create or buy: a comparative analysis of liquidity and transaction

118 CREATE OR BUY: A COMPARATIVE ANALYSIS OF LIQUIDITY AND TRANSACTION COSTS FOR SELECTED U.S. ETFS SUMMER 2013

Create or Buy: A Comparative Analysis of Liquidity and Transaction Costs for Selected U.S. ETFsMILAN BORKOVEC AND VITALY SERBIN

MILAN BORKOVEC

is the managing director and head of financial engineering at ITG in Boston, [email protected]

VITALY SERBIN

is the director of financial engineering’s portfolio analytics research group at ITG in Boston, [email protected]

Attention to transaction costs has risen in recent years in both finan-cial theory and practice. Although for brokers and dealers, transac-

tion costs constitute the bread and butter of their daily routine, the significance of trans-action costs for buy-side market participants is often misunderstood or interpreted too narrowly.

Traditionally the effect of transac-tion costs, as applied to buy-side practices, is summarized by one number: the differ-ence between paper and net returns. “Paper” refers to investment returns calculated without accounting for trading costs, while “net” includes the cost of trading. In other words, the classical view of transaction costs eliminates part of an investment strategy’s notional, or “paper” return and therefore should be controlled at the trading stage.

Some recent papers demonstrate that accounting for transaction costs at the port-folio construction level 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 et al. [2011] extend this evidence and show that the inclusion of stock-specific transaction costs at the port-folio construction stage permits higher turn-over levels and allows portfolio managers to run larger portfolios without facing detri-mental cost effects.

Exchange-traded funds (ETFs) are rela-tively 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 U.S. exchanges, as a pro-portion of all equity turnover, has oscillated between 25 and 35 percent 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&P 500 index, the MSCI EAFE Index, the Russell 2000 index, and the Vanguard Total Stocks Market index. Together they hold 81 percent of the assets in the top ten ETFs.”

As important as it is, an ETF’s tracking error, 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 percent of institutional funds and 79 percent of asset managers cite liquidity as one of the top ETF selection criteria. Managing the ETF trading process requires

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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 article 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 impor-tant to bear in mind that by construction, an ETF’s liquidity is closely tied to the liquidity of its underlying basket, since the net asset value (NAV) of an ETF ref lects 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 ETF shares, but also by the liquidity of the underlying securities via the creation/redemption mechanism. Not taking the creation/redemption process into consider-ation when assessing an ETF’s liquidity and trading cost can severely understate its true liquidity. As a result, this can inadvertently reduce the ETF universe considered for portfolio inclusion and inf late the estimated implementa-tion shortfall cost of ETF trading in general.

This ETF-basket duality is only one corollary of the fact that ETFs are derivative products. Their liquidity and trading characteristics are affected by numerous factors not commonly captured within a single secu-rity-market microstructure framework. Another dis-tinguishing feature of ETFs is that their liquidity and price-determination mechanism can be strongly affected by trading activity in related derivate products, such as futures and options. Some ETF transactions, such as exchange for physicals (or EFPs), leave no mark on the ETF price and can often occur outside of an exchange altogether. Though these complications exist for common stocks as well, they are more pronounced for ETFs, especially for those that track standard indices, for which the trading volume in futures, options, and EFPs is typically much higher than for single stocks.

Investigating the links between different deriva-tives markets in the context of their effect on ETF trading costs is outside of the scope of this article. We limit our-selves to a more manageable task of documenting the

differences between the liquidity profiles of ETFs and single stocks, from the point of view of an investor who is determined to trade ETFs and who wants to know if the trading practices that work for common stocks still apply. We start by being agnostic about any links between ETFs and the underlying baskets, treating ETFs as if they were regular stocks. Later, we take an in-depth look at the relationship between ETFs’ liquidity in the secondary market and the liquidity of their underlying baskets.

Our main findings:

• 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 sur-rounding the mid-quote. This is true even without accounting for the 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 secondary-market liquidity remains an important determinant of ETF costs.

• In order to ref lect these differences, a good transaction-cost model needs to be properly cali-brated and parameterized. Failure to do so may lead to inaccurate estimates of ETF trading costs. For instance, the model design should ref lect that the permanent price-impact costs of most ETF trades are significantly lower than the price-impact costs for a matched common stock. It also should ref lect that ETF liquidity is determined not only on the secondary market, but also through other means, such as the creation/redemption process. In addi-tion, the model calibration should be performed on a database subsample that contains only ETF trades. We illustrate these points using the newly developed ITG’s Smart Cost Estimator (SCE) model; however, they hold true for any quantita-tive transaction-cost model.

