Create or Buy: A Comparative Analysis of Liquidity And Transaction Costs For Selected U.S. ETFs

ABSTRACT

Our examination of twelve popular Exchange Traded Funds (ETFs) reveals that ETFs exhibit qualitatively different liquidity and cost characteristics than common stocks. The limit order book for ETFs is deeper than that of common stocks with similar daily share volume, price, spread, and volatility characteristics, especially at price levels immediately surrounding the prevailing mid-quote. The creation/redemption mechanism is an integral part of the ETF liquidity provision. The differences in trading mechanisms and liquidity characteristics between ETFs and common stocks affect transaction costs. The price impact costs for trading $10mln with optimal trade scheduling throughout the day are two to five times lower for ETFs than for the matched common stocks. In order to reflect these differences, an appropriate transaction cost model should be properly calibrated and parameterized. In addition, it should also consolidate the entire accessible ETF liquidity on the secondary market and through the creation/redemption procedure. We demonstrate that looking at the cost estimates for trading the underlying constituents of ETFs provides additional clarity with respect to the “true” cost of an ETF. Finally, our comparison of ETF and basket costs in conjunction with a look at creation/redemption fees suggests that ETF providers are trying to differentiate their products on the basis of liquidity provision mechanisms.

The authors would like to thank Charlie Behette, Doug Clark, Ian Domowitz, Laura Tuttle, Konstantin Tyurin, Olav Van Genabeek and Ian Williams for their support, comments, and suggestions. Any opinions expressed herein reflect the judgment of the individual authors at this date and are subject to change, and do not necessarily represent the opinions or views of Investment Technology Group, Inc.

CONTRIBUTORS

Milan BorkovecHead of Financial Engineering, ITG, Inc.+1.617.692.6733 milan.borkovec@itg.com

Vitaly Serbin Manager of Financial Engineering’s Portfolio Analytics Research Group, ITG, Inc. +1.617.692.6746 vitaly.serbin@itg.com

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Introduction

Attention to transaction cost considerations has risen in recent years in both financial theory and practice. While for brokers and dealers transaction costs constitute the bread and butter of their daily routine, the significance of transaction costs for buy side market participants is often misunderstood or is interpreted too narrowly.

Traditionally, the impact of transaction costs, as applied to buy-side practices, is summarized by one number: the difference between so-called “paper” and “net” returns. The former refers to investment returns calculated without accounting for trading costs, while the latter includes the cost of trading. In other words, the classical view of transaction costs is that they eliminate part of the notional or “paper” return of an investment strategy and, therefore, should be controlled at the trading stage. Some recent papers demonstrate that accounting for transaction costs at the level of portfolio construction could lead to better investment allocations. Borkovec et al. [2010] show that cost-aware portfolio construction yields portfolios with higher net returns and lower variances. Brandes, Domowitz and Serbin [2011] extend this evidence and show that the inclusion of stock-specific transaction costs at the portfolio construction stage permits higher turnover levels and allows portfolio manager to run larger portfolios without facing detrimental cost effects.

Exchange-traded funds (ETFs) are relatively new investment tools that are similar to mutual funds, but trade more like stocks. In contrast to mutual funds, ETFs can be bought or sold at any time during the trading day. This is one of the main drivers of ETF’s popularity and importance in the investment community. BlackRock [2011] estimates that ETF turnover on all US exchanges, as a proportion of all equity turnover, has been oscillating between 25 and 35% for most of the period between January 2008 and June 2011. Some of the most popular ETF strategies, including cash equitization and hedging, rely on accurate index tracking, available liquidity and low cost. According to a recent TABB Group report, see Berke [2009], “seven of the ten largest [ETFs] are broad market indices, including the S&P500 index, the MSCI EAFE Index, the Russell2000 index and the Vanguard Total Stocks Market index. Together they hold 81% of the assets in the top ten ETFs”. As important as it is, the tracking error of an ETF, with respect to a broad market index, is only one of the top institutional priorities when it comes to selecting an ETF product. According to a Greenwich Associates [2011] survey, 61% of institutional funds and 79% of asset managers cite liquidity as one of the top ETF selection criteria. Managing the ETF trading process requires knowledge and effort from investors. Failure to tap available ETF liquidity, or to fully understand the nature of ETF costs, severely limits the usefulness of ETFs in a typical buy-side investment application. These facts, and the steady rise of ETF trading volume in the past decade, clearly suggest the need to better understand the liquidity characteristics and trading costs of ETFs and their underlying securities.

The purpose of this paper is to explore the liquidity and trading costs of a selected group of ETFs that track popular U.S. equity indices. We adhere to the accepted definition of liquidity as the “ability to transact quickly without exerting a material effect on prices”1. It is important to bear in mind that by construction, the liquidity of an ETF is closely tied to the liquidity of its underlying basket, since the net asset value (NAV) of an ETF reflects the value of the underlying securities at any time. In other words, although an ETF appears to “trade like a stock”, its liquidity is determined not only by supply and demand for the ETF shares, but also by the liquidity of the underlying securities via the creation/redemption mechanism. Not taking the

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creation/redemption process into consideration when assessing the liquidity and trading cost of an ETF can inadvertently reduce the ETF universe considered for inclusion into a portfolio and inflate the estimated implementation shortfall cost of ETF trading in general.

Our main findings are as follows.

• ETFs exhibit qualitatively different liquidity and cost characteristics than common stocks with comparable volume, volatility, spread and price levels. The limit order book (LOB) for ETFs is deeper, especially at the levels immediately surrounding the midquote. This is true even without accounting for implicit liquidity available via the underlying basket.

• The creation and redemption process is crucial for accessing the entire ETF liquidity. The differences in trading mechanisms and liquidity characteristics result in disparate transaction costs between ETFs and common stocks. Nevertheless, we see that the liquidity in the secondary market remains an important determinant of ETF costs.

