Pricing Efficiency of Exchange Traded Funds

Author: Pavel Prusevic Student Number: 20084266 Class: U15 Pricing Efficiency of Exchange Traded Funds Business and Social Sciences 2012

Table of Contents Abstract... 1 Introduction... 2 Problem Statement and Outline of the Paper... 3 Delimitations... 3 The Background and Recent Development of ETPs Industry... 4 Technical Characteristics and Versatility of ETFs... 6 Cost Advantage of ETFs... 6 Tax Efficiency of ETFs... 7 Transparency of ETFs... 7 Trading Flexibility of ETFs ETFs as Unique Beta Instruments... 7 Physical Replication and Synthetic Structure... 8 Leveraged and Inverse ETFs: Gambling Instrument or an Important Innovation?... 9 Actively-Managed ETFs... 9 Mechanics of ETFs: Pricing vs. Tracking Efficiency... 10 Intraday Creation-Redemption Process Pricing Efficiency of ETFs... 11 Multidimensional Role of Authorized Participant... 12 Existing Research on Pricing Efficiency of ETFs... 13 Tracking Efficiency Indexation Techniques... 14 Market Microstructure Theoretical Background... 15 Markets, their Functions and Benefits... 15 Evolution of Research Topics within the Field of Market Microstructure and Their Practical Applicability 16 Price formation and price discovery... 16 Market Architecture and Price Formation... 18 Market Transparency and Behavior of Market Participants... 19 Market Microstructure and Other Areas of Theory of Finance... 19 ETFs and Market Microstructure... 19 Study of Liquidity of ETFs by Sanchez and Wei (2010)... 20 Comparison of Active and Passive ETFs by Rompotis (2010)... 21 Ackert and Tian (2008) Analysis of Pricing Efficiency of ETFs and Liquidity... 21 The concept of Liquidity... 22 Pricing Efficiency and Liquidity Measures... 23

Quoted Spread... 23 Effective Spread... 23 Realized Spread... 23 Stoll s Version of Realized Spread Traded Spread... 24 Measures of Depth... 24 Amihud s Illiquidity Measure... 25 Price Impact Regression... 25 Premiums, Discounts and Pricing Error... 25 Methods of Analysis of Pricing Efficiency... 26 Pricing Efficiency Model of ETFs... 26 Net Fund Flows Indicator of Intensity of Creation-Redemption Process... 27 Turnover Ratio Indicator of Trading Intensity... 27 Indicators of Price Volatility... 28 Panel Data Estimation Cross-Sectional Unobserved Effects Control... 28 Specification of Key Pricing Efficiency Regressions... 29 General Data Description and Selection Rationale... 30 Cross-sectional Selection... 30 Time Period Selection... 31 Estimation Procedure and Empirical Results... 32 Descriptive Statistics of the Sample... 32 Descriptive Statistics of Pricing Error Variable... 33 Descriptive Statistics of Fund Flows Variable... 35 Descriptive Statistics of Other Variables of the Model... 37 Unit Root Tests of the Key Variables... 38 Correlations Between the Key Variables of the Pricing Efficiency Model... 39 Estimation Procedure and Results of Pricing Efficiency Model... 40 Nominal Pricing Error and Creation-Redemption Process... 40 Absolute Pricing Error Regression... 41 Conclusions... 43 Appendix 1 Global ETFs Industry by Geographical Region (as of the end of 2011)... 45 Appendix 2 Global Leading ETP Providers (as of the end of 2011)... 46 Appendix 3 Summary of the ETFs Landscape in the US... 47 Appendix 4 Growth of ETFs Listings on the US Exchanges... 49

Appendix 5 Replication Methods of US ETFs... 50 Appendix 6 Complete List of Variables of Pricing Efficiency Model... 51 Appendix 7 Selected Sample of ETFs... 53 Appendix 8 Hausman Test Results... 54 Appendix 9 Choice Between First Differencing and Fixed Effects Estimation... 55 Reference List... 56 Articles:... 56 Books:... 57 Websites:... 57

Abstract Exchange traded funds are relatively recent innovation of the financial industry. Their structural characteristics which turned out to be appealing to both short-term and long-term traders allowed them to achieve very strong growth during the last 12 years. This tremendous success, naturally, attracted the attention of market microstructure scholars whose concern has been to determine whether the existing microstructure theory which has been primarily developed in the context of stocks trading process is applicable to these financial instruments. Notably, liquidity and tracking efficiency of exchange traded funds received far more academic attention than pricing efficiency. Therefore the purpose is the present paper is to partially fulfill the gap of research on pricing efficiency of exchange traded funds, with special attention being placed on its relationship with creation-redemption process of the shares. The paper is segmented into three parts. Firstly, general background of exchange traded funds is presented including historical evolution, versatility and mechanics of their functioning. The second part of the paper puts into perspective the relation of the present topic to existing research of market microstructure. More detailed discussion is dedicated to some of the more relevant findings and tools of microstructure analysis. In the third part of the present paper a model specifically tailored for the analysis of pricing efficiency of exchange traded funds is introduced. The main purpose of the model is to examine the significance of creation-redemption process and general market conditions, such as trading intensity and price volatility, for pricing efficiency of exchange traded funds. Before the presentation of model estimation results a thorough descriptive analysis of the behavior of the main variables in the model is conducted. By means of this description a number of insightful characteristics of the dynamics of creation-redemption process and the market-makers role in it are demonstrated. The model is applied to a relatively large sample, covering 64 trading sessions of 115 most actively traded exchange traded funds listed on the US exchanges. Actual empirical results of estimation of the pricing efficiency model suggest an inferior pricing efficiency of exchange traded funds which have an international exposure in their underlying portfolios. Alternatively, domestic exchange traded funds are found to be exceptionally efficiently priced, since neither unbalanced order-flow, nor volatile prices or high intensity of trading does not seem to increase the likelihood of domestic exchange traded funds being traded at a premium or discount. The rationale for his development is suggested to be the complexities related to the execution of cross-border arbitrage activities. Overall, the findings of the present research can be interpreted as a solid manifestation of the role of arbitrage forces and market-makers in ensuring efficiency of financial markets. Whenever, restrictions are imposed on arbitrage activities financial markets are likely to lose some degree of efficiency. 1

Introduction Market microstructure analysis is relatively young field within the broad academic topic of the Theory of Finance. Primary focus of these studies is put on investigation of frictions arising in the process of functioning of financial markets as market participants seek to transact with each other. These frictions are often referred to as different dimensions of liquidity available on the markets. During the last 40 years fair amount of research has been dedicated towards the analysis of market microstructure, however, it has been largely concentrated on the study of trading process of stocks, to a large extent ignoring other types of securities. Late 1990s marked the beginning of the tremendous growth of Exchange Traded Funds innovative investment vehicles first introduced in the year 1990 in Canada. By construction ETFs are very similar to index mutual funds, in a sense that they are passively traded portfolios of securities, but in contrast to index mutual funds ETFs are, like individual stocks, listed on the exchanges and therefore can be traded continuously throughout the trading session. To a large extent the interest towards ETFs can be explained by their relative cost and tax advantages relative to traditional index mutual funds, however it should also be noted that the success of ETFs would be unlikely if the markets for these securities would be illiquid or inefficient. Therefore there should be a natural interest towards microstructure studies of the markets of ETFs, since it is not clear whether the existing academic results related to trading process of stocks should also apply to ETFs. Specifically, it is of interest to examine the relationship of pricing efficiency of ETFs and the net fund flows to and from the ETFs. Is the creation-redemption process efficient enough to sustain efficient pricing of ETFs during the periods of intensive movement of capital to or from the funds? Similarly, is pricing efficiency related to the general conditions of the financial markets should pricing errors be expected to increase in more volatile and active markets? Another rather interesting approach is to examine ETFs for cross-sectional differences. What are pricing efficiency characteristics of different types of ETFs, could it be so that certain categories of ETFs are on average priced less efficiently than other categories of ETFs? Similarly, such cross-sectional analysis could be applied to the general dynamics of net fund flows, which are in a sense a barometer of the intensity of creation-redemption process of shares of ETFs. What principles are guiding the behavior of market-makers receiving unbalanced order-flow for ETFs? How likely they are to accept the risks of additional inventory or otherwise to execute the creation-redemption transactions of ETFs? These are some of the issues this research aims to look into. 2

Problem Statement and Outline of the Paper The primary purpose of the present paper is the analysis of pricing efficiency of exchange traded funds whereby it is sought to identify the importance of the role of creation-redemption process for efficient pricing of the funds. Theoretical background of the paper can be seen as segmented into two distinctive parts. In order to elucidate the research objective of the present paper a thorough discussion of the background of ETFs is provided which is followed by an extensive review of existing theory of market microstructure. The paper begins with a broad description of the global exchange traded products industry which is then progressively narrowed in the scope to present in more detail the most mature segment of the global industry ETFs market of the United States. Further, the attention is directed to the discussion of the key characteristics of ETFs which should be credited for the solid growth of these investment vehicles, which is then followed by an assessment of versatility of ETFs. The discussion of the background of ETFs is finalized by an in-depth description of the mechanics of ETFs with particular emphasis being placed on the role of market-makers in the creation-redemption process. The second part of theoretical background review of the existing studies with the field of market microstructure serves as a source of inspiration for the pricing efficiency model developed in the present paper. The section is organized to start with a general theory of organization of exchanges and their functioning, which is then followed by a brief review of the main four stages of academic development of the theory of market microstructure. The segment of the paper is then finalized with presentation of tools and methods of measuring microstructure characteristics of financial markets. In the empirical part of the present study pricing efficiency model is developed which aims to explain pricing error of ETFs by means of indicators of intensity of creation-redemption process, general trading activity, and price volatility of the markets. This model is applied to the sample of 64 trading sessions in 115 ETFs listed on the US exchanges. The paper is concluded by a summary of the findings and their possible interpretations. Delimitations The most significant aspect in term of delimitation of the paper is its focus primarily on the pricing efficiency of ETFs. This should not be confused with perhaps more widely debated issue of tracking efficiency of ETFs. Similarly, this scope of the research is even more far apart from the conventional performance evaluation studies of different investment vehicles, such as mutual funds or for that reason ETFs. Whereas the latter category of studies is focused mostly on long-term performance evaluation of fund managers or other passive investments vehicles, microstructure analysis is centered around the efficiency of functioning of different types of exchanges and investment products which are traded there. Among the other more significant delimitations of the present paper is its focus on exchange traded funds, and not any other types of exchange traded products. However, no restrictions are placed within the large family of exchange traded funds. Implying that ETFs of all replication strategies or objective portfolio exposure are included in the analysis. However, it should be noted that given the sample selection procedure the scope of the research covers only the most actively traded ETFs. Furthermore, the study is 3

carried out in the context of ETFs industry of the US, leaving out from the analysis other geographical regions of global ETFs industry. The Background and Recent Development of ETPs Industry The broader category of investment vehicles under the umbrella of which fall Exchange Traded Funds (ETFs) as well as Exchange Traded Vehicles (ETVs), Exchange Traded Notes (ETNs) and Certificates are Exchange Traded Products (ETPs). These securities are typically derivatively priced and can trade intraday on securities exchanges. The benchmarks which ETPs promise to replicate are often stock indices or static baskets of stocks, futures, swaps, commodities or alternatively these can be actively managed (NYSE Euronext, 2012). Since the introduction of the first ETP on March 9 th of 1990 on Toronto Stock Exchange in Canada the dominant category in this class of investment vehicles have been ETFs benchmarked to replicate the performance of various equity assets. Recent development of the whole ETPs industry is presented in the Figure 1 below. Figure 1 Development of Global ETPs Industry (2000-2011) Assets Under Management (US$ Bn) 1800.0 1600.0 1400.0 1200.0 1000.0 800.0 600.0 400.0 200.0 0.0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 ETF Equity 74.3 104.7 137.5 205.9 286.3 389.6 526.5 729.9 596.4 841.6 1053.81057.4 ETF Fixed Income 0.1 0.1 4.0 5.8 23.1 21.3 35.8 59.9 104.0 167.0 207.3 257.7 ETF Commodity 0.0 0.0 0.1 0.3 0.5 1.2 3.4 6.3 10.0 25.6 45.7 31.1 ETF Other 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.6 1.8 4.5 4.6 Other ETPs total 5.1 3.9 4.1 6.3 9.3 15.9 32.5 54.6 61.2 119.7 171.3 173.5 Number of ETPs 106 219 297 300 358 525 884 1542 2211 2683 3526 4221 4500 4000 3500 3000 2500 2000 1500 1000 500 0 Source: BlackRock, ETF Landscape: Global Handbook Q4 2011 Notes: Rright hand side axis measured in number of ETPs The early development of the industry is omitted in the graph, but it can be partially justified for the reason that the most explosive growth of the ETPs started around the year 2000. It can be seen that during the period of 11 years till the year 2011 the growth of the industry has followed almost the pattern of geometrical expansion. The only notable disruption of the growth occurred in the year 2008, the year distinctively adverse for the overwhelming majority of global financial industry. However, even though according to one measure of the industry growth total assets under management ETPs industry contracted, the total number of ETPs actually increased even during the most uncertain and volatile period of 2008 and 2009, which demonstrates the strength of the underlying investors interest in the products. 4

Overall, assets under management of ETPs globally increased by a factor of 19 over the mentioned 11 years period - from just below USD 80 bn to more than USD 1.5 tn, which is effectively constitutes an annual growth of around 30 percent. Increase in the number of ETPs has been even more dramatic as of the end of 2011 there have been more than 4000 ETPs available for investors globally, whereas in the year 2000 there had been just around 100 of them. Focusing specifically on ETFs the largest segment of ETPs, which is the course of this paper, it can be noticed that the industry is relatively concentrated in a number of aspects. First of all the dominance of the market of the United States should be noted. If this might not be completely visible in terms of the number of ETFs US market accounts for around one third of total number of ETFs the dominance in other two metrics of the industry is apparent almost 70 percent of global assets under management and more than 90 percent of trading in ETFs is concentrated in the US. For detailed geographical distribution see Appendix 1. The situation is rather similar in connection to the concentration of ETF providers or as sometimes they are referred to as ETF Sponsors. Top four ETF providers account for 71.2 percent of global assets under management and all have their primary business in the US markets. Appendix 2 contains detailed statistics of top 50 ETF providers. Looking specifically at the US market, as of the end of 2011 it was nearly USD 1 trillion industry measuring by combined assets under management. Thirty ETF sponsors were present on the market together providing 1098 ETFs. Average daily volume totaled USD 52.5 bn, which as seen before represents more than 90 percent of global average daily trading volume in ETFs. During the last decade ETFs market of the US has definitely experienced a huge proliferation, as of the year 2011 by means of ETFs an investor could obtain portfolio exposure to literally all asset classes equity, fixed income, internationals, commodities, even if an investor wished so he could magnify his bets by leveraged ETFs or obtain negative exposure by inverse ETFs. Appendix 3 contains a comprehensive summary of the ETFs landscape in the US. In order to demonstrate the growth of ETFs market in the US a simple metric the number of new ETF listings per quarter can be used. Figure 2 presents this development since the introduction of the first ETF in 1993. The graph shows that up to the year 2000, ETFs were exotic investment vehicles, with just 30 ETFs being available as of the year 1999. Thus the year 2000 became a turning point in the development of ETFs industry in the US, steady and solid growth of the industry continued up to the year 2005, after what a sharp acceleration of the growth was observed. It is seen that just like for the global ETFs industry, the period of 2008 and 2009 revealed to be a grave bump in the road for US ETFs growth, however, soon afterwards the high growth rates recovered and has actually been sustained as of the end of 2011. Similarly to the global situation, ETF industry within the US is also relatively concentrated, again that is not completely visible by the distribution of the number of ETFs, but that becomes more apparent measuring the concentration in terms of assets under management and even more so in terms of trading volume. Just one most actively traded ETF, the so called Spiders for its ticker SPY (notably, that is the oldest ETF on the US exchanges), accounts for around 40 percent of all daily trading volume in ETFs. Similarly top five ETFs account for almost 58 percent of all trading volume. 5