• Looking jointly at cost estimates for ETFs and underlying baskets provides additional clarity with respect to an ETF’s true cost. We argue that



these cost estimates can be used to derive upper and lower bounds of the true average costs. One could interpret these bounds as limits to arbitrage that could be performed by authorized participants (APs).

• Comparing ETF and basket costs, in conjunction with a look at creation and redemption fees, sug-gests that it is important to monitor relative ETF liquidity, as the optimal switching points between trading an ETF and creating or redeeming the underlying basket can vary widely across ETFs.

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 ETF sample used in this study, along with 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 creating or redeeming the underlying securities basket. 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 exchange’s rebate structure, 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 about both client prefer-ences and the functional form of the dependencies.

To abstract from the modeling assumptions, we begin by directly examining the liquidity profiles of ETFs that use 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 dis-parities 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 exe-cution (sometimes called “climbing the book”) as an aggregated liquidity measure.

The proliferation of electronic trading means that the majority of large orders are executed algorithmically, via a strategy that usually aims at minimizing overall implementation shortfall cost. In this article, we use ITG’s newly developed Smart Cost Estimator (SCE) model to simulate this process and generate associated cost estimates.

As its inputs, SCE uses the order attributes (size, date, and time stamp), security characteristics (type, expected liquidity, volatility, and daily average spread), recently observed market conditions (deviations of vola-tility, volume, and spread from historical patterns), and the traders’ subjective market sentiment and its persis-tence (expressed through the magnitude of trade imbal-ance). In contrast to a static trading-cost estimator, the SCE model can dynamically update execution trajec-tories based on the market response to order f low and the observed market conditions. The SCE cost estimates presented in this document were obtained under the assumption that the trader’s subjective expectation of future trade imbalances is neutral, with 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 33 million orders filled by more than 140 institutional clients from 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 that 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.

We estimate and calibrate several key SCE com-ponents and parameters separately for ETFs, to ref lect 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. Our model also explicitly incorporates the underlying basket’s extra available liquidity. More details on SCE’s methodology and definitions can be found in Borkovec et al. [2011].



ETF Universe

We select our study sample of equity ETFs on the basis of practical relevance and to ensure a fair repre-sentation of different liquidity categories and market segments. Where possible, we select ETFs that track the same index, but have different liquidity characteristics (for instance, SPY, IVV, VOO, and RSP track the S&P 500 index). There are twelve ETFs in our sample, all of them with U.S.-only constituents. Five ETFs track large-cap indices (S&P 500 and Russell 1000), three track mid-cap indices (S&P 400 and MSCI US Mid Cap 450), and four track small-cap indices (S&P 600, Russell 2000, Russell Microcap, and MSCI US Small Cap 1750). Our ETF sample also lets us compare trading costs across ETFs supplied by different vendors.

Our study liquidity categories are based on the 21-day median daily trading volume (MDV) in the secondary market. Exhibit 1 reports our classification thresholds and the selected ETFs that fall into each liquidity group.

Exhibit 2 provides descriptive and trade-related statistics for selected ETFs. Overall, there appears to be a positive relationship between the fund’s inception year and its secondary market volume, which supports the notion of a first-mover advantage. All ETFs have cre-ation/redemption fees of $500, regardless of the number of units raised, with a few exceptions. Those excep-tions seem to be historical artifacts, as higher creation/redemption fees belong to ETFs that began before 2000 (except for Guggenheim’s RSP).

Not surprisingly, the ETFs’ spread values 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 percent, and are roughly proportional to the volatilities of the underlying indexes.

A substantial part of this article is devoted to comparing liquidity and cost characteristics of ETFs and common stocks. In order to accomplish this com-parison, 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, 2011.3 We added the latter date, which represents different market conditions, 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). During the two-week period in 2012, the average VIX was 18.1 (normal volatility).

LOB AND SCE COMPARATIVE COST ANALYSIS FOR ETFs AND COMMON STOCKS

We characterize the liquidity profile of our sample ETFs 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.

