Garbage In, Garbage Out: An Optical Tour of the Role of Strategy in Venue Analysis

Transcription

1 AUTHORS Ian Domowitz Managing Director Head of Analytics Kristi Reitnauer, Assistant Vice President Analytics Research Colleen Ruane Director Analytics Research CONTACT Asia Pacific Canada EMEA United States Garbage In, Garbage Out: An Optical Tour of the Role of Strategy in Venue Analysis ABSTRACT We address a single question in this paper: is consideration of trading strategy an essential component in assessing venue performance? The answer is, yes. We arrive at this conclusion through comparisons of strategy use across venues and performance metrics, by venue, venue type, and strategy. INTRODUCTION Venue analysis is all the rage these days, but an old theme. Nasdaq and the NYSE once fought to advertise the relative execution quality on their venues, motivating academic work purporting to make the comparison. With the arrival of electronic trading venues, initial efforts concentrated on explaining the mechanics of different venues, followed by empirical studies, which attempted to contrast execution on electronic and floor systems. 1 The analogue in the past few months has been the race to publish Reg ATS filings in the interest of transparency. Venue quality reporting via regulatory mandate appeared with Reg NMS. Closely following, the interaction of dark pools with routing schemes generated work on information leakage. 2 Comparisons of lit and dark markets began to appear. 3 In the midst of all this activity, sponsors of alternative execution venues began to publish material on the relative benefits of their own dark pools. 4 Flash forward to present day, and the furor surrounding the recent Michael Lewis book has generated collective amnesia with respect to evolution of our knowledge of venues over the past twenty years. The focus is on microseconds. We reject that line of inquiry here, and we are not alone. Firms such as Pragma, whose business is arguably in tiny fractions of a second, note that drawing conclusions from microsecond snaps of fills is a fruitless undertaking. 5 After investigating several popular methods of venue analysis, Pragma concludes that such methods do not help understanding or improve performance; they turn their attention instead to venue fee structures and the patterns of routing behavior created by them. 1 Ian Domowitz, A Taxonomy of Automated Trade Execution Systems, Journal of International Money and Finance 12, 1993; for a review of papers comparing electronic to floor trading, see Ian Domowitz and Benn Steil, Automation, Trading Costs, and the Structure of the Securities Trading Industry, Brookings-Wharton Papers on Financial Services, Ian Domowitz, Ilya Finkelstein, and Henry Yegerman, Cul de Sacs and Highways: An Optical Tour of Dark Pool Trading Performance, Journal of Trading, Ian Domowitz, Ilya Finkelstein, and Henry Yegerman, Cul de Sacs and Highways: An Optical Tour of Dark Pool Trading Performance, Journal of Trading, 2008; Yossi Brandes and Ian Domowitz, Alternative Trading Systems in Europe: Trading Performance by European Venues Post MIFID, Journal of Trading, Summer 2010; Yossi Brandes and Ian Domowitz, Alternative Trading Systems in Europe: Trading Performance by European Venues Post MIFID, An Update for 2010, Journal of Trading, Spring 2011; Quantitative Services Group, QSG Shines a Light on a Dark Pool, Research Note, June See, for example, Hitesh Mittal, Are You Playing in a Toxic Dark Pool? A Guide to Preventing information Leakage, Journal of Trading, Summer 2008; and George Sofianos and David Jeria, Quantifying the SIGMA X Crossing Benefit in Street Smart, Goldman Sachs, March, Venue Analysis: What is it Good for?, Pragma Research Note No. 7, May 2014.

