each expanding ETN matched to one ETF (K = 1)

Panel B: Each competing ETN matched to one ETF (K = 1)

46

Mixed

5

40.00%

60.00%

40.00%

0.00%

80.00%

60.00%

Other

0

N/A

N/A

N/A

N/A

N/A

N/A

200 136 35 29 95 10 38 29 2 14 12

35.50% 24.26% 54.29% 65.52% 33.68% 80.00% 34.21% 41.38% 50.00% 28.57% 8.33%

45.50% 33.82% 62.86% 79.31% 43.16% 80.00% 47.37% 48.28% 100.00% 50.00% 8.33%

36.79% 23.85% 58.82% 68.97% 34.78% 80.00% 38.89% 41.38% 50.00% 25.00% 8.33%

45.08% 31.54% 64.71% 82.76% 42.39% 80.00% 50.00% 51.72% 50.00% 41.67% 8.33%

7.25% 6.92% 2.94% 13.79% 5.43% 10.00% 5.56% 6.90% 50.00% 25.00% 0.00%

20.21% 19.23% 17.65% 27.59% 14.13% 30.00% 25.00% 27.59% 50.00% 33.33% 8.33%

30 5 13 12 1 0 20 2 2 5 0

53.33% 40.00% 61.54% 50.00% 100.00% N/A 55.00% 50.00% 50.00% 40.00% N/A

60.00% 40.00% 69.23% 58.33% 100.00% N/A 60.00% 100.00% 50.00% 40.00% N/A

46.67% 40.00% 46.15% 50.00% 100.00% N/A 45.00% 50.00% 50.00% 40.00% N/A

53.33% 40.00% 61.54% 50.00% 0.00% N/A 25.00% 0.00% 0.00% 20.00% N/A

20.00% 0.00% 23.08% 25.00% 0.00% N/A 45.00% 100.00% 0.00% 40.00% N/A

43.33% 20.00% 46.15% 50.00% 100.00% N/A 55.00% 50.00% 50.00% 40.00% N/A

Panel C: Each expanding ETN matched to ten ETFs (K = 10) All Unlevered, long Levered Inverse Commodities Currency Domestic equity Fixed income Foreign equity Mixed Other Panel D: Each competing ETN matched to ten ETFs (K = 10) All Unlevered, long Levered Inverse Commodities Currency Domestic equity Fixed income Foreign equity Mixed Other

47

Table 7. Mean-variance spanning tests and the market for ETNs, by competing and expanding. This table presents the coefficient estimates and p-values (in parentheses) from the estimation of Eq. (2). Competing exchange-traded notes (ETNs) are those that track the same index as an exchange-traded fund (ETF). Expanding ETNs are those that track an index that cannot be matched with a corresponding ETF. The dependent variables are Volume, calculated as the average daily volume divided by the total shares outstanding, Size, calculated as the natural logarithm of market capitalization, Illiquidity, calculated as in Eq. (1), Market Share, calculated as the average size of the ETN over the month relative to the size of the entire ETN market that month, and Net Issuance, calculated as the percentage change in shares outstanding over the month. The independent variables of interest are the mean-variance spanning test statistics for the minimum variance portfolio test (Min Var Test) and the tangency portfolio test (Tangency Test). Control variables are the average daily percentage premium (or discount) of the ETN market price relative to its indicative value (Premium), Volume, Size, the annual expense ratio (Expenses), the log average age of the ETN (Age), and the standard deviation of the ETN’s daily returns (Volatility). Control variables are averages or totals for the full sample period for all ETNs with at least 20 valid observations. All independent variables are standardized to have mean zero and standard deviation of one. The significance of coefficient estimates are indicated by ***, **, and * at the 1%, 5%, and 10% level, respectively. Competing ETNs

Variable Min Var Test

Tangency Test

TE

Size

Illiquidity

Market Share

Net Issuance

Volume

Size

Illiquidity

Market Share

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

-0.491

-9.821

17.379

-0.165

0.067

-0.001

-0.086

0.046

-0.001

0.001 (0.72)

(0.52)

(0.54)

(0.23)

(0.84)

(0.77)

(0.43)

(0.80)

(0.46)

0.017

1.459***

-1.139

0.009*

-0.013

0.060***

-0.039

-0.583

0.002

0.002

(0.73)

(0.01)

(0.25)

(0.06)

(0.32)

0.00

(0.88)

(0.13)

(0.68)

(0.79)

0.016

-0.665***

0.175

-0.003

-0.006

0.000

0.124

-0.490**

-0.002

0.008**

(0.38)

