A Side-by-Side Comparison Between ITG s Size-Adjusted Spread Cost Estimates and the True Realized Costs of Institutional Investors

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1 ITG Financial Engineering, August 2016 A Side-by-Side Comparison Between ITG s Size-Adjusted Spread Cost Estimates and the True Realized Costs of Institutional Investors AUTHORS Onur Albayrak, PhD, Researcher, Financial Engineering Milan Borkovec, Managing Director, Head of Financial Engineering INTRODUCTION ITG s size-adjusted spread (SaS) cost estimates provide guidance on the anticipated costs associated with instantaneous spot trade executions, measured relative to the prevailing mid-quote rate at the trade time 1. The underlying data for our model contains dealer quotes from 6 global banks and 5 major ECNs. By varying the manner in which we consolidate the limit order book across trading venues / liquidity providers, we are able to reflect different trading styles and credit tiers as well as varying degrees of sophistication of market participants. The use of the empirical limit order book enables us to construct cost estimates for instantaneous trading at various consolidation levels, deal sizes, as well as at various times of the day 2. 1 For details of the methodology and applications, see for instance Borkovec, Cochrane, Domowitz, and Escobar, The Cost of Liquidity in the FX Market, Journal of Trading, Vol. 20, No. 1 (Winter 2015): 6-19, and Brandes Y., Spread Revisited: How Size Adjusted Spread Metrics Can Deliver FX Trading Insights 2 ITG s SaS cost estimates are currently available for four different consolidation types reflecting the cost of instantaneously sweeping the limit order book for four different aggregation schemes ( ALL, CECN, AVG, MIN ) depending on the end users objectives and capabilities. ALL and CECN correspond to consolidating liquidity across all available venues and all ECN venues, respectively. AVG and MIN do not involve limit order book consolidation across different trading venues. These latter schemes rely on the liquidity available from a single bank dealer. AVG computes the cost of climbing the limit order book of each dealer bank separately, and then calculates the average cost (across banks) for a given trade quantity. MIN starts in a similar manner, but instead of calculating the average cost across all participating banks, it determines the lowest cost. The cost computed in this manner is typical for a trader who has good business relationships and sufficient credit standing with all the bank dealers and thus can act on the best dealer quote provided by the banks for any deal size at any moment in time. 1

2 In this study, we provide answers to the following important questions: Are ITG s SaS cost estimates reflective of observed costs of spot trades for large institutional investors? Which of the supported consolidation levels best represents the realized costs of ITG s FX Peer clients? Is the execution performance of ITG s TCA clients uniform and consistent across various trade scenarios? For our empirical analysis, we utilize twenty four most liquid currency pairs in our ITG FX Peer database in ITG FX Peer data includes contributions from a majority of the ITG FX TCA clients. As such, it is not limited to a specific execution style or trading pattern. The data retrieved from ITG FX Peer database is cleansed by ensuring the validity of the information in the benchmark and execution prices. Details are given in the following sections. Our findings can be summarized in the following bullet points: ITG s SaS cost estimates serve as meaningful benchmarks to assess the realized FX costs of large buy-side investors. ITG s Peer group database clients pay attention to their executions, as their average costs mimic remarkably well the cost estimates for the consolidation type MIN, especially for larger deal sizes. Our analysis shows that the execution performance can be further improved for very small deal sizes, as well as during off-london trading hours. SETUP OF ANALYSIS ITG FX Peer database holds the execution information of most of ITG s FX TCA buy-side clients in the foreign exchange market. For the year 2015, the ITG FX Peer database encompases more than 755 thousand trades, for a total allocation of 2.4T US dollars. At the initial stage of our analysis, we study the reliability of the trade timestamps and benchmark rates for all of the spot transactions. As such, we retrieve the benchmark rates from ITG s internal Market Data (MD) group, check their validity by comparing it with a second data source and subsequently perform an actual cost analysis to check whether the trade timestamps of spot trades are reliable and can be included in our analysis. Results will be presented in terms of the empirical costs defined by the below formulas for Buy trades and for the Sell trades, where is the mid-quote rate at execution time, and denotes the execution rate of the trade. Then, we bucket the trades according to the deal size in base currency. TESTING BENCHMARK PRICES An individual spot trade in the ITG FX Peer database contains its trade timestamp and spot rate that the deal was executed with. In order to compute the associated realized SaS cost, the prevailing mid-quote rates at trade execution were collected. To verify the validity of the benchmark rates, we leverage the existing market database used in ITG s FX TCA service which aggregates data from 6 global banks and 5 major ECNs and provides the best bid and offer for tradeable quotes for any given day/time and currency pair. For robustness of the test, rates were also used from a secondary data set that uses the same data 2