2 2 Venue performance cannot be separated from trading strategy, market conditions, or even constraints on traders workflows. We have made this point before, in the context of comparisons of algorithmic trading strategies. 6 More broadly, venue analysis is most useful when viewed in the larger context of overall performance. Understood in that light, a better grasp of routing practices and performance permit traders to use the full suite of electronic tools at their disposal effectively, as well as generating more useful conversations with their brokers. We address a single question in this paper: is consideration of trading strategy an essential component in assessing venue performance? Before this issue is settled empirically, it is impossible to construct comparisons of individual venues, or even contrast dark versus lit markets in the aggregate. The answer to the question is yes. Even examination of the distribution of trading strategies across venue types suggests that different venues are used to implement disparate strategy choices. We begin with some comments on our data and a few definitions, which are required to make the analysis transparent. DATA AND TERMINOLOGY A blizzard of numbers and charts is avoided by sharply limiting the universe of data for this analysis. Trading activity is for the U.S. only, and restricted to large cap stocks. High frequency trading is concentrated in this capitalization subset. All activity took place over the 2013 calendar year. Small orders are analyzed, up to one percent of median daily volume. This last restriction is not terribly binding, since the vast majority of orders submitted for algorithmic trading fall into this category. The data are not limited to ITG executions; all buy-side participants represented in the sample trade with a variety of brokers. This means, in particular, that we study order routing and venues in the large, and the results do not reflect directly on ITG s methodology. 7 There sometimes is confusion over terminology, and we want to be as clear in this regard as we are with the data. An algorithm or trading strategy determines how many shares to be traded at any particular point in time. The router takes these instructions and determines venues for the execution. A trade or execution is a single fill, and is typically only a partial fill of the order. Venues are characterized as being either lit or dark. In terms of pricing models, we restrict most of the analysis to maker/taker venues. An inverted model is taker/maker, and comprises about five percent of the data. We present differences between the two as suits the context; if a chart does not differentiate between the two, it is based on maker/taker only. Algorithms are aggregated across brokers into several categories. 8 We refer to scheduled strategies, which include VWAP, TWAP, and Participation strategies. Dark denotes a liquidity-seeking strategy concentrating on dark pools, while opportunistic is general liquidity-seeking in nature. IS is shorthand for implementation shortfall, creating execution patterns based on cost and risk minimization. Non-algo in the charts is generally desk trading, while Other is dominated by direct market access and and includes some pairs trading. 6 Ian Domowitz and Henry Yegerman, Algorithmic Trading Usage Patterns and their Costs, Journal of Trading, Summer We also exclude extreme cost outliers, defined by plus or minus 100 basis points (bps) versus the order decision price, as well as any orders for which an implementation shortfall calculation is not possible for any reason; similarly, individual trades for which t+1 and t+5 second reversion cannot be computed are excluded, as well as orders with limits greater than 100 bps away from next price post decision price. 8 See Ian Domowitz and Henry Yegerman, Algorithmic Trading Usage Patterns and their Costs, Journal of Trading, Summer 2011, for further detail and additional characterizations of strategies from broker algorithms.

3 3 FIGURE 1 Strategy Distribution by Venue Type 100% 80% 60% 40% 20% 0% DARK IMPLEMENTATION OPPORTUNISTIC OTHER SCHEDULED SOME VENUE CHARACTERISTICS BY STRATEGY TYPE Trading strategies can affect venue analysis in several ways, given the profile of fills flowing back to the algorithm from routing information, and disparities in how strategies cycle child orders into the markets. Routers also may be tuned to favor certain venues depending on strategy, a more direct approach. Venue types are characterized by different mixes of strategies which feed them. Some disparities in venues across strategies are simply intuitive. Dark pool trading is dominated by dark aggregation strategies, no surprise there. Scheduled strategies favor lit markets, but even within that category, the taker/maker model exhibits proportionately higher activity, possibly due to the interaction between strategy and fee structure. Implementation shortfall algorithms make up about a quarter of lit market activity, but account for less than 10 percent of what we see in dark pools. Similarly, a small fraction of DMA orders hit the dark pools directly, compared to the lit markets. Fill size typically is considered a performance metric, but may also be a characteristic stemming from segmentation of participant types in any given venue. We cannot observe segmentation directly. Fill size is traceable to the nature of strategies, however, given the interaction of trade size with sizes determined by the strategy itself. The disparity across venue types is illustrated in Figure 2. FIGURE 2 Average Fill Size by Venue Type and Strategy Fill Size (sh) DARK OPPORTUNISTIC IMPLEMENTATION SCHEDULED