0.00

(0.61)

(0.14)

(0.21)

(0.96)

(0.40)

(0.04)

(0.42)

(0.04)

0.270

-0.002

0.031***

0.664***

0.005

0.0351***

(0.50)

(0.52)

(0.01)

(0.25)

0.019

Expenses

Age

Volatility

-0.039***

0.00 0.006

(0.50) Premium

Net Issuance

(0.68)

Volume

Size

Expanding ETNs

Volume

-0.237

0.144

-0.002

0.011***

(0.44)

(0.01)

0.009**

-0.006

0.00 0.012*** 0.00

-0.130

0.141

0.000

0.004

(0.01)

(0.24)

(0.63)

(0.30)

(0.05)

(0.20)

(0.31)

(0.50)

(0.82)

(0.23)

0.008

-0.087

1.026***

0.000

-0.004

0.025***

-0.152

-0.589**

0.002

0.012***

(0.55)

(0.60)

0.00

(0.95)

(0.32)

0.00

(0.41)

(0.05)

(0.38)

(0.01)

0.045

0.084

0.098

0.008**

-0.014

0.016***

0.037

-0.763***

0.005**

0.003

(0.15)

(0.84)

(0.89)

(0.04)

(0.11)

0.00

(0.81)

0.00

(0.02)

(0.49)

0.069***

-0.087

0.098

0.003

0.000

0.036***

0.057

0.241

0.000

0.005

48

R-squared Observations

(0.01)

(0.80)

(0.86)

(0.86)

(0.99)

0.00

(0.75)

(0.38)

(0.38)

(0.32)

42.33%

42.03%

25.90%

17.08%

43.31%

47.26%

3.19%

5.71%

1.65%

44.90%

30

30

30

30

30

195

195

194

195

195

49

Table 8. Leverage positions and the market for ETNs. This table reports coefficient estimates and p-values (in parentheses) for the regression model given in Eq. (3). Competing exchange-traded notes (ETNs) are those that track the same index as an exchange-traded fund (ETF). Expanding ETNs are those that track an index that cannot be matched with a corresponding ETF. The dependent variables are Volume, calculated as the average daily volume divided by the total shares outstanding, Size, calculated as the natural logarithm of market capitalization, Illiquidity, calculated as in Eq. (1), Market Share, calculated as the average size of the ETN over the month relative to the size of the entire ETN market that month, and Net Issuance, calculated as the percentage change in shares outstanding over the month. The independent variables of interest are the indicator variables Long 200, Long 300, Short 100, Short 200, and Short 300, which are set equal to one if the given ETN is classified with the associated leverage position. Control variables are the average daily percentage premium (or discount) of the ETN market price relative to its indicative value (Premium), Volume, Size, the annual expense ratio (Expenses), and the average age of the ETN (Age). All control variables are calculated in month t - 1 and are standardized to have mean zero and standard deviation of one. Errors are clustered at the ETN level, and objective and time fixed effects are included. The significance of coefficient estimates are indicated by ***, **, and * at the 1%, 5%, and 10% level, respectively. FE = fixed effects. Competing ETNs

Expanding ETNs

Volume

Size

Illiquidity

Market Share

Net Issuance

Volume

Size

Illiquidity

Market Share

Variable

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Net Issuance (10)

Long 200

0.060

-1.276

0.428

-2.899

-0.057

-0.001

1.056**

-1.203***

0.724

0.017

(0.62)

(0.36)

(0.80)

(0.17)

(0.33)

(0.94)

(0.02)

(0.01)

(0.12)

(0.15)

Long 300

0.124

-3.067***

6.416***

-2.584*

-0.087**

0.051**

0.836*

-1.147*

-0.468

0.074*

(0.36)

(0.01)

(0.00)

(0.10)

(0.04)

(0.05)

(0.08)

(0.09)

(0.21)

(0.08)

Short

-0.004

-1.786

1.832*

-1.194

-0.069**

0.008

-0.119

-0.507

-0.572**

0.006

(0.98)

(0.18)

(0.08)

(0.38)

(0.04)

(0.32)

(0.76)

(0.32)

(0.04)

(0.55) -0.010

Short 200

Short 300

0.093

-3.216***

4.942**

-2.544

-0.042

0.039*

-0.039

0.452

-0.645*

(0.48)

(0.00)

(0.02)

(0.11)

(0.37)

(0.07)

(0.94)

(0.59)

(0.06)

(0.29)

