Master Thesis: The efficiency of Exchange-Traded Funds as a market investment

Transcription

1 Master Thesis: The efficiency of Exchange-Traded Funds as a market investment Author: R.Bernabeu Verdu ANR: Supervisor: Lieven Baele Faculty: Tilburg School of Economics and Management Department: Finance Programme: Master in Finance Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 1

2 Abstract Exchange-Traded Funds (ETFs) have become an important innovation in the financial markets nowadays. They are low cost products designed to pursue passive replication strategies that respond to investor s demands of liquidity and efficiency. However, they suffer from tracking errors and price mismatches even when they are designed to avoid them. I will show that, in general, ETFs underperform their benchmarks by around 50 basis points every year and which factors are responsible for this underperformance. For Leveraged or Inverse funds the results are much worse as the underperformance is around 6% every year. Moreover, tracking errors will be analyzed for both ETFs and LIETFs in order to find the reasons behind the underperformance of the benchmarks. Finally, I will show that ETFs and LIETFs are very efficient in keeping the fund s market price close to the NAV. Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 2

3 Table of Contents Abstract Introduction ETF structure and competitive advantages Current state of literature Research questions and methodology Sample description Data selection Sample and descriptive statistics ETFs LIETF Empirical results ETFs Capital Asset Pricing Model test ETF Tracking errors ETF Price efficiency LIETFs Capital Asset Pricing Model test LIEFTs tracking errors LIETFs price efficiency Conclusions References: Appendix 1: Figures Appendix 2: Tables Appendix 3: List of variables and definitions Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 3

4 1-Introduction Since the development of Modern Portfolio theory by Markowitz (1952) investors have been looking for efficient ways to diversity their portfolio in order to eliminate idiosyncratic risk and obtain efficient portfolios that maximize return minimizing risk. The most direct way to do this is replicating indices by buying all stocks or, at least, a representative sample of them. However, this strategy is only available to large investors as retail investors would experience severe transaction costs. Due to these problems, retail investors started demanding equity funds that could buy stocks in large quantities resulting in lower transaction costs; causing the appearance of the first passive mutual funds. These funds are intended to replicate indices charging fewer fees to their customers than active funds, which look to outperform a market index. For this purpose, passive funds hire fund managers that create portfolios of stocks (usually replicating an index benchmark) offering the fund s stock to retail investors with lower costs that it would be for them to buy all the equity on their own. On the other hand, there are active mutual funds that follow active strategies based on the knowledge of professional managers, paying higher fees in exchange. In these funds, investors give capital to the fund and it follows different strategies in order to obtain abnormal returns compared with an index benchmark. Active funds usually pursue investment strategies that focus in finding of α stocks (stocks that offer more (less) returns given their market risk (β)), these stocks offer higher (lower) returns that companies with the same risk (following CAPM) and therefore can be a productive investment if active funds are able to find them. Passively managed mutual funds have revolutionized the way retail investors behave, as they have made it very easy and relatively cheap to get broad market exposure. However they had, Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 4

5 and still have, limitations in terms of liquidity and pricing efficiency. Mutual funds are structured in two different ways: On the one hand, there are open-end funds that have important liquidity problems as their shares do not trade in organized markets. The fund s shares can only be bought or sold back to the fund at the end of trading sessions for the Net Asset Value (NAV). The advantage of this approach is that the difference between the price of the fund and the value of the underlying assets is guaranteed to remain low as if they differ, arbitrageurs will intervene buying or selling shares back to the fund until the difference is gone. However, the fact that the shares can only be bought or sold at the end of the trading sessions is not optimal as it increases transaction costs and reduces the ability of investors to liquid their investment. Moreover, they also charge fees when buying or selling the fund s shares decreasing even more liquidity and increasing transaction costs. These additional fees are known as front-end loads 1 if there are paid when entering into a long position or back-end loads if they are charged when selling the long position. This liquidity problem is an important concern for short term investors as they need to realize multiple transactions and to be able to recover their money fast and with as low as possible transaction costs. For long term investors, liquidity supposes less a challenge as they plan to keep the money invested in the fund for a long period of time. However, the increasing costs and higher probability of default of less liquid funds make them consider these restrictions before making a final investment decision. On the other hand, closed-end funds are funds that are exchanged between individuals in organized markets, they are traded like shares and individuals can exchange them using brokers and traders. The problem with these funds is that once the fund has issued shares they cannot be redeem back, which means that shares can only be purchased or sold in the market and not back 1 12b-1 fees in United States Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 5

