4 September 2012 | Volume 3 | Issue 22

The Blotter presents ITG’s insights on complex global market structure, technology, and policy issues.

CONTRIBUTORS

Jeff BacidoreManaging Director, Head of Algorithmic Trading, ITG, Inc.Jeff.Bacidore@itg.com+1.212.588.4327

Kathryn BerkowQuantitative Analyst, Algorithmic Trading, ITG, Inc.Kathryn.Berkow@itg.com+1.212.444.6146

Nigam SaraiyaVice President, Algorithmic Trading, ITG, Inc.Nigam.Saraiya@itg.com1.212.444.6479

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Imbalance Feeds for Auction Size PredictionExperts from ITG discuss the limitations of historical models in predicting auction size. They suggest a better alternative: imbalance feeds.

An accurate estimate of auction volume is critical to optimal auction participation. Volume executed in an opening auction depends on a myriad of factors, from news and earnings to past volumes and recent volatility. Often, historical models are used to predict auction size, based on an average or median size as a percentage of typical volume over a recent time period. However, with the dramatic swings in volatility and volume of the last few years, the accuracy of such historical models is called into question.

One way to improve auction volume prediction is to use the real-time exchange “imbalance feeds”. The NYSE imbalance feed, for example, provides real-time information about the potential opening price as well as information on paired volume and buy/sell imbalances1 that reflect the state of the pre-open market. The power of this information is that it directly incorporates the effects of news, special days, and any other abnormal liquidity environments. For example, if a company announces earnings pre-open, auction volume would likely be much higher than anticipated. Without real-time context, historical models will not be able to determine how much higher volume will be since they cannot capture the nuanced information contained in each earnings announcement. The imbalance feeds, on the other hand, will better capture announcement-specific information, for example, in the form of dramatically higher paired volumes.

As an illustration, consider the opening auction for Abbott Laboratories (NYSE: ABT) on January 18, 2012. News on the stock was released at 8:30 AM, information that is difficult to incorporate systematically into a historical volume prediction model. As shown in the table below, the actual opening size that day was 584,838 shares. Historical models likely predicted volumes of around 93,000 shares, as the news was unanticipated. A model incorporating real-time imbalance feed information, on the other hand, predicted volume of 513,877 shares in the auction, over five times higher

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1 For detailed information on the imbalance feed, see Bacidore, Berkow, and Wong “Inside the Opening Auction” Winter 2012, Journal of Trading.

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than the historical model and significantly closer to the actual opening auction volume.

Table 1: Volume News Releases Impact Auction Size PredictionExample: News on Ticker ABT is released on 1/18/2012 at 8:30am

Shares Error

Median Daily Volume (previous 21 days)

6,413,963

Sophisticated Historical Model Prediction2

92,579 Under-predicted by 84%

Real-Time Imbalance Feed Model

513,877 Under-predicted by 12%

Actual Opening Auction Size 584,838

Source: ITG

Of course, a single anecdote is insufficient to prove the superiority of using imbalance feed information. Below is a chart illustrating variability in the auction size, and the corresponding variance reduction when using sophisticated historical and real-time models of auction size.3 For very liquid stocks, much of the variability in opening size is explained by a historical model, but the real-time imbalance feed model still has a 39% smaller error variance. For very illiquid stocks, the value added by the real-time model becomes much clearer. Here, the historical model can explain some of the variation, but the real-time model is a dramatic improvement.

Exhibit 1: Variance in Predicting Opening Auction Volume

Source: ITG

2 The “sophisticated” historical model is one that attempts to capture factors such as earnings, special days, etc. into its forecast, but uses only historical data to make its forecast, i.e., uses only data available as of the previous day..

3 Values in the chart represent the mean square error, with size and errors normalized by the 21-day median volume for the stock. The sample includes all NYSE-listed stocks over a period of fourteen months ending in February 2012.

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Real-time models of auction size can improve prediction dramatically, reducing the number of missed opportunities and the possibility of over-participation or price impact. For institutional investors benchmarked to the day’s open price, over- or under-prediction—yielding over- or under-participation—can create price impact or trigger a missed opportunity to execute at the benchmark. In contrast, optimal auction participation can mean better performance against the benchmark of interest, leading to substantial cost savings over time.

*Real-time imbalance information is incorporated into auction volume prediction within ITG’s Dynamic Open algorithm. The algorithm optimizes auction participation by maximizing participation rate while minimizing price impact. Alternatively, users can define their own minimum expected participation rate, while relying on the algorithm to forecast the size of the auction itself.

©2012 Investment Technology Group, Inc. All rights reserved. Not to be reproduced or retransmitted without permission. 71912-21140

The opinions, positions, and/or predictions taken or made in this document reflect the judgment of the individual author(s) and are not necessarily those of ITG. 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. Nothing contained herein should be relied upon as a representation, guarantee, or warranty as to the reasonableness of the assumptions or the accuracy of the sources used by the author(s). These materials do not provide any form of advice (investment, tax or legal). ITG Inc. is not a registered investment adviser and does not provide investment advice or recommendations to buy or sell securities, to hire any investment adviser or to pursue any investment or trading strategy.