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Algorithm Selection: A Quantitative Approach

JIAN YANG AND BRETT JIU

April 25, 2006

Abstract

The widespread use of algorithmic trading has led to the question of whether the most suitable

algorithm is always being used. We propose a practical framework to help traders qualitatively

characterize algorithms as well as quantitatively evaluate comparative performance among vari-

ous algorithms. We demonstrate the applicability of the quantitative model using historical data

from orders executed through ITG Algorithms.

BRETT JIU is a senior research analyst at ITG Solutions Network, Inc., 44 Farnsworth

Street, Boston MA 02210, Tel: (617)-692-6741; E-mail:

bjiu@itginc.com

IAN YANG is a senior vice president at ITG Solutions Network, Inc., 44 Farnsworth

Street, Boston MA 02210, Tel: (617)-692-6860; E-mail:

jyang@itginc.com

The authors wish to thank Milan Borkovec, Gabe Butler, Vitaly Serbin, Xiangyang Wang, James Wong, Henry

Yegerman and Ian Domowitz all of ITG Inc., as well as Yingchuan Wang for their support and comments. The

information contained herein is for informational purposes only. Nothing herein is investment advice as de-

fined by the Investment Advisers Act of 1940. ITG Inc. does not guarantee its accuracy or completeness and

ITG Inc. does not make any warranties regarding results from usage. Any opinions expressed herein reflect

the judgment of the authors at the time of publication and are subject to change without notice and may not

reflect the opinion of ITG Inc. This communication is neither an offer to sell nor a solicitation of an offer to

buy any security or financial instrument in any jurisdiction where such offer or solicitation would be illegal.

All trademarks not owned by ITG are owned by their respective owners.

he relentless pursuit of lower transaction costs has led to increasing demand for sophis-

ticated trading tools and algorithms, which in turn has led to an explosion in the num-

ber of algorithmic products offered in the marketplace today. Yang and Borkovec

[2005] predict that this trend will continue as more investment management firms em-

brace best execution as a top priority.

Having more algorithms at their disposal offers traders both opportunities and challenges.

On the up side, a trader now has the opportunity to pick the suitable algorithm that will most

likely achieve the trading objective for each order. On the down side, the number of algorithm

choices can be so large as to make it difficult to make a quick and correct choice.

Adding to the algorithm selection challenge is the fact that algorithms offered by sell-side

vendors usually come in the form of a “black box,” with inner workings hidden to the end users.

Because of this lack of transparency, users may find it difficult to clearly understand the per-

formance characteristics of a particular algorithm, which, in turn, further complicates the algo-

rithm selection decision.

Instead of looking inside an algorithm, we propose a systematic, quantitative approach to

evaluating an algorithm’s historical performance by identifying the determining factors of rela-

tive performance across alternative algorithms, and we present a framework for algorithmic se-

lection based on these underlying factors. Our methodology is easy to implement in practice and

provides a quantitative framework for conducting performance attribution on algorithmic prod-

ucts. We will demonstrate how we perform empirical analysis on the algorithm performance and

how we turn historical data-based model parameters into forward-looking algorithm selection

criteria. Our proposed approach can also help investment managers and traders become more

proactive in selecting algorithms that are of the highest value to them, and help to ensure the

alignment of algorithmic trading with their investment objectives.

ALGORITHMIC STRATEGY SPECTRUM

The significance of conducting pre-trade “homework” on algorithms is well understood.

The need to understand the nature of an algorithm starts at the point when an algorithm is offered

by a third-party vendor. We begin our discussion of algorithm choice with a look at how algo-

rithms can be categorized.

trade schedule will be similar to the pre-defined one, absent significant, unusual changes in li-

quidity over the order horizon.

Between these two ends is a category that we call evaluative strategies. Not surprisingly,

these strategies combine approaches of both opportunistic and schedule-driven algorithms. At

the macro level, these algorithms suggest how to optimally slice a large order in different time

intervals, for example, half-hour bins. At the micro level, intelligent rules – often quantitative in

nature – are employed to execute each part of the original order while balancing the tradeoff be-

tween cost and risk. Oftentimes these micro rules require the input of substantial real-time in-

formation, which makes them similar to opportunistic strategies. The trader will have a good

idea of what the execution trajectory may look like, but the ex post trajectory may differ little or

greatly from the ex ante prediction. An example is ITG ACE, a highly quantitative strategy that

actively evaluates the potential price impact of each slice and continuously adjusts how and

when each slice of the big order is executed in order to minimize the impact.

While our three-part categorization of algorithms is only a guide,

dividing algorithms

into different categories is a necessary first step in deciphering the nature of the myriad strategies

available. It is important to see beyond general descriptions and get a clear sense of what kind of

strategy any given algorithm is at its core.

ALGORITHM SELECTION: A QUANTITATIVE FRAMEWORK

Given the availability of a basket of algorithmic strategies, attention is now turned to or-

der-specific pre-trade analysis. Specifically, there are two questions concerning algorithm selec-

tion:

1. Is the order at hand suitable for algorithmic trading?

2. If so, which algorithm is the optimal one for trading this order?

It is well known that not all orders can be traded using an algorithmic approach. This is

because, essentially, algorithms are pre-programmed logic run on computers. As such, algo-

rithmic trading is not, and will never be, the magic bullet that solves all transaction cost-related

problems. This is an important pre-trade analysis issue that is beyond the scope of this paper; in-

stead, we focus on the optimal algorithm selection question.

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