0.177

-3.619***

5.688***

-2.748*

-0.068

0.173***

0.375

-2.053***

-0.888**

0.082*

(0.00)

(0.01)

(0.10)

(0.17)

(0.01)

(0.44)

(0.00)

(0.02)

(0.06)

(0.25) Volume

Size

Premium

Expenses

0.635*

0.331

(0.07)

(0.16)

0.275***

0.143

(0.00)

(0.23)

0.076

-0.004

0.019

(0.12)

(0.63)

(0.10)

0.001

-0.002

-0.026

-0.008

0.043

0.084

-0.024

-0.006 (0.10) 0.011

-0.014

-0.003

(0.37)

(0.18)

(0.68)

(0.52)

(0.83)

(0.11)

(0.25)

(0.80)

(0.61)

(0.13)

0.018

-0.059

2.148***

-0.011

-0.009

0.017***

0.035

-0.216

0.386**

0.011**

(0.38)

(0.75)

(0.00)

(0.91)

(0.13)

(0.00)

(0.80)

(0.14)

(0.05)

(0.02)

50

Age

0.031

-0.542

0.937

-1.220

-0.033

0.008

0.282*

-0.245

0.593***

-0.013***

(0.49)

(0.15)

(0.22)

(0.11)

(0.12)

(0.13)

(0.09)

(0.29)

(0.01)

(0.00)

Time FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Objective FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

R-squared

38.1%

44.6%

29.4%

34.1%

2.8%

15.9%

18.8%

12.2%

13.0%

1.6%

Observations

1,584

1,584

1,467

1,584

1,584

14,355

14,355

14,175

14,355

14,355

51

Table 9. Differences in tracking errors and the market for ETNs. This table reports coefficient estimates and p-values (in parentheses) for the regression model given in Eq. (5). Competing exchange-traded notes (ETNs) are those that track the same index as an exchange-traded fund (ETF). Expanding ETNs are those that track an index that cannot be matched with a corresponding ETF. The dependent variables are Volume, calculated as the average daily volume divided by the total shares outstanding, Size, calculated as the natural logarithm of market capitalization, Illiquidity, calculated as in Eq. (1), Market Share, calculated as the average size of the ETN over the month relative to the size of the entire ETN market that month, and Net Issuance, calculated as the percentage change in shares outstanding over the month. The independent variable of interest is TE Difference, the ETN monthly tracking error subtracted from the matched ETF monthly tracking error. Control variables are the average daily percentage premium (or discount) of the ETN market price relative to its indicative value (Premium), Volume, Size, the annual expense ratio (Expenses), the average age of the ETN (Age), and the standard deviation of the ETN’s daily returns (Volatility). All independent variables are calculated in month t - 1 and are standardized to have mean zero and standard deviation of one. Errors are clustered at the ETN level, and objective and time fixed effects are included. The significance of coefficient estimates are indicated by ***, **, and * at the 1%, 5%, and 10% level, respectively. FE = fixed effects. Competing ETNs

Variable

Expanding ETNs

Volume

Size

Illiquidity

Market Share

Net Issuance

Volume

Size

Illiquidity

Market Share

Net Issuance

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

0.048***

0.565***

-1.213***

0.187*

0.010*

0.008

-0.050

0.099

0.240***

-0.000

(0.00)

(0.00)

(0.00)

(0.06)

(0.10)

(0.37)

(0.44)

(0.24)

(0.01)

(0.89)

Panel A: Full sample TE Difference

Volume

Size

Premium

Expenses

Age

Volatility

0.543***

0.249*

(0.01)

(0.06)

0.265***

0.100

(0.00)

(0.45)

0.039*

0.002

0.017*

(0.07)

(0.62)

(0.09)

0.004

-0.005**

-0.013

-0.016*

0.159**

-0.334

0.037

-0.005* (0.09) 0.005

0.003

-0.004***

(0.08)

(0.04)

(0.20)

(0.19)

(0.45)

(0.03)

(0.50)

(0.88)

(0.88)

(0.00)

0.010

-0.217

1.936*

-0.096

-0.006*

0.018**

0.065

-0.393***

0.269*

0.011**

(0.35)

(0.19)

(0.09)

(0.27)

(0.07)

(0.02)

(0.57)

(0.01)

(0.06)

(0.04)

0.016

-0.098

0.633

-0.277

-0.012**

0.006

0.174

-0.116

0.531**

-0.019***

(0.28)

(0.80)

(0.36)

(0.13)

(0.03)

(0.26)

(0.28)

(0.63)

(0.02)

