Algorithmic trading cuts costs, but performance levels vary widely

Algorithmic trading cuts costs, but performance levels vary widely

Comparative research by ITG into automated order execution strategies suggests that algorithmic trading engines can cut costs, but warns that performance levels between brokers can be far from uniform especially as order sizes increase.

The ITG study evaluated over 2.5 million orders, consisting of about 10 billion shares (valued at over $80 billion) traded in 2004, from more than 40 institutions. Researchers looked not only at the performance of algorithmic trading versus non-algorithmic trading but also compared the performance of different algorithmic systems offered by six unidentified brokers.

The study estimates that the use of algorithmic trading can shave up to 11 basis points from investor costs. Average performance differences across providers for very small orders were few, but gaps between providers grew as order size increased.

Selectivity is key, says ITG, as the research indicates that certain algorithms perform better than others at higher volumes. Certainty of outcome, as opposed to quality of outcome, was also found to vary widely between brokers.

Alasdair Haynes, ITG Europe chief says the research helps to shed light on a dynamic and growing market: "This study shows that algorithmic trading can play an important part in helping firms achieve best execution, but that users have to be discerning as to which product they choose and under which circumstances it is appropriate for them."

Algorithmic trading is currently used by an estimated 60% of US buy-side firms and this percentage is set to grow. The take up in Europe is currently thought to be about half of that in the US, but is expected to rise dramatically.

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