Figure 2 Growth of the US ETFs Industry Measured by New Listings per Quarter Number of ETF Listings during the Quarter 100 90 80 70 60 50 40 30 20 10 0 1993-Q1 1993-Q4 1994-Q3 1995-Q2 1996-Q1 1996-Q4 1997-Q3 1998-Q2 1999-Q1 1999-Q4 2000-Q3 2001-Q2 2002-Q1 2002-Q4 2003-Q3 2004-Q2 2005-Q1 2005-Q4 2006-Q3 2007-Q2 2008-Q1 2008-Q4 2009-Q3 2010-Q2 2011-Q1 2011-Q4 Source: BlackRock, 2011 ETF Landscape: Global Handbook Q4 2011 and own calculations; Notes: The black trend line is represented by a one year (four quarters) moving average. Another characteristic of ETFs which should be of interest for an investor is the replication strategy employed by the ETF provider. Specifically, two broad types of replication techniques can be distinguished: physical and synthetic; the essence of both is discussed in more detail later in the text. Additionally, third type of replication method, which is relatively immature, should be noted which is based on active management of ETFs. in the US ETFs market physical replication dominate over the other two techniques, almost 83 percent of ETFs employ this replication scheme, whereas around 15 percent of ETFs can be classified as synthetic and only 1.5 percent of ETFs are actively managed. Appendix 4 contains detailed statistics on replication strategies. Technical Characteristics and Versatility of ETFs Technically ETFs can be classified either as open-ended mutual funds or unit investment trusts (UITs) which typically are index-based and therefore allow traders to gain diversified exposure to different asset classes both domestic or international, sectoral or stylized. The key distinction from the mutual funds and similarity to closed-end funds is that ETFs are traded on exchanges like individual stocks, meaning that they can be sold short or bought on margin. Besides intraday flexibility investment community also appreciates ETFs for their relative cost and tax efficiency and high levels of transparency. These characteristics are discussed in more detail below (SEC, 2012). Cost Advantage of ETFs Majority of ETFs are passively managed baskets of securities, with relatively low portfolio turnover, which is the main reason for their cost advantage over the similar investment vehicles. Firstly, it should be noted that passive attempts to replicate an index should always be more cost efficient than active management. Even more, the functioning structure of ETFs allows them to charge lower expense ratios than other passively managed investment vehicles, such as index mutual funds. Specifically, the cost advantage of ETFs stems from the mechanics of creation and redemption of ETFs shares, which is one of the key research 6

topics of the present Thesis. To support these statements the average expense ratios for US equity mutual funds and US equity ETFs can be provided which are 1.42 percent and 0.53 percent respectively (IndexUniverse, 2012). Tax Efficiency of ETFs Mechanics of capital flows to and out of ETFs is also the reason for their relative tax efficiency. Specifically, in the events of net funds outflows from the ETFs no taxable capital gains are realized. In contrast, the same situations in the context of mutual funds do represent a taxable event. This setting can seem particularly unfair for conventional mutual fund investors who have to bear tax expenditures on their investments when other mutual fund investors leave the fund which forces the managers to sell-off some of the assets which is likely to create a taxable event. In ETFs setting each investor has much more control over the realization of taxable capital gains, rare exceptions are when the ETF is changing its investment strategy or its replication benchmark changes its composition (Pinsky, 2011). Transparency of ETFs Investors generally do value the ability to monitor and be aware of all the issues related to their investment. Firstly, in the context of all collective investment schemes principal-agent problem is inherent to the relationship between the actual investors and the managers of the scheme, therefore the more transparent investment decisions and actions of managers the less likely it is that the managers will exploit their superior positions at the expense of individual investors. Secondly, information about the actual holdings of the investment vehicles is needed for an investor to correctly evaluate the risks involved. In terms of transparency ETFs also have high assessments: first of all simply due to their passive replication strategies, whereby the process of investment management becomes nearly automated, which in turn leaves little to no room for dysfunctional behavior of managers. Another important feature of ETFs is that the components of the fund, their weights in it and net asset value (NAVs) of the fund s shares are disseminated to the public daily, thus an investor can be completely aware of how his money are invested at any point in time. Moreover, in the US markets all ETF providers are required to publish indicative values of their ETFs every 15 seconds, which can be seen as a great manifestation of transparency of these products (Engle and Sarker, 2006). Trading Flexibility of ETFs ETFs as Unique Beta Instruments One of the distinguishing characteristics of ETFs is that they are available for trading throughout the day. This allows traders particularly cheaply enter broadly diversified positions, such as broad-market indices, which possibly can be composed of thousands of stocks, within very short periods of time. Evidently in the absence of ETFs traders willing to gain equivalent exposure would have to complete thousands of transactions, which is affordable only to a small fraction of traders on the financial markets, primarily institutional traders. ETFs besides receiving the merit for allowing traders to circumvent potentially heavy amounts of commissions which traders would have to bear for this amount of transactions, should also be credited for the creation and provision of cheap liquidity which otherwise would not be present for traders who have interest in securities which individually are illiquid. An example of this situation is trading in stocks of small capitalization companies. These individual positions frequently are quite illiquid and therefore a trader requiring relatively quick execution of his orders would have to accept high transaction costs. Essentially, little available sizes at the bid and ask, the dimension of liquidity referred to as depth, and big differences between bid and ask prices, the dimension of liquidity referred to as width or simply 7

spread, would imply that impatient traders would have to move the market in the direction of their trades and therefore receive inferior prices for their transactions (Harris, 2003). In contrast to that ETFs allow to gain equivalent exposure in just a single trade. Further, the costs of transacting in ETFs are also improved due to the diversification effect researched by Subrahmanyam (1993) all unsystemic risk, which is particularly important in small-cap stocks, is diversified away, which makes it very unlikely to observe informed trading in ETFs. Essentially the statement of Subrahmanyam (1993) is equivalent to the argument that it is very difficult to be informed at the level of broad-market indices. This is very similar in its spirit to the notion, on which majority of financial studies consolidated, that over the long run it is highly unlikely that any individual investor has enough market timing skills to beat the market. That is definitely good news for uninformed traders, among whom marketmakers, other dealers, specialists and brokers can be identified. This implies that more depth, narrower quotes or both can be provided by liquidity suppliers, since they are not afraid of being exploited by informed traders, which would eventually end up in losses for them, since by definition informed traders trade on the right side of the market. All this implies that effectively traders can gain broad market exposure just in the matter of minutes if not seconds, hence the introduction of ETFs made possible theretofore unavailable intraday high-frequency trading strategies. In principle this implies that traders can instantly change the betas of their portfolio simply buying and selling ETFs tracking some specific indices, hence the term unique beta instruments. In fact, it has been suggested in academic literature that the primary clientele for ETFs are exactly short-term traders. (Poterba and Shoven, 2002). Even more, with the availability of inverse and leveraged ETFs the flexibility of short-term traders became even greater. Physical Replication and Synthetic Structure Like any business financial institutions are constantly seeking for new designs of their products which would be capable to meet dynamic needs of their customers. One of such customer-driven innovations was an introduction of synthetically structured ETFs. Particularly, this structure emerged to address investors desire to add exposure to emerging markets to their portfolios. ETFs based on physical replication strategies (or plain vanilla structures as they are called in financial jargon) whereby an exposure is obtained by buying up the assets representing the risks of interest, revealed to be quite costly for investment in emerging markets. The main reasons for that were that emerging markets which were offering lucrative returns were often illiquid, various capital movement restrictions or taxation rules were also significant obstacles. The introduction of synthetic ETFs allowed to circumvent these problems. As opposed to owning the physical assets synthetic ETFs achieve replication of an index using derivatives. The mechanics of this type of ETFs is based on total return swaps, whereby ETF sponsors enter into a derivative position, which effectively, provides them with required benchmark index return in exchange for the return of the preagreed basket of securities. This type of replication strategy greatly reduces the tracking error risk to investors, however, that does not come as a free gift, investors should realize that this type of risk reduction is offset by an increased counterparty risk. This constitutes the risk that swap counterparty will default on its obligations to provide the required return. Synthetic ETFs gained significant popularity in the European Markets, where they account for around half of ETFs industry, however, in the United States nearly all ETFs use physical replication schemes (Ramaswamy, 2011). Given that their presence in target research market of the present paper is insignificant, more detailed description of the functioning of ETFs will be made in the context of physical replication schemes. 8

Leveraged and Inverse ETFs: Gambling Instrument or an Important Innovation? Another rather successful innovation in the ETFs industry are inverse and leveraged ETFs. These ETFs employ synthetic replication schemes in order to achieve daily returns which are multiples (e.g. 2, 2) of a given benchmark daily returns. It is important to stress the point that these ETFs do not promise any long term performance in relation to the underlying benchmark, instead the objective is to provide a specified leverage on a daily basis. In order to achieve this target the ETFs have to be rebalanced each day to maintain a constant leverage ratio. That is the main reason why returns of leveraged ETFs over the periods longer than one day are expected to deviate from their target. Therefore this instrument is generally not suitable for long-term investors. Interestingly, leveraged and inverse ETFs after they were first introduced in the United States in 2006 have grown strongly in popularity and as of the end of 2011 total assets under management comprised USD 26.5 bn. Even more revealing is the fact that these ETFs account for disproportionately large share of trading volume even though in the year 2009 in the United States leveraged and inverse accounted for around 5% of total ETFs assets under management trading volume of these securities constituted approximately 40% of total trading in ETFs. Lastly, the size of individual trades are smaller than the sizes of trades in traditional ETFs. These characteristics of trading in leveraged and inverse ETFs has made many practitioners to suggest that these ETFs might be used by a different clientele than traditional ETFs. Namely, these are tools used by short-term traders, who wish to play their speculative views on the underlying benchmarks (Charupat and Miu, 2011). Harris (2003) in his classification of traders framework points out for some market participants somewhat inconvenient practicality that among the traders there are such market participants who are using financial markets for gambling reasons. Larry Harris defines gamblers as the traders who firstly are uninformed about the fundamental values of the securities they trade and secondly the intrinsic reason for their trading is the satisfaction obtained through the process of trading. Additionally, it should be noted that gamblers do prefer liquid and volatile markets. Although neither it is necessarily the case nor it is the unanimous consensus of the market microstructure practitioners that financial markets would be better off without gamblers, but there is quite strong argument that gamblers should serve their desires at the specifically designated for that places. The reason why this is uncertain is that gamblers also do contribute with liquidity with their trading capital and by definition their long term losses convert to profits of informed traders, which in turn encourages the later group to engage in the research of fundamental values of trading instruments, which makes market prices more informative and therefore beneficial for financial markets and economies as a whole. As previously mentioned typical traders of leveraged and inverse ETFs are small frequently trading retail investors, which incidentally are the best proxies for gamblers. Further, it is the case that markets for leveraged ETFs are relatively liquid and at the same time the leverage effect magnifies the volatility, which is what gamblers value. This does provide some indication that trading in leveraged and inverse ETFs is likely to contain relatively high fraction of noisy or in other words uninformed trading. Actively-Managed ETFs Another product of innovation of ETF sponsors are actively managed ETFs. As seen from its title, these investment vehicles employ portfolio managers in order to outperform passive investments. This is the sharpest distinction of this type of ETFs from the rest of the family, which represents a step towards conventional mutual funds. However, some of the authoritative practitioners actually do criticize these ETFs for exactly this deviation from the traditional course of passive management. According to them active 9

management potentially can remove the features of ETFs for which they have deserved the recognition of investors in the first place. Among the worrying issues is the potential tax efficiency decrease, since active management of the fund can lead to much frequent occurrence of taxable events than in the case of traditional passive management of ETFs. Furthermore, upside potential in ETFs returns stemming from active management certainly does not come at no price expense ratios of these ETFs are expected to be higher to compensate additional management efforts (Buttell, 2011). This argument can actually be supported by statistics: as of the end of 2011 total expense ratio of actively managed ETFs was 0.83 percent per annum, whereas the average total expense ratio of all ETFs listed in the US was around 0.54 percent per annum (BlackRock, 2011). Interestingly, like leveraged and inverse ETFs, actively managed ETFs were first introduced in the year 2006, however since then leveraged ETFs has been way more successful at attracting investors as of the end of 2011 assets under management of active ETFs were more than ten times smaller than those of leveraged and inverse ETFs or a little more than USD 4 bn (BlackRock, 2011). By no means this comparison should be interpreted as an indication of failure of these investment products, however, it is an indication that active ETFs have had hard times competing with conventional mutual funds, which for a long time have been traditional choice for investors seeking to delegate their portfolio to active portfolio managers (Buttell, 2011). Mechanics of ETFs: Pricing vs. Tracking Efficiency There are two approaches for studying the mechanics of the functioning of ETFs. Both are indicative of trading efficiency and therefore equally important for a market practitioner. However, the present paper is focused solely on one of them pricing efficiency. Therefore it is crucially important to draw a distinction between these two dimension of trading efficiency. The essence of these characteristics is demonstrated in Figure 3 below. Figure 3 Pricing and Tracking Efficiency Pricing Efficiency Tracking Efficiency Price NAV Benchmark Source: Charupat and Miu, 2011 The pricing efficiency is the indicator showing to what extent the market price of ETF follows its NAV, frequently this is narrowed down to daily or even intraday analysis since such market inefficiencies as sustained premiums or discounts are not expected to persist over the long run. The second dimension, alternatively, is focused on the trading efficiency of the longer time periods and shows the degree to which NAV of an ETF follows its replication benchmark. Tracking efficiency of ETFs has received far more academic attention than pricing efficiency, perhaps, in the first place because of the relative data-intensity of intraday analysis and secondly simply because tracking efficiency is way more intriguing for investors in efficient markets pricing errors are expected to be small and within transaction costs (Charupat and Miu, 2011). Therefore, it is the purpose of this paper to contribute to the narrowing of this academic gap. In the following sections more detailed discussion of both dimensions is presented. 10

Intraday Creation-Redemption Process Pricing Efficiency of ETFs Direct implication of being structured as open-end funds is that ETFs are allowed to receive new capital or otherwise accept outflows of capital after the launching of the fund. That is in contrast to closed-end funds which rarely issue new shares after their initial public offering (SEC, 2012). These characteristics of closeend funds in combination with the fact that the price of closed-end fund is determined on the market makes it highly possible that the price of the shares of closed-end fund can be traded with significant premiums or discounts to their NAVs, what implies a poor pricing efficiency. In turn conventional mutual funds circumvent this problem by simply arranging trades of its shares only after the trading session ends and only at the prices of NAVs of funds shares, which virtually eliminates the risk of pricing errors. Creation- redemption process of ETFs allows to reconcile the best of the two worlds closed-end and openend funds. Namely, this process creates sufficiently strong arbitrage forces to ensure that intraday transactions are made at the prices very close to NAVs of ETFs shares. Mechanics of the process which is employed by ETFs which use physical replication schemes is demonstrated in the Figure 1 below. Figure 4 Operational Structure of ETFs ETF Sponsor Creation Units Baskets of Securities Authorized Participant / Market Maker Securities Cash Markets ETF Shares Cash Primary Market Exchange ETF Shares Cash Secondary Market Investors Source: Ramaswamy, 2011 From the chart it can be seen that in operational structure of ETFs an important role is performed by authorized participants (APs), who are essentially market-makers of ETFs. The process starts when these 11

designated market makers observe unbalanced order flow, which consequently can lead to emergence of premiums or discounts in the price of ETFs that is a potential arbitrage opportunity. In order to exploit an arbitrage situation through which ETF shares are created authorized participants firstly purchase the basket of securities of the underlying index which ETF promises to replicate. Then these securities are deposited in the custody account of the ETF sponsor which in exchange for them provides authorized participant with ETF creation units. These transactions are often bulky (ETF creation units are exchanged in 50.000 or multiples thereof) and take place at the primary market. After actual ETF shares are created the price basis of which is determined by the market value of securities deposited, authorized participant is ready to satisfy demand at the secondary market by selling newly created ETF shares. Redemption process goes in reverse, when ETF shares are bought, exchanged for the underlying basket of securities and then the securities are sold on the secondary market (Ramaswamy, 2011). Multidimensional Role of Authorized Participant At this stage it is useful to discuss in more detail the role of authorized participant, since it is the key entity in ensuring smooth trading of ETFs. This entity is often represented by an investment bank units or smaller trading outfits and should not be confused with actual ETFs sponsor companies. Basically, authorized participants are the ones who make sure that at the end of the day the net capital flows are settled at the accounts of ETFs sponsors (Hartley, 2009). As a designated primary market makers they can be viewed as performing threefold role meaning that they can act as dealers, as brokers and also as exchange officials (Harris, 2003). This multidimensional role is schematically presented in the Figure 5. It can be seen that the task of the primary market maker is rather controversial, since as a dealer trading for his own account he should be interested in maximizing his trading profits, however, this interest can seriously collide with his best execution responsibility as a broker or as an exchange official s responsibility to conduct fair and orderly markets in the securities he trades. This potential conflict of interest situation is complicated further given a number of privileges which specialists possess, such as: Ability to create market quotes Access to superior information about the order flow and limit order book The right to make decisions after other traders make them (stopping orders) The right to collect brokerage commissions from execution of system order flow This demonstrates the necessity to regulate trading activities of primary market makers. This is done by two sets of regulation, namely by negative and affirmative obligations. Negative obligations are mainly governed by the principle of public order precedence implying that market makers are not allowed to frontrun the incoming public orders and by a principle of public liquidity preservation implying that they are restricted from consuming liquidity originating from the public limit order traders. Affirmative obligations in contrast require market-makers to trade at certain situations. Essentially that implies that they have to step in with their own capital to reduce intraday volatility. Principles governing this obligation are implying that market makers should be traders of last resort in the sense that they should provide liquidity at reasonable terms when no one else is willing to provide it. This comes as a responsibility as provision of liquidity can be costly especially in the markets with high likelihood of informed trading (Harris, 2003). However, as previously mentioned broadly diversified positions such as ETFs are likely to be the places where the fraction of uninformed trading is relatively high (Subrahmanyam, 1993). 12