• In order to reflect these differences, a good transaction cost model needs to be properly calibrated and parameterized. Failure to do so may lead to inaccurate estimates of ETF trading costs. For instance, the model design should reflect that the permanent price impact costs of most ETF trades are significantly lower than the price impact costs for matched common stock. It also should reflect that ETF liquidity is determined not only on the secondary market but also through the creation/redemption procedure. In addition, the model calibration should be performed on a subsample of a database which contains only ETF trades. These points are illustrated utilizing the newly developed ITG’s Smart Cost Estimator (SCE) model; however, they hold true for any quantitative transaction cost model.

• Looking jointly at the cost estimates of ETFs and the underlying baskets provides additional clarity with respect to the “true” cost of an ETF. We argue that these cost estimates can be utilized to derive upper and lower bounds of the “true” average costs. These bounds could be interpreted as limits to arbitrage that could be performed by authorized participants (APs).

• The comparison of ETF and basket costs in conjunction with a look at creation/redemption fees suggests that ETF providers try to differentiate their products in terms of accessing liquidity.

This paper is organized as follows. In the next section, we discuss ITG’s Limit Order Book (LOB) database and present ITG’s Smart Cost Estimator (SCE) model. Section 3 introduces the sample of ETFs used in this study, along with their basic trade-related statistics. Section 4 presents the main results of our ETF liquidity and trading cost analysis. Section 5 extends the analysis by comparing the costs of trading ETFs with the cost of creation/redemption of the underlying basket of securities. Section 6 summarizes our conclusions.

Transaction Cost Estimates and Data

Cost estimates for any security vary widely depending on a multitude of factors, such as order size, time of day, prevailing market conditions (buy/sell imbalance, volatility, volume and spread), the rebate structure of the exchange, and the desired immediacy of order execution, among others. A good cost estimator should take most of these factors into account, usually at the expense of making assumptions on the functional form of the dependencies and on client preferences.

In order to abstract from the modeling assumptions, we begin by directly examining

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the liquidity profiles of ETFs utilizing the consolidated LOB aggregated from NYSE, NASDAQ, ARCA, BATS, as well as the NASDAQ OMX BX facility. The size and distribution of LOB liquidity across different price levels reveals disparities between ETFs and common stocks, and offers clues to the sources of differences in realized trading costs. In addition to measuring depth sizes at different price levels, we compute the cost of instantaneous execution (“climbing the book”) as an aggregated liquidity measure.

The proliferation of electronic trading results in the majority of large orders being executed algorithmically via a strategy that usually aims at minimizing overall implementation shortfall cost. In this paper, we use ITG’s newly developed Smart Cost Estimator (SCE) model to simulate this process and to generate associated cost estimates. SCE uses as its inputs the order attributes (size, date, and time stamp), security characteristics (type, expected liquidity, volatility, and daily average spread), recently observed market conditions (deviations of volatility, volume, and spread from historical patterns), and the traders’ subjective market sentiment and its persistence (expressed through the magnitude of trade imbalance). In contrast to a static trading cost estimator, the SCE model has the capability to dynamically update execution trajectories based on the market response to order flow and the observed market conditions. The SCE cost estimates presented in this document are obtained under the assumption that the trader’s subjective expectation of future trade imbalances is neutral (no directional prediction of market sentiment).

The SCE model is calibrated for various market conditions using ITG’s Peer Analysis™ database, which contains execution details for more than 33mln orders filled by more than 140 institutional clients within the time period of March 2010 to February 2012. We define an order as a cluster of trades that occur on the same date, in the same security and direction (buy, sell, short, or cover), have the same broker and client IDs, and identical arrival time stamps corresponding to the time when the package was sent by a trading desk to a broker for execution. This fairly standard methodology allows us to identify the unit of analysis granular enough to distinguish the clusters of trades performed by different brokers, or by the same broker on behalf of different ITG Peer Analysis™ clients.

Several key SCE components and parameters are estimated and calibrated separately for ETFs to reflect the distinct nature of those securities. For instance, the permanent price impact, which is estimated directly from the ITG’s Peer Analysis™ database, is significantly lower for ETFs than for common stocks. We also explicitly incorporate the extra available liquidity of the underlying basket in our model. More details on SCE’s methodology and definitions can be found in Borkovec et al. [2011].

ETF Universe

The sample of equity ETFs utilized for the study is selected on the basis of practical relevance and ensuring a fair representation of different liquidity categories and market segments. Where possible, we select ETFs which track the same index, but have different liquidity characteristics (for instance, SPY, IVV, VOO and RSP track the S&P500 index). There are twelve ETFs in our sample, all of them having US-only constituents. Five ETFs track large cap indexes (S&P500 and Russell 1000), three mid-cap indexes (S&P400 and MSCI US Mid Cap 450), and four small-cap indexes (S&P600, Russell 2000, Russell Microcap and MSCI US Small Cap 1750). Our ETF sample also allows for a comparison of trading costs across ETFs supplied by

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different vendors.

The liquidity categories utilized in the paper are based on the 21-day median daily trading volume (MDV) in the secondary market. Exhibit 1 reports the thresholds used for the classification and the selected ETFs falling into each liquidity group.