6 to the fund. As the price is not guaranteed to reflect the real value of the underlying assets, these shares have the risk of important price deviations between their market price and the value of the assets held by the fund. These deviations usually take form as a discounted price relative to the fund s NAV; signaling that investors value the fund s shares less than the assets that back them. The problem is that there is not a mechanism though which investors can use arbitrage and eliminate deviations. The logic of this anomaly has become an important puzzle for finance academics as prices should not differ that much from their fundamental value and it is subject to important research (see e.g. Boudreaux (1973) or Pontiff (1996)) With the rise of new empirical research that showed that, in general, active funds underperform their index benchmarks (e.g. Malkiel (1995)); and the acceptance that low cost passive strategies can deliver superior results than traditional actively managed mutual funds, investors started looking for low cost methods of replicating indices. They started demanding funds that could be easily tradable and not prone to substantial discounts or premiums from the NAV. As a consequence of these increasing demands, the first generation of Exchange-Traded Funds (ETFs) known as Spiders were introduced in the year 1990 in Canada. Spiders were a hybrid between open end funds and closed end funds. By construction there were very similar to passive mutual funds in a sense that they were passively traded portfolios of securities but, in contrast to open-end index mutual funds, Spiders were listed on exchanges like individual stocks and therefore could be traded continuously throughout the trading session. Moreover they could be redeemed back to the fund provider for the NAV. These products will later on be named Exchange-Traded Funds and were created to avoid price deviations while allowing continuous trading. Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 6

7 Because of the relative advantages of ETFs, their rapid expansion was inevitable. From Canada they jumped to the rest of the world experiencing a very fast expansion starting in 2000 until today. Next to ETFs a new variety of products were born with similar structures included in the category of Exchanged Traded Products (ETPs).These products are Exchanged Traded Notes (ETNs), Exchanged Traded Commodities (ETCs), alternative ETFs, currency ETFs, active ETFs, inverse ETFs and leveraged ETFs. In the year 2000, there were 106 ETPs (92 ETFs) with asset value of 79.4 billion U.S dollars (74.3 billion corresponding only to ETFs). By the end of 2012 there were 4748 ETPs (3297 ETFs) with asset value of 1.8 trillion dollars (1.6 trillion) an average annual growth of 34% (see graph 1 and 2 in appendix 1). This exponential growth shows how important these products have become in today s financial markets. If we take a look to the ETPs providers there are a total of 195 ETPs providers worldwide. ETFs are the most important component with 89% of the total ETP market leaving the rest 11% for the other types of funds. Furthermore, 84% of the value of the ETF s assets is concentrated in only 10 providers that cope the market, leaving the other 16% for the other 185 providers (see figure 3 in appendix 1). By provider, ishares is the largest ETP provider with 39% market share followed by State Street with 18% and Vanguard with 13%, all three supposing almost 70% of the market. These numbers show that the ETP market is very concentrated; having the main players an important advantage compared with smaller less known families of funds. The picture in the ETP market in the U.S in very similar to the global overview as it accounts for 71% of the global ETP market (1.3 trillion U.S dollars) using data from September There are 49 ETF sponsors providing 1465 ETFs. The average growth of assets under management for the last 13 years has been of 27% per year (see figure 4 in appendix 1) which is a little bit lower Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 7

8 than the global industry growth. ETFs are the most important component with 89% of the total ETP market (see figure 4 in appendix 1). There are a total of 49 ETP providers, but as in the global case their assets are very concentrated. The three most important providers account for 82.5% of the total assets under management, being ishares again the largest provider with 40% of market share as shown by figure 5 in appendix 1. The increased popularity and the rise of more complex products like Leveraged and Inverse ETFs (LIETFs) augmented the possibilities for institutional asset allocation as well as for private investing but at the cost of increasing complexity. In this context, LIETFs have acquired an important role in the financial markets. These funds use synthetic replication in order to achieve daily returns that are multiples (2, 3,-1,-2,-3 are the most usual) of the targeted benchmark. They do not promise long term performance regarding the benchmark; instead the objective is to provide a specific leverage on a daily basis. LIETFs must rebalance their assets continuously in order to keep their promised multiple; this causes higher transaction costs that may explain their higher fees and their relative underperformance (this will be discussed later). Another important characteristic of LIETFs is their high trading volume, for example as of September 2009 they supposed approximately 40% of the total trading volume for ETP in the U.S when their assets are only a small fraction (around 5%) of the ETP market as stated by Charupat and Miu (2010). These characteristics make them optimal for short term traders who wish to pursue speculative trades in specific benchmarks as opposed to the more long term diversified approach of conventional ETFs. LIETFs are also associated with gambling as they are perfect for investors looking for high volatility liquid markets where it is possible to take very risky positions for the sake of it. Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 8

9 The rest of the paper is organized as follows. The next section provides a more detailed explanation of the structure and functioning of ETFs and the advantages of these funds. Section 3 provides a literature review consisting of related research. Section 4 will set the main research questions and purposes of this paper. Section 5 will describe the data and methodology. Then, section 6 will show the main empirical results. Finally, section 7 will present the main conclusions of this paper. Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 9