3 sources. The secondary data set aggregates these data sources independently and adopts 15 second buckets to report the prevailing mid-quote rate at the end time of each bucket. In order to validate the benchmark rate for every trade in the FX Peer database, we determine which 15 seconds trading interval the trade corresponds to and subsequently compare the two data points at the end of that interval. If the two mid-quote rates are more than 1 bps 3 away from each other, we mark this trade as a bad benchmark trade and exclude it from the subsequent analysis. TESTING TIMESTAMPS One reason to suspect that trades have incorrect timestamps is exemplified when execution rates are very different compared to their corresponding benchmark rates. In order to evaluate the seriousness of this issue, we create an acceptable window around the executed rate, depending on whether the trade is a buy or sell. We measure how far away from this acceptable window the execution rate is using ITG s SaS cost estimates for consolidation type ALL, as well as the minimum, and the maximum rate surrounding the trade. More specifically, we require that the executed rate meets the following two criteria in order to pass our timestamp test: for Buy trades, and for Sell trades, where is the size-adjusted spread cost for the consolidation type ALL, is a factor showing how far away (in terms of the size-adjusted spreads) the execution is from the mid-quote rate, and / is the minimum/maximum rate within the three 15 second bins surrounding the execution. A chart demonstrating this requirement is shown in Exibit 1. Exhibit 1: Illustration of the filtration algorithm for trades with bad timestamps. 3 The 1 bps threshold is a reasonable threshold for the 24 currency pairs that are used in this analysis. Robustness checks in changing the threshold have not resulted in material different results. 3

4 In our subsequent analysis we use the value 4, which is chosen to avoid discarding too many trades and biasing our results. The asymmetric requirement on the costs of trading can be justified by the argument that our institutional clients are not in the business of making the market in FX. In other words, they are the initiators of their trades, most of the time. Our heuristic makes sure that we only accept trades where execution timestamps are reasonable, in the sense that transactions are not associated with huge profits and losses. We tested our heuristic on a subset of clients trades that are known to have valid timestamps in the ITG s FX Peer universe and performing very well in terms of transaction costs, encompassing approximately 20% of the US dollar amount of the EURUSD trades. Exhibit 2 shows the resulting cost curves for EURUSD during London hours (i.e. 7:00 to 18:00 GMT) for two filtration methods: the red curve corresponds to our heuristics discussed above (asymmetric filtration, i.e. size-adjusted spread costs are added to the trade-initiated side only), while the black curve corresponds to symmetric filtration. In both cases, we set 4 and 10 (shown by yellow and orange curves for asymmetric and symmetric requirements, respectively). The proximity of the curves in Exhibit 2 and the fact that the choice of did not have a major impact on the cost curves suggests that our filtration heuristic is meaningful 4. These findings are consistent across other currency pairs as well. Exhibit 2: Cost curve comparison as a result of the symmetric and asymmetric SaS requirement on trade filtration, for EURUSD during London hours. 4 The subset of trades shown in Exibit 2, have very low execution costs, pointing to excellent trading performance compared to their peers. (Compare red curve in Exhibit 2 with orange line in Exhibit 3) 4