Figure 5 Roles of Primary Market Makers Roles of Primary Market Makers Dealers Allowed to trade for their own account Brokers Broker orders and trades for other brokers Exchange Officials Responsible for conducting orderly and fair markets Trading activities are subject to two sets of regulation: Negative Obligations restrain from trading at certain circumstances Public order precedence rule Public liquidity preservation principle Affirmative Obligations obligated to offer liquidity at certain circumstances Traders of last resort Ensure price continuity, but not restrain market movements Source: Harris, 2003, Trading and Exchanges Lastly, measures of performance evaluation of authorized participants could be mentioned. For the broad investing community performance of primary market-makers in their role of exchange official is the one of interest. Therefore in this context they are evaluated based on liquidity characteristics of the market where they trade, such liquidity measures as average spread, average depth at best bid and ask or a number of large price reversals could be used for this purpose (Harris, 2003). Finally, as market-makers specifically in ETFs they should be concerned with the extent to which ETFs follow their NAV, essentially the pricing efficiency of ETFs, that is why it is essential to the present analysis. In contrast to their responsibilities as exchange officials represented by negative and affirmative obligations which can be costly, ensuring the price efficiency of ETFs is incentivized by arbitrage profits, therefore it makes it reasonable to expect that in well-developed markets price deviations of ETFs should be small and dependent on transaction costs (Charupat & Miu, 2011). Existing Research on Pricing Efficiency of ETFs Among the more significant studies conducted in this field is the analysis of Engle and Sarkar (2006) of daily as well as intraday transaction data for US listed domestic and international ETFs in which they conclude that price deviations of domestic ETFs are generally small and transient typically lasting for only several 13

minutes; while international ETFs are subject to larger and persistent premiums (discounts) lasting for up to several days. More complex creation-redemption process for international ETFs is provided as an explanation for the phenomena. Another quite revealing point is that premiums within the day are less volatile comparing to end-of-day premiums. Authors suggest that partially this phenomena can be explained by general intraday trading pattern, when markets are more volatile at the opening and closing hours of the trading session. Adding the point that variability of premiums is closely-related to the variability of the underlying index it should not be surprising to see this type of behavior. Finally, Engle and Sarkar (2006) touch upon important concept in microstructure analysis of staleness of prices. Specifically, the authors tried to address the staleness of the estimate of NAV. They calculate three versions of premiums - relative to Indicative Optimized Portfolio Value (IOPV), to the cash index, and to the futures price. First order autocorrelation of the premiums relative to futures prices were found to be the smallest which supported their hypothesis that the first two measures of premium suffered from staleness. However, the most important point which can be drawn from the synthesis of existing research in this field is that the arbitrage process of creations and redemptions has to be credited for the high pricing efficiency of ETFs and therefore is the central principle in studies of market efficiency of ETFs (Ackert and Tian, 2008). Finally, it should be noted that significant pricing inefficiencies can be observed simply due to little trading activity. In the situations when the last trade of the session happens well before the closing it is quite likely it will not reflect the NAV, which is recorded at the closing of the session. Similar argument, although to a way lesser degree, is also valid for actively traded ETFs. Even though the last trade in ETF might be executed just before the closing, it is expected to be filled at or close to either bid or ask price, which is expected to be skewed downwards or upwards from the NAV, respectively (Flood, 2010). Tracking Efficiency Indexation Techniques Passively managed ETFs which promise investors to track a specific index are all about ETFs gaining proper market exposure. Importantly, this is to a large extent a task of ETF provider, and not the authorized participant as is the case in ensuring pricing efficiency of ETFs (Ramaswamy, 2011). Apparently, the replication strategy is the first thing an ETF provider should consider, as has been discussed earlier in case of synthetic replication tracking error is literally removed, whereas in case of physical replication strategies performance of ETFs potentially can deviate from the performance of target benchmarks. Hereby a number of circumstances leading to this kind of deviations of the returns of ETFs employing physical replication strategies are discussed. To begin with, one of the most significant problems for ETF providers is the treatment of dividends. Being baskets of frequently large amount of securities each with some degree of dividend yield, ETFs are expected to receive dividends in form of cash inflows on a daily basis, naturally in best interests of ETF shareholders this cash should be the sooner the better reinvested or otherwise be distributed to them. Some ETFs, especially those focusing on fixed-income securities or high dividend yield stocks, distribute dividend income monthly, however, majority of ETFs listed in the US do it quarterly or on an annual basis. Notably, dividend reinvestment strategy is not frequently used by ETF providers. Importantly, benchmark indices are often created to indicate the return of a basket of securities whereby dividends are assumed to be reinvested immediately. Apparently, in such situations dividend inflows create difficulty for replication, known as cash drag. Additional challenge stems from the fact that ex-dividend and payment dates are not the same. Whereas indices with immediate reinvestment assumption reflect reinvestment just on the exdividend date, ETFs naturally cannot implement reinvestment before the actual payment date. Another 14

potential source of tracking error is discriminatory dividend taxing practices for foreign investors, consequently returns of ETFs investing abroad can fall short of the returns of the replication benchmarks (Flood, 2010). Noticeably, in contrast to the conventional mutual funds tracking ability of ETFs does not suffer from market frictions inherent to the trading process. Whereas conventional mutual funds do incur transaction costs which reduce the value of the fund whenever purchase or sale transactions on the exchanges are made (such as paying the bid-ask spreads), in case of ETFs these transaction costs are borne by authorized participants who are responsible for creation-redemption process. (Ramaswamy, 2011). Finally, the most significant aspect investors should address when evaluating potential tracking error of an ETF is annual management fee charged by the fund. Elton, Gruber and Comer (2002) conducted a research studying the tracking ability of the afore mentioned ETF replicating S&P 500 index: Standard and Poor s Depository Receipts. Their results indicate that the largest source of underperformance stems from the management fee of an ETF, whereas the underperformance related to losses from dividend reinvestment practices is less significant. Market Microstructure Theoretical Background Academic field of market microstructure encompasses the analysis of trading mechanisms used for financial securities. (Hasbrouck, 2007) Specifically, the central concept of this academic field around which research in market microstructure has developed is the notion that asset prices determined in the process of trading need not necessarily be equal to the fundamental values of those assets. The reason for these price deviations are frictions inherent to the financial markets. These theories represented a serious challenge for traditional assumptions about efficient and therefore frictionless financial markets (Stoll, 2000). Notably studies of frictions are quite common in economics and frequently the results of the studies are quite insightful and can be efficiently applied in practice. As an example of the efforts directed to and resonance often brought by the research of frictions within the labor market, which are manifested by the existence of unemployment, can be provided. Given this compact introduction to market microstructure field it can be seen that the phenomenon of pricing error is largely within its scope. Therefore, now the attention of the reader is directed to a brief review of the main theoretical postulates of market microstructure as well as some more narrow yet more relevant to the present paper academic findings. Markets, their Functions and Benefits Before getting into more complex issues of market microstructure basic theoretical discussion about the purpose of markets, their possible architecture and entities who use them is provided. A market is the place where traders gather to trade financial instruments, which among others include ETFs. That place may be a physical trading floor, or it may be an electronic system facilitating communication of traders with each other. There can be distinguished two basic functions of markets: Facilitate an exchange of an item being traded Produce price discovery for the items being traded It can be seen that by performing these functions markets produce benefits which can be accrued both privately and publicly. Specifically, whereas the benefits of an exchange of an item can be attributed individually to the trading parties, the prices which prevail in these trades produce positive externalities 15

from which benefit the whole economies. Namely, efficient price discovery of financial instruments greatly contributes to capital and asset allocation in market-based economies (Harris, 2003). Evolution of Research Topics within the Field of Market Microstructure and Their Practical Applicability According to Madhavan (2002) the whole evolution of studies within the microstructure field can be segmented into four stages, during which academic discussion was dominated by certain distinct topics. Hereby, short description of these areas along with the main research findings and their applicability is presented. Price formation and price discovery The first studies in this field were concentrated on the price formation and price discovery such static issues as determinants of trading costs and dynamic ones relating to the process through which prices adjust to reflect the newly arriving information about the valuations were analyzed. As Madhavan (2002) puts it: efforts were made to look into the black box by which latent demands of traders are translated into prices and volumes. Role of Dealers and Determinants of Spread One of the more weighty apprehensions at this stage was that of the crucial role market makers perform in the process of price formation. Traditionally, market makers have been viewed as passive liquidity providers who given their information about the order flow try to set such prices at which the order flow would be balanced, or in other words to keep the price at the level where the incoming buy and sell orders would be equally likely (Harris, 2003). However, in the course of time a number of important refinements to this traditional view were made. Close examination of the nature of activity of market-makers served as an inspiration for decomposition of the bid-ask spread difference between the highest bidding and lowest selling prices. Research conducted by Huang and Stoll (1997) consolidates research conducted on the decomposition of spread, by coming up with the model which reconciles majority of the theretofore existing models. They state that spread can be partitioned into three distinct components, namely the ones relating to order processing, inventory holding and adverse selection costs. Applying the model to the intraday trading data in the 20 largest and most actively traded stocks on the US exchanges they also demonstrate several quite revealing phenomena. To begin with it is demonstrated that on average order processing component of the traded spread accounts for 61.8 percent of it while information and inventory cost components are 9.6 percent and 28.7 percent respectively. Likewise it is showed that bid-ask spread tends to be wider for larger trades, reflecting the impact of possible anticipation of the order due to information leakage in the upstairs market. Interestingly, it is also found that the components of bid-ask spread are a function of the trade size large trades are characterized with smaller information components than small and medium trades, the reasoning is that pre-negotiated price of the trade largely reflects information conveyed by the trade. Another highly insightful finding in the field was made in a similar research by Madhavan, Richardson, and Roomans (1996), who investigated intraday variations of price volatility. Results of their study suggested that components of the spread do vary throughout the day, specifically, adverse information cost component decreases while the other two components increase. This is a nice manifestation of the uncertainty inherent to the beginning of any trading session, when market participants try to digest newly arrived information and come up with new valuations. 16

These ideas about static issues relating to the determinants of costs of trading are quite closely related to the studies of dynamic issues of price formation. The presence of these different types of costs in market making activity demonstrates its complexity which implies that traditional approximations of market makers being simply passive liquidity suppliers are not accurate enough. Later research demonstrated that in order to stay in business market-makers must also actively participate in price discovery process and have inventory management skills quickly turn over inventory without accumulating significant positions on one side of the market (Madhavan, 2002). This became an inspiration for the development of two types of frameworks modeling the so called transitory price movements departure of prices from the fundamental expectations of values. Inventory Control Models One class of models is focused on the importance of inventory control mechanisms of market makers. Garman (1976) was one of the first to show that dealer inventory should have an impact on stock prices. His argument was based on a simple fact that dealer s capital is finite, while dealer s exposure by means of inventory steadily increases with the number of trades, therefore making it certain for dealer to run out of capital if he would be about to passively trade with incoming orders of both directions. Adding an assumption that the market is purely dealer driven, described situation would constitute a market failure. Thus to avoid this situation market makers must actively adjust prices in relation to inventory. Generally, the intuition of inventory control models goes as follows: as the dealer trades his actual and target positions diverge, which in turn forces him to adjust prices, lowering them in cases of surplus long position and raising in cases of surplus short position. Since this price adjustment happens irrespectively of fundamental valuation, then it should eventually result in losses, which should be covered should market making be sustainable business. This implies that at least some fraction of the spread should be attributable to the inventory holding costs. A straightforward result is that spread should be narrowest when dealer is at the target level of inventory and it should widen as inventory deviations accumulate. Another practical implication stems from market-makers reluctance to accept additional inventory on the side of the market where his inventory is already over-weighted. This unwillingness should translate into progressively larger price effects following a prolonged sequence of trades on one side of the market (Madhavan, 2002). Further, as shown by Cushing and Madhavan (2001) dealers should be less comfortable with inventory deviations during the closing periods of the session than during the other periods, implying also larger price impact costs towards the end of the session. The reasoning for that is higher overnight inventory holding risk which certainly has to be compensated. As an implication order imbalances during the last hours of trading session can unjustifiably move the valuations, which for that reason should be transitory and therefore subsequent significant price reversals should be expected once market-makers get rid of unwanted inventories or otherwise hedge these risks. Finally, inventory models manifest a great importance of dealers and their market making activities. Just like exchanges which bring traders together in space, dealers through their inventory connect buyers and sellers together in time should there be no trading interest on any one side of the market dealer is expected to fills this gap and supply liquidity to impatient traders. This argument nicely meets the above mentioned central concept in market microstructure about the deviation of asset prices from their fundamentals as Madhavan (2002) puts it: for markets to be efficient someone has to make them efficient implying that market efficiency is decently dependent on behavior of dealers and constraints, such as working capital, imposed on them. 17

Asymmetric Information Models Focal point of another class of models of price formation is the expected loss of market-makers when trading with informed traders. Here the risk of inventories diverging from target levels is substituted by the risk of providing liquidity to traders well informed about future valuations. As a direct implication trading with these market participants leads to expected losses which consequently have to be recovered from trading with uninformed traders, or as sometimes referred to as noise traders. This profit recovery comes in the form of wider spreads than otherwise would prevail be there no informed traders in the markets. For the simplicity ignoring order processing cost an alternative formulation of the issue, which provides more insight into the dynamics of price formation can be made. Bid and ask prices set by dealer represent his conditional security value estimates given that the next market order trader is seller or a buyer respectively. In turn, midpoint of dealer s quoted spread should represent his unconditional security value estimate, given that incoming buy and sell orders are equally likely. Having in mind the practical impossibility for dealer to be able to identify informed trader, this setting suggests that dealer s estimates and therefore actual bid and ask prices should change every time new orders arrive. This model delineates market maker as the one who constantly receives to some degree noisy signals about the valuation of securities (actual degree depends on the probability of informed trading) and subsequently updates his quotes in the direction of new estimate of fundamental value (Harris, 2003). Integration of Inventory Control and Asymmetric Information Models Given that both types of models are quite appealing and it would seem likely that behavior of a dealer would be driven by both inventory and informational motives, there also have been attempts to reconcile the frameworks in a single model. However, this approach is constrained by the fact that under both settings, although for different reasons, prices adjust to a single variable order flow. This obstacle creates a need to find new structural conditions which in turn would allow to take into consideration the two effects. Dynamic-programming inventory control model developed by Madhavan and Smidt (1993) is one such attempt. The essence of their modeling was to allow for dual role of a market maker as a dealer controlling his inventory and as an active investor managing his portfolio. Therefore, besides viewing dealer as setting quotes to induce reversion of inventory towards its target levels the authors also allowed for periodic strategic adjustment of actual target inventory levels, which would proxy his activities as an active investor. This study indicated a presence of both informational and inventory effects. Market Architecture and Price Formation The next stage of the development of microstructure academic field is distinguished by the focus on the investigation of different market structures, specifically their likely impact on the process of price formation and generally on market quality. Among the more insightful notion in this context is the notion of built-in mechanisms forcing consolidation of trading activity in a single marketplace. This development is related to the concept of liquidity: the more market participants there are concentrated in a market the cheaper the liquidity price in that market. Making a link to the previous discussion of market-makers the higher the trading volumes the shorter the holding period of inventory and hence, the lower the inventory control costs for market-makers. This implies that as soon as one marketplace gains advantage over the other marketplaces, mechanics of competition will force the situation to move further and further from the equilibrium (Harris, 2003). Other notable research findings are focused on the relationship between the market structure and liquidity characteristics of the trading process. It has been numerously documented 18

that spreads tend to be wider in dealer markets than in auction markets, and the rationale for this tendency is the possibility of public traders to compete with dealers in offering liquidity. Market Transparency and Behavior of Market Participants The third stage in the evolution was marked by an interest towards informational characteristics of the markets specifically implications of different levels of market transparency on the actual behavior of market participants were the research objective. Firstly, in connection to market transparency an important distinction is made between pre-trade and post-trade transparency, which encompass information about prevailing market conditions (best bid and ask prices and sizes offered) and timely trading data dissemination (trade execution details), respectively. It should be said that the general idea conveyed by the research conducted in this segment of market microstructure suggests that there is a positive relationship between market transparency and the quality of markets, e.g. more informative prices. However, some studies demonstrated that higher levels of transparency are not necessarily beneficial, e.g. increase in market transparency can lead to a decrease of liquidity, since traders might be reluctant to disclose their valuations of securities by placing limit orders (Bloomfield and O'Hara, 1999). Market Microstructure and Other Areas of Theory of Finance Finally, the fourth stage of the evolution can be distinguished for the increased efforts to integrate the findings within the microstructure field with other areas of finance such as asset pricing or corporate finance. Among the more significant papers in this segment is the one of Amihud and Mendelson (1986) who suggested that liquidity of the markets where the financial security is traded does play a role in determination the expected return of this security. Specifically, the idea is that investors expect to be compensated for the additional risks stemming from taking positions in less liquid securities, therefore their hypothesis is that expected returns are a decreasing function of liquidity. Likewise, the topic of stock splits can be an example of a synthesis of microstructure studies and corporate finance. Early research on this topic suggested that stock split might be motivated by signaling motives of management of companies splitting the stock might be interpreted as managers signal that the newly achieved price levels are sustainable. However, market microstructure researchers pointed out that stock splits might be viewed as an adjustment of price relative to tick size in an attempt to affect the trading costs and liquidity of the market where the stock is traded. The research of Angel (1997) addresses exactly this issue making a suggestion that in the event of stock splits, which result in a wider relative tick size, liquidity in the market for the stock is enhanced due to reduced bargaining and order processing costs which in turn might motivate market makers and other limit order traders to supply liquidity in the stock. Angel (1997) went further by estimating a model with the aim to explain a post-split target price of the stock. The results suggested that proxies for firm s idiosyncratic risk, visibility 1 of the company and the size are useful at predicting the optimal post-split share price. Specifically, the post-split price was suggested to be a negative function of idiosyncratic risk and positive function of size and visibility of the firm. ETFs and Market Microstructure Since it is only a bit more than 10 years since ETFs have become popular in the investment community, it is not surprising that there have not been accumulated as much academic literature about their functioning, as there have been about other more established investment vehicles, such as mutual funds, stocks or 1 The variables used to proxy visibility of the company were the number of analysts covering the stock and the time period since the company became publicly listed. 19