The results presented in this section are based on two weeks of data that span the ten-day window from February 27 through March 9, 2012.4 We compute all LOB statistics 25 times daily for each trading day in our sample period (every 15 minutes from 9:45 a.m. to 4:00 p.m.) and average them across the two-week period. We use ITG’s SCE to calculate the expected costs corre-sponding 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. We calculate SCE cost estimates daily, assuming a trading horizon of one day (starting at 9:30 a.m.), and average them 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 10:00 a.m. on February 28, 2012, when the LOB

E X H I B I T 1ETF Liquidity Thresholds



ask-price levels are a step function of the depth. The cumulative depth across the first ten levels corresponds to approximately 570,000 shares.

To put the distribution of LOB liquidity into per-spective and 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 f irst 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 corre-sponding averages for the matched common stocks.

Even a cursory look at Exhibit 4 reveals that ETF LOBs are typically much deeper than the LOBs of 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 percent).

The difference is especially significant when we constrain our analysis to the depth volume on the first ten levels of the LOBs. As an example, consider IVV, which tracks the S&P 500 index. This ETF has, on average, 5.9 percent 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 just 1.1 percent. The numbers for the entire

E X H I B I T 2Summary 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 five-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), and VO and VB (both 100,000 shares).

Creation/redemption fees for each ETF are gathered from SEC filing.



LOB are 18.2 percent and 13.3 percent, respectively. The values of 5.9 percent and 18.2 percent correspond to 24.4 million and 73.2 million dollars, respectively. Overall, it appears that the average liquidity in the ETF LOBs is more concentrated around the mid quotes and decreases to zero faster at price levels far from the mid quotes.5

Although 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 percent of MDV on the first ten price levels), the abso-lute numbers reveal the opposite. The last two columns of Exhibit 4 show that these ETFs have substantial avail-able immediate liquidity. Sweeping the first ten levels of the LOB would allow for trading 22 units worth of SPY, 9 units worth of IWM, and 2 units worth of MDY. Traversing the entire LOB would obtain 85, 21, and 13 units, respectively.

The distribution of the visible liquidity across multiple LOB price levels indicates substantial differences between ETFs and common stocks. Yet this evidence is mute on another impor-tant liquidity component: the spread. Therefore, we combine the depth and spread values on all LOB levels 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 5 summarizes the results.

The evidence from Exhibit 5 sub-stantiates our earlier findings on the dif-ferences in liquidity between ETFs and common stocks. Perhaps the NA values tell the most compelling story. For instance, instantaneously trading $10 million 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: instantaneously trading $10 million in IVV costs only 1.6 basis points (bps), while the similar instanta-

neous trade for the matched common stocks would cost nearly 100 bps.

In the next section, we examine the ETF cost esti-mates, provided by the SCE model, that ref lect the true costs of large institutional clients, and compare these costs with the costs for the matched common stocks.

E X H I B I T 4LOB Liquidity for ETFs and Matched Common Stocks

The rows are sorted in descending order by the number of units that can be raised by sweeping the entire LOB.

E X H I B I T 3Limit Order Book for SPY, February 28, 2012, Around 10:00 a.m.



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 order-splitting into account by specifying a trading horizon. An inno-vative feature of SCE is that the execution horizon is not specified as a hard parameter, but defined as a function of 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 ETF transaction cost estimates obtained from the SCE model calibrated from ITG’s Peer Analysis database. In contrast to the instan-taneous costs of climbing up the LOB, which 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 6 pres-ents the SCE cost estimates of trading $10 million, $100

million, and $400 million worth of each of the selected ETFs. In addition, as in Exhibit 5, we include the average cost estimates for the matched common stocks.

It is obvious that across all ETFs and trade quan-tities, the SCE cost estimates for selected ETFs are lower than those for the matched common stocks. Exhibit 7 illustrates this for IVV and MDY. The chart contains SCE cost estimates for those two ETFs and for the matched common stocks. In order to link these results with the previous section, the chart also includes the instantaneous costs of climbing up the LOB from Exhibit 5. Exhibits 6 and 7 show clearly that it is essential for pre-trade models to recognize ETFs’ different nature. Failure to do so could lead to situations where the cost estimates of trading ETFs optimally across the whole day are higher than the instantaneous costs of climbing the LOB, which is impossible.6

We believe that the arbitrage mechanism avail-able 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 the underlying basket. We discuss the ETF-basket liquidity link and the creation/redemption mechanism in section 5.