4 4 At the lower levels of the percentage of average daily volume, there are similarities across scheduled strategies and implementation shortfall, but also differences. Opportunistic trading may look similar to dark aggregation, venue type by venue type, but it is quite different from the other two categories in the chart. Such results are not figments of the aggregation process. Results on fill size, disaggregated by venue, appear in Figure 3. FIGURE 3 Distribution of Fill Sizes for Selected Strategy Types and Venues SCHEDULED ALGORITHMS BATS YEXCHANGE, INC. NASDAQ OMX BX OPPORTUNISTIC ALGORITHMS DEUTSCHE BANK SUPER X CREDIT SUISSE AES CROSSFINDER A)1-199 B) C) D)>1000 Although it is possible to compare these charts line by line, that is not the goal of the exercise. Rather, the two charts, representing two strategy choices, should be viewed as heat maps, or mosaiques, with respect to underlying activity. In that sense, the two graphs look very different. Strategy type clearly matters with respect to fill size, regardless of whether it is viewed as a performance metric or as simply a characteristic of venues.

5 5 COMMON PERFORMANCE METRICS If venue characteristics vary across strategies, performance comparisons also must be affected. Even the simplest performance statistic, average spread capture, varies across venues by strategy. FIGURE 4 Spread Capture by Venue Type and Strategy Spread Capture DARK OPPORTUNISTIC IMPLEMENTATION SCHEDULED Differences in spread capture for maker/taker markets vary by as much as 16 percent, for example. That figure rises to 45 percent for the taker/maker paradigm. Dark market spread capture can vary by 46 percent across strategy types. In terms of the classic measure of trading performance, these differences translate into variation across venues, but traceable to trading strategies, as opposed to distinct properties of the pools themselves. Figure 5 contains results on implementation shortfall cost at the order level. FIGURE 5 Implementation Shortfall Cost by Venue and Strategy Cost (bps) DARK OPPORTUNISTIC IMPLEMENTATION SCHEDULED NON-ALGO OTHER

6 6 We expand the strategy set in this chart to include DMA activity (Other) and trading desk activity (Non-Algo). The difference between these categories for the maker/ taker venues is as much as 80 percent. Even a comparison between implementation shortfall and opportunistic strategies for lit markets differs by 29 percent. Dark aggregation juxtaposed with scheduled strategies yields a 49 percent difference in cost for the same venue type. Any comparison of dark pools to lit markets fails, if strategy is not taken into account. Given the idiosyncratic nature of time stamps in trading systems, calculation of implementation shortfall cost is typically possible only when a broker strategy is considered. For this reason, many have resorted to reversion metrics, which depend only on a snapshot of price, at and after, an individual fill. We now turn to the effect of strategy on such analysis. REVERSION METRICS Reversion measures differ by the time after the fill. The idea is that if a great deal of reversion is exhibited in price over very short time horizons, then the fill is bad relative to what might have happened in the absence of toxicity in a venue. We illustrate one second and five second timeframes in Figure 6. FIGURE 6 Reversion Seconds After the Fill ONE SECOND POST TRADE REVERSION BY VENUE TYPE AND STRATEGY Cost (bps) FIVE SECOND POST TRADE REVERSION BY VENUE TYPE AND STRATEGY Cost (bps) DARK IMPLEMENTATION NON-ALGO OPPORTUNISTIC OTHER SCHEDULED