10 2-ETF structure and competitive advantages As a way to avoid price deviations ETFs have engineered a method called creation-redemption process (view Figure 6 in the appendix) that guarantees that prices do not vary much from the NAV. In order to do this, when an ETF provider wants to create new shares of its fund it turns to an Authorized Participant (AP). An AP can be a market maker, a specialist or any other large financial institution with buying power. The AP buys all the shares that compose the benchmark index (or a representative sample) the ETF tries to replicate, and exchanges them with the ETF provider for the ETF s equally valued own shares at their NAV 2. The exchange takes place in a fair value basis as the block of underlying shares and the ETF shares are equally valued at the NAV. Both participants benefit from the transaction because the ETF provider gets the stocks it needs to track the index and the AP gets ETF shares to resell for profit. The process also works in reverse, which means that AP can buy large blocks of the ETF shares and exchange them for the underlying benchmark shares at the NAV. The process guarantees that the ETF market price does not differs much from its NAV as if mismatch occurs, APs will intervene exchanging ETF shares for underlying shares, or the other way around, using arbitrage until both, market price and NAV, are equal. This system also saves money to the ETF provider as it does not incur in transaction costs when the portfolio is constructed, it is the AP who buys the underlying stocks (usually with low transaction costs as it is a big player in the market). This facts cause ETFs to charge less fees than equivalent passive mutual funds. Moreover, there are other characteristics that make ETFs more efficient and more attractive than mutual funds. 2 This process is only done for large blocks of shares, usually 50,000 or more. Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 10

11 Cost Advantage: The majority of ETFs are passive investment products that usually try to track the performance of a market index. This means that their turnover is low, as share transactions only occur for rebalancing purposes. Furthermore, the passive approach causes lower costs for ETFs as providers do not need to hire expensive managers and perform complex market analysis. This cost efficiency is translated in lower expense ratios that make them more attractive as investors usually consider fees as one of the main determinants when choosing among similar funds. Transparency: ETFs are generally listed in market exchanges and trade like normal shares which forces them to comply with the exchange transparency and transmission of information rules. ETFs are obliged to publish their financial statements and prospectus informing investors of all relevant changes in the fund s policy. Moreover, they also have to disclosure the components of the fund, their weights and the NAV of their shares daily, which helps investors to be aware about what the fund is doing and how it is doing it. Avoidance of principal-agent problem: The importance of the principal-agent problem is well documented in many research papers like Grossman and Hart (1983) and Haubrich (1994). This problem occurs when the manager of the fund, the agent, makes decisions on behalf of the owners of the fund (investors), it is usual that the manager acts in his own interest instead of acting in the interest of the investors. The problem is exacerbated in active mutual funds when managers have a bonus or a stock-based compensation if they are able to beat the market. This form of salary incentivizes them to pursue riskier strategies that offer higher expected returns. In the ETF case, this issue is avoided as managers are specifically instructed to replicate a market index and therefore assume the same risk than the benchmark. However, recently there have appeared new types of ETFs Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 11

12 called active ETFs that try to outperform their benchmarks by pursuing active (alpha) strategies. These products are usually cheaper than equivalent active mutual funds, but suffer from the same problems when trying to outperform market benchmarks (e.g.malkiel (1995)). Tax efficiency: The in and out capital flows of ETFs are also a source of improvement compared to mutual funds. ETFs are structured in a manner that taxes are minimized for the holder of the ETF and the ultimate tax bill (when the ETF is sold and investors pay taxes for capital gains) is less than what the investor would have paid in a similarly structured mutual fund. A mutual fund must constantly rebalance the fund by selling securities to accommodate shareholder redemptions or to reallocate assets. The sale of securities within the mutual fund portfolio creates capital gains for shareholders that are subject to taxes. In contrast, ETFs administrate inflows and outflows by creating or redeeming blocks of ETF shares that are exchanged for an equally valued amount of benchmark stock, not creating any capital gains in the process. As a result, investors usually are not exposed to capital gains on any individual security in the underlying structure. As ETP started to be more and more interesting for investors, investment banks started to enter the market offering different new features, like new derivative structures, tailored for specific investor s demands and, as a consequence, the market started to be more and more complex. The new and different types of replication methods used made ETFs more dissimilar to each other and comparable investment products. Investors have now to differentiate between full replication, optimized sampling and synthetic, swap-based, replication strategies: Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 12