Exhibit 7 also shows quite vividly that the imme-diate liquidity would come at a steep price. For example, instantaneously trading five units of IVV would cost more than 30 bps, while the SCE cost estimate for this ETF is only around 2 or 3 bps.

A comparison analysis of Exhibits 5 and 6 con-firms that, in general, ETFs’ relative rankings, in terms of their LOB and SCE costs, are similar. Looking at the trading quantity of $10 million, one can note the change in VOO’s relative ranking. VOO ranks as the sixth-cheapest in our sample of ETFs based on SCE, but ranks only as ninth, based on LOB costs. This indicates that the LOB for VOO appears to be less concentrated around the mid-quote than are all other large-cap ETFs in our sample. This observation is consistent with the evidence in Exhibit 4.

In summary, the evidence presented in the last two subsections shows quite clearly that ETFs’ liquidity characteristics are very different from those of common stocks. These differences naturally translate into differ-ences in trading costs. These qualitative results appear

E X H I B I T 5Instantaneous Average Costs for Trading ETFs and the Matched Sample of Common Stocks

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 $1 million worth of each ETF on the secondary market. The NA values indicate that there is insuff icient avail-able visible depth in the LOB, on average, to execute the specified quantity.



to be robust with respect to the liquidity or trading cost measures we use. In the next section, we discuss what we believe is at the heart of the ETFs’ liquidity provision: the creation/redemption process.

CREATION/REDEMPTION AND ETF LIQUIDITY

An ETF’s true liquidity does not become apparent until the creation/redemption process comes into play.

The creation and redemption process lets us source the 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 8 examines the liquidity of the basket of underlying securities for the selected ETFs. Different panels show the liquidity of ETFs tracking large-, mid- and small-cap indices. Solid lines depict the SCE cost estimates for trading ETFs on the secondary market. Dashed lines show the SCE cost estimates for the bas-kets of underlying constituents, including creation/redemption fees. We assume that the ETF creation/redemption costs are a lump-sum payment (see Exhibit 2) inde-pendent of the quantities raised.

E X H I B I T 6SCE Cost Estimates for Trading ETFs and the Matched Common Stocks

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

E X H I B I T 7Cost Curves for Selected ETFs and Matched Common Stocks

The black solid lines represent the optimal costs of trading an ETF; the grey solid lines show the average optimal cost (across stocks) of trading a matched common stock. The black dashed lines illustrate the cost of instantaneously trading an ETF; the dashed grey lines represent the average cost (across stocks) of instantaneously trading a matched common stock. The lower horizontal axis depicts the number of ETF units; the upper horizontal axis shows the corresponding dollar amount (in millions).



Displaying the trading cost estimates for ETFs and as the underlying securities baskets within a given market cap segment on the same chart sheds light on the rela-tionship between an ETF’s liquidity and its basket. The charts confirm that liquidity sourced from underlying securities baskets for alternative ETFs that passively track the same index has nearly identical costs. The dashed lines for SPY and VOO (two ETFs tracking the S&P 500 index) on the chart for large-cap ETFs in Exhibit 8 converge as the order size increases and the lump-sum creation/redemption fees become less relevant. The cre-ation/redemption cost estimates for IWB, which tracks

the Russell 1000 large-cap index, are comparable with those tracking the S&P 500 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, ref lecting the fact that less-liquid securities from the index would have to be accumulated in the same proportions as more liquid securities. By the same token, the cost-estimate ranking for large-cap ETFs on the secondary market (solid lines) is different than the cost-estimate ranking for the cor-responding baskets.

E X H I B I T 8Cost Curves for Selected ETFs and Associated Baskets

The solid lines represent the optimal costs of trading an ETF; the dashed lines illustrate the optimal cost of trading the basket of stocks necessary for creation/redemption (with the creation/redemption fees added on top). All costs are displayed for the number of ETF units indicated on the horizontal axis.



The optimal switching point between trading an ETF and using the creation/redemption mechanism dif-fers widely in the selected ETF universe. 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 $100 million trade size. On the other hand, Vanguard’s VOO ETF, which was launched in 2010 and has an identical under-lying basket, 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 two units, corresponding to a $6 million trade size.