(0.00)

0.067***

-0.151

0.601

-0.002

0.012

0.039***

0.057

-0.006

0.090

0.025***

(0.00)

(0.28)

(0.13)

(0.98)

(0.13)

(0.00)

(0.62)

(0.97)

(0.59)

(0.00)

Time FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Objective FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

52

R-squared

47.9%

26.8%

19.7%

25.2%

2.2%

17.8%

15.6%

11.4%

12.6%

1.8%

Observations

1,570

1,570

1,455

1,570

1,570

13,531

13,531

13,362

13,531

13,531

0.078**

0.414***

-0.600**

0.108

0.015*

0.016

0.028

-0.018

0.232**

0.007**

(0.04)

(0.01)

(0.04)

(0.64)

(0.06)

(0.15)

(0.66)

(0.88)

(0.03)

(0.01)

Panel B: No-leverage subsample TE Difference

Volume

Size

Premium

Expenses

Age

Volatility

0.422

0.332

(0.14)

(0.17)

0.218**

0.201***

(0.02)

(0.01)

0.093

0.022

0.015

(0.11)

(0.28)

(0.26)

-0.004

-0.004***

0.010

-0.023**

-0.006* (0.07)

0.078**

0.062*

-0.008

(0.02)

(0.03)

(0.06)

(0.86)

(0.11)

(0.00)

(0.66)

0.054

0.979**

0.187

0.360

-0.034*

0.002

0.148

(0.43)

(0.04)

(0.28)

(0.54)

(0.06)

(0.54)

(0.14)

-0.006

0.991***

-0.789**

-1.023

-0.053

0.008*

(0.84)

(0.00)

(0.02)

(0.33)

(0.13)

(0.06)

0.043

0.185

-0.170

0.084

0.022**

-0.036

0.022

-0.004**

(0.25)

(0.11)

(0.03)

-0.225

0.224**

0.002

(0.13)

(0.03)

(0.57)

0.585***

-0.698**

0.836***

-0.012***

(0.00)

(0.01)

(0.01)

(0.00)

0.026

-0.136

0.219

-0.100

0.011**

(0.26)

(0.23)

(0.19)

(0.62)

(0.02)

(0.14)

(0.25)

(0.22)

(0.51)

(0.03)

Time FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Objective FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

61.6%

74.3%

21.9%

37.9%

7.0%

12.4%

23.1%

14.4%

14.5%

1.1%

665

665

658

665

665

10,571

10,571

10,406

10,571

10,571

-0.001

0.101

-0.465

0.008

-0.001

-0.002

-0.187

0.223

0.085

-0.014

(0.85)

(0.15)

(0.12)

(0.81)

(0.89)

(0.87)

(0.13)

(0.13)

(0.36)

(0.13)

R-squared Observations

Panel C: Leverage subsample TE Difference

Volume

Size

Premium

0.103***

-0.001

(0.01)

(0.95)

0.005

0.003

0.018**

(0.39)

(0.74)

(0.02)

0.317**

-0.246

(0.03)

(0.50) -0.011 (0.19)

0.001

0.079

0.190

0.017

0.006

-0.006***

-0.186*

0.348

-0.376

-0.006

(0.24)

(0.10)

(0.64)

(0.44)

(0.32)

(0.00)

(0.06)

(0.19)

(0.12)

(0.20)

53

Expenses

Age

Volatility

-0.006***

-0.123

2.397**

-0.016

-0.005

0.054***

-0.344*

-0.596**

-0.073

0.014

(0.01)

(0.36)

(0.01)

(0.78)

(0.17)

(0.00)

(0.08)

(0.03)

(0.68)

(0.39) -0.035***

0.007

-1.298***

4.170***

-0.503**

-0.012

0.010*

-0.452*

0.861*

-0.271

(0.30)

(0.00)

(0.00)

(0.02)

(0.48)

(0.09)

(0.06)

(0.08)

(0.11)

(0.00)

0.001

-0.181

0.966*

0.024

-0.001

0.060***

0.111

0.038

0.357

0.056***

(0.86)

(0.18)

(0.08)

(0.65)

(0.93)

(0.00)

(0.57)

(0.89)

(0.24)

(0.00)

Time FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Objective FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