Exhibit 1. Liquidity Thresholds for ETFs

Liquidity Category MDV Thresholds Selected ETFs

Very Liquid > 10 mln shares SPY, IWM

Liquid 1.5..10 mln shares IVV, MDY

Medium Liquidity 0.2..1.5 mln shares VOO, IWB, RSP, IJH, IJR, VB

Illiquid < 0.2 mln shares VO, IWC

Exhibit 2 provides descriptive and trade-related statistics for each selected ETF. Overall, there appears to be a positive relationship between the fund’s inception year and its secondary market volume. The creation/redemption fees for all ETFs are $500, regardless of the number of units raised, with a few exceptions. Not surprisingly, the spread values of the ETFs are strongly correlated with their corresponding liquidity group: very liquid ETFs have an average spread of 1 cent, followed by average spreads for liquid ETFs of 1.43 cents, medium liquidity ETFs of 1.62 cents and illiquid ETFs of 3.25 cents. Daily volatilities range between 0.7 and 1.2%, and are roughly proportional to the volatilities of the underlying indexes.

Exhibit 2. Summary Profiles for ETFs Selected

Price is the official closing price on February 28, 2012. MDV, daily volatility and spread are the 21-day median daily share volume, 60-day average historical daily volatility and the 5-day time-weighted average spread on February 28, 2012, respectively. The number of shares in one creation redemption unit for all ETFs in our sample is 50,000 shares with the exception of MDY (25,000 shares), VO and VB (both 100,000 shares). Creation/redemption fees

ETF Index Provider Inception Years

# Stocks

Liquidity Group

MDV (‘000)

Price ($)

Volatility (%, day)

Spread (cents)

Creation/redemption fee

SPY S&P500 Large Cap SSGA 1993 500 Very Liquid 115,800 137.56 0.8 1.0 $3000

IVV S&P500 Large Cap iShares 2000 500 Liquid 3,100 138.05 0.7 1.2 $500

VOO S&P500 Large Cap Vanguard 2010 500 Medium Liquidity

370 62.93 0.8 1.6 $500

IWB Russell1000 Large Cap

iShares 2000 976 Medium Liquidity

1,070 76.29 0.8 1.1 $500

RSP S&P500 Equal Weights Large Cap

Guggenheim 2003 500 Medium Liquidity

450 51.22 0.9 1.4 $2000

MDY S&P400 Mid Cap SSGA 1995 400 Liquid 2,020 179.02 1.0 2.1 $3000

IJH S&P400 Mid Cap iShares 2000 400 Medium Liquidity

680 98.29 1.0 2.1 $500

VO MSCI US Mid Cap 450 Index

Vanguard 2004 448 Illiquid 160 80.53 0.9 3.4 $500

IWM Russell 2000 Small Cap

iShares 2000 1,944 Very Liquid 40,400 82.28 1.2 1.0 $500

IJR S&P600 Small Cap iShares 2000 600 Medium Liquidity

1,180 75.5 1.2 1.1 $500

VB MSCI US Small Cap 1750 Small Cap

Vanguard 2004 1,732 Medium Liquidity

370 77.93 1.1 2.4 $500

IWC Russell Microcap iShares 2005 1,373 Illiquid 110 50.55 1.2 3.1 $500

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for each ETF are gathered from SEC filing.

A substantial part of this paper is devoted to the comparison of liquidity and cost characteristics of ETFs and common stocks. In order to accomplish this comparison, we use a matching technique introduced in Huang and Stoll [1996]2. For each ETF, we select five stocks that come closest to an ETF in terms of their median daily dollar volume (MDDV) , price, historical volatility and spread on February 27, 2012 (displayed in Exhibit 2), as well as on September 26, 20113. The latter date, which represents different market conditions, was added as a robustness check for the results discussed in the next sections. The average VIX during the two-week period in 2011 was 37.6 (high volatility), and, during the two-week period in 2012, 18.1 (normal volatility).

LOB and SCE Comparative Cost Analysis for ETFs and Common Stocks

We characterize the liquidity profile of the ETFs in our sample by looking at the cumulative depth sizes and “climbing the book” costs for various price levels and trade sizes. We compare these quantities across ETFs, as well as between an ETF and its matched common stocks. This comparison is complicated by the presence of the implicit link between an ETF and its underlying basket of stocks, which defines the well-known price-NAV relationship (the ETF price and the NAV of the basket are bound together by arbitrage argument), and greatly affects determinants of ETF liquidity and trading costs. For instance, it is clear that looking at the LOB depth of an ETF systematically understates its true liquidity, at least to the extent that additional liquidity is implicit in the quotes of the equity basket. In this section, we are agnostic about this link and treat ETFs as they were regular stocks, while the next section offers an in-depth look at the relationship between the secondary market liquidity of ETFs and the liquidity of its underlying baskets.

The results presented in this section are based on two weeks of data that span the ten-day window from February 27 until March 9, 20124. All LOB statistics are computed 25 times daily for each trading day in our sample period (every 15min from 9:45am to 4:00pm) and then averaged across the two week period. We utilize ITG’s SCE to calculate the expected costs corresponding to an optimal execution schedule for different trade sizes. We compare the costs of trading the ETFs on the secondary market with the costs of acquiring/selling the baskets of underlying stocks. SCE cost estimates are calculated daily assuming a trading horizon of one day (starting at 9:30am) and then averaged across the ten-day window.

LOB LIQUIDITY FOR ETFS AND THE MATCHED COMMON STOCKS

Exhibit 3 shows the ask side of the LOB for SPY, around 10:00am on February 28, 2012, where the ask price levels of the LOB are a step function of the depth. The cumulative depth across the first ten levels corresponds to approximately 570K shares.

Exhibit 3

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To put the distribution of liquidity of the LOB into perspective and to allow a comparison across securities, we normalize the cumulative depth sizes by the median daily share volume (MDV) and/or the unit size of the corresponding ETF. In the example shown in Exhibit 3, the normalized LOB depth on the first ten levels is 570K/115,800K*100% = 0.49% of MDV or 570K/50K = 11.4 units (the MDV of SPY is defined in Exhibit 2). Exhibit 4 presents a comprehensive summary of these results for the twelve selected ETFs and the corresponding averages for the matched common stocks.