The chart for mid-cap ETFs in Exhibit 8 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 160,000 shares against 2 million shares of MDY on the secondary market, Vanguard compensates by tracking a slightly broader mid-cap index (450 versus 400 basket stocks) and setting a low creation/redemption fee ($500 versus $3,000). 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 an ETF’s underlying securities are important. All small-cap ETFs presented in Exhibit 10 track different indices: IWM tracks the Russell 2000, VB tracks the MSCI US 1750, and IWC tracks the Rus-sell Microcap index. The slopes and intercepts of the dashed lines corresponding to these ETFs perfectly ref lect the differences in liquidity between the under-lying securities of these indices.

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 increase 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 those costs for 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.

Exhibit 9 presents a more detailed cost breakdown for three selected trade quantities: $10 million, $100 million, and $400 million. The ETFs are sorted in ascending order by the SCE cost estimates of creating/redeeming $10 million worth of each ETF. Note that an ETF’s ranking changes as the quantity traded varies. 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 $10 million via creation/redemption. However, due to economies of scale and the abundant liquidity in its underlying securities, SPY moves from tenth to fourth position (still lagging after three other large-cap ETFs) when it comes to creating/redeeming $400 million worth of the underlying basket. Overall, the number of ETFs for which creation/redemption is cheaper for the same trade amount grows from three for a $10 mln trade to 10 for a $400 mln trade.

However, the creation/redemption mechanism is not a magic cure that would substantially enhance the liquidity of all ETFs. The numbers presented in Exhibit 9 for some liquid ETFs (IWM, SPY, and 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 quantities ($400 million), while for IWM, trading on the secondary market appears to be cheaper, regardless of the trade amount. Clearly, ETFs’ trading costs still depend to a large extent on their sec-ondary market volumes and liquidity.

Earlier in the paper we mention that the arbitrage mechanism via creation/redemption ensures that the costs of trading an ETF on the secondary market are sub-stantially lower than the costs of trading a common stock with comparable volume, spread, volatility, and price characteristics. However, the evidence in Exhibits 8 and 9 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 for this. First, trading an ETF basket usually involves higher commis-sions and other implicit costs, simply because there are more shares traded,7 which can make it harder to execute this strategy successfully. Second, basket securities often need to be accumulated in a non-discretionary fashion



(e.g., via market orders), to ensure that the ETF can be created or redeemed at the end of the day. The cost estimates presented so far are based on SCE’s default trade imbalance function, which maps own trading into aggregated order f low 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 downwards to some extent.8

Despite these caveats, we believe that cost esti-mates for the underlying stock baskets provide additional clarity with respect to the true average costs of large ETF orders. The joint analysis of ETF and basket cost estimates presents the user with a realistic range of pos-sible outcomes. For most ETFs and larger order sizes, the costs of trading the basket (illustrated by the dashed lines in Exhibit 8) define a lower limit on the costs of ETF trading. These costs correspond to a hypothetical situation in which there are no implicit fees, other than creation/redemption fees, and the execution implemen-tation is a mix of opportunistic and non-discretionary trading.

One can observe that the difference between trans-action 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 370,000 shares

daily; however, it tracks a very liquid S&P 500 index. On the other hand, for SPY, IWM, or another ETF with abundant liquidity on the secondary market, the transaction cost uncertainty band becomes much nar-rower, possibly indicating that the creation/redemption process is less practical.

Exhibit 10 offers a more detailed, graphical rep-resentation of the effect of different trading styles on the market order f low and ultimately on 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 basket stocks are traded only via market orders (dashed light-grey line). The data points marked on Exhibit 10 correspond to the numbers highlighted in bold in Exhibit 9.

Both charts in Exhibit 10 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 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

E X H I B I T 9SCE Cost Estimates for Trading ETFs and Creation/Redemption Costs

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

*The cells with asterisks indicate the instances in which creation/redemption is cheaper than trading the same quantity on the secondary market.



that affect the liquidity and trading costs of ETFs and their underlying baskets in real time.9

CONCLUSIONS

We examine liquidity and trading-cost character-istics for 12 equity ETFs that track small-cap, mid-cap and large-cap U.S. 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 characteris-tics. The ETFs’ LOBs are deeper and more concentrated around the prevailing mid-quotes. The costs of instan-taneous execution (climbing up the book) are signifi-cantly lower for ETFs than for the matched common stocks.

At the same time, we present empirical evidence that ETFs’ LOBs are quite volatile. The number of ETF units that can be raised by climbing the LOB at any par-ticular moment of time can change dramatically within a single trading day.

