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March 23, 2012

Equities wins electronic trading award

Deutsche Bank was voted the best provider of algorithmic trading products to European clients in The Trade magazine's annual awards in April.

The award was based on a survey of over 200 institutional investors, making it one of the most comprehensive electronic trading polls globally.

Algorithmic or "algo" trading - in which computer programmes are used to make trading decisions - is one of the fastest growing areas of the financial markets.

Andrew Morgan, Head of Autobahn Equity Europe, described the award as "a great result for Equities and for Autobahn which confirms the huge gains we're making and the opportunities ahead of us this year."

The award is the latest in a string of successes for Deutsche Bank's electronic equity business. In December, Super X, its in-house broker crossing platform for equities, was ranked the biggest dark pool in Europe* for the first time ever.

Ten years ago, only a handful of funds used these models and they accounted for a small portion of equity trading volumes. Now, algo models are used by thousands of organisations, and account for the majority of trades in the US and Europe. The driving force behind this trend has been the increase in the number of high frequency trading (HFT) funds which rely on algorithmic analysis to find trading opportunities. This, in turn, has encouraged real money investors to increase their use of algorithms so that they can take advantage of the HFT funds and opportunities in a fraction of a second.

Deutsche Bank began offering algo products and strategies in 2005 alongside Autobahn, its award winning electronic distribution platform.

Between 2005 and 2009, it was just one of many firms offering these services but at the end of 2010 it made a critical breakthrough with a revolutionary new dynamic trading strategy called Stealth. Stealth is perfectly adapted for the current market, leveraging quantitative models to interact efficiently with high frequency liquidity while trading opportunistically in dark pools.

It is a common phenomenon in the equity markets that if a large order for a stock becomes known - as is often the case in today's transparent markets - then the price of that stock will often go up as traders try to profit from the information by buying the stock before the large order is fully executed.

The traditional way round this problem was to split the order up into lots of small orders which will not be noticed by rival traders. While traditional schedule based algorithms can achieve this, they still leave a footprint that high frequency traders can use to their advantage.

Stealth avoids this problem by leveraging models similar to those used by HFTs while continuously interacting with dark pools in order to identify opportunities to trade in size discretely.

Stealth is a dynamic strategy that constantly adapts to price and liquidity in real time, this smarter logic allows it to adjust its strategy mid-operation in response to changing market conditions.

An example: a client wants to buy 10 million shares in Company X, Stealth analyses the market and identifies that splitting the order up into 50k tranches and executing them on dark pool X and public market Y will be the best strategy. So it moves ahead placing some initial orders in the market.

But while the first order is being executed, dark pool Y becomes extremely liquid. Suddenly there is an opportunity for the client to get the whole deal done in one go at the current market price  Other strategies might not pick this up. With Stealth, the client can switch mid-stream.

Stealth has quickly become among the most popular algo trading tools in Europe. Launched in Europe last year, it is already being used by more over 200 different institutions including many of the region's leading investors and Morgan says the potential for growth is significant.

"We've got a huge opportunity to grow our market share," he says. "Stealth is the only product of its kind out in the market right now with a proven track record of success."


*footnote: Please refer to our awards section


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