Exhibit 4: LOB Liquidity for ETFs and Matched Common Stocks

ETF MDV of ETF, ‘000 shares

Cumulative Visible Depth, as % of MDV, ETF (5th-pctile...95th-pctile)

Cumulative Depth, as % of MDV, matched common stock

Average # of ETF Units that can be raised (5th-pctile...95th-pctile)

levels 1..10 all levels levels 1..10 all levels levels 1..10 all levels

SPY 115,800 0.9 (0…1)

3.5 (3…4)

0.04 (0…1)

0.6 (0…1)

22 (16...28)

85 (73...98)

IWM 40,400 1.1 (1…2)

2.5 (2…3)

0.2 (0…1)

2.0 (2…3)

9 (7…11)

21 (16...26)

MDY 2,020 2.8 (2…4)

16.7 (15…17)

1.4 (0…2)

18.4 (17…20)

2 (1...3)

13 (12...14)

IWB 1,070 22.1 (15…27)

53.0 (45…62)

3.3 (1…6)

13.7 (10…17)

5 (3...6)

11 (10...13)

RSP 450 37.2

(27…44)

116.6 (105…127)

4.2 (2…7)

24.6 (20…29)

3 (2...4)

10 (9...11)

IVV 3,100 5.9 (4…7)

18.2 (16…20)

1.1 (0…2)

13.3(12…15)

3 (2...4)

10 (9...11)

IJH 680 15.0 (10…16)

62.6 (57…66)

3.5 (1…6)

19.5 (16…23)

2 (1...2)

8 (8...9)

VOO 370 26.0 (21…31)

96.8 (89..103)

3.9 (1…6)

30.3 (25…35)

2 (2...2)

7 (7...8)

IJR 1,180 3.9 (2…5)

28.3 (26…30)

2.1 (0…4)

11.3 10…13)

1 (1...1)

7 (6...7)

VB 370 20.0 (10…24)

113.6 (105…118)

2.9 (0…6)

21.6 (18…25)

1 (0…1)

4 (3...4)

VO 160 18.0 (11…21)

139.1 (129…143)

2.9 (1…5)

36.2 (30…42)

0 (0…0)

2 (2...2)

IWC 110 19.2 (8…31)

52.0 (25…54)

3.0 (1…5)

32.7 (27…38)

0.6 (0...1)

1.5 (1...2)

Average 14.3 58.6 2.4 18.7 4.4 15.1

The rows are sorted in descending order by the number of units that can be raised by sweeping the entire LOB. The average % of MDV available only at the best price level varies between <0.1% and 7% for ETFs and between <0.1% and 0.6% for the matched sample of common stocks. The number of ETF units that can be raised only at the best price level varies between 0 and 0.6 units. These numbers are not shown in the table for the sake of brevity.

Even a cursory look at Exhibit 4 reveals that the LOBs of ETFs are typically much deeper than the LOBs for matched common stocks (with the exception of MDY, where average total depth of the entire LOB for the matched common stocks exceeds the depth for the ETF by 1.4%). The difference is especially significant when we constrain our analysis to the depth volume on the first ten levels of the LOBs only. As an example consider IVV, which tracks the S&P 500 index. This ETF has, on average, 5.9% of its own MDV available on the first ten bid (or ask) price levels, while the corresponding average value for the matched sample of five common stocks is 1.1%

only. The numbers for the entire LOB are 18.2% and 13.3%, respectively. The values 5.9% and 18.2% correspond to 24.4 mln and 73.2 mln dollars, respectively. Overall, it appears that the average liquidity in the LOBs of the ETFs is more concentrated around the mid quotes and decreases to zero faster at price levels far from the mid quotes.

Exhibit 5 below offers an illustration of the average relative depths available in the LOBs for IVV, IWM and the common stocks matched to those ETFs.

Exhibit 5

While the relative depth size numbers for SPY, IWM and MDY appear to be quite small at first glance (e.g., for IWM, 1-1.2% of MDV on the first ten price levels), the absolute numbers reveal the opposite. The last two columns of Exhibit 4 show that the available immediate liquidity for these ETFs is substantial. Sweeping the first ten levels of the LOB would allow for the trading of 22 units worth of SPY, 9 units worth of IWM and 2 units worth of MDY, while traversing the entire LOB would obtain 85, 21 and 13 units, respectively. Exhibit 6 summarizes the available depth as a function of units for selected large- and small-cap ETFs6 used in this study.

Exhibit 6

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A quick glance at the entire distributions (i.e. 5th- and 95th-percentiles) reveals that the time-series variability of depth is higher for ETFs than for common stocks. This is best observed by looking at the variation in the number of units which could be traded via traversing multiple LOB levels. For instance, for SPY, one could trade, on average, 22 units by picking up all shares at the first ten levels of the LOB. However, at certain times, the liquidity on those levels of SPY’s LOB is sufficient for trading only 16 units. The same qualitative conclusions could be drawn for other ETFs and LOB levels7.

The distribution of the visible liquidity across multiple price levels of the LOB indicates substantial differences between ETFs and common stocks. Yet, the evidence above is mute on another important liquidity component: the spread. Therefore, we combine the depth and spread values on all levels of the LOB and use the implied cost of instantaneous execution as an aggregate measure of liquidity. As before, we compare this liquidity metric across ETFs and across the matched common stocks. Exhibit 7 below summarizes the results.