Our findings confirm that the creation and redemp-tion mechanism is crucial for ETFs’ liquidity provision. For many (but not all) ETFs studied, the costs of cre-ation/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 we take creation/redemption fees into account.

Nevertheless, we see that secondary market liquid ity remains an important determinant of ETF costs. For instance, $10 million of SPY can be traded instanta-neously at a cost of less than 1 bp, whereas trading the same quantity via creation/redemption would cost more than 4 bps. 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 the difference between secondary market costs and the costs of trading the basket in check. How-ever, for some ETFs (VO, VOO, and 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 cali-brated transaction-cost model can serve as a useful tool for measuring and analyzing ETF liquidity and trading costs. The model design should ref lect the fact that the permanent price-impact costs of most ETF trades are sig-nificantly lower than the price-impact costs for matched

E X H I B I T 1 0Cost Curves for Selected ETFs and Associated Baskets for Trading Styles

The black solid lines represent the optimal costs of trading an ETF; the black (grey) dashed lines depict the cost of trading the underlying basket of stocks necessary for creation/redemption using a mix of market and limit orders (market orders) only. The lower horizontal axis shows the number of ETF units; the upper horizontal axis represents the corresponding dollar amount (in millions).



common stock. Model parameters should be estimated and calibrated using the ETF-only sub-universe of trading data, incorporating 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.

The comparison of ETF and basket costs, in con-junction with a look at creation/redemption fees, indi-cates the need for monitoring relative ETF liquidity, as the optimal switching points between trading an ETF and creating/redeeming can vary widely across ETFs. For instance, the low creation/redemption fees for two Vanguard ETFs (VOO and VO) appear to make them appear to be 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 com-petitors’ 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 cre-ation or redemption mechanism for those ETFs subop-timal across the entire range of trade sizes of practical relevance.

ENDNOTES

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, com-ments, and suggestions. Any opinions expressed herein ref lect the judgment of the individual authors at this date, are subject to change, and do not necessarily represent the opinions or views of Investment Technology Group, Inc.

1See, for instance, Warsh [2007].2Davies and Kim [2009] provide an extensive list of

papers using similar matching techniques.3For instance, the five stocks matched to IWB are ECL,

PGN, ED, ETR, and ADP.4We use an alternative two-week period between

September 26, 2011 and October 7, 2011 to run robustness checks. The results are qualitatively very similar and available upon request.

5The f inding is consistent with the CFTC and SEC Report [2010].

6Note that a big chunk of the solid grey line lies above the dashed black line for IVV.

7For example, creation/redemption of one unit of IWB requires trading approximately 95,000 shares of underlying stocks (buy only 50,000 shares of the ETF itself ).

8For instance, the price-impact costs for individual basket securities are not independent of each other. Once an AP starts actively accumulating basket securities, the price-impact costs of other securities from the same basket are likely to go up as well.

9See, for example, Borkovec et al. [2011].

REFERENCES

Berke, L. “The Optimal Implementation. ETFs, Futures and Swaps.” TABB Group Report, 2009.

BlackRock. “ETF Landscape: Industry Highlights.” ETF Research Report, Q1 2011.

Borkovec, M., I. Domowitz, B. Kiernan, and V. Serbin. “Portfolio Optimization and the Cost of Trading.” The Journal of Investing, 19 (2010), pp. 63-76.

Borkovec, M., 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.

Brandes, Y., I. Domowitz, and V. Serbin. “Transaction Costs and Equity Portfolio Capacity Analysis.” In The Oxford Hand-book of Quantitative Asset Management, edited by B. Scherer and K. Winston. Oxford University Press, 2011, pp. 398-420.

CFTC and SEC Report. “Findings Regarding the Market Events of May 6, 2010.” September 2010.

Chan, L.K.C., and J. Lakonishok. “The Behavior of Stock Prices Around Institutional Trades.” The Journal of Finance, 50 (1995), pp. 1147-1174.

Davies, R. and S. Kim. “Using Matched Samples to Test for Differences in Trade Execution Costs.” Journal of Financial Markets, 12 (2009), pp. 173-202.

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



Huang, R., and H. Stoll. “Dealer Versus Auction Markets: A Paired Comparison of Execution Costs on NASDAQ and the NYSE.” Journal of Financial Economics, 41 (1996), pp. 313-357.

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To order reprints of this article, please contact Dewey Palmieri at [email protected] or 212-224-3675.


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