13 Physical replication: This strategy consists in holding all, or substantially all, the stocks with the same weights than the target benchmark. This method supposes higher transaction costs as more stocks must be bought and rebalanced, but the risk of tracking error is lower. Sampling replication: The strategy consists in holding only a representative sample of the benchmark that is supposed to give approximately the same returns than the benchmark. This method supposes lower transactions costs as the fund needs to operate with less stocks but increases the change of higher tracking errors. Synthetic replication: This method tries to replicate an index benchmark using different derivatives like swaps. They have the advantage of lower costs and more favorable tax treatment (in some countries) than physical and sampling replication but may be prone to higher errors and other risks as counterparty risk and lack of transparency. In the equity market, physical and sampling ETFs are predominant in the U.S and synthetic replication is only used for bond, commodity or leveraged/inversed replication (only 3% of the ETFs used synthetic replication in the U.S as stated by Dickson et al. (2013)). In the U.S legislation, swap-based replication has less favorable treatment than the rest of replication methods because swap income has higher tax liability than capital gains incurred when trading physical stocks 3. 3 In Europe, however, synthetic replication is more common because of tax reasons. Physical or sampling based ETFs must pay a tax called stamp duty (0.5% of the value of physical underlying securities in U.K) that swap-based ETFs do not have to pay (Dickson et al., 2013). Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 13

14 3-Current state of literature Due to their relative youth (the first ETF was created in 1990 in Canada, but they started to be popular from 2002) research on these products is limited and focused mainly in U.S market. Many of the current research finds that ETF prices are very close to NAV in United States (Ackert and Tian (2008), Elton et.al.(2002) but country ETFs are not (Engle and Sakar (2006)), meaning that although ETF are designed to be efficiently priced, evidence signals persistent premiums or discounts especially for ETF that are not based in U.S. Engle and Sarkar (2006) started examining the end-of-day and intra-day data, measuring the premiums magnitude as well as their persistence. They focused on the influences of the creation-redemption process on domestic and international funds. They arrive to the conclusion that international funds face higher premiums than their domestic counterparts as market discrepancies cannot be capitalized efficiently. For international funds the creation-redemption process involved in the arbitrage mechanism for institutional investors is more complicated and costly while the ability of hedging risk is also tremendously reduced. Based on this, research papers have focused on trying to find the logic behind tracking errors and price deviations. Some important factors have been detected, Elton et.al. (2002) finds that the most important sources of mispricing are management fees and dividends received but not yet paid out, this cash flows are not put in interest bearing accounts which causes underperformance. On the other hand Gastineau (2004) finds that the expense ratio alone explains much of the difference in pricing. Ackert and Tian (2008) find that mispricing of country funds is related to momentum, illiquidity and size effects concluding that the relationship between fund premiums and market illiquidity shapes as an inverted U, they arrive to this conclusion after finding that the Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 14

15 premium is positively related to liquidity but negatively related to squared liquidity. Blitz, Huij and Swinkels (2010) find that taxes are determinant for the mispricing, especially dividend taxes. Another important factor researchers have found significant is liquidity. Blitz and Huij (2012) conclude that when there is a high spread of cross-sectional returns, the tracking errors tend to be higher than when this spread is low because of bad liquidity (especially in Emerging Markets). Another important topic is the method of replication each ETF uses, as stated before, physical, sampling or synthetic replication. Although research is even scarcer in this topic, there are some papers that focus in the performance differences between synthetic and physical replication. William (2014) concludes that physical ETFs replicate the performance of benchmarks similarly to synthetic ETFs meaning that synthetic holders are not compensated for the additional (counterparty) risk they bear. Elia (2012), on the other hand, states that ETFs that follow a synthetic replication strategy instead of holding the Benchmark s securities enjoy a lower tracking error and higher tax efficiency, even though they underperform both the benchmarks and the traditional counterparts. However, none of these papers focuses on the differences between physical and sampling replication (which is one of the points of this paper) that are the predominant replication methods in the U.S, market that accounts for 70% of the total ETF market by assets under management. Research has also covered the differences between ETF and conventional passive mutual funds, trying to assess which of both products is more efficient. Agapova (2011) finds that conventional index funds and ETFs are substitutes, but not perfect substitutes. ETFs have not replaced the conventional index funds, but they are a new investment vehicle that has added new features previously unavailable in conventional mutual funds, helping completing the market. Harper, Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 15

16 Madura and Schnusenberg (2006) obtained evidence that ETFs have higher mean return and higher Sharpe ratio than foreign closed-end funds, while closed-end country funds show negative alphas. This indicates that passive investment strategies using ETFs could be superior to active investment strategies using closed-end mutual funds. Regarding LIETFs, there is not much research available as they started to become popular very recently ( ). The main objective of the scarce research has been to find out the reasons of tracking errors and mispricing. Charupat and Miu (2010) analyze leveraged ETFs and find that they are usually held by very short term investors causing occasional large premiums and discounts from NAV. Moreover, the behaviour of premiums is different between bull and bear ETFs. Lu,Wang and Zhang (2009) analyze the long term performance of leveraged ETFs in order to check if they achieve the promised multiples in the long run (they usually promise to achieve this performance in the short run but not for longer periods of time). Their results confirm that leveraged ETFs track reasonably well over one month or less but get significant deviations for longer periods of time. They arrive to the conclusion that leveraged ETFs are not long term substitutes for long or short positions in the index benchmarks. Finally, Avellaneda and Zhang (2010) study the underperformance of leveraged ETF and arrive to the formula that links the return of leveraged funds with the corresponding multiple of the unleveraged fund return and its variance. Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 16