Exhibit 7:: Instantaneous Average Costs for Trading ETFs and the Matched Sample of Common Stocks

ETF Cost of immediate execution $1mln, bp

Cost of immediate execution $10mln, bp

Cost of immediate execution $50mln, bp

ETF Matched ETF Matched ETF Matched

SPY 0.4 2.5 0.8 33.9 1.1 135.2

IWM 0.6 3.0 2.7 58.7 4.4 256.1

IVV 0.9 3.6 1.6 98.2 2.9 NA

IWB 1.4 3.9 5 93.3 26.4 NA

MDY 1.5 5.9 3.9 NA 18 NA

VOO 1.6 29.7 53 NA NA NA

IJH 1.8 6.5 8.1 NA 57.8 NA

RSP 2.7 14.8 21.1 NA 191.1 NA

VB 3.4 6.2 63.8 NA 264.4 NA

IJR 3.6 23.5 40.9 NA 176.4 NA

VO 5.7 82.6 105.7 NA NA NA

IWC NA 176.6 NA NA NA NA

The rows are sorted in ascending order of the instantaneous costs in the second column (marked with ), i.e. the implied instantaneous costs of trading $1mln worth of each ETF on the secondary market. The “NA” values indicate that there is insufficient available visible depth in the LOB on average to execute the specified quantity.

The evidence from Exhibit 7 substantiates our earlier findings on the differences in liquidity between ETFs and common stocks. Perhaps, the “NA” values tell the most compelling story. For instance, trading instantaneously $10mln is possible, on average, for all but one ETF (IWC). However, trading the same dollar amount for the matched stocks exhausts the LOB liquidity for the majority of matched common stocks. The differences in actual costs can be staggering: trading instantaneously $10mln in IVV costs only 1.6bp, while the similar instantaneous trade for the matched common stocks would cost nearly 100bp.

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In the next section, we examine the ETF cost estimates provided by the SCE model that reflect “true” costs of large institutional clients, and compare them with the costs for the matched common stocks.

TRADING COST ESTIMATES USING OPTIMAL TRADE SCHEDULING FOR ETFS AND THE MATCHED COMMON STOCKS

It has long been known that institutions split their large orders to minimize the trading costs; see, for example, Chan and Lakonishok [1995]. The proliferation of algorithmic trading in recent years has intensified this trend. Most transaction cost models take the order-splitting practice into account by specifying a trading horizon. An innovative feature of SCE is that the execution horizon is not specified as a hard parameter, but defined as a function of the prevailing market conditions. If the conditions are good, the order will be completed quickly; if the conditions are unfavorable, trading will slow down, resulting in a more passive execution schedule.

In this section, we present estimates of ETF transaction costs obtained from the SCE model calibrated from ITG’s Peer Analysis™ database. In contrast to the instantaneous costs of climbing up the LOB that could be viewed only as an upper limit on the actual costs, the cost estimates reviewed in this section represent typical, observed implementation shortfall costs. Exhibit 8 below presents the SCE cost estimates of trading $10mln, $100mln and $400mln worth of each of the selected ETFs. In addition, as in Exhibit 7, we include the average cost estimates for the matched common stocks. For comparison we include the half spread values in the last two columns of the table as well.

Exhibit 8: SCE Cost Estimates for Trading ETFs and the Matched Common Stocks

ETF Cost of trading $10mln, bp

Cost of trading $100mln, bp

Cost of trading 400mln, bp

Half Spread, bp

ETF Macthed ETF Matched ETF Macthed ETF Matched

SPY 0.6 3.7 1.9 17.6 3.8 30.3 0.4 1.8

IVV 1.1 4.0 2.9 19.5 5.3 38.3 0.6 1.2

MDY 1.4 4.3 4.6 20.7 8.5 40.2 0.4 0.6

IWB 1.4 5.6 3.1 19.8 6.4 32.4 0.7 0.8

IWM 1.5 4.1 5.1 23.1 7.8 38.1 0.6 0.7

VOO 2.1 12.5 4.0 30.5 7.8 44.9 1 0.8

RSP 2.2 11.6 6.2 29.2 9.7 43.2 0.7 0.8

IJR 2.5 5.8 8.0 21.7 12.6 36.3 1.2 1.2

IJH 2.6 6.1 7.4 21.9 11.3 36.2 1.3 1.2

VB 4.8 13.8 10.9 35.3 14.8 51.9 1.5 1.5

VO 5.0 16.2 9.1 38.4 11.1 56.7 2.1 1.8

IWC 9.2 20.2 17.6 48.2 29.9 70.4 3.9 1.8

The rows are sorted in ascending order by the SCE cost estimates in the second column, i.e. the cost estimates of trading $1mln worth of an ETF on the secondary market (marked with ).

It is obvious that across all ETFs and trade quantities, the SCE cost estimates for the selected ETFs are lower than those for the matched common stocks. Exhibit 9 below illustrates this for IVV and MDY, where the SCE cost estimates for the matched common stocks are averaged and presented as solid grey lines. To link these results with the previous section, it is instructive to include the instantaneous costs of

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climbing up the LOB from Exhibit 7. These cost estimates for matched common stocks are averaged and presented on the chart as dashed grey lines. The SCE cost estimates and the instantaneous costs for the ETFs are depicted as solid and dashed black lines, respectively. Exhibits 8 and 9 show clearly that it is essential for pre-trade models to recognize the different nature of ETFs. Failure to do so would lead to situations where the cost estimates of trading ETFs are higher than instantaneous costs of climbing the LOB which is, indeed, impossible.

We believe that the arbitrage mechanism available to authorized participants (APs) is largely behind the lower ETF costs. For example, whenever an ETF is getting overbought in the secondary market, an AP who closely watches the discrepancy between the IOPV and the ETF price could start selling the ETF short and buying stocks in underlying basket. We discuss the ETF-basket liquidity link and creation/redemption mechanism in Section 5.