7 7 The differences across strategies, within and between types of venue, are clearly evident. Within maker/taker markets, at the five second level, the variation between scheduled and opportunistic strategies is over 60 percent. For 1 second reversion, the difference in cost for dark pools across strategy types ranges up to 73 percent. Most of such deviations jump right off the chart. Disaggregated results appear in Figure 7 below. We limit these charts to 1-second reversion, but the five-second graphs tell the same story. FIGURE 7 T+1 Second Reversion DARK ALGORITHMS NASDAQ/NMS (GLOBAL MARKET) MS POOL ATS ITG POSIT DEUTSCHE BANK SUPER X GOLDMAN SACH MTF IS ALGORITHMS BATS YEXCHANGE, INC. NASDAQ OMX BX ITG POSIT A)<-1 bps B)-1 to -0.5 bps C)-0.5 to 0.5 bps D)0.5 to 1 bps E)> 1 bps

8 8 FIGURE 7 (CONT.): Distribution of T+1 Second Reversion Opportunistic Algorithms OPPORTUNISTIC ALGORITHMS DEUTSCHE BANK SUPER X CREDIT SUISSE AES CROSSFINDER SCHEDULED ALGORITHMS BATS YEXCHANGE, INC. NASDAQ OMX BX A)<-1 bps B)-1 to -0.5 bps C)-0.5 to 0.5 bps D)0.5 to 1 bps E)> 1 bps

9 9 Viewed as heat maps, the pictures are obviously different. Within each strategy category, one might now attempt a comparison across individual venues. We give a flavor of this approach through an examination of lit and dark markets along the dimension of price reversion in Figure 8. FIGURE 8 Reversion in Dark and Lit Markets for Implementation Shortfall Strategies SELECTED DARK POOLS - IS ALGORITHMS BANK OF AMERICA MERRILL LYNCH ATS CITI MATCH CREDIT SUISSE AES CROSSFINDER DEUTSCHE BANK SUPER X GOLDMAN SACH MTF ITG POSIT KNIGHT MATCH ATS MS POOL ATS SELECTED LIT VENUES - IS ALGORITHMS BATS YEXCHANGE, INC. NASDAQ OMX BX NASDAQ/NMS (GLOBAL MARKET) A)<-1 bps B)-1 to -0.5 bps C)-0.5 to 0 bps D)0 to 0.5 bps E)0.5 to 1 bps F)> 1 bps Comparisons across charts illustrate performance in terms of price reversion between dark and lit markets. Within each chart, individual venues may be compared, controlling for strategy. We are not at the point of league tables, however. Controlling for market conditions should be a part of such an exercise. Nevertheless, examination of results for implementation shortfall strategies yields a couple of discussion points. The first point is the appropriate comparison, which depends upon one s perspective. If the issue at hand is the proportion of activity which exhibits little reversion, then the correct comparison within and across charts might be a range around zero, between -0.5 bps and 0.5 bps. If the probability of a bad outcome is of interest, the tails of the distribution matter, and the correct item of attention might be the unlimited range of less than -1 bps.