28.8%

61.4%

37.3%

29.8%

2.0%

36.1%

18.1%

12.0%

16.3%

6.7%

905

905

797

905

905

2,960

2,960

2,956

2,960

2,960

R-squared Observations

54

Table 10. Differences in factor loadings and the market for ETNs. This table reports coefficient estimates and p-values (in parentheses) for the regression model given in Eq. (6). Competing exchange-traded notes (ETNs) are those that track the same index as an exchange-traded fund (ETF). Expanding ETNs are those that track an index that cannot be matched with a corresponding ETF. The dependent variables are Volume, calculated as the average daily volume divided by the total shares outstanding, Size, calculated as the natural logarithm of market capitalization, Illiquidity, calculated as in Eq. (1), Market Share, calculated as the average size of the ETN over the month relative to the size of the entire ETN market that month, and Net Issuance, calculated as the percentage change in shares outstanding over the month. The independent variables of interest are Beta Difference, SMB Difference, HML Difference, and MOM Difference, which are the absolute differences in the monthly factor loadings between the ETN and matched ETF based on a daily return four-factor model. Control variables are the average daily percentage premium (or discount) of the ETN market price relative to its indicative value (Premium), Volume, Size, the annual expense ratio (Expenses), and the average age of the ETN (Age). All control variables are calculated in month t - 1 and are standardized to have mean zero and standard deviation of one. Errors are clustered at the ETN level, and objective and time fixed effects are included. The significance of coefficient estimates are indicated by ***, **, and * at the 1%, 5%, and 10% level, respectively. FE = fixed effects. Competing ETNs

Variable

Expanding ETNs

Volume

Size

Illiquidity

Market Share

Net Issuance

Volume

Size

Illiquidity

Market Share

Net Issuance

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

0.046***

-0.354***

0.177

-0.228

-0.002

0.011

0.094*

-0.195***

0.023

-0.003

(0.00)

(0.01)

(0.51)

(0.14)

(0.82)

(0.33)

(0.08)

(0.00)

(0.75)

(0.44)

0.005

-0.145*

0.408

-0.035

-0.000

0.006**

-0.038

0.088

-0.114**

0.006

(0.23)

(0.08)

(0.21)

(0.30)

(0.98)

(0.03)

(0.26)

(0.18)

(0.04)

(0.13)

-0.001

0.164**

0.367

0.218

0.007

0.008***

-0.016

0.029

-0.025

0.007

(0.92)

(0.04)

(0.34)

(0.13)

(0.21)

(0.01)

(0.60)

(0.58)

(0.33)

(0.10)

-0.012

-0.024

-0.491

0.008

0.002

0.006*

0.021

-0.238***

-0.009

0.009

(0.19)

(0.78)

(0.26)

(0.91)

(0.67)

(0.10)

(0.52)

(0.00)

(0.78)

(0.35)

Panel A: Full sample Beta Difference

SMB Difference

HML Difference MOM Difference

Volume

Size

Premium

Expenses

Age

0.709***

0.335*

0.278***

0.129

(0.01)

(0.08)

(0.00)

(0.27)

0.065*

0.006*

0.018*

(0.07)

(0.09)

(0.06)

0.004

-0.002

-0.008

-0.010

0.044

-0.004 (0.18)

0.157**

-0.299

(0.13)

(0.03)

(0.28)

(0.13)

(0.34)

(0.16)

(0.68)

(0.95)

(0.71)

(0.11)

0.018

-0.219

1.749

-0.151

-0.006

0.024***

0.074

-0.326***

0.301*

0.016***

(0.24)

(0.21)

(0.13)

(0.25)

(0.15)

(0.00)

(0.52)

(0.01)

(0.07)

(0.01)

0.014

-0.123

0.589

-0.334

-0.014**

0.008

0.182

-0.112

0.506***

-0.017***

55

0.002

0.007

-0.003

(0.40)

(0.74)

(0.41)

(0.11)

(0.02)

(0.19)

(0.25)

(0.63)

(0.01)

(0.00)

Time FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Objective FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

R-squared

38.4%

24.4%

16.7%

24.3%

1.8%

15.0%

15.7%

11.7%

12.3%

1.6%

Observations

1,584

1,584

1,467

1,584

1,584

14,355

14,355

14,175

14,355

14,355

Panel B: No-leverage subsample Beta Difference

SMB Difference

HML Difference

MOM Difference

0.069***

-0.309

0.046

-0.411

0.001

0.017

0.131*

-0.276***

0.057

0.003

(0.00)

(0.14)

(0.72)

(0.27)

(0.94)

(0.23)

(0.08)

(0.00)

(0.43)

(0.41)

-0.004

0.071

0.038

-0.017

0.012

-0.001

-0.058

0.165*

-0.132

0.002

(0.21)

(0.29)

(0.73)