Exhibit 9 also visualizes quite vividly that the immediate liquidity would come at a steep price. For example, trading instantaneously 5 units of IVV would cost over 30bp, while the SCE cost estimate for this ETF is only around 2-3bp.

Exhibit 9

A comparison analysis of Exhibits 7 and 8 confirms that, in general, the relative rankings of ETFs, in terms of their LOB and SCE costs, are similar. Looking at the trading quantity of $10mln, one can note the change in the relative ranking for VOO. VOO ranks as the 6th cheapest in our sample of ETFs based on SCE, but ranks only as 9th based on LOB costs. This indicates that the LOB for VOO appears to be less concentrated around the mid quote than for all other large-cap ETFs in our sample. This observation is consistent with the evidence in Exhibit 6.

In summary, the evidence presented in the last two subsections shows quite clearly that the liquidity characteristics of ETFs are very different from those of common stocks. These differences naturally translate into differences in trading costs. These qualitative results appear to be robust with respect to the liquidity or trading cost measures utilized. In the next section, we discuss what we believe is at the heart of the liquidity provision of ETFs: the creation/redemption process.

Creation/Redemption and ETF Liquidity

TThe true liquidity of an ETF does not become apparent until the creation/redemption process comes into play. The creation and redemption process allows sourcing the

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liquidity for the ETF trade from the liquidity in the securities that comprise the underlying basket. Consequently, ETFs tracking the same index should have, in principle, very similar market impact costs.

Exhibit 10 below examines the liquidity of the basket of the underlying securities for the selected ETFs. Different panels show the liquidity for ETFs tracking large-, mid- and small-cap indices. SCE cost estimates for trading ETFs on the secondary market are depicted by solid lines. SCE cost estimates for the baskets of underlying constituents are shown by dashed lines and include creation/redemption fees. We assume that the ETF creation/redemption costs are a lump-sum payment (see Exhibit 2) independent of the quantities raised.

Displaying on the same chart the trading cost estimates for ETFs and the underlying baskets of securities within a given market cap segment sheds light on the relationship between the liquidity of an ETF and its basket. The charts confirm that sourcing liquidity from the underlying baskets of securities for alternative ETFs that passively track the same index have nearly identical costs. The dashed lines for SPY and VOO (two ETFs tracking the S&P500 index) on the chart for large-cap ETFs in Exhibit 10 converge as the order size increases and the lump-sum creation/redemption fees become less relevant. The creation/redemption cost estimates for IWB, which tracks the Russell 1000 large-cap index, are comparable with those tracking the S&P500 index. The basket cost estimates for RSP which tracks the equally-weighted S&P 500 index (not shown) are slightly higher than those for all other large-cap ETFs, reflecting the fact that less-liquid securities from the index would have to be accumulated in the same proportions as more liquid securities. At the same token, the ranking of the cost estimates for large-cap ETFs on the secondary market (solid lines) is different from the ranking of the cost estimates for the corresponding baskets. We hypothesize that ETF providers set up different liquidity provision mechanisms for their products. For instance, SPY, with its abundant secondary market liquidity, does not appear to benefit from creation/redemption up to 15 units, corresponding to approximately a $100mln trade size. On the other hand, Vanguard’s VOO ETF, which was launched relatively recently in 2010, is lagging with respect to IVV and SPY in terms of secondary market volume. Therefore, investors who trade VOO appear to be better off taking advantage of the creation/redemption process for quantities as small as 2 units, corresponding to a $6mln trade size.

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Exhibit 10

Similar to VOO, the chart for mid-cap ETFs in Exhibit 10 shows that another ETF by Vanguard, VO (tracking the MSCI US Mid Cap 450 index), appears to be competitive against the more established MDY, provided by SSGA. With a median daily volume of only 160K shares against 2mln shares of MDY on the secondary market, Vanguard compensates by tracking a slightly broader mid-cap index (450 vs. 400 stocks in the basket) and setting a low creation/redemption fee ($500 vs. $3000). As a result, for trade quantities of 10 units or more, creation/redemption costs for VO are, essentially, in line with the secondary market costs for MDY.

A quick look at small-cap ETFs shows that liquidity considerations regarding the underlying securities of an ETF are important. All small-cap ETFs presented in Exhibit 10 track different indices: IWM tracks the Russell 2000, VB – MSCI US 1750, and IWC – Russell Microcap index. The slopes and intercepts of the dashed lines corresponding to these ETFs reflect perfectly the differences in liquidity between the underlying securities of these indexes. For instance, the basket costs for VB which tracks the narrower index (S&P 1750), starts a bit lower than the basket costs for IWM, which tracks the Russell 2000 index. However, as the trade quantities increase, the basket costs for IWM increases at a slower rate than the basket costs for VB, as larger trade volume is spread among more securities. The illiquidity of the stocks belonging to the Russell Microcap index places the creation/redemption costs for IWC well above the other three ETFs. Taking the creation/redemption fees into consideration does not change the relative ranking of small-cap ETFs as much as it did for large-cap ETFs.

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Exhibit 11 below presents a more detailed cost breakdown for three selected trade quantities: $10mln, $100mln and $400mln. The ETFs are sorted in ascending order by the SCE cost estimates of creating/redeeming $10mln worth of each ETF. Note that the ranking of an ETF changes as the quantity traded is varied. For instance, SPY’s underlying stocks are very liquid securities, but the ETF itself also has one of the highest creation/redemption costs ($3,000, regardless of the number of units created). Consequently, SPY ranks only 10th when trading $10mln via creation/redemption. However, due to “economies of scale” and the abundant liquidity in its underlying securities, SPY moves from 10th to 4th position (still lagging after three other large-cap ETFs) when it comes to creating/redeeming $400mln worth of the underlying basket.