10 10 On the basis of little reversion, and for implementation shortfall strategies only, the New York Stock Exchange exhibits only about 13 percent of activity within the range around zero. The range between best and worst performers is rather narrow in the lit markets, however. BATS, for example, exhibits about 16 percent of activity around zero reversion. There are more significant differences in the extreme tail behavior. The difference between best and worst performance across lit venues with respect to the probability of a bad outcome is roughly 44 percent. Comparisons across dark pools exhibit more variation in the distribution of outcomes. The difference between pools with respect to straddling zero reversion is as high as 65 percent. For ITG s POSIT, 43 percent of fills are close to zero reversion, and few venues fall below 30 percent in that category. On that basis, dark pools, controlling for this particular strategy type, dominate lit markets in terms of performance. THE ANSWER TO THE QUESTION: A LAST LOOK A complete set of comparisons across lit venues, dark pools, and comparisons between the two types, is beyond the simple goal of this paper; we will return to that exercise in the near future. We emphasize our main point one more time before closing, however. Figure 9 illustrates dark pool performance in reversion terms, now for dark aggregator strategies only. FIGURE 9 Distribution of Price Reversion: Dark Pools Dark Aggregator Strategies BANK OF AMERICA MERRILL LYNCH ATS CREDIT SUISSE AES CROSSFINDER DEUTSCHE BANK SUPER X GOLDMAN SACH MTF ITG POSIT KNIGHT MATCH ATS MS POOL ATS UBS PIN (UBS PRICEIMPROVEMENT NETWORK) A)<-1 bps B)-1 to -0.5 bps C)-0.5 to 0 bps D)0 to 0.5 bps E)0.5 to 1 bps F)> 1 bps This chart should be compared to that of dark pool reversion in Figure 8. The percentage of fills for which there is reversion around zero is now much higher than observed for implementation shortfall strategies. The range for the latter is roughly between 15 and 45 percent. The use of a dark algorithm, which by its nature is tuned to dark pools, yields a range between 25 and over 60 percent; in fact, for nine of ten pools, the bottom of the range is above 40 percent. The percentage of zeroreversion fills for ITG s POSIT, for example, is 50 percent higher for dark aggregation strategies, as opposed to implementation shortfall.

11 11 The difference in comparisons, and the potential advantage to using an algorithm tuned to the venue type, are also evident in an examination of the tails of the price reversion distribution. We noted large percentages of poor outcomes, in the case of the implementation shortfall strategies. The tail of the distribution for reversion in the dark, using dark aggregation, is much smaller, overall and for nine of the ten individual venues. Performance around zero reversion improves, and the probability of high reversion numbers falls. The answer to our original question is now easily stated: consideration of trading strategy is an essential component in assessing venue performance. This is particularly true, when attempting to compare and contrast different market structures, such as dark pools versus lit markets. It also is important in assessing venue quality on a disaggregated basis, within market structure categories. The results suggest that proper tuning of routing functionality to strategy may improve performance as well. A proper treatment of this suggestion is a natural next, and constructive, step in venue research Investment Technology Group, Inc. All rights reserved. Not to be reproduced or retransmitted without permission These materials are for informational purposes only and are not intended to be used for trading or investment purposes or as an offer to sell or the solicitation of an offer to buy any security or financial product. The information contained herein has been taken from trade and statistical services and other sources we deem reliable but we do not represent that such information is accurate or complete and it should not be relied upon as such. No guarantee or warranty is made as to the reasonableness of the assumptions or the accuracy of the models or market data used by ITG. These materials do not provide any form of advice (investment, tax or legal). All trademarks, service marks, and trade names not owned by ITG are the property of their respective owners. The positions taken in this document reflect the judgement of the individual author(s) and are not necessarily those of ITG. Broker-dealer products and services are offered by: in the U.S., ITG Inc., member FINRA, SIPC; in Canada, ITG Canada Corp., member Canadian Investor Protection Fund ( CIPF ) and Investment Industry Regulatory Organization of Canada ( IIROC ); in Europe, Investment Technology Group Limited, registered in Ireland No ( ITGL ) and/or Investment Technology Group Europe Limited, registered in Ireland No ( ITGEL ) (the registered office of ITGL and ITGEL is First Floor, Block A, Georges Quay, Dublin 2, Ireland and ITGL is a member of the London Stock Exchange, Euronext and Deutsche Börse). ITGL and ITGEL are authorised and regulated by the Central Bank of Ireland; in Asia, ITG Hong Kong Limited, licensed with the SFC (License No. AHD810), ITG Singapore Pte Limited (CMS Licence No ), and ITG Australia Limited (AFS License No ). All of the above entities are subsidiaries of Investment Technology Group, Inc. MATCH NowSM is a product offering of TriAct Canada Marketplace LP ( TriAct ), member CIPF and IIROC. TriAct is a wholly owned subsidiary of ITG Canada Corp.