17 4-Research questions and methodology One of the aims of this paper is to assess the efficiency of ETFs as substitute for investment in a particular market index as a whole. For this purpose, the first step is to use the Capital Asset Pricing Model (CAPM) to find out if the returns of ETFs and LIETF achieve perfect replication or if they underperform somehow their respective index benchmarks. This method to test the performance of funds against the market has already been used in other papers like Rompotis (2009) and Chong et al. (2011). The formulas are shown below: Equation 1: ( ) Equation 2: ( ) Being and the monthly returns of the fund s NAV. α is the abnormal return of the asset. is the sensitivity of the of the ETF or LIETF excess return to the excess return of the market. the monthly return of the risk free rate defined as the 3-months U.S T-Bill. the leveraged fund s promised multiple. In the ETF case, if ETFs are perfect trackers, I expect to obtain a beta of 1 as ETFs track benchmark indices and their sensitivity should be approximately equal to the market. Theoretically α should be insignificantly different from 0 as ETFs are not constructed to obtain Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 17

18 any extra return than the market, neither positive nor negative. However, given the fact that ETFs charge an expense ratio for their services, the value of α is expected to be negative and close to the average monthly expense ratio of 0.033% or 0.4% in yearly terms. For the case of LIETFs it is first necessary to multiply the benchmark return by the fund s promised multiple to obtain the equivalent promised return of the respective fund. I do not expect a beta of exactly 1 but relatively near, as these funds are not created to track indices in the long run precisely and are prone to subtantial tracking errors. Regarding α, it is expected to be negative and even lower than the average expense ratio. LIETFs tend to underperform their benchmarks, this is due to the fact that they are designed to track benchmarks during short periods of time and therefore they are more prone to underperformance in longer periods as explained by Lu, Wang and Zhang (2009). After this analysis, the two dimensions of efficiency in funds, pricing efficiency and tracking efficiency, will be studied. Pricing efficiency indicates how closely the price of an ETF or a LIETF follows its NAV and analyzes the reasons for premiums or discounts. Pricing efficiency is measured using the formula bellow: ( ) Being the market price of the fund at the end of the month. the Net Asset Value of the fund s assets at the end of the month. Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 18

19 ETFs are designed to be price efficient and, following the creation-redemption process, all possible premiums or discounts should disappear almost immediately due to the action of AP looking for arbitrage returns. When an AP observes a price discount it can buy enough ETFs shares to create a block and then exchange it for a block of the underlying stock that composes the index. As the NAV is higher than the market price, the market value of the shares is higher than the ETF price; the AP can sell the underlying shares in the market obtaining profit in the process. The opposite process can also happen when the price of the fund trades at a premium. The AP will buy a blocks of shares of the underlying stocks and trade it for a block of the ETF shares. As the ETF shares are valued higher than the underlying stocks the AP will obtain returns with no additional risk. The same argument applies for LIETFs but using derivatives and other products as underlying assets. In reality, this process is not as straightforward as it seems as premium and discounts are likely to appear and persist. These deviations are, however, smaller than for closed-end mutual funds. One of the points of this paper is to find out the amounts and importance of the deviations and the reasons behind them so investors can have better criteria when choosing among possible funds that replicate an index. The other dimension this paper aims to analyze is tracking efficiency, defined as the degree to which the NAV return of a fund follows the return of its benchmark index. Tracking errors measure how closely an ETF tracks its index benchmark (or a multiple times the index in the case of LIETFs). ( ) Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 19

20 : the NAV monthly returns. : the index benchmark monthly returns. the leveraged fund s promised multiple. For common ETFs this number is 1. In theory it should not be any significant difference as ETFs and LIETFs are created to replicate an index but this is not always the case and tracking errors are a good method to measure how much returns differ from the benchmarks. The analysis of tracking errors is essential in order to find out if buying an ETF (LIETF) is the same than buying an equivalent part of an index (multiple of the index) in terms of risk and return. The objective is to determine which factors and variables are responsible for the difference between the fund s and the benchmark s performance. Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 20