5 FIXING BAD TIMESTAMPS Timestamps in FX execution data is notoriously bad, therefore it is crucial to design a good algorithm to assign correct timestamps to the executions that are outside of our acceptable window definition. Instead of just discarding these trades, we search for a time period where the execution would be more reasonable such that it is not associated with huge profits, or huge losses. Starting from the reported execution time, we search for a 15 second interval before the trade to find the time period where the execution is inside the acceptable window. The closest time period to the reported execution time is chosen to be the new timestamp for the execution and the average mid-quote within that period is used as the benchmark to calculate costs 5. We tested this algorithm on the executions reported in ITG s FX Peer database and observed that including the fixed executions have minimal impact on the empirical costs. Results are shown for EURUSD on Exhibit 3, for the trades during the London hours in 2015, other currency pairs provide similar behaviour as well. Exhibit 3: Cost curve comparison for the corrections applied to the timestamps, for EURUSD during London hours. METHODOLOGY AND DESCRIPTIVE STATISTICS After the removal of the trades that failed our filters, we move on to calculate the empirical costs. All clients are aggregated together, except for the clients with irregular trading patterns (e.g. clients with the majority of their executions at and around the London close) and trades that are very close to London Close (as 5 If the algorithm could not find an acceptable window within the last 3 hours the trade is excluded from further analysis. The 3 hours horizon has proven to be a conservative horizon in the sense that most of the trades can be kept. 5

6 these trades have different SaS cost characteristics from regular trading). It is also worthwhile to mention that trades that share the same set of information (i.e. same client, side, currency, date, and execution time) are combined to form a single trade, as we consider them as allocation trades that technically belong to the same deal. In order to make a meaningful aggregation across all currencies, times of the day and clients, actual costs are normalized using ITG s SaS cost estimates of the consolidation type ALL (i.e. available liquidity is consolidated across all venues and liquidity providers and SaS cost estimates are computed based on that aggregated liquidity pool). Other ITG SaS cost curves ( ALL, AVG, MIN, and CECN ), which are also normalized, are also included in the analysis to present insights about the best fit to the actual trading costs. Trades are analyzed for two distinct trading periods separately: London hours (between 7:00-18:00 GMT) and off-london hours. Descriptive statistics of our spot transaction sample before and after the data clean-up are shown in Tables 1 and 2 below, together with the US dollar (in millions) amount that was discarded with our filtration. Table 1 displays the number of observations and US dollar volume traded for the average client (aggregated across all currencies for each particular client) in Similarly, Table 2 shows the number of trades and the nominal US dollar amount in 2015 for an average currency pair (aggregated across all clients). The results in the tables are based on all the trades across the day. Both tables indicate that we can keep most of the trades in ITG s FX Peer database. The Removed Trades column also includes trades that were discarded due to activity spikes (explained above). Table 1: Average number of trades and US dollar volume (in mln) for spot transactions in 2015 for a representative client in ITG FX Peer Database (before and after the data cleaning). Client Packaged Trades Removed Trades Remaining Trades Average % Client Packaged Trades USD Removed Trades USD Remaining Trades USD Average $83, $9, $73, % Remaining % Remaining % Table 2: Average number of trades and US dollar volume (in mln) for spot transactions in 2015 for a representing currency pair in ITG FX Peer Database (before and after the data cleaning). Currency Packaged Trades Removed Trades Remaining Trades Average % Currency Packaged Trades USD Removed Trades USD Remaining Trades USD Average $58, $8, $50, % Remaining % Remaining % 6

