Algorithmic trading, using mathematic models developed by PhD’s at various investment banks and trading firms, is quickly growing in favor. In fact, according to the Aite Group, a Boston, MA based consultancy firm, by the year 2010 algorithmic trading may be responsible for over 50% of all U.S. equity trades.
Apparently, the investment experts are becoming much more comfortable with this method of efficiently trading equities and the returns being generated, particularly during rapidly vacillating markets. All are in agreement that algorithmic trading can move large numbers of shares quickly.
Based on objectives, parameters, and constraints of mathematic algorithms, traders plan the timing of buy/sell orders using these quantitative models. The models then specify both the timing and size of the trades to be made. As market changes come ever faster, algorithmic trading is becoming a more important and accepted strategy.
The Aite Group found that, in the ongoing quest of creating or “capturing” alpha (excess return above the stated benchmark), algorithmic trading is becoming a major tool and action plan. In the highly competitive arena of hedge funds, many are now developing proprietary algorithms that they hope will help them generate better returns than their rivals.
The Aite report predicts that by the end of 2007, algorithmic trading may account for upwards of 33% of the total equity trading volume. Their data indicates that its influence will increase to around 53% by 2010.
This does not mean there are no longer doubts, misgivings, and misconceptions about algorithmic trading. There are still many firms and traders that distrust this cutting edge trading strategy. Some feel uneasy leaving trade “decisions” to an algorithm designed by a PhD who is not an active trader.
The algorithms currently being used are considered first generation and may be nearing the end of their run. The next generation algorithms should be more sophisticated and will be capable of actually adapting to changing market spikes, seeking out liquidity, and general portfolio trading decisions. They are of particular interest to buy-side traders as the methods of trading and locating liquidity become more circuitous.
Estimates speculate that in 2006, around $230 Million was spent in the U.S. equity markets just on the technology required to support algorithmic trading. Forecasts predict that, by 2008, over $300 Million technological dollars will be consumed by algorithmic trading. As it becomes more sophisticated and reliable, algorithmic trading will be accepted and used by more and more traders and firms.