bonds. Even more, nearly all findings relating to the topics such as determinants of trading costs, price discovery or dealer s impact on price formation have been made before the introduction of ETFs, primarily the findings were made in the context of stock trading. Therefore, in the beginning of 00s when investment community interest in ETFs translated into increased academic attention towards ETFs, there arose a natural question to what extent the results of microstructure studies of 70s - 90s related to stocks trading are applicable to ETFs trading (Sanchez and Wei, 2010). As a demonstration of the issue cross-sectional relation of spreads to firms and its stock trading characteristics can be provided. According to Stoll (2000) empirical relation of the following form is one of the most stable in the theory of finance: s = α 0 + α 1 log V + α 2 σ 2 + α 3 log MV + α 4 log P + α 5 log N + e Where s is the stock s proportional quoted spread half-spread, V is daily dollar volume, σ 2 is the return variance of the stock, MV is the stock s market value, P is the stock s closing price, N is the number of trades per day and e is the error term. The relation and rationale for it is the following: measures of trading activity such as dollar volumes or number of trades increase the probability of locating a counterparty for a trade, thereby reducing inventory costs which implies negative relation to spreads. Other variables in the equation represent proxies for the probability of dealer suffering adverse price movements. Notably, since inventory of a dealer, who is often a market-maker in 3 to 5 stocks, is fairly undiversified, the variance of returns and not systematic risk of a stock, in theory of finance referred to as beta, is relevant in this context. Thus, increase of the risk should be associated with wider spreads, which means spreads are related negatively with market value of a company and positively with volatility of a stock. Logarithm of price in this model controls for the effect discreteness and is an additional proxy risk, since low price stocks tend to be riskier (Stoll, 2000). It is evident that even if the same variables happen to be related to spreads of ETFs in the same manner as to spreads of stocks the underlying rationale should still be different. Apparently, whereas individual stocks are materially risky instruments, ETFs are more often than not broadly diversified baskets of securities, which likely has significant impact on inventory and asymmetric information related components of the spread. Even though academic field addressing specifically trading microstructure characteristics of ETFs is relatively fresh, still, there have been conducted a number of studies which produced insightful conclusions. In addition to already mentioned study of Engle and Sarkar (2006) few other relevant microstructures studies of ETFs are discussed. Study of Liquidity of ETFs by Sanchez and Wei (2010) In this study two dimensions of ETF liquidity were examined, specifically bid ask spreads and holding periods of ETFs. Interestingly, instead of simply measuring trading intensity by trading volume, authors use holding period indicator, which is an inverse of turnover ratio. The rationale for this choice is that in contrast to stocks trading where the number of shares in a given position which is fixed 2, the number of outstanding ETF shares varies over time due to redemption and creation of shares of ETFs which makes it a better liquidity proxy. Additionally, authors using various methods estimated information component of the spread. The following hypotheses were confirmed in their study: 2 Except for the fairly rare situations of equity issuance. 20

Compared to less diversified sector funds, broad-based ETFs are found to have lower spreads; however, information component is found to be insignificantly different in the two types of ETFs. Information component of spread is found to be lower for ETFs than stocks Overall liquidity of ETFs is not unambiguously better compared to their top-holding stocks: ETFs have relatively greater spreads, but are traded more frequently (lower holding periods) Comparison of Active and Passive ETFs by Rompotis (2010) Besides distinguishing ETFs based on their degree of diversification, which was done by Sanchez and Wei (2010), ETFs could be distinguished based on their replication strategies. Rompotis (2010) studied the differences in liquidity characteristics between passively and actively managed ETFs, however not segmenting further passively managed ETFs into those following physical and synthetic replication strategies. The research was based on German ETFs market, where actively managed ETFs are relatively more popular than in the US markets. In the analysis four alternative methods of spread estimation were used and the general conclusion was the following: Compared to passively managed ETFs measures of bid-ask spread were on average lower for actively managed ETFs. Although the author did not emphasize that but passively managed ETFs were significantly more intensively traded, which makes it ambiguous whether active ETFs are associated with higher liquidity levels. Ackert and Tian (2008) Analysis of Pricing Efficiency of ETFs and Liquidity Analysis of premium and discount of ETFs to their NAVs of Ackert and Tian (2008) is of similar nature as the present paper. It is focused on both domestic and international ETFs, therefore making here an important distinction. Rationale for this distinction is related to the fact that holdings of international ETFs are often listed not in the US markets which creates obstacles for efficient creation-redemption process what in turn harms pricing efficiency of ETFs. Using several alternative methods to measure liquidity the authors reported the following relationship of the tendency of ETFs to be traded at a premium or discount and the liquidity of ETFs: Domestic ETFs exhibit, although generally insignificant, but negative relationship between ETFs tendency to be traded at a premium or discount and the liquidity of ETF, which is in line with expectations, since more liquid markets tend to be more efficient. In connection with international ETFs the same relationship is found to be nonlinear, following an inverted-u shape. Specifically, less liquid international ETFs exhibit positive relationship of ETFs premiums and liquidity, whereas the relationship is negative for more liquid ETFs. The nonlinear relationship found to be present in the context of international ETFs was surprising, however, the authors explanation for this peculiar pattern of behavior pricing error was centered around the ideas expressed in the paper of French and Roll (1996) that trading itself contributes greatly to volatility of prices and therefore financial markets have to be actively managed by someone in order to be efficient. This notion well fits as an explanation of the positive relationship between liquidity and pricing errors of less liquid segment of international ETFs. Specifically, before reaching certain levels of liquidity trading in the ETFs does contribute to liquidity indicators of ETFs, however, since this trading is likely to be uninformed, at the same time it leads to deterioration of pricing efficiency. 21

The concept of Liquidity The concept of pricing efficiency in financial markets is mostly relevant in relation to ETFs, since it is not meaningful to discuss pricing efficiency of stocks or mutual funds. This is due to the reason that objective fundamental valuation of stocks is not an accessible public information, which is the case in the context of ETFs, which fundamental value is reflected by its NAV. Discussion of pricing efficiency of mutual funds is not meaningful, since all trades in mutual funds take place at their NAV prices. Due to this ETF-specific nature of the concept it is not surprising that research is not as abundant on this topic as it is on other microstructure topics, such as liquidity. Further, it has been demonstrated previously by the research findings of Ackert and Tian (2008) that there is significant relationship of ETF liquidity and pricing efficiency. Therefore, for the proper analysis of pricing efficiency it is essential to integrated it with the concept of liquidity. This necessity is addressed in this section, whereby, the concept of liquidity is introduced followed by presentation of different liquidity measures along the measure of pricing efficiency. Even though the concept of liquidity is one of the central ones in the whole field of market microstructure not all researchers do agree on its precise meaning. At best the following abstract definition of liquidity could be provided: Liquidity is complex, multidimensional characteristic of bilateral search of buyers and sellers on financial markets. (Harris, 2003) The key phrase of the definition above is bilateral search, which emphasize the presence of two sides willing to engage in an exchange. However, be it financial instruments or any other tradable item, it is not always easy for agents to search the whole market for the best deal, in the most severe situation these difficulties can preclude an agent from trading all together. All the circumstances which surround this process of an agent s search for a deal are covered by the concept of liquidity. It is often the case that execution of a trade happens as a result of one of the sides being more aggressive than the other and as a direct implication accepting the terms of trade of the more passive side. In such situations researchers speak about the liquidity premiums which are earned by the more passive side. Further, comprehension of the concept of liquidity could be enhanced by distinguishing between its three dimensions: Immediacy is related to how quickly traders are able to arrange their trades of a given size at a given cost. Width or as sometimes referred to as breadth, is indicative of how expensive it is to make trades of a given size. This is usually identified by the bid-ask spread quoted in the market. Depth is related to the size which can be obtain by traders at a given cost. This dimensions is usually identified by the units available at a given price of liquidity. This multidimensionality of liquidity is the reason there is such vast amount of ways measuring it, each method addressing some specific dimension of it and certainly being tailored to the needs of the user. Additionally, it should be noted that width and depth are two closely related concepts, which convey essentially the same information about liquidity condition. Interestingly, the whole academic area of measurement of transaction costs can be seen to be segmented into two parts differing by their fundamental assumptions. One way is to view the frictions as the time required to optimally execute a 22

given order, fulfilling both its quantity and price dimensions (Lippman and McCall, 1986), which apparently is related to the immediacy. The other approach is to see the frictions as price concession that has to be paid in order to execute the order immediately (Demsetz, 1968), which is related to width and depth dimensions of liquidity. Both approaches can be equally useful, so that there is no one superior approach, however, the one suggested by Demsetz (1968) in the work which is one of the very first ones in the whole field of market microstructure is more popular among the researchers (Stoll, 2000). Pricing Efficiency and Liquidity Measures In this section various liquidity measures are presented beginning with static measures of liquidity and progressively moving to more complex dynamic methods. Special attention is directed to the price impact regression since this measure of liquidity, serves as a technical background for the methods used in the empirical part of the present paper. Additionally, the most widely used pricing efficiency measure is introduced, which plays an important role in the pricing efficiency model developed in the present paper. Quoted Spread One of most widely used methods of measuring liquidity is quoted spread. The reason for this is its simplicity. All what is needed for its construction is just the best available prices of buyers and sellers on the market, what is frequently referred to as national best bid and offer (NBBO). The difference between these two prices is the quoted spread, which would be indicative of the so called a round trip cost market buy followed by a market sell order or vice verse. However, more often market practitioners are concerned with just the cost of a single trade, therefore half-spread is more operational, and is defined as: S = (A B)/2 Where A - is the best available ask (offer) price and B is the best available bid price. This measure can further be refined to bring more information, for example, daily average spread could be used in an analysis of liquidity, whereby each spread is weighted by the number of trades executed at that spread. Importantly, this measure of liquidity is classified as a static one, since there is no information revealed in connection to the possible price impact of a trade, it simply assumes that there is none of such (Stoll, 2000). Effective Spread One further step in the analysis of liquidity can be made when looking at the effective spread. Although the measure is also static, in that no dynamic implications of the trade are investigated, however, it captures important for traders implication of price improvement by market makers. Specifically, the measure is defined as: ES = P M Where P is the price at which trade is executed and M is the midpoint of the quote associated with the trade. Several studies have shown that effective spreads tend to be lower than quoted, which is indicative of the presence price improvement of market makers (Fialkowski and Petersen, 1994). Realized Spread A simple way to address dynamic implications of liquidity consumption of trading is to analyze the realized spreads. The measure is very similar to the effective spread with the only difference being that the benchmark against which trade price is compared is not the midpoint of the quote immediately preceding 23

the trade but rather the midpoint of the quote prevailing some time after the execution of the trade. It can defined as: RS = P t M t+n Where P t is the execution price of the trade at time t, and M t+n is the midpoint of the quote at time t + n. Notably, the time when the midpoint of the quote should be measured is not strictly defined, that is something left for the discretion of the user of the indicator, it could be five to ten minutes in active markets, and as much as days in passive markets. Quite insightful theoretical relationship between the quoted and realized spread can be noted. Whereas quoted spread can be seen as the price for liquidity charged by market makers, which includes all components of his costs, realized spread is the amount which market makers end up earning, and this amount theoretically should cover only order-processing costs and possibly monopoly rents, in case market makers enjoy such superior position (Jones and Lipson, 2001). Stoll s Version of Realized Spread Traded Spread Another way to arrive at the daily profit of a market maker is to compare average purchase and sale prices of market-makers. In cases, inventory does not change during the trading session the difference between these two weighted prices should represent their profit. Specifically, there can be used two versions of the indicator, which differ by the price weighting method: trades could be weighted equally or otherwise by volume. Further, focusing of the liquidity price of a single trade, and not a round trip, the traded halfspread with volume based weighting method can be defined as follows: Where, P A = 1 m A w i TS = P A P B 2 w i A A 1 P i, P B = 1 n w B w i B B 1 P i i w i A - share volume of the i th purchase, w i A - share volume of the i th sale. In order to understand why this measure is not only informative for market-makers wishing to measure their performance, but also is indicative of the degree of frictions in the market it has to be established that trading is a zero-sum game, which implies that earnings of liquidity providers are costs for liquidity consumers. Therefore the larger the traded spread the more expensive liquidity in the analyzed markets (Stoll, 2000). Measures of Depth Most operational measures of depth are quite straightforward, following the definition of depth dimension of liquidity these measure should reflect the sizes which are available at certain price levels. Perhaps, most popular measure of depth is simply the sizes which are available at the best bid and ask price levels. Certainly, these measures could quite noise if measured at some specific moment of trading session, therefore the time-averages should be preferred in this case. Similarly, the measures could be modified in 24

way to convey information not only about the sizes offered at the top of limit order-book, but e.g. at the range of 0.5% from prevailing market price or the midpoint of the market quote (Harris, 2003). Amihud s Illiquidity Measure Another measure, which has been quite extensively used in the market microstructure research is Amihud s illiquidity measure. The essence of the indicator is to show how much prices are sensitive to the intensity of trading. This measure is defined as: ILLIQ t = R t /VOL t Where, R t is the absolute return on the security on day t and VOL t is dollar trading volume in the security. Essentially, the measure produces expected absolute percentage return per dollar of trading volume, or the daily price impact of the order flow (Amihud and Mendelson, 1986). Price Impact Regression In the spirit of the Amihud s measure of illiquidity Stoll (2000) came up with a measure of price impact. Firstly, instead of absolute price return of a security for the day, it is suggested to use a return adjusted for the change in the S&P 500 index, specifically: P t = C t C t 1 1 + R SP,t Where, C t is the closing price on day t and R SP,t is daily return on the S&P 500 index. Further, instead of using dollar trading volume as an explanation of the movement of prices, the author suggests to use trading imbalance for the day. Specifically, percentage imbalance variable is defined as: I = m 1 w i A n B 1 w i m A 1 w i + n B (100) 1 w i Where w i A and w i B are the share volume of the i th purchase and sale, respectively. After, the following regression is run to estimate the price impact coefficient, λ, which is interpreted as the sensitivity of the price change over a day to the daily imbalance: P t = λ 0 + λi t + λ 2 I t 1 + e t It can be noticed that the regression equation as explanatory variable besides contemporaneous trading imbalance also includes one lag of the imbalance. This is done in order to determine whether prices bounce back the day after an imbalance is observed. In the context of stocks trading, Stoll claims that the price impact coefficient reflects information content of the day s imbalance, whereas significant coefficient on the variable of the lagged imbalance should be indicative of the presence of real order processing factors. Premiums, Discounts and Pricing Error Given the complexity of the concept of liquidity it is no surprise there are numerous methods for its measurement. In contrast, the concept of pricing efficiency is way more specific, that is the deviation of market prices from the NAV of ETFs. Therefore, methods for its measurement are not as abundant as the one of liquidity measurement. Generally, pricing efficiency is evaluated based on the analysis of premiums and discounts measured by the percentage difference between the average of the closing bid and ask 25

prices (midpoint of the quote) (Engle and Sarkar, 2006). Thus, the indicator of pricing efficiency, in the present Thesis referred to as pricing error is defined in the following way: PER = (M NAV) NAV Where M is ETF market value benchmark, which could be closing price or the midpoint of closing quote, and NAV is net asset value of ETF share. It can be seen that the value of variable can be both positive and negative, which in referred to as premiums and discounts, respectively. Methods of Analysis of Pricing Efficiency In this section methods of analysis and selected data sample are described and rationale for the choice is discussed. It starts with presentation of the pricing efficiency model and detailed description of the variables involved. It is followed by sample description clarifying both cross-sectional and time period choices. Pricing Efficiency Model of ETFs In the spirit of Stoll s price impact regression, a model which is tailored specifically to analyze the pricing efficiency of ETFs is introduced. In the model pricing efficiency indicator expressed as a ratio of price deviation of ETF from its NAV to contemporaneous NAV of ETF is the dependent variable. More specifically, the deviation of the midpoint of the quote prevailing at the close of trading session from NAV of an ETF is used. This is done due to the tendency of closing prices to be subject to a significant amount of noise which can distort the pricing efficiency indicator 3, therefore the benchmark of closing market value of an ETF is defined as: M t,i = A t,i + B t,i 2 Where A t,i is closing ask price of ETF i at day t, and B t,i is closing bid price of ETF i at day t. Then daily pricing error variable is defined as follows: PER t,i = M t,i NAV t,i NAV t,i Where NAV t,i is net asset value (NAV) of ETF i at day t. Additionally, regressions focusing on the explanation of absolute values of pricing errors are estimated, therefore the absolute version of pricing error variable, is the following: APER t,i = M t,i NAV t,i NAV t,i = PER t,i 3 For detailed discussion of the noise inherent to the closing prices see section Existing Research on Pricing Efficiency of ETFs p.13 26