7 SUMMARY OF ACTUAL SaS COSTS IN ITG FX PEER UNIVERSE In what follows, we report the results for trades during London hours and off- London hours separately. Not surprisingly, using only the trades remaining after filtration (see Tables 1 and 2), we observe that most of the clients are more active during the London hours, i.e. there are significantly less observations for off-london hours. Also, the deal sizes are usually smaller for spot transactions during off hours. As a consequence, we show and discuss cost curves (i.e. costs as a function of deal size) for London hours only. Costs for small deal sizes are subsequently compared for the two trading periods separately. Normalized aggregated cost curves for the most liquid currency pairs are given in Exhibit 4, overlaid with ITG s normalized SaS cost curves for all consolidation types. The currency pairs EURUSD, USDJPY, GBPUSD, USDCHF, AUDUSD, USDCAD, and EURGBP are used for the aggregation. The average cost curve is shown by the solid red line and the min/max values (displaying the lowest/highest normalized average costs for the set of currency pairs) are presented by the by the same red color with dashed curves. Unlike the CECN cost curve, the relative difference between the actual cost curve and the ALL consolidation type decrease for large trade sizes. We observe a similar behaviour of actual costs to the MIN, where execution is always done with the best (cheapest) dealer in our dealer universe. Results clearly show that clients appear to execute large deals mostly when they receive good quotes and thus can outperform our CECN and AVG cost estimates. Particularly for the smallest deal sizes, there is still room for improvement in best execution, as the average aggregated cost in this size segment is 1.7 times the SaS cost estimate for consolidation type ALL and, most importantly, it is much higher than the CECN cost estimate. We should also note that the AVG cost curve decreases relative to the ALL for large order sizes, which could be the result of the banks getting more competitive for the larger deal sizes. 7

8 Exhibit 4: Average normalized aggregated cost curves for the eight most liquid currency pairs in ITG FX Peer Database in 2015 during London hours. Next, we study the performance of the smallest deal sizes (corresponding to the first observation points in Exhibit 3) in more detail. Normalized costs for deals with notional value of 1 million in the base currency and smaller, together with the corresponding normalized cost estimates of ITG s FX cost curves for active currency pairs, are shown in Exhibit 5A for London hours and in Exhibit 5B for off- London hours. Similar to Exhibit 4, we see that the performance of ITG s Peer clients is in the vicinity of the SaS cost estimates for the consolidation type MIN. These results imply that our SaS cost estimates are meaningful cost benchmarks and that the institutional clients in ITG s FX Peer database have the potential to lower costs further by actively monitoring and executing their spot transactions through ECNs during London hours. As one can easily see, costs for small deal sizes are higher during the off hours relative to our SaS MIN and CECN cost estimates, suggesting that ITG s institutional clients should monitor transactions that are being handled off-hours with at least as much attention as the London hours. 8

9 Exhibit 5A: Average normalized costs for small deal sizes in 2015 during London hours. Exhibit 5B: Average normalized costs for small deal sizes in 2015 during off-london hours. CONCLUSIONS The demand for FX measurement and best execution services is growing. Our analysis shows that ITG s SaS cost estimates for FX spot transactions provide a meaningful benchmark for actual costs of our institutional investor clients in the ITG FX Peer universe. The FX trading environment is getting equitized, with the recent market shifts to electronic trading through ECNs, the introduction of nolast-look trading venues, and the development of the foreign exchange market global code by the Bank of International Settlements 6. It is expected that we will observe further declines in transaction costs in the future. ITG s SaS cost estimates can guide institutional investors toward that goal. 6 Bank for International Settlements, FX Global Code: May 2016 Update, 9

10 The results presented in this paper demonstrate that the ITG Peer universe clients pay particular attention to executions of larger deal sizes during regular London hours. Their average empirical costs in those size segments are close to ITG s cost estimates for consolidation type MIN, but slightly higher. We believe that the performance of those clients can be improved even more if they carry through the same process for trades of smaller notional values, on and off the London trading hours Investment Technology Group, Inc. All rights reserved. Not to be reproduced or retransmitted without permission. XXXX-XXXX These materials are for informational purposes only and are not intended to be used for investment purposes. The information contained herein has been taken from 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 data used by ITG or the actual results that may be achieved. 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 ) (the registered office of ITGL is Block A, Georges Quay, Dublin 2, Ireland). ITGL is authorised and regulated by the Central Bank of Ireland; in Asia, ITG Hong Kong Limited (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. 10