21 5-Sample description 5.1-Data selection In this section I will comment the construction of the dataset and its main characteristics using descriptive statistics. For the realization of this paper it has been needed to construct two datasets, one containing 127 normal ETF with data from January 2006 until December 2013, the other one contains 62 LIETFs also with data from January 2006 until December The first dataset contains a total number of observations and the second one a total of One of the purposes of this paper is to analyze the tracking and pricing efficiency of equity ETFs and LIETFs. For this reason all the chosen funds only track equity indices priced in U.S dollars, and therefore currency, bond and commodity ETFs are excluded. Also ETN and other ETP, excepting leveraged and inverse ETFs, are not included as they are not in the scope of this research. Regarding the selection of the ETFs or LIETFs that will be part of the chosen data, the Blackrock Global Handbook Q was used. This document contains a comprehensive directory of all 4748 Exchanged Traded Products with 1.8 US$ trillion in assets from 195 providers on 54 exchanges around the world. Using this report, 150 normal ETFs and 70 LIETFs were selected based in the following criteria: For the normal ETFs dataset only funds that follow a passive investing approach, based on replicating a market index, were selected. For LIETFs only funds that follow a passive leveraged or inverse replication approach were selected. Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 21

22 Funds have to trade in U.S exchanges. The reason of this is because U.S is the biggest market of ETPs as it accounts for 71% of the global ETP market. ETFs have to track a broad and general market index well-known in the investment world. For this reason, the datasets are mainly composed by well-known index provider companies like MSCI, S&P, FTSE, Stoxx and Russell among others. Table 1 in appendix 2 shows the selected benchmarks. For data availability purposes, only ETFs created before January 2010 were selected. The selection tries to get the maximum diversity respecting countries and index providers given the other criteria. An overview of selected ETFs by ETF provider can be consulted in table 2 in appendix 2. The time period selected for the analysis runs from January 2006 until December The reason for this selection is due to data and fund s availability. As ETF and LIETFs are very recent products, not many were created before 2006 and this paper tries to extract general conclusions for the equity ETF and LIETF markets. For this reason it was necessary to have enough funds working and data available since the beginning. However, due to this constrain it is difficult to get overall conclusions as the available data comprises a concrete period of time characterized by high volatility in financial markets as a consequence of the economic crisis of Once, the funds were selected, it was time to get all the relevant variables that would be used for the empirical analysis. The datasets are constructed using four different sources of data with different variables collected in each of them: Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 22

23 CRSP database: Many of the variables used in this paper come directly or indirectly from the mutual fund tool in CRSP database. The variables obtained in CRSP are Price, Net Asset Value (NAV), Total return per share, Total Net Assets (TNA), dividend cash payments, volume, bid, ask, shares outstanding, percentage of the fund s asset held in cash, year first offered, expense ratio, and fund turnover ratio (aggregated sales of securities divided by the average TNA of the fund). Thompson Datastream: This tool was used in order to get the total return of the index benchmark and also the total return of the risk free rate, in this case the 3 Months U.S T- Bill. Morningstar webpage: This web page specialized in funds was used in order to generate dummy variables for style and size and also in order to classify the ETFs and LIETFs in geographic areas. It was also necessary in order to find out which index benchmark each fund tracks. Fund s prospectus: In order to find out which type of replication each ETF uses it was necessary to check the prospectus of all ETFs included in the dataset. In the LIETFs case this was not necessary because all of these funds use synthetic replication. Once all the data was collected and assembled into two datasets, one for normal ETF and the other for LIETFs, it was time to create the variables that would be used in the empirical analysis. Apart of the dependent variables, tracking error and price deviation, described in part 3 of this paper, there are other important variables, for a description of them please look at the appendix 3: list of variables and definitions. Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 23

24 Due to missing data and other difficulties in completing the data, the final datasets are composed by 134 ETFs and 61 LIETFs supposing and 4222 observations respectively Sample and descriptive statistics ETFs In this part, a summary statistics analysis for ETFs will be performed. In table 3 of the appendix 2 a selection of summary statistics for ETFs can be found. The results are shown in a monthly basis, so it is necessary to multiply by 12 the results in order to get comparable annual numbers. The first thing that can be seen in the table is that average index monthly returns are higher than ETFs returns by 4 basis points (bp) with similar conclusion when the median is.this means that ETFs underperform their benchmarks by 50 bp each year on average. This difference in returns is also responsible for the annual average tracking error of 2.4% which is a considerable amount to be taken into account by investors when choosing a fund given the fact that, on average, tracking errors are translated into underperformance, even when the underperformance is partially offset by mean-reversion. These results are similar to related papers in which ETFs usually suffer from similar underperformance figures. In a similar analysis we can also check the price deviation variable. On average the price of a fund is higher (premium) that the assets backing the ETF share in near 0.01 dollars in absolute terms or 1.8 bp in relative terms (the median is 0). This value is low giving a first impression that ETFs are doing a good job in keeping premiums or discounts low as designed to. Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 24