(0.85)

(0.51)

(0.41)

(0.15)

(0.07)

(0.11)

(0.65)

-0.017*

0.209

0.001

0.312

-0.005

0.000

-0.019

-0.009

0.019

0.007

(0.09)

(0.13)

(0.99)

(0.19)

(0.22)

(0.92)

(0.55)

(0.87)

(0.63)

(0.12)

-0.026

-0.013

-0.029

-0.024

-0.003

-0.000

0.026

-0.280***

0.006

-0.000

(0.14)

(0.87)

(0.83)

(0.85)

(0.49)

(0.89)

(0.43)

(0.00)

(0.84)

(0.95)

Volume

Size

Premium

0.724**

0.514

(0.02)

(0.19)

0.187**

0.206**

(0.04)

(0.02)

0.161**

0.041

0.013

(0.02)

(0.02)

(0.30)

-0.001

-0.003**

0.005

-0.013*

0.134***

-0.001

0.006

-0.007* (0.07) -0.037

0.015

-0.004** (0.05)

(0.10)

(0.00)

(0.99)

(0.87)

(0.81)

(0.03)

(0.82)

(0.11)

(0.23)

0.068

1.098**

-0.212

0.320

-0.028

0.001

0.102

-0.118

0.204*

0.002

(0.37)

(0.02)

(0.51)

(0.59)

(0.27)

(0.68)

(0.33)

(0.42)

(0.05)

(0.57)

-0.031

0.919***

-0.785**

-1.335

-0.062

0.011*

0.539***

-0.600**

0.756***

-0.011**

(0.30)

(0.01)

(0.04)

(0.26)

(0.05)

(0.07)

(0.00)

(0.03)

(0.01)

(0.01)

Time FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Objective FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

13.5%

72.5%

15.9%

38.4%

5.9%

10.0%

22.7%

14.5%

14.2%

1.1%

668

668

661

668

668

11,171

11,171

10,995

11,171

11,171

-0.134**

0.066

-0.030

-0.004

0.009***

-0.070

0.017

-0.109

-0.009

Expenses

Age

R-squared Observations

Panel C: Leverage subsample Beta Difference

0.005*

56

SMB Difference

HML Difference

MOM Difference

(0.08)

(0.05)

(0.86)

(0.37)

(0.69)

(0.01)

(0.13)

(0.83)

(0.17)

0.001

-0.128*

0.550

-0.052**

-0.010*

0.009**

-0.074

0.052

-0.006

0.013

(0.56)

(0.09)

(0.15)

(0.05)

(0.08)

(0.05)

(0.16)

(0.55)

(0.91)

(0.12)

0.002

0.024

0.922

0.055

0.015**

0.012**

-0.044

0.146

-0.000

0.004

(0.53)

(0.72)

(0.22)

(0.21)

(0.04)

(0.02)

(0.48)

(0.11)

(1.00)

(0.68)

-0.005*

-0.164*

-0.813

-0.086

-0.000

0.010

-0.021

-0.124

0.007

0.024

(0.09)

(0.10)

(0.24)

(0.12)

(0.96)

(0.20)

(0.75)

(0.27)

(0.92)

(0.37)

Volume

Size

0.092**

-0.004

0.421***

-0.100

(0.02)

(0.80)

(0.00)

(0.73)

0.006

0.002

0.028***

(0.39)

(0.84)

(0.00)

0.006

-0.002

Premium

0.001 (0.12)

Expenses

-0.004

Age

(0.32)

0.080*

0.323

0.026

(0.09)

(0.50)

(0.26)

(0.38)

(0.60)

-0.044

2.077**

0.001

-0.008

0.072***

-0.004 (0.60) -0.179**

0.385

-0.335

-0.002

(0.05)

(0.16)

(0.13)

(0.75)

-0.216

-0.672***

0.055

0.035*

(0.12)

(0.75)

(0.02)

(0.99)

(0.11)

(0.00)

(0.28)

(0.01)

(0.78)

(0.06)

0.007

-1.310***

4.345***

-0.492**

-0.012

0.012

-0.406*

0.817*

-0.267

-0.032***

(0.27)

(0.00)

(0.00)

(0.02)

(0.47)

(0.10)

(0.08)

(0.08)

(0.11)

(0.00)

Time FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Objective FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

30.4%

62.7%

37.1%

31.0%

2.5%

30.7%

17.6%

12.4%

15.1%

4.9%

916

916

806

916

916

3,184

3,184

3,180

3,184

3,184

R-squared Observations

57

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