Further, three types of independent variables which are expected to be related to pricing efficiency of ETF are used in the regression, which now are discussed in turn. Essentially, the model developed here represents a testing of three hypotheses: Creation-redemption process is a significant obstruction of pricing efficiency of ETFs Trading intensity is a significant obstruction of pricing efficiency of ETFs Price volatility is a significant obstruction of pricing efficiency of ETFs Significance of the coefficients of these variables would indicate a statistical evidence of the arguments. Net Fund Flows Indicator of Intensity of Creation-Redemption Process One of the goals of study is to analyze to what extent creation-redemption process constitutes an obstruction for market-makers to sustain pricing efficiency throughout the trading session. This is done using a variable of net fund flows to and out of ETFs. This variable is measured in nominal dollar volumes and is expected to reflect the workload or pressure to which authorized participant is exposed during the day, making sure that the price of ETF does not deviate from its NAV when investors for some reason appear to be trading on one side of the market 4. It is not clear whether this variable should be adjusted for the capitalization of ETF or not. On the one hand, larger ETFs in terms of capitalization tend to be more actively traded, and as is demonstrated later, fund flows in these ETFs also tend to be larger. However, from market maker s point of view, it seems that they should be concerned with actual volumes of fund flows, which are indicative of difficulties borne during creation-redemption process, and not with supposed accounting measures. Yet again, given that the capitalization of ETFs tends to be fairly stable the nominal and adjusted for capitalization variables of fund flows should be highly correlated in time. Nevertheless, price impact regression is estimated with both adjusted and unadjusted fund flows variables. Similarly to the pricing error variable absolute versions of the variables are used, therefore: FFC t,i = FF t,i CAPt,i ; AFF t,i = FF t,i ; AFFC t,i = AFF t,i CAPt,i Where FF t,i is the variable of nominal fund flows of ETF i at day t, FFC t,i is adjusted for capitalization fund flows variable, CAP t,i is capitalization of ETF i at day t and variables AFF t,i and AFFC t,i are absolute versions of nominal and adjusted for capitalization fund flows variables, respectively. Turnover Ratio Indicator of Trading Intensity Further, it is expected that trading intensity of a given trading session should also create obstructions for market-makers to sustain pricing efficiency. Following the approach of Sanchez and Wei (2010) in order to reflect trading intensity, turnover ratio and holding period of ETFs are used, which are defined in the following way: TURN t,i = VOL CAP ; HPER t,i = CAP t,i t,i VOLt,i 4 For detailed discussion of the role of authorized participant see section Intraday Creations and Redemptions Pricing Efficiency p. 11 27

Where VOL t,i is share trading volume of ETF i at day t. At this stage, it is important to highlight the distinction between turnover ratio and net fund flows variable. These two variables proxy two different characteristics of a trading session: turnover ratio reflects the general trading activity in the market, whereas fund flows variable reflects to what extent the order-flow is unbalanced on a given trading session. Apparently, it can be the case that highly active trading sessions are associated with one-sided order-flow, however, the underlying rationale for their inclusion should not be uniformed. Indicators of Price Volatility Besides unbalanced order-flow and trading intensity it is reasonable to expect that sharp price movements can also place barrier for efficient pricing of ETFs. In the present analysis two measures of pricing efficiency are used. The first measure is based on the intraday spread between the highest and lowest prices, while the second is simply a standard deviation statistic for sets of prices of ETF, namely, the opening, highest, lowest and closing. The two variables are defined in the following way: PVOL t,i = P high,t,i P low,t,i ; P average,t,i PSQD t,i = σ Popen,t,i,P high,t,i,p low,t,i,p close,t,i = = P open,t,i P average,t,i 2 + P high,t,i P average,t,i 2 + P low,t,i P average,t,i 2 + P close,t,i P average,t,i 2 3 Where P opening,t,i, P high,t,i, P low,t,i, P average,t,i, and P close,t,i are opening, the highest, the lowest, average and closing prices of ETF i at day t. Since these are expected to be highly correlated both of them are not included in a single estimation. A complete list of variables of the model can be found in Appendix 7. Panel Data Estimation Cross-Sectional Unobserved Effects Control A crucial characteristic of the methods employed to study pricing efficiency is that regression is applied to panel data. This structure of sample can greatly contribute to the reliability of results and provide additional options in estimation procedure. The key purpose of structuring sample as panel data is to allow controlling for unobserved characteristics. Specifically, in the model of pricing efficiency this estimation design allows to control for unobserved characteristics, such as liquidity of ETFs, which potentially can impact their pricing efficiency. In the discussion of liquidity in earlier sections 5 it has been demonstrated that liquidity is a complex object, which would be difficult to represent in a model by a single variable. In the context of present analysis, there are three available techniques, namely: Fixed effects estimation First Differencing estimation Random effects estimation In order to choose among the three alternative techniques the first thing to consider is the relation of unobserved effects and observed explanatory variables include in pricing efficiency regression. Particularly, 5 For detailed discussion of the concept of liquidity see section The Concept of Liquidity p. 22 28

random effects estimation should be chosen if unobserved effects are assumed to be uncorrelated with observed explanatory variables, denoting unobserved effects as c i and a set of observed variables as x i : E(c i x i ) = E(c i ) = 0 In contrast, first differencing or fixed effects estimation should be employed if unobserved effects are allowed to be correlated with observed explanatory variables: E(c i x i ) 0 Firstly, this issue can be addressed by an educated reasoning about the underlying structural forces. In the present analysis it would be too restrictive to disallow any correlation between liquidity characteristics of ETFs and the variables used in the model. Turnover ratio, in fact, is itself one of the characteristics of liquidity, therefore there is a strong case for not using random effects estimation. To add statistical evidence for this decision Hausman test can be performed. The idea of the test is to perform both regression with fixed effects and random effects, and to examine the estimates for statistical significant difference. Statistically significant estimates would serve as an evidence of corr(x i, c i ) 0, which in turn would justify fixed effects or first differencing estimation. Hausman test has been performed for the main model specification and the results are supporting the choice of fixed effects estimation. The results of the test can be found in Appendix 8. However, it could be noticed that random effects estimation does allow to incorporate in a model timeconstant variables, which is not feasible in fixed effects and first differencing methods. Therefore an alternative for the chosen fixed effects estimation could be random effects estimation, whereby, sufficient amount of information would be gathered to proxy for unobserved liquidity characteristics by dummy variables, for example categorizing ETFs based on their levels of liquidity. Finally, the choice has to be made between fixed effects and first differencing. This is done based on the relative efficiency of the two estimators. The results of the test are presented in Appendix 9, and there is a strong case for using fixed effects estimator. Specification of Key Pricing Efficiency Regressions In the present analysis a number of different specifications of the model are undertaken, however, in accordance with general research objectives the following two specifications should be considered as the primary ones: PER t,i = α 0 + α 1 FF t,i + α 2 FF t 1,i + e t,i (1) APER t,i = α 0 + α 1 APER t 1,i + α 2 TURN t,i + α 3 PSQD t,i + α 4 AFF t,i + e t,i (2) Equation (1) models the relationship of pricing efficiency and net fund flows to ETFs. Its coefficient estimates, except for the intercept which is not of immediate importance, should be interpreted in the following way: α 1 is contemporaneous effect of unbalanced order-flow on pricing efficiency. Significance of this variable would provide some indication that creation-redemption process is not completely frictionless and therefore unbalanced order-flow can lead to significant barriers for market-makers 29

to sustain pricing efficiency throughout the trading session. The sign of the coefficient is expected to be positive, since net creations are supposed to be done to offset buy side traders of ETF in order to prevent ETFs to be traded at a premium. Accordingly, net redemptions are carried out to prevent ETFs from trading at a discount. α 1 significance of this coefficient indicates persistence or alternatively tendency of reversal of the effect of unbalanced order-flow the next trading session. In other words, it could be interpreted as an indication of whether the effects of unbalanced order-flow are long-lasting. Equation (2) can be seen as a more rigorous approach for explaining pricing efficiency of ETFs. Noticeably, nominal versions of both pricing efficiency and fund flows variables are replaced by absolute ones. This is done due to the introduction of trading intensity and price volatility variables, relation of which to nominal pricing error is not meaningful, as deviations of prices from NAVs in both directions imply symmetrical effect on pricing efficiency, while price volatility and trading intensity are expected to create obstructions for pricing efficiency only when being of increased levels. Therefore: α 2 and α 3 are interpreted as effects of general price volatility and trading intensity on pricing efficiency. If significant, positive sign of the estimates is expected. α 3 similarly to the first model, the coefficient is expected to reflect the importance of creationredemption process for pricing efficiency of ETFs. Importantly, this specification of the model is capable of providing more powerful evidence on the significance of the process, since in this framework it is explicitly controlled for other variables potentially affecting pricing efficiency. Finally, fixed effects estimation method is expected to control for the unobserved characteristics of ETFs, such as their different dimensions of liquidity. General Data Description and Selection Rationale Daily data on prices, trading volumes and capitalization of ETFs is obtained from COMPUSTAT North America database. Funds flow data is obtained from the online resource of IndexUniverse, it contains nominal values of daily net fund flows to ETFs. Classification of ETFs is based on the system used in fourth quarter report of BlackRock (2011) ETF Landscape. Precise composition of the categories is presented in Appendix 10. Cross-sectional Selection The initial sample of the present analysis consists of 1098 ETFs available in the US market. However, given the data-intensity of the analysis and certain statistical requirements for the variables, initial sample is significantly reduced applying several filters. One of the objectives of the present research is to perform a cross-sectional analysis of pricing efficiency of ETFs, therefore it should be established that each category of ETFs is reasonably represented. Choices of ETFs were made based on average daily dollar volume, namely, with several exceptions, the most actively traded ETFs in each category were chosen. Specifically, the following rule was applied to determine the number of ETFs for each category: Less than 50 in the category 5 ETFs to be chosen Between 50 and 100 ETFs in the category 10 ETFs to be chosen More than 100 ETFs in the category 15 ETFs to be chosen 30

Further, given the quantitative techniques employed in the analysis it is required that in order to be included in the sample ETFs should exhibit at least some variation in net fund flows during the research period. For this reason four ETFs were replaced from the sample. Further, a natural requirement of simply being listed as of the beginning of March 2011 is imposed. One sector ETF which qualified the previous requirements, however, revealed to be listed later than in the beginning of March, and therefore was substituted for another ETF. This selection procedure resulted in a sample of 115 ETFs representing 10 different categories of their primary investment exposure. Precise cross-sectional composition of the sample is presented in Appendix 10. Time Period Selection Choice of the time period for any analysis of financial markets is also not a straightforward procedure. First of all, the issue of volatility clustering inherent to the financial markets should be taken into consideration. Essentially, volatility clustering implies the tendency of large changes in asset prices to follow large changes and small changes to follow small changes (Bentes et. al. 2008). This should not imply that time periods when markets are volatile should be excluded from the analysis, but it should be apprehended what impact on the results would have lumping together in one sample periods of high and low volatility. To some extent this decision is similar to a common in econometrics decision about whether certain observations should be considered outliers and therefore excluded from the analysis. In the situation at hand, lumping time periods of high volatility with the ones with low volatility potentially could raise concerns about the structural breaks in the data. This implies that the underlying structural models could differ in the two periods, and therefore lumping them together would bias the estimates of the parameters of the model. Further, after the decision has been made not to mix distinct time periods, it is not apparent which period the one of high or low volatility should be chosen. In this situation the choice should be guided by the actual objectives of the research. In the view of the recent development in the financial markets, namely, the periods of high volatility of 2008 and 2009 related to the advent of the global financial crisis, heightened volatility of markets in 2010 and 2011 related to the sovereign debt crisis in Europe and the US, it could easily be justified to concentrate the research on high volatility periods. However, the approach of the present thesis is conservative, thus the choice is to focus on less volatile period, studying normal course of trading. Finally, it should be noted that the ETFs market has developed immensely in the last several years and still is expected to continue to grow, therefore a natural requirement is to focus on the latest available data, in order for the research findings to be relevant. Figure 6 contains a graph of the development of CBOE volatility index benchmarked against the volatility of S&P 500 and also the development of actual S&P 500 index during the years 2010 and 2011. From the highlighted area of the graph in Figure 6 it can be seen that three months of the year 2011 have been chosen as a research period. Specifically, 64 trading sessions from the 1st of March till the 31st of May compose a source of data. In the context of market microstructure analysis the period of three months can be considered as a reasonably wide. Research target of microstructure analysis is the mechanics of the trading process, which is fairly homogenous from day to day, therefore extending the research time period burdens the computations while brings little new information about this process. Therefore, taking into consideration that the analysis is quite broad in cross-sectional terms (115 ETFs are analyzed) time period covering three months was decided to be the most appropriate in the situation. In connection to the volatility of the research period it can be seen, that the period is characterized by relatively smooth price 31

Figure 6 Development of S&P 500 and CBOE Volatility Index (2010-2011) 1600 S&P 500 CBOE Volatility Index 60 1400 50 1200 1000 40 800 30 600 20 400 200 10 0 0 Source: COMPUSTAT North America, CBOE Indexes, and own calculations. Notes: Period between 2011 March 01 and 2011 May 31 is highlighted by grey color. development, with the exception of several trading sessions in the middle of March when CBOE volatility index spiked to the level of almost 30 points. The reason time period was not shifted to earlier period to exclude this spike is that before March markets were quite strongly trending upwards, which could also be an issue. Similarly, shifting time period further in time would induce inclusion of summer months, which are generally associated with lower activity and therefore trading volumes. Estimation Procedure and Empirical Results In this section empirical results of the study are presented. Firstly, the basic descriptive statistics of certain dimensions of the sample are presented, after, correlations of the key variables are presented and finally the estimation results from pricing efficiency regressions are presented and possible interpretations are provided. Descriptive Statistics of the Sample Given the research objectives of the present study, variables of pricing error and fund flows are of particular interest, therefore, further, these variables are discussed in more detail, what is then followed by a brief overview of other variables of the model. 32

Descriptive Statistics of Pricing Error Variable The key variable of the present research is pricing error of ETF. Since premiums are expected to offset discounts making average value of pricing error close to zero, it would be more intuitive to start description with absolute measure of pricing error (APER t,i ). Figure 7 below in an unusual, yet informative format presents the broad picture of this variable. The figure consists out of two graph each approaching the pricing error variable from a different perspective and a table with the key statistics. The first graph depicts daily behavior of the variable, which is done in the context of three ETFs which have be chosen as representative of the three segments dominating in the sample. Presentation technique of the second graph is used to capture both inter-categorical and intra-categorical distribution of pricing error. Vertical axis depicts actual values of pricing errors while horizontal axis contains cross-sections segmented according to their objective investment exposure. Since it would be quite cumbersome and uninformative to aggregate all data on pricing error behavior in a single graph or table the first graph of the Figure 7 is constructed from the data on just three ETFs which, however, can be seen as representative of the three segments of ETFs sharing similar pricing error behavior. Specifically, Vanguard Total Stock Market ETF (VTI) is representative of the segment of ETFs which exhibit pricing errors on the order of close to zero as well as little variation, with no apparent persistence or otherwise reversal tendency. The situation is little different in the context of ETFs which exhibit higher pricing errors, in the graph this segment is represented by leveraged ETF Direxion Daily Small Cap Bull (TNA). It can be seen that although pricing errors are larger, they are still centered around zero without no apparent tendency to be traded on either premium or discount. Additionally, it can be noted the pricing errors are quite transient, or in other words, tend to be short-lived, thus exhibiting some degree of tendency of reversals. The third segment of ETFs is represented by Vanguard Total International Stock ETF (VXUS) which holdings are purely international. It can be seen that pricing efficiency of this segment of ETFs is considerably inferior to the other two segments. Particularly, a strong tendency of the ETFs to be traded at a premium can be distinguished. Notably, this is in line with the existing research on pricing efficiency of international ETFs. Besides the observed persistence of pricing errors, they also are found to be of significantly higher magnitudes. The key message that can be extracted from the second graph of Figure 7 is that stylized and sectoral ETFs can be distinguished for their high levels of pricing efficiency pricing errors are both low and exhibit very little intra-categorical variation. Little worse pricing efficiency is seen to be present in ETFs with Broad US objective exposure, Leveraged and Inverse ETFs and Actively Managed ETFs. Interestingly, the graph strongly supports the findings of Ackert and Tian (2008) who noted that for various reasons ETFs having some degree of international exposure have poor records of pricing efficiency. Notably, two other categories of ETF, namely Fixed Income and Thematic ETFs also seem to be characterized with inferior pricing efficiency, however, the closer examination reveals that the reasons for that have little to do with particularly fixed income or thematic exposure categories. ETFs in these categories having poor pricing efficiency simply have international holdings among their assets. Talking about the actual magnitudes of pricing errors, the smallest pricing errors typical for stylized and sectoral ETFs account for around 0.025% of NAV, which is way lower than pricing errors of 0.4-0.5% typical for ETFs having international exposure in their assets. Interestingly, for leveraged and inverse ETFs, which are the only ones using synthetic replication strategy, pricing errors of around 0.07% are typical. Although 33