25 Other interesting variable is Total Net Assets (TNA) with an average of 3.6 billion per fund which decreases to 0.6 billion when using the median. The difference indicates an important concentration of capital into the top up funds in the distribution. Also expense ratio is an important variable as it indicates how much it does cost to invest in the fund in a monthly basis. The average is 0.034% per month or 0.40% per year (the median is very similar) which is lower than an average equivalent mutual fund. It is also interesting to know that the average ETF is 12 years old indicating that they are very recent products that have become very important in a short period of time. The variable cash shows the percentage of the TNA ETFs have in cash and indicates that they have an average of 0.25% of their funds in cash (0.19% median) evidencing that, as was expected, funds keep the majority of their capital invested in the respective market index. Finally if we take a look to volume figures, it can be seen that the average fund has a number of 825,000 transactions every month (almost 10 million in year terms) with value of 66,862,000 U.S dollars (800 million in yearly terms). Again, the median of these variables suffer from severe Skewness to the right and kurtosis showing and important concentration of transactions in some funds and dates. After this superficial analysis it is time to take a look at the correlation between the main variables used in the paper. Table 4 in appendix 2 shows the correlation matrix of selected variables. From this table it can be extracted that: first, return and benchmark have, as expected, a very high correlation of 99.8%. Second, the rest of the variables like tracking error, TNA, Cash Dividends, Liquidity, among others, are not very highly related as correlations are below Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 25

26 The second highest correlation is between Amihud s illiquidity measure and spread, both measures of liquidity LIETF In the case of LIETFs the statistics are substantially different. In table 5 in appendix 2 a table with summary statistics for LIETFs can be found. The first thing that can be appreciated is that the LIETF monthly return over the period is negative -0.78% (9.5% in annual terms) and also that the return of the benchmarks times the multiple is also negative but smaller -0.28% (- 3.46%). These returns can lead to misleading conclusions as the sample includes inverse ETFs that show positive returns when the market is bearish. What it is informative is the difference between both of them, LIETFs underperform their benchmarks by almost 50 bp on average per month (64 bp if the median is considered) causing considerable tracking errors of 1.38% per month or 16.5% per year (0.64% and 7.7% respectively, indicating concentration of tracking errors in some funds and dates). If we take a look a price deviation, the average deviation is -2 bp in relative terms or dollars in absolute terms with even lower figures for the median. The number is negative which means that the NAV is higher than the fund s market price, meaning that LIETFs products sell with discount. Investors value the product less than the value of the assets that back them. Regarding TNA, the average assets of a LIETF is 0.25 billion dollar with a smaller median of 44 million dollars, again this difference shows an important concentration of assets in a few funds and dates. When these results are compared with the ones we found for ETFs, it can be seen that LIETFs are much smaller in size that normal ETFs. This is due to their relative youth and speculative nature that makes them short term financial instruments. Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 26

27 Furthermore, LIETFs are on average only 7 years old, which confirms our previous statements talking about how recent an innovative these funds are, even compared with ETFs. Taking a look to cash, the table tells us that funds usually keep 63% of their funds in cash (32% if we look at the median) indicating that an important percentage of their assets are kept in cash. This is not a surprise as cash serves as a guarantee when using derivatives to achieve the desired leverage. Finally, if we take a look to volume figures, the average fund has 0.62 million transactions every month (almost 7.5 million every year) valued in 24 million U.S dollars (almost 300 million in yearly terms). Again the median is considerably smaller indicating a severe concentration of assets in some funds. If this data is compared with the data obtained for normal ETFs, volumes are relatively high, as ETFs volumes are only three times higher than LIETFs but the value of their assets is 15 times the TNA of LIETFs. This last fact reassures the short term nature of the product. Table 6 of appendix 2 shows the correlation coefficients of selected variables for LIETFs. LIETF s and benchmark returns are correlated by 98.8%, slightly lower than for normal ETFs. For the rest of the variables an increase in correlations compared with ETFs can be appreciated, but still lower than 37%.The second highest correlation is between turnover and fund age indicating that older funds have less turnover that younger ones. Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 27