Exhibit 11: SCE Cost Estimates for Trading ETFs and Creation/Redemption Costs

ETF SCE, $10 mln SCE, $100 mln SCE, $400 mln

ETF Basket Basket + cr/rdm fee

ETF Basket Basket + cr/rdm fee

ETF Basket Basket + cr/rdm fee

VOO 2.1 1.3 1.9* 4 1.6 1.7* 7.8 2.4 2.4*

IVV 1.1 1.3 1.9 2.9 1.6 1.7* 5.3 2.4 2.4*

IWB 1.4 1.4 2.0 3.1 1.6 1.7* 6.4 2.4 2.4*

VO 5 2.1 2.7* 9.1 2.7 2.8* 11.1 4.6 4.6*

IJH 2.6 2.3 2.9 7.4 3.7 3.8* 11.3 7.5 7.5*

VB 4.8 3.1 3.7* 10.9 4.1 4.2* 14.8 6.9 6.9*

IJR 2.5 3.2 3.8 8 6.2 6.3* 12.6 13.2 13.2

RSP 2.2 1.7 4.2 6.2 2 2.2* 9.7 3.1 3.2*

IWM 1.5 3.7 4.3 5.1 5.1 5.2 7.8 8.9 8.9

SPY 0.6 1.3 5.1 1.9 1.6 1.9 3.8 2.4 2.5*

MDY 1.4 2.3 6.1 4.6 3.7 4.0* 8.5 7.5 7.6*

IWC 9.15 9.2 9.8 17.6 16.1 16.2* 29.9 25.1 25.1*

The rows are sorted in ascending order by the cost of creating/redeeming $10mln worth of each ETF, taking fees into account (marked with ).

As has been previously demonstrated in Exhibit 10, the trading costs of ETFs still depend to a large extent on their secondary market volumes and liquidity. The cells with asterisks indicate the instances when creation/redemption is cheaper than trading the same quantity on the secondary market. The number of ETFs for which creation/redemption is cheaper for the same trade amount grows from 3 for a $10mln trade to 10 for a $400mln trade. However, the creation/redemption mecha-nism is not a magic cure that would substantially enhance the liquidity of all ETFs. The numbers presented in Exhibit 11 for some liquid ETFs (IWM, SPY, MDY) suggest that sourcing liquidity from the underlying securities can only partially alleviate the burden of high trading costs on the secondary market. In particular, for MDY and SPY, creation/redemption starts to get cheaper only for extremely large trade quan-tities ($400mln), while for IWM, trading on the secondary market appears to be cheaper, regardless of the trade amount.

Earlier in the paper we mention that the arbitrage mechanism via creation/redemp-tion ensures that the costs of trading an ETF on the secondary market are

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substantially lower than the costs of trading a common stock with comparable volume, spread, volatility and price characteristics. However, the evidence in Exhibits 10 and 11 suggests that creation/redemption arbitrage does not completely reduce the gap between the secondary market costs and the basket costs for large order sizes. We believe that there are several reasons behind this. First, trading an ETF basket usually involves higher commissions and other implicit costs, simply because there are more shares traded8, which can make it harder to execute this strategy successfully. Second, securities in the basket often need to be accumulated in a non-discretionary fashion (e.g. via market orders) in order to ensure that the ETF can be created or redeemed at the end of the day. The cost estimates that were presented so far are based on SCE’s default trade imbalance function, which maps own trading into aggregated order flow and is estimated using ITG’s Peer Analysis™ data. Since many client orders stored in the Peer Analysis™ database are executed opportunistically, it is possible that the resulting cost estimates are biased down-wards to some extent9.

Despite the caveats discussed above, we believe that the cost estimates for the underlying baskets of stocks provide additional clarity with respect to the “true” average costs of large ETF orders. The joint analysis of ETF and basket cost esti-mates presents the user with a realistic range of possible outcomes. For most ETFs and larger order sizes, the costs of trading the basket (illustrated by the dashed lines in Exhibit 10) define a lower limit on the costs of ETF trading. These costs correspond to the hypothetical situation, in which there are no implicit fees other than creation/redemption fees, and the execution implementation is a mix of oppor-tunistic and non-discretionary trading. One can observe that the difference between transaction cost estimates for an ETF and for the underlying basket becomes wider when the ETF’s secondary market liquidity and the liquidity of underlying securities are disparate. For instance, VOO trades only 370K shares daily; however, it tracks a very liquid S&P500 index. If, on the other hand, for an ETF with abundant liquidity on the secondary market, such as SPY or IWM, the transaction cost uncertainty band becomes much narrower, possibly indicating that the creation/redemption process is less practical.

Exhibit 12:

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Exhibit 12 offers a more detailed, graphical representation of the effect of different trading styles on the market order flow and, ultimately, on the trading costs for IJH and VB. In addition to SCE’s default trade imbalance function, which corresponds to a mix of opportunistic and non-discretionary trading (dashed dark-grey line), we compute the basket costs assuming that the stocks in the basket are traded only via market orders (dashed light-grey line). The data points marked on Exhibit 12 correspond to the numbers highlighted in bold in Exhibit 11. Both charts in Exhibit 12 demonstrate that the creation/redemption mechanism can substantially reduce implementation shortfall costs, particularly for large quantities, when the liquidity diversification effect for the underlying large cap constituents becomes apparent. However, the cost savings can be realized only if the trading is properly implemented. Trading the underlying basket only via market orders would have a substantial adverse effect on trading costs. In order to make optimal trading decisions, it is thus important to assess market conditions that affect the liquidity and trading costs of ETFs and their underlying baskets in real-time10.