Figure 7 Average Absolute Pricing Errors Segmented by Exposure Categories of ETFs 1.000% Daily Behavior of Nominal Pricing Errors TNA VTI VXUS 0.800% Net Fund Flows (USD mn) 0.600% 0.400% 0.200% 0.000% -0.200% -0.400% Average Absolute Pricing Error (% of NAV) 0.01 0.00875 0.0075 0.00625 0.005 0.00375 0.0025 0.00125-1.56E-17 Average Absolute Pricing Errors Segmented by Exposure Categories of ETFs Category Broad US Stylized Sectoral Fixed Income Thematic Leveraged and Inverse Average Pricing 0.057% 0.026% 0.024% 0.167% 0.309% 0.069% 0.089% 0.393% 0.385% 0.504% 0.204% Error Source: COMPUSTAT North America and own calculations. Actively Managed Global International (Europe and Canada) International (Rest of the World) All 34

the difference with sectoral and stylized ETFs is noticeable, however, given that the pricing errors of other categories of ETFs which do not have international exposure are of about the same level, the statement that poorer pricing efficiency is typical for synthetic ETFs would lack validity. Similar argument, could be applicable for actively managed ETFs. Generally, given quite high levels of intra-categorical variation it can be suggested that pricing efficiency is not easily explained by the category of objective investment exposure. Descriptive Statistics of Fund Flows Variable Another key variable in the pricing efficiency model is net fund flows indicator. Figure 8 presents the behavior of the variable, for presentational purposes the graph is split into three parts each focusing on a single category of ETFs. Due to fairly cumbersome process of statistics presentation of relatively large sample of ETFs, three categories of ETFs have been chosen as representative of the whole sample specifically the categories of Broad US, International (Rest of the World) and Leveraged and Inverse ETFs. Notably, the three most actively traded ETFs from each category are selected for presentation. The first notable aspect is the difference of intensity of creation-redemption process between the categories of ETFs. In fact, the chosen categories well approximate the whole sample, particularly, the segmentation in terms of the intensity of the process into three groups is quite evident. Broad US ETFs together with Sectoral and Stylized ETFs appear to exhibit the most active creation redemption process, there are nearly no trading sessions during the research period with zero activity in fund flows. The second group could be represented by Leveraged and Inverse, Fixed Income and the most active ETFs with international exposure, which would be characterized with moderate activity levels in creation-redemption process. Thus, the third group consisting of Actively Managed, Thematic and less actively traded International ETFs would be characterized with the lowest activity levels. Furthermore, more rigorous approach of analysis of the behavior of fund flows variable sheds some light on several quite insightful aspects of creation-redemption process. Firstly, relatively little activity in creationredemption process of international ETFs should be noted. This could be interpreted as a result of relatively more complicated creation-redemption process which involves international transactions 6. For this reason creation-redemption activities in international ETFs are expected to be rare but bulky, which as seen from the graph is exactly the case. Further, quite insightful inferences can be made in relation to Leveraged and Inverse ETFs based on moderate, or at least not as intensive creation-redemption process as in ETFs of other categories which are distinguished by their high trading volumes. Particularly, little fund flows yet high trading volumes should imply short holding periods of investors or relatively balanced order-flow. This is yet another manifestation of the point that leveraged and inverse ETFs have short-term aggressive traders as their primary clientele. 6 For more detailed discussion of potential problem of international transactions see section Tracking Efficiency Indexation Techniques on p. 14 35

Figure 8 Behavior of Net Fund Flows Variable Source: IndexUniverse online resource and own calculations Notes: Top three ETFs in terms of trading volume from three selected categories; Legend indicates ticker of an ETF. 36

Finally, it should be noted that although little creation redemption activity is most typical for international ETFs, similar fund flows behavior is also observed in other ETFs with domestic exposure. This demonstrates, the presence of a tradeoff which market-makers are facing each time they receive an unbalanced orderflow. Specifically, they have to choose among the following two options: Absorb net fund flows by accepting excess inventories, whereby accepting increased inventory holding risks; Initiate creation-redemption process, whereby accepting transaction execution risks. Following this setting ETFs characterized with relatively frequent still fund flows should be associated with higher levels of inventory holding risks for market-makers, as zero creation-redemption activity indicates that market-maker made a decision to absorb net fund flows by accepting excess inventory. However, the argument should be approached with caution since to the most of author s knowledge there have not been structured approaches of investigation of the issue. Descriptive Statistics of Other Variables of the Model Hereby a brief discussion of other variables in the pricing efficiency model is provided. Specifically, mean, median and standard deviation of the variables of turnover ratio, holding period, capitalization and two measures of price volatility are analyzed. This set of statics is presented in Table 1. In connection with the two variables indicating trading intensity turnover ratio and holding period, the most noticeable characteristic is extraordinarily high trading intensity in Leveraged and Inverse ETFs, e.g. mean holding period of these ETFs is just around 3 trading sessions, whereas the average of all ETFs is 84 trading sessions. That is a strong evidence of the huge short-term speculative interest in these ETFs. Similar although notably higher trading intensity is observed in Sectoral ETFs, which is not surprising, since these ETFs in fact also can be seen as a speculative instrument, since even though Sectoral ETFs may contain a large number of holdings in their portfolios, the risks of these companies are still strongly correlated. Further, in terms of capitalization it is noticeable that Stylized ETFs are the largest and the backlog of other ETFs is quite significant. This can be explained by that Stylized ETFs are the most appealing choice for majority of utilitarian investors, i.e. those seeking to preserve capital or wish to optimize their long-term capital growth. Notably, ETFs with international exposure appear to be nearly of the same sizes as domestic ETFs. Thematic and Actively Managed ETFs both in terms of sizes and trading intensity appear to be lagging behind other ETFs, especially Actively Managed ETFs can distinguished for their relatively small capitalizations, which, in fact can be partly justified by their relative immaturity 7. Finally, price volatility is seen to be particularly high in leveraged ETFs, while the lowest is observed in fixed income ETFs, which is certainly an anticipated phenomena. Otherwise, price volatility is quite similar in other categories of ETFs, perhaps only with a small exception of Broad US ETFs, which probably is a result of their broad diversification. 7 For more detailed discussion of Actively Managed ETFs see section Actively-Managed ETFs p. 9 37

Table 1 Basic Descriptive Statistics of Other Variables of the Model Category Variable and Statistic Mean Turnover Ratio (TURNt,i) Median Standard Dviation Broad US Stylized Sectoral Fixed Income Thematic Leveraged and Inverse Actively Managed 0.01 0.07 0.21 0.03 0.01 0.44 0.02 0.02 0.02 0.05 0.11 0.01 0.02 0.12 0.01 0.01 0.36 0.01 0.01 0.02 0.04 0.02 0.01 0.10 0.43 0.06 0.03 0.30 0.03 0.03 0.02 0.05 0.24 Global International (Europe and Canada) International (Rest of the World) All Holding Period (HPERt,i, #Sessions) Mean Median Standard Dviation 183.3 71.6 16.2 140.3 178.1 4.1 133.4 128.0 71.7 35.8 84.0 167.9 54.1 8.6 127.6 162.9 2.8 109.9 103.5 56.8 24.4 44.0 92.8 64.0 22.6 98.1 106.8 5.1 106.0 114.4 55.3 35.2 97.2 Capitalization (CAPt,i; USD mn) Mean Median Standard Dviation 6460 16800 4100 7180 2290 852 186 4930 3130 5640 5770 3410 11200 3320 7350 1090 662 130 1820 3010 2800 2710 6940 21100 2960 4870 2380 629 152 9520 1790 9160 10500 Price Volatility (PVOLt,i, %) Mean Median Standard Dviation 0.74 1.20 1.48 0.28 0.94 3.00 0.92 1.44 1.31 1.21 1.33 0.63 1.09 1.30 0.23 0.87 2.65 0.67 1.19 1.15 1.07 1.06 0.49 0.57 0.78 0.24 0.56 1.66 0.72 0.92 0.63 0.60 1.13 Price Volatility (PSQDt,i, USD) Mean 0.19 0.53 0.33 0.13 0.21 0.80 0.13 0.32 0.21 0.21 0.34 Median 0.13 0.46 0.23 0.10 0.19 0.60 0.09 0.26 0.18 0.18 0.23 Standard Dviation 0.18 0.31 0.28 0.11 0.15 0.66 0.13 0.22 0.13 0.16 0.38 Source: COMPUSTAT North America nad own calculations. Unit Root Tests of the Key Variables In order to safeguard against the risk of running spurious regression, all key variables of the model are examined on the order of integration, using several test. The results of the tests are presented in Table 2 below. It can be seen that the hypotheses of presence of unit root in the variables is strongly rejected both in level and first difference specifications of test equations, therefore suggesting that the variables are I(0) or stationary, which means there are no risks of running a spurious regression. In fact, order of integration of variables of pricing error and fund flows could have been already detected from their behavior demonstrated in Figures 7 an 8 earlier. 38

Table 2 Results of Unit Root Tests Source: Compustat North America and own calculations Notes: All test equations contain intercept and no trend and two lags; p-values are computed based on asymptotic Chi-square distribution and can materially differ from conventional Chi-square distributions. Correlations Between the Key Variables of the Pricing Efficiency Model Before the actual estimation of any regression model it is often useful examine relationship of the key variables involved. Hereby, it is done by means of analysis of coefficients of correlation, which are based on the complete sample of ETFs and presented in Table 9 below. Figure 9 Correlation Coefficients of the Main Variables of the Model (Complete Sample of ETFs) All ETFs Methods and Indicators Augmented Dickey-Fuller Test (Assumes Individual Unit Root Process) Levin, Lin and Chu Test (Assumes Common Unit Root Process) Level Form First Difference Level Form First Difference Variable Statistic P-Value Statistic P-Value Statistic P-Value Statistic P-Value Pricing Error (PER t,i ) 1532.0 0.0 4203.8 0.0-17.7 0.0-28.7 0.0 Fund Flows (FF t,i ) 1544.6 0.0 3926.3 0.0-24.7 0.0-29.9 0.0 Capitalization Adjusted Fund Flows (FFC t,i ) 1600.0 0.0 3950.5 0.0-25.8 0.0-30.1 0.0 Measure of Price Volatility (PVOL t,i ) 1083.7 0.0 3555.1 0.0-18.1 0.0-16.5 0.0 Measure of Price Volatility (PSQD t,i ) 1111.4 0.0 3699.1 0.0-14.6 0.0-7.7 0.0 Turnover Ratio (TURN t,i ) 935.9 0.0 3646.2 0.0-14.9 0.0-27.6 0.0 CORR(X t,i,x t-1,i ) PER t,i APER t,i FF t,i AFF t,i FFC t,i AFFC t,i TURN t,i HPER t,i PVOL t,i PSQD t,i PER t,i 0.25 1 0.22 0.03-0.02 0.04 0.00-0.08 0.12-0.14-0.12 APER t,i 0.60-1 0.03-0.07 0.03-0.04-0.12-0.01 0.03-0.10 FF t,i -0.17 - - 1-0.05 0.19 0.02 0.00 0.01-0.01-0.02 AFF t,i 0.52 - - - 1 0.02 0.16 0.10-0.14 0.00 0.09 FFC t,i 0.17 - - - - 1 0.62 0.25-0.02 0.01 0.00 AFFC t,i 0.33 - - - - - 1 0.52-0.14 0.11 0.09 TURN t,i 0.89 - - - - - - 1-0.35 0.56 0.50 HPER t,i 0.77 - - - - - - - 1-0.39-0.31 PVOL t,i 0.78 - - - - - - - - 1 0.82 PSQD t,i 0.81 - - - - - - - - - 1 Source: COMPUSTAT North America and own calculations. Notes: Sizeable coefficients are marked with italics. The first point to notice is that correlations between the variables based on a complete sample of ETFs appear to be of relatively small magnitudes and in some situations even of counterintuitive sign, e.g. according to this set of statistics absolute pricing error is expected to be negatively related to one measure of price volatility or to one of trading intensity. Mostly strong correlations are observed between the variables of price volatility and trading intensity. The only more intuitive coeffient is observed between capitalization adjusted absolute fund flows and turnover ratio, which is of expected positive sign. Additionally, first order autocorrelations are computed for each variable. Most sizeable autocorrelation is observed in trading intensity and price volatility variables, which is indicative of the earlier discussed volatility clustering issue. Signals conveyed by autocorrelation coefficients of other variables are conflicting and not as reliable, however. The only noticeable characteristic is quite strong persistence of absolute 39

versions of both pricing error and fund flows variable. In fact, the lack of informativeness of the statistics can be explained by the fact that these are constructed based on a pooled sample of ETFs and the issues of unobserved characteristics of different ETFs, which are addressed by means of e.g. fixed effects method are not addressed in a simple autocorrelation analysis. Therefore, it could be of interest to examine the same statistics, but which instead would be based on more homogeneous sample. Table 10 presents correlation coefficients computed based on a narrowed sample including 15 Stylized ETFs. Figure 10 Correlation Coefficients of the Main Variables of the Model (Stylized ETFs) Stylized CORR(X t,i,x t-1,i ) PER t,i APER t,i FF t,i AFF t,i FFC t,i AFFC t,i TURN t,i HPER t,i PVOL t,i PSQD t,i PER t,i -0.10 1-0.18 0.00 0.03 0.04 0.04 0.02-0.01 0.04 0.02 APER t,i 0.28-1 0.04-0.01 0.01 0.08 0.13-0.17 0.20 0.10 FF t,i -0.19 - - 1-0.07 0.67-0.01-0.01 0.03-0.02-0.02 AFF t,i 0.49 - - - 1-0.01 0.61 0.56-0.34 0.00 0.11 FFC t,i 0.05 - - - - 1-0.01-0.01 0.02-0.05-0.04 AFFC t,i 0.31 - - - - - 1 0.49-0.42 0.04 0.08 TURN t,i 0.91 - - - - - - 1-0.59 0.20 0.20 HPER t,i 0.80 - - - - - - - 1-0.20-0.29 PVOL t,i 0.44 - - - - - - - - 1 0.75 PSQD t,i 0.60 - - - - - - - - - 1 Source: COMPUSTAT North America, various ETF providers websites and own caluctions. Note: Sizeable coefficients are marked with italics. It can be noticed, that the picture although still not convincing but improved materially. Particularly encouraging is the relationship of absolute pricing error and variables of trading intensity and price volatility, which all are now of expected sign, while still of low magnitudes. Also, persistence of absolute pricing error and fund flows is seen to have decreased. Estimation Procedure and Results of Pricing Efficiency Model This section is segmented into two parts. The first part aims at explaining both the magnitude and direction of pricing error exclusively by means of fund flows variable. The second part is focused on the explanation of the absolute magnitude of pricing error besides fund flows also employing price volatility and trading intensity variables. Nominal Pricing Error and Creation-Redemption Process As one of the key objectives of the present paper is to examine the relationship between pricing efficiency and creation-redemption process, firstly several regressions are estimated only with nominal pricing error as a dependent variable and fund flows variables both nominal and adjusted for capitalization as independent variables. Notably, including nominal pricing error as a dependent variable also allows to study the direction of the effects of independent variables of the model. Then, in order to examine tendency of pricing error to reverse one lag is included in model specification. Additionally, one equation is estimated assuming no fixed effects, to demonstrate the improvement of the estimation related to choice of fixed effects method. The results of the regression of these five model specifications are presented in Table 3 below. 40