28 6-Empirical results From last section some interesting results were obtained indicating that, in general, ETFs and LEITFs are successful in keeping price deviations low. These funds are created to keep price deviation low and it seems that they are achieving it. However, they struggle significantly when tracking benchmark returns. In this section of the paper a more formal analysis will be performed, the objective is to find out if both products can be considered as substitutes for market indices and finding out which are the reasons of their deviations in returns and prices (even though the last one has been shown as quite low). The structure of the section is the following, first ETFs will be analyzed followed by LIETFs. For each product a CAPM test will be performed to check if ETFs or LIETFs replicate their respective benchmarks correctly followed by an econometric analysis of tracking errors and price deviations ETFs Capital Asset Pricing Model test In this subsection I will check if ETFs returns follow the CAPM model from equation 1 in section 4. The results of the regression are shown in the first specification of table 7 in appendix 2. As explained in section 3, in this regression the variable Beta is expected to be equal to 1 and the constant negative and similar to the monthly average expense ratio of 0.03% as, in theory, the returns of ETFs should replicate perfectly the returns of their benchmarks excepting for the fees charged to investors. The coefficient for Beta is equal to which is approximately 1 although if a t-test is performed, the results indicate that the coefficient is statistically different from 1 (t-statistic is equal to -6.7). It is more interesting to look at the coefficient of the constant, Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 28

29 its value is -0.04% and it is significant at 1% level indicating underperformance of 48 bp per year. The ETFs underperformance is, as expected, near the average expense ratio of 0.03%, possibly indicating that much of the underperformance is due to the fees charged by the ETF. More discussion about this will be done in next paragraph and in next section when analyzing tracking errors. This result is very similar to the one extracted from the summary statistics ( table 3), which adds more evidence to the fact that ETFs underperform their benchmarks indices for an amount slightly higher than the expense ratio. Regarding the goodness of fit, the is which means that almost all movements in ETF returns are explained by movements in the benchmark returns. Also in table 7 in specification 2 a CAPM test with a new variable added, expense ratio, can be found. This test is made in order to confirm if the underperformance is mainly due to the expense ratio or there are more variables responsible. Checking the results it can be confirmed that much of the underperformance measured in specification 1 by the constant disappears when including the expense ratio as α becomes insignificantly different form 0 and the expense ratio is negative and significant. Moreover, does not change with the inclusion of the expense ratio indicating that the it absorbs the effect previously captured by the constant. Given this, we can confirm that an important amount of the underperformance is due to the fees charged by the ETFs ETF Tracking errors As stated before, ETFs have problems in tracking index benchmarks and this causes average annual tracking errors of 2.4% that are directly responsible for the underperformance of 48 bp per year analyzed in section In this part we are going to study this problem deeper by Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 29

30 analyzing tracking errors (for a more detailed explanation of the variables, look at appendix 3: List of variables and definitions). As stated by Elton et al. (2002), two of the most important sources of tracking errors and price deviations are the expense ratio and dividends. In order to capture these effects, the expense ratio and one dummy variable that signals if the fund has paid dividends during the month are included in the regressions. These variables are expected to be economically and statistically significant. One of the most important hypotheses of this paper is that the method of replication matters regarding tracking errors and price deviation. For the testing of this hypothesis, a dummy variable signaling if the replication approach selected by the provider is sampling replication is included. Sampling is cheaper but more prone to systematic errors; physical replication is more expensive (higher expenses are related to higher tracking errors) as it implies more transactions but is less likely to suffer tracking errors. Depending on which effect is stronger the variable will have a positive or negative sign. Other variables which are expected to have a significant impact are the ones related with liquidity like Amihud s illiquidity measure and the variable called liquidity (check appendix 3 for more details). Liquidity is one of the fundamental reasons why ETFs were created, it is expected that more liquid funds have less problems in tracking their indices and also in keeping price deviations low. Turnover is also expected to be significant as it captures the number of transactions a fund does when buying or selling stock mainly due to rebalancing purposes. Fund s that make more Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 30

31 transactions, pay more costs and are more prone to underperform. Other variables like age and cash can also play a role, even though smaller in determining tracking errors and price deviations. Table 8 in appendix 2 shows the results of several regressions performed using tracking errors as the dependent variable. The table is separated in 4 specifications, the first shows the most usual variables used in research to determine tracking errors and the sampling dummy, specification 2 includes the variables of the first one and adds more variables (extended model), specifications 3 and 4 contain the same variables than 1 and 2 but with date fixed effects and provider effects 4. The variable Sampling dummy is positive and significant at 5%, which means that funds with sampling replication suffer 4 bp higher tracking errors per month than funds with physical replication (48 bp in annual terms). This value supposes 20% of the average tracking errors which does not seem extremely important but if the median is used as a comparison, the coefficient supposes around 60% of it, which is a more considerable fraction. Even though, it is a considerable percentage, the value of 48 bp per year seems at first sight low to be considered economically significant. Regarding the other variables, as expected the expense ratio is positive and significant with a coefficient of 2.91 indicating than if the expense ratio increases by 1% the tracking errors increase by 35 bp per year. The sign is positive meaning that higher expenses cause higher tracking errors, in line with previous research. The value seems significant, but taking into account that the average expense ratio is around 0.34%, it makes us think that maybe is not 4 One dummy variable per index provider. Master Thesis: The Efficiency of Exchange-Traded Funds as a market investment Page 31