Conclusions

We examine liquidity and trading cost characteristics for twelve equity ETFs that track small-cap, mid-cap and large-cap US market indices, and compare them with their counterparts for matched common stocks with similar MDDV, spread, historical volatility and price levels. Our analysis reveals that ETFs and matched common stocks exhibit different liquidity characteristics. The LOBs of ETFs are deeper and more concentrated around the prevailing mid quotes. The costs of instantaneous execution (climbing up the book) are significantly lower for ETFs than for the matched common stocks. At the same time, we present empirical evidence that the LOBs of ETFs are quite volatile. The number of ETF units that can be raised by climbing the LOB at any particular moment of time can change dramatically within a single trading day.

Our findings confirm that the creation and redemption mechanism is crucial for the liquidity provision of ETFs. For many (but not all) ETFs studied, the costs of creation/redemption are lower than the costs of acquiring/selling the ETF in the secondary market across a wide range of notional trade quantities. Our conclusions hold when creation/redemption fees are taken into account. Nevertheless, we see that the liquidity in the secondary market remains an important determinant of ETF costs. For instance, $10mln of SPY can be traded instantaneously at a cost of less than 1bp, whereas trading the same quantity via creation/redemption would cost over 4bp. For some liquid ETFs, the costs of trading the underlying basket remain higher than secondary market costs throughout the entire practical range of trade quantities. Overall, it appears that the arbitrage mechanism associated with the creation/redemption mechanism is likely to keep in check the difference between secondary market costs and the costs of trading the basket. However, for some ETFs (VO, VOO, RSP), we observe noticeable differences between these two cost estimates for larger order sizes. We offer several reasons that explain the cost differences.

We also argue that a properly estimated and calibrated transaction cost model can serve as a useful tool for measuring and analyzing ETF liquidity and trading costs. The model design should reflect the fact that the permanent price impact costs of most ETF trades are significantly lower than the price impact costs for matched common stock. Model parameters should be estimated and calibrated using the ETF-only sub-universe of trading data and incorporate liquidity available through the creation/redemption mechanism. Provided that proper care has been taken in building a transaction cost model, its cost estimates for ETF trades on the secondary market and for its underlying basket can serve as upper and lower bands for the actual ETF transaction costs.

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Finally, the comparison of ETF and basket costs, in conjunction with a look at creation/redemption fees, suggests that ETF providers try to differentiate their products in terms of accessing liquidity. For instance, the low creation/redemption fees for two ETFs of Vanguard (VOO, VO) appear to make them very attractive options for creation/redemption, as soon as the trade sizes exceed two ETF units. These low fees seem to compensate for the lack of liquidity on the secondary market relative to the competitors’ ETFs that track similar indices. On the other hand, the relatively steep creation/redemption fees for SPY and IWM, combined with the abundant liquidity of ETF shares on the secondary market, make the creation or redemption mechanism for those ETFs suboptimal across the entire range of trade sizes of practical relevance.

ENDNOTES1 See, for instance, Warsh [2007].2 Davies and Kim [2007] provide an extensive list of papers using similar matching techniques.3 For instance, the five stocks matched to IWB are: ECL, PGN, ED, ETR and ADP.4 We use an alternative two-week period between September 26 and October 7, 2011 to run robustness checks. The results are qualitatively very similar and available upon request.5 The finding is consistent with the CFTC and SEC Report [2010].6 The chart for mid-cap ETF is available upon request. 7 It is important to note that this variation is not primarily due to common intraday effects (e.g. scarcity of liquidity at the beginning of a trading day). Instead, market-wide events or news play a significant role. For instance, we observed a considerable drop in liquidity immediately prior to the release of the ISM non-manufacturing index at 10:00am on March 5, 2012.8 For example, creation/redemption of 1 unit of IWB requires trading ~95,000 shares of underlying stocks (while only 50,000 shares of ETF itself). 9 For instance, the price impact costs for individual securities in the basket are not independent of each other. Once an AP starts actively accumulating securities in the basket, the price impact costs of other securities from the same basket are likely to go up as well.10 See, for example, Borkovec et al. [2011].

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REFERENCES

[1] Berke Laura, “The Optimal Implementation. ETFs, Futures and Swaps”, TABB Group Report, 2009.

[2] Borkovec, Milan, K. Tyurin, Q. Fang, and J. Cheng, “What Does It Take to Work Large Orders in Real Time? Introducing ITG Smart Cost Estimator”, ITG technical report, 2011.

[3] Brandes, Yossi, I. Domowitz, and V. Serbin, “Transaction Costs and Equity Portfolio Capacity Analysis”, Chapter 18 in the Oxford Handbook of Quantitative Asset Management, Oxford University Press, 2012.

[4] Borkovec, Milan, I. Domowitz, B. Kiernan and V. Serbin, “Portfolio Optimization and the Cost of Trading”, Journal of Investing, 2010.

[5] Chan, Louis K.C., and J. Lakonishok, “The behavior of stock prices around institutional trades”, Journal of Finance 50, 1995.

[6] Ryan Davies and S. Kim, Using Matched Samples to Test for Differences in Trade Execution Costs, working paper, 2007, available at SSRN: http://ssrn.com/abstract=555182

[7] Fuhr, Deborah, “ETF Landscape. Industry Highlights”, BlackRock Report, 2011.

[8] Greenwich Associates, “Institutional Demand for Exchange-Traded Funds Continues to Climb”, May 2011.

[9] Findings Regarding the Market Events of May 6, 2010, CFTC and SEC Report, September 2010

[10] Roger Huang and H. Stoll, “Dealer versus auction markets: A paired comparison of execution costs on NASDAQ and the NYSE”, Journal of Financial Economics 41(3), 1996.

[11] Kevin Warsh, “Market Liquidity: Definitions and Implications”, speech at the Institute of International Bankers Annual Washington Conference, 2007, available at http://www.federalreserve.gov/newsevents/speech/warsh20070305a.htm.

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