Table 3 Results of Different Specifications of Nominal Pricing Error Regression Dependent Intercept FF t,i FF t-1,i PER t-1,i FFC t,i FFC t-1,i F-statistic Adj. R 2 Equation (1) Equation (2) Equation (3) Equation (4) PER t,i PER t,i PER t,i PER t,i 0.00076 0.00071 0.00076 0.000711 0.0000005 0.0000005 0.00000002-0.00000003 0.0723 0.0725 1470.1 1362.8-702.23-788.82 14.8 15.1 14.8 15.1 19.2 (0.00) 17.6 (0.00) 19.2 (0.00) 17.6 (0.00) 1.9 (0.055) 1.8 (0.076) 0.1 (0.95) -0.1 (0.92) 6.3 (0.00) 6.3 (0.00) 1.6 (0.11) 1.5 (0.13) -0.8 (0.44) -0.9 (0.39) (0.000) (0.000) (0.000) (0.000) 0.181 0.185 0.181 0.185 Equation (5) PER t,i 0.00059 0.2427 2149.5 66.36 159.0 0.061 (No Fixed Effects) 13.6 (0.00) 21.6 (0.00) 2.2 (0.03) 0.1 (0.95) (0.000) Source: COMPUSTAT North America, various ETF providers websites and own calculations. Notes: Below the estimates T-statistics are provided together with P-value in the brackets. First of all, the coefficient estimates on contemporaneous fund flows variable are of the greatest interest. In the Equation specifications 1 to 4 which should be seen as the ones appropriate for interpretation of coefficient estimates the effect of contemporaneous fund flows variable has been found to vary between being significant to weakly significant, specifically p-values of 0.05 to 0.13 are observed. Furthermore, the direction of the effects is positive, which has been widely anticipated. However, it should also be admitted that the economic significance is little of the coefficients, e.g. given the estimates of the first equation fund inflow of USD 1 bn should imply premium of only about 0.05%. Nevertheless, it can be seen that under the current framework it is shown that there is a statistically significant positive relationship of net fund flows and pricing error, which can be accreditted as one of the key findings of the present thesis. In relation to the persistence of the fund flows it can be said that the effects of fund flows are not long-lasting, not a single specification of the model produced significant estimates at any reasonable significance level. Positive but quite small coefficient estimate on the lagged variable of pricing error has been found, which does not allow one to state that persistence of pricing errors are economically significant, although they are statistically. Generally, all model specifications have been found to have modest explanatory power, that is said judging from adjusted R-squared, which indicates that around 18% of variation of pricing error has been explained in the current specifications of the model. However, it should be noted that in this set of model specification is has not been sought to maximize the explanation of variation within pricing error, rather to obtain some intuition about the likely relationship of pricing error and creation-redemption process. Finally, it can be seen that leaving out the assumption of fixed effects in cross-sections and estimating the model by pooled OLS the model fit decreases materially, which could serve as some indication of the appropriateness of the chosen method. Absolute Pricing Error Regression In this section attention is directed at the explanation of absolute pricing error. This is done due to the fact that nominal pricing error is not meaningful to relate to both trading intensity and price volatility. Similarly to the previous section five different specifications of the pricing efficiency model are estimated and the results of the estimation are presented in Table 4. 41

Table 4 Results of Different Specifications of Absolute Pricing Error Regression Dependent Intercept AFF t,i HPER t,i TURN t,i PSQD t,i PVOL t,i APER t-1,i F-statistic Adj. R 2 Equation (9) APER t,i 0.001 0.0000004 0.00036 0.0012 0.18 68.4 20.3 (0.00) 1.87 (0.06) 1.72 (0.08) 8.5 (0.00) 15.7 (0.00) (0.00) Equation (10) PER t,i 0.00090-0.0000005-0.00055 0.00004 0.58 1017.6 (No fixes effects) 18.8 (0.00) -2.7 (0.01) -3.7 (0.00) 0.4 (0.65) 62.1 (0.00) (0.00) Equation (6) Equation (7) Equation (8) APER t,i APER t,i APER t,i 0.0008 0.0013 0.0008 0.0000004 0.0000005 0.0000004-0.000001-0.000001-0.00023 0.0012 0.07 0.07 0.17 0.17 0.17 72.3 68.4 72.4 12.5 (0.00) 17.8 (0.00) 10.7 (0.00) 1.81 (0.07) 1.88 (0.06) 1.76 (0.08) -2.7 (0.01) -1.58 (0.11) -1.1 (0.27) 8.9 (0.00) 17.13 (0.00) 17.3 (0.00) 15.0 (0.00) 15.6 (0.00) 14.9 (0.00) (0.00) (0.00) (0.00) 0.54 0.52 0.54 0.52 0.36 Source: COMPUSTAT North America, various ETF providers websites and own calculations. Notes: Below the estimates T-statistics are provided together with P-value in the brackets. The first thing to notice from the estimation results is that even after accounting for such factors as price volatility and trading intensity the effect of creation-redemption process still remains significantly related to pricing error. Even more, the magnitude of the effect remained to be approximately the same, which adds robustness to the argument that net fund flows to ETFs are positively related to pricing error. Further, the level of persistence of pricing error remained approximately at the same relatively low level. In fact, the estimate is higher than the one calculated in the framework of nominal pricing errors, however, this is likely to be related to its conversion to absolute value. Price volatility and trading intensity seem to be both related to pricing efficiency, however, price volatility appears to be related more strongly. Not a single specification of the model produces non-significant estimates on the price volatility variable, while turnover ratio and holding period each only once appears to be significant whilst once weakly significant. It could be, however, a result of collinearity of these two variables, since as has been demonstrated earlier price volatility and trading intensity are decently correlated, therefore for this reason increased standard errors could be observed. Normally, it could be an option to exclude one of the variables from the model, however, in this particular setting both of the variables are essential from theoretical point of view, since they proxy two quite different aspects of general market conditions, therefore both are left in the model. For demonstrative purposes few examples of the model prediction based price volatility and trading intensity can be provided. Thus, e.g. the value of price volatility variable PVOL t,i of 0.013 which is a mean value of the variable (what implies daily spread between the highest and lowest prices of 1.3% of the average price for the day) would predict an additional 0.09% of pricing error. Similarly, if the mean value of trading intensity HPER t,i is used for prediction of pricing error, then decrease of trading intensity of the magnitude translating to an increase of holding period by 84 trading sessions would imply an expected decrease of pricing error by 0.0084%. Given that average value of pricing error is 0.002 or 0.2% the estimates of the coefficients could be admitted to be practically significant. This is also seen from the fraction of explained variation of the absolute pricing error, which indicates that more than half of the variation is explained. Once again, the last regression which has been run leaving out the assumption of fixed cross-sectional effects produces even more pronounced evidence of inappropriateness of pooled OLS estimation method. This is seen since coefficients of price volatility and trading intensity become either insignificant or of counterintuitive sign, while nearly all variation of pricing error is attempted to be 42

captured simply by the lagged pricing error. It is always an indication of a poor model, if even given an obvious presence of variation of explained variable the model merely provides the last observation of explained variable as the best guess. Given the level heterogeneity of pricing error behavior between ETFs with purely domestic exposure and ETFs with primarily international exposure it could be logical to split the sample into these two groups and re-estimate the model. In case of materially different results of estimation it could be suggested that pricing error behavior is structurally different in these two groups. The results of the estimation of pricing efficiency model based on specification of the kind of Equation 6 (which can be found in Table 4), are presented in Table 5. Table 5 Results of Pricing Efficiency Regression Applied to two Segments of the Sample Estimation Based on ETFs with Primarily International Exposure (35 ETFs) Dependent Intercept AFF t,i TURN t,i PVOL t,i APER t-1,i F-statistic Adj. R 2 Equation (11) APER t,i 0.0062 0.0000037 0.03 0.17 0.08 34.9 0.37 3.6 (0.00) 1.87 (0.06) 10.85 (0.00) 13.9 (0.00) 4.32 (0.00) (0.00) Estimation Based on ETFs with Primarily Domestic Exposure (80 ETFs) Equation (12) APER t,i 0.0008 0.000000005 0.0001 0.002 0.18 150.4 0.71 21.0 (0.00) 0.4 (0.66) 0.9 (0.36) 0.8 (0.41) 13.7 (0.00) (0.00) Source: COMPUSTAT North America, various ETF providers websites and own calculations. Notes: Below the estimates T-statistics are provided together with P-value in the brackets. The results presented in Table 5 can be interpreted as a strong manifestation of the notion that financial markets can differ by the level of their efficiency. It could be a bit disappointing for the author to give up some of the scope of the previous findings, but the results of estimation of Equations 11 and 12 strongly suggest that ETFs with their primary domestic exposure are priced very efficiently and neither fund flows, nor such factors as price volatility or trading intensity are a significant obstruction for efficient pricing. However, in connection with international ETFs it can be seen that the previous findings about the relevance of creation-redemption process, price volatility and trading intensity are sustained. Conclusions The general purpose of the present study was to conduct an extensive analysis of pricing efficiency of exchange traded funds, in order to understand the importance of creation-redemption process and other drivers of pricing error. Firstly, a wide background of ETFs was provided, which included discussion about recent development, versatility and mechanics of the functioning of these investment vehicles. Afterwards, it was demonstrated how the topic of price efficiency of ETFs fits into the field of market microstructure and some specific research findings within the field which had the greatest impact on the development of the analysis were discussed at length. By means of descriptive statistical techniques it has been shown that international ETFs exhibit significantly worse pricing efficiency than other categories of ETFs, which is largely in line with existing research. In 43

contrast, ETFs with primarily domestic exposure of their portfolios tend to exhibit relatively small pricing errors, in this respect Stylized and Sectoral ETFs have been found to be the most price efficient. Next, a model of pricing efficiency has been introduced and applied to a relatively large sample of trading data, which included 64 trading sessions of 115 ETFs. By means of fixed effects panel data estimation method it has been shown that creation-redemption process of ETFs does not create significant obstacles for pricing efficiency of ETFs with primarily domestic exposure. Similarly, neither price volatility nor trading intensity were found to be a significant obstacle for market-makers to sustain efficient pricing of ETFs. However, opposite results were obtained in relation to international ETFs, where creation-redemption process, trading intensity and price volatility were revealed to be significantly related to pricing errors. These results can be interpreted as a great manifestation of the importance of market-makers in ensuring efficiency of financial markets. Whenever, significant obstacles are created for the activities of marketmakers, efficiency of markets where they operate is expected to decrease. Finally, it should be noted that the market microstructure of ETFs or specifically the issue of pricing efficiency of ETFs is quite far from being completely explained. A natural extension of the present paper would be an enlargement of the sample including less liquid ETFs. The present analysis covered around 10 percent of the whole population of ETFs in US markets, however, even in this relatively narrow segment a number of different types of pricing error or creation-redemption process behavior were found. Further, it should be noted that the key variables of the model were related to the closing period of the trading session, which is known for relatively high amounts of noise. Therefore another suggested direction for extending research of ETFs pricing efficiency is to examine the creation-redemption process and its relationship to pricing efficiency at higher intraday frequencies. 44

Appendix 1 Global ETFs Industry by Geographical Region (as of the end of 2011) Number of ETFs AUM (USD Bn) 20 Day ADV (USD Mn) Asia Pacific 393 89.2 1123.8 Australia 31 3.3 20.0 China 37 11.8 256.0 Hong Kong 48 22.9 165.7 India 21 0.3 5.8 Indonesia 1 0.0 0.0 Japan 90 34.9 104.0 Malaysia 4 0.3 1.4 New Zealand 6 0.3 0.2 Singapore 31 2.3 7.5 South Korea 103 8.6 530.3 Taiwan 17 4.3 32.3 Thailand 4 0.1 0.4 Americas 1356 993.0 53458.7 Brazil 10 1.5 29.5 Canada 227 42.3 771.5 Colombia 1 0.7 1.1 Mexico 20 8.1 220.5 Unites States 1098 940.4 52435.9 - Percent of Global Total 36.5 69.6 91.1 Europe 1220 266.1 2927.1 Austria 1 0.0 0.0 Belgium 1 0.0 0.0 Finland 1 0.2 4.2 France 277 43.5 345.6 Germany 448 103.8 767.5 Greece 3 0.0 0.0 Hungary 1 0.0 0.0 Ireland 1 0.0 0.0 Italy 23 2.2 261.3 Netherlands 26 0.6 58.5 Norway 7 0.6 41.9 Poland 1 0.0 0.2 Portugal 3 0.1 0.2 Russia 1 0.0 2.5 Spain 14 0.5 18.8 Sweden 24 2.7 122.6 Switzerland 130 44.3 269.2 United Kingdom 258 67.6 1034.6 Middle East and Africa 42 2.3 16.2 Saudi Arabia 3 0.0 0.1 South Africa 26 2.1 5.0 Turkey 12 0.2 11.1 UAE 1 0.0 0.0 Global Total 3011 1350.9 57547.4 Source: BlackRock, ETF Landscape: Global Handbook Q4 2011 45

Appendix 2 Global Leading ETP Providers (as of the end of 2011) Year End of 2011 ETP Provider # ETPs AUM (US$ Bn) Market Share (%) ishares 504 599.1 39.3 State Street Global Advisors 146 270.3 17.7 Vanguard 75 170.7 11.2 PowerShares 146 46.5 3.0 db x-trackers 213 41.8 2.7 Lyxor Asset Management 172 34.9 2.3 ETF Securities 260 24.8 1.6 Van Eck Associates Corp 43 23.5 1.5 ProShares 126 23.1 1.5 Nomura Asset Management 37 18.4 1.2 Credit Suisse Asset Management 58 15.5 1.0 Zurich Cantonal Bank 7 14.0 0.9 Deutsche Bank 45 12.9 0.8 WisdomTree Investments 47 12.2 0.8 UBS Global Asset Management 62 9.7 0.6 Bank of New York 1 8.9 0.6 Amundi ETF 100 8.4 0.6 Nikko Asset Management 20 7.9 0.5 HSBC/Hang Seng 35 7.8 0.5 Rydex SGI 37 7.7 0.5 Source Markets 96 7.3 0.5 Commerzbank 96 7.1 0.5 Daiwa Asset Management 23 6.8 0.4 Direxion Shares 52 6.7 0.4 Claymore Investments 34 6.6 0.4 First Trust Advisors 60 6.3 0.4 Barclays (ipath) 86 6.3 0.4 Swiss & Global Asset Management 16 5.8 0.4 Charles Schwab Investment Management 15 5.0 0.3 Samsung Investment Trust Management 24 4.8 0.3 ETFlab Investment 40 4.5 0.3 EasyETF 48 4.5 0.3 Polaris 11 4.2 0.3 UBS AG 221 4.1 0.3 PIMCO 17 4.0 0.3 China Asset Management 2 3.8 0.3 BMO Asset Management 44 3.8 0.2 JPMorgan Chase 4 3.7 0.2 Societe Generale 41 3.2 0.2 Guggenheim Funds 44 3.0 0.2 BetaPro Management 75 3.0 0.2 XACT Fonder 25 3.0 0.2 United States Commodity Funds 10 2.9 0.2 E Fund Management 3 2.9 0.2 db ETC 54 2.2 0.1 ALPS ETF Trust 3 2.1 0.1 Absa Capital 8 2.1 0.1 Mitsubishi UFJ Asset Managemennt 6 1.8 0.1 BBVA Asset Management 16 1.5 0.1 RBS 56 1.5 0.1 Source: BlackRock, ETF Landscape: Global Handbook Q4 2011; Providers conducting business in the US marked with italics. 46

Appendix 3 US ETF Landscape # / AUM / ADV Broad US Stylized Thematic Sectoral Emerging Markets Equity Global Equity Dividends Actively Managed Fixed Income Alternative Inverse and Leveraged Total Number of ETFs 16 172 18 179 112 205 24 47 135 31 159 1098 AUM (USD mn) 35440.3 222546.1 1279.2 102093.9 112746.9 125073.6 36405.6 4295.0 177076.2 1646.2 26551.6 845154.6 ADV (USD mn) 219.0 30118.2 10.4 5323.9 4790.7 2850.8 306.5 37.7 2443.2 24.1 6425.6 52549.9 AdvisorShares ALPS ETF Trust BONY Columbia Mgt DBX Strategic Adv Direxion EGA FFCM LLC Fidelity First Trust Focus Shares Global X Funds Guggenheim IndexIQ ishares 1 / 12.1 / 0.3 2 / 11.2 / 0.0 6 / 11273.2 / 71.4 1 / 62.3 / 0.6 1 / 8851.9 / 470.8 1 / 154.5 / 0.7 16 / 1227.2 / 9.7 3 / 14.0 / 0.3 3 / 54.5 / 0.4 37 / 131716.4 / 4949.1 2 / 7.9 / 0.4 3 / 77.6 / 0.3 5 / 277.5 / 3.8 2 / 326.9 / 1.5 1 / 1993.2 / 19.7 19 / 3626.0 / 38.2 11 / 54.4 / 0.4 2 / 4.9 / 0.0 1 / 8.0 / 0.1 1 / 9.7 / 0.1 36 / 19411.3 / 518.1 1 / 4.46 / 0.00 19 / 512.0 / 5.4 7 / 158.9 / 1.9 15 / 335.1 / 2.8 6 / 772.5 / 8.0 2 / 10.6 / 0.2 29 / 60818.7 / 3788.9 1 / 50.5 / 0.3 4 / 39.71 / 0.15 12 / 357.3 / 4.2 21 / 721.2 / 10.5 9 / 597.9 / 5.5 5 / 108.4 / 1.2 65 / 77679.3 / 1724.3 3 / 824.0 / 6.5 1 / 28.7 / 0.4 2 / 59.9 / 0.9 3 / 11166.1 / 110.1 11 / 427.3 / 6.5 5 / 22.7 / 0.1 7 / 51.1 / 0.4 2 / 487.0 / 2.8 4 / 268.7 / 2.3 14 / 768.4 / 4.6 38 / 117623.2 / 1857.4 5 / 119.8 / 0.1 14 / 1789.6 / 1256.6 50 / 6687.2 / 3686.7 11 /427.3 / 6.5 3 / 2106.0 / 20.6 1 / 8851.9 / 470.8 5 / 22.7 / 0.10 10 / 163.93 / 0.24 52 / 6695.1 / 3687.0 19 / 512.0 / 5.4 7 / 51.1 / 0.4 1 / 154.5 / 0.7 60 / 6271.0 / 60.7 15 / 80.5 / 1.0 39 / 1139.8 / 13.7 44 / 3036.9 / 26.1 12 / 330.3 / 3.1 230 / 430552.7 / 13025.4 47

(Continued) # / AUM / ADV Broad US Stylized Thematic Sectoral Emerging Markets Equity Global Equity Dividends Actively Managed Fixed Income Alternative Inverse and Leveraged Total Number of ETFs 16 172 18 179 112 205 24 47 135 31 159 1098 AUM (USD mn) 35440.3 222546.1 1279.2 102093.9 112746.9 125073.6 36405.6 4295.0 177076.2 1646.2 26551.6 845154.6 ADV (USD mn) 219.0 30118.2 10.4 5323.9 4790.7 2850.8 306.5 37.7 2443.2 24.1 6425.6 52549.9 Jefferies AM Northern Trust Pax World PIMCO PowerShares Precidian ProShares RevenueShares Russell Rydex SGI Schwab ETFs SSga Van Eck Vanguard WisdomTree 2 / 2527.4 / 9.4 1 / 815.9 / 11.2 1 / 126.5 / 0.6 3 / 20674.0 / 126.1 21 / 28412.4 / 2458.7 1 / 90.8 / 0.9 3 / 394.0 / 1.6 20 / 234.5 / 3.9 13 / 4221.5 / 60.0 5 / 1920.2 / 18.1 11 / 11888.64 / 21946.93 27 / 30507.0 / 173.9 8 / 2796.3 / 22.8 2 / 9.6 / 0.1 2 / 515.1 / 4.2 2 / 64.6 / 0.1 1 / 163.4 / 2.1 47 / 4881.1 / 35.7 1 / 7.2 / 0.0 9 / 333.0 / 3.7 1/ 191.1 / 1.9 30 / 51821.8 / 4454.2 8 / 1855.5 / 114.5 11 / 17733.7 / 135.1 7 / 1525.6 / 23.2 1 / 12.4 / 0.2 1 / 439.1 / 6.57 9 / 2577.5 / 22.45 13 / 2965.7 / 160.7 1 / 41899.9 / 731.59 1 / 714.5 / 38.9 2 / 95.0 / 0.8 18 / 1283.7 / 10.6 1 / 163.3 / 1.7 1 / 38.1 / 0.2 3 / 14.8 / 0.0 2 / 13.5 / 0.1 2 / 772.9 / 7.5 24 / 4979.7 / 58.8 11 / 17231.5 / 748.7 8 / 18528.7 / 252.5 16 / 2310.5 / 23.9 2 / 843.0 / 8.4 1 / 9.0 / 0.0 1 / 134.8 / 4.2 2 / 8412.4 / 54.3 2 / 11345.6 / 83.0 7 / 3582.2 / 38.7 1 / 26.0 / 0.8 4 / 1966.4 / 14.3 8 / 341.4 / 3.0 2 / 30.9 / 0.3 1 / 6.4 / 0.1 2 / 667.1 / 7.2 2 / 296.0 / 1.5 13 / 2030.14 / 15.0 13 / 4404.8 / 48.0 4 / 769.5 / 12.0 26 / 18988.7 / 248.9 11 / 1449.5 / 12.4 12 / 29645.3 / 231.8 2 / 1100.7 / 11.6 1 / 16.3 / 0.03 2 / 15.1 / 6.2 9 / 957.4 / 13.2 107 / 19739.6 / 2735.0 2 / 124.9 / 3.9 Source: BlackRock, 2011: ETF Landscape: Global Handbook Q4 2011; Notes: format of each cell: Number of ETFs/Combined assets under management/combined average daily volume. 2 / 95.0 / 0.8 4 / 485.4 / 4.3 2 / 9.6 / 0.1 17 / 3996.6 / 29.2 121 / 44750.9 / 2601.1 1 / 163.3 / 1.7 112 / 19876.5 / 2742.4 6 / 448.2 / 1.8 24 / 255.7 / 4.0 27 / 4705.2 / 67.8 15 / 5043.6 / 61.4 104 / 98795.2 / 26786.3 43 / 23502.2 / 1036.3 64 / 170333.9 / 1734.1 47 / 12193.2 / 156.1 48

Appendix 4 Growth of ETFs Listings on the US Exchanges Time Period Count Percent Cumulative Count Cumulative Percent Time Period Count Percent Cumulative Count Cumulative Percent 1993-Q1 1 0.09 1 0.09 2005-Q3 7 0.64 176 16.03 1995-Q2 1 0.09 2 0.18 2005-Q4 19 1.73 195 17.76 1996-Q1 15 1.37 17 1.55 2006-Q1 12 1.09 207 18.85 1998-Q1 3 0.27 20 1.82 2006-Q2 49 4.46 256 23.32 1998-Q4 9 0.82 29 2.64 2006-Q3 18 1.64 274 24.95 1999-Q1 1 0.09 30 2.73 2006-Q4 44 4.01 318 28.96 2000-Q2 23 2.09 53 4.83 2007-Q1 68 6.19 386 35.15 2000-Q3 18 1.64 71 6.47 2007-Q2 53 4.83 439 39.98 2000-Q4 3 0.27 74 6.74 2007-Q3 13 1.18 452 41.17 2001-Q1 3 0.27 77 7.01 2007-Q4 52 4.74 504 45.90 2001-Q2 2 0.18 79 7.19 2008-Q1 20 1.82 524 47.72 2001-Q3 7 0.64 86 7.83 2008-Q2 34 3.10 558 50.82 2001-Q4 11 1.00 97 8.83 2008-Q3 20 1.82 578 52.64 2002-Q1 1 0.09 98 8.93 2008-Q4 34 3.10 612 55.74 2002-Q3 4 0.36 102 9.29 2009-Q1 15 1.37 627 57.10 2002-Q4 4 0.36 106 9.65 2009-Q2 26 2.37 653 59.47 2003-Q1 1 0.09 107 9.74 2009-Q3 23 2.09 676 61.57 2003-Q2 4 0.36 111 10.11 2009-Q4 42 3.83 718 65.39 2003-Q3 1 0.09 112 10.20 2010-Q1 48 4.37 766 69.76 2003-Q4 4 0.36 116 10.56 2010-Q2 43 3.92 809 73.68 2004-Q1 15 1.37 131 11.93 2010-Q3 53 4.83 862 78.51 2004-Q2 2 0.18 133 12.11 2010-Q4 23 2.09 885 80.60 2004-Q3 13 1.18 146 13.30 2011-Q1 53 4.83 938 85.43 2004-Q4 3 0.27 149 13.57 2011-Q2 88 8.01 1026 93.44 2005-Q1 11 1.00 160 14.57 2011-Q3 31 2.82 1057 96.27 2005-Q2 9 0.82 169 15.39 2011-Q4 41 3.73 1098 100.00 Source: BlackRock, ETF Landscape: Global Handbook Q4 2011 49

Appendix 5 Replication Methods of US ETFs Cumulative Cumulative Count Percent Replication Method Count Percent 10 Yr Bond, Swaps, Futures 1 0.09 1 0.09 30 Yr Bond, Swaps, Futures 1 0.09 2 0.18 Bonds, Futures 1 0.09 3 0.27 Bonds, Swaps 2 0.18 5 0.45 ETFs, Swaps 1 0.09 6 0.54 Futures 3 0.27 9 0.81 Futures, Swaps 44 4.01 53 4.82 Stock, Options 1 0.09 54 4.91 Stocks (Opt.), Swaps, Futures 14 1.28 68 6.19 Stocks, Forwards 1 0.09 69 6.28 Stocks, Futures 2 0.18 71 6.46 Stocks, Swaps 1 0.09 72 6.55 Stocks, Swaps, Futures 5 0.46 77 7.01 Swaps 93 8.47 170 15.48 Swaps, Futures 2 0.18 172 15.66 ADRs 1 0.09 173 15.75 Bonds 66 6.01 239 21.76 Bonds (Fully Rep) 7 0.64 246 22.4 Bonds (Optimized) 61 5.56 307 27.96 CEFs 1 0.09 308 28.05 ETFs 21 1.91 329 29.96 Fixed Income 9 0.82 338 30.78 Fixed Income, Forwards 1 0.09 339 30.87 Stocks 420 38.25 759 69.12 Stocks (Fully Rep) 261 23.77 1020 92.89 Stocks (Optimized) 47 4.28 1067 97.17 Stocks (Optimized), ETFs 1 0.09 1068 97.26 Stocks, ADRs 2 0.18 1070 97.44 Stocks, Bonds 6 0.55 1076 97.99 Stocks, ETFs 1 0.09 1077 98.08 Active Bonds 1 0.36 1078 98.17 Replication Class Synthetic Physical Actively Managed Actively Managed 4 1.47 1082 98.53 N/A 16 0.09 1098 100 N/A Source: BlackRock, ETF Landscape: Global Handbook Q4 2011 and own calculations; Notes: ETFs using swaps, or futures, or options, or forwards, or any combination of these have been classified as synthetic. 50

Appendix 6 Complete List of Variables of Pricing Efficiency Model P open,t,i Opening price of ETF i at day t; P high,t,i Highest trading price of ETF i at day t; P low,t,i Lowest trading price of ETF i at day t; P close,t,i Closing price of ETF i at day t; P average,t,i Average of opening, high, low and closing prices of ETF i at day t; NAV t,i Net asset value of ETF i at day t; VOL t,i Share volume of ETF i at day t; SOUT t,i Number of outstanding shares of ETF i at day t; FF t,i Net fund flows of ETF i at day t; BID t,i Closing bid price of ETF i at day t; ASK t,i Closing ask price of ETF i at day t; M t,i = BID t,i + ASK t,i /2 CAP t,i = P close,t,i SOUT t,i Midpoint of closing quote of ETF i at day t; Market Capitalization of ETF i at the end of day t; PER t,i = M t,i NAV t,i Pricing error of ETF i at day t; NAV t,i APER t,i = M t,i NAV t,i Absolute pricing error of ETF i at day t; NAV t,i FFC t,i = FF t,i CAPt,i Net fund flows adjusted for capitalization of ETF i at day t; AFF t,i = FF t,i Absolute net fund flows of ETF i at day t; 51

AFFC t,i = AFF t,i CAPt,i Net fund flows adjusted for capitalization of ETF i at day t; PVOL t,i = P high,t,i P low,t,i Measure of Price volatility of ETF i at day t, based on the P average,t,i spread between the highest and lowest prices; PSQD t,i = σ Popen,t,i,P high,t,i,p low,t,i,p close,t,i = = P open,t,i P average,t,i 2 + P high,t,i P average,t,i 2 + P low,t,i P average,t,i 2 + P close,t,i P average,t,i 2 3 Measure of price volatility of ETF i at day t based on standard deviation of opening, highest, lowest and closing prices; TURN t,i = CAP t,i VOLt,i Turnover ratio of ETF i at day t; HPER t,i = VOL t,i CAPt,i Holding period of ETF i at day t; 52

Appendix 7 List of Selected ETFs Objective Exposure Categories Name of the Category in the Analysis Included Categories of BlackRock Classification ETF Tickers Number of Available ETFs in the Category Number of Selected ETFs in the Category Broad US Broad US VTI, PFF, IWV, SCHB, PGX 16 5 Stylized Sectoral Fixed Income Thematic Leveraged and Inverse Style Exposure Sector Exposure Fixed Income Dividend US Domestic, Dividend International, Alternative, Asset Allocation, Currency Inverse Equity/Fixed Income, Leveraged Equity/Fixed Income, Leveraged Inverse Equity/Fixed Income/Commodities SPY, IWM, QQQ, DIA, MDY, IVV, IWF, IWD, IWO, IJR, IWN, IJH, IWB, OEF, IWR XLE, XLF, XLI, XLB, XRT, IYR, XOP, XLK, XLV, XLU, XLY, XLP, XME, VNQ, XHB TLT, HYG, SHY, AGG, JNK, LQD, BND, TIP, IEF, BIL, CSJ, BSV, EMB, MBB, SHV DVY, VIG, SDY, DEM, VYM, PID, IDV, DGS, EDIV, CYB TNA, TZA, FAS, FAZ, SDS, SSO, QLD, QID, UPRO, SH, SPXU, ERX, TWM, BGU, SKF 172 15 197 15 135 15 55 10 159 15 Actively Managed Active QAI, HDGE, WDTI, CVY, PCEF 47 5 Global Global Equity, Global (ex-us) Equity EFA, GDX, VEA, GDXJ, VEU, ACWI, MOO, ACWX, RWX, SCZ, IXC, VT, DWX, VXUS, SCHF 147 15 International (Europe and Canada) Canadian Equity, Europe Equity VGK, EWC, EWG, EWU, IEV 30 5 International (Rest of the World) Asia Pacific Equity, Emerging Markets Equity, Middle East and Africa Equity, Latin America Equity EEM, RSX, EWW, FXI, EWY, EWJ, EWT, ILF, EWA, EWH, EPI, AAXJ, EPP, EWS, EWM 140 15 53

Appendix 8 Hausman Test Results The rationale of Hausman test is comparison of the estimates using fixed effects and random effects methods, and given that both estimators are consistent in the case when Corr(x t,i, c i ), then statistically significant difference between the estimates would serve as an evidence of corr(xit,ci) 0, which in turn would justify fixed effects estimation. Therefore the hypotheses of the test are the following: H 0 : Cov x t,i, c i = 0 H 1 : Cov x t,i, c i 0 And the decision should be based on the following test statistic which is Chi-squared distributed with degrees of freedom equal to the number of the time-varying variables in the estimated equation (n), thus the test statistic is the following: H t = β FE β RE V FE V RE 1 β FE β RE ~χ n 2 Where: β FE denotes 1 n vector of coefficient estimates using fixed effects method; β RE denotes 1 n vector coefficient estimates using random effects; V FE denotes n n estimated covariance matrix (fixed effects); V RE denotes n n estimated covariance matrix (random effects) The model which has been estimated using both fixed and random effects is the following: APER t,i = α 0 + α 1 APER t 1,i + α 2 TURN t,i + α 3 PSQD t,i + α 4 AFF t,i + e t,i The summary of Hausman test results are presented in the table below: Estimation Based on Assumption of Fixed Effects Dependent AFF t,i TURN t,i PVOL t,i APER t-1,i APER t,i 0.0000004-0.0002 0.07 0.17 Estimation Based on Assumption of Random Effects Dependent AFF t,i TURN t,i PVOL t,i APER t-1,i APER t,i -0.0000004-0.0013 0.03 0.58 Chi-Sq. d.f. = 4 Chi-Sq. Statistic = 2776.9 P-Value = 0.00 It can be seen that with overwhelming degree of confidence it can be concluded that there are significant differences between the estimates, therefore fixed effect estimation should be employed. 54

Appendix 9 Choice Between First Differencing and Fixed Effects Estimation Choice between fixed effects and first differencing method should depend on relative efficiency of estimators, i.e. variance of estimators. Relative efficiency can be examined based on the pattern of behavior of error terms. Specifically the assumption which can make fixed effects estimation better than first differencing is: E(u i, u i x i, c i ) = σ u 2 I T This implies that the error terms u i should be serially uncorrelated. Alternatively, equivalent assumption in first differencing estimation is: E( u i, u i x i, c i ) = σ 2 u I T 1 Which implies, that original errors ui should follow a random walk. Relying on this difference one way to decide whether to use first difference or fixed effects technique is to run a regression of residuals from first difference regression on their lagged values and to test whether the coefficient is different from zero. Significant negative coefficient should be treated as evidence in favor of autocorrelation of Δu i terms, and consequently uncorrelatedness of u i terms. Therefore, the equation of interest is: The results of this regression are the following: u i,t = ρ u i,t 1 + v i,t Variable Coefficient (ρ) Std. Error T-Statistic P-Value u i,t 1-0.1514 0.012-12.9 0.000 The resulting coefficient is negative and is significantly different from zero. This can be treated as sufficiently strong evidence in favor of no correlation in the original errors u i. Therefore: After taking into consideration behavior of the error terms it can be concluded that fixed effects estimator is more efficient than the first difference estimator, thus fixed estimation is chosen. 55

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