What Is Algorithmic Trading
Algorithmic trading is an approach whereby an order is entered into a computer program built based on mathematical models or algorithms
. The models themselves can range from simple linear regression to more complex genetic programming and game theory based algorithms. The result is that the program created by quant specialists and software programmers determines the parameters of the order such as timing, price and quantity/volume.Algorithmic trading is more so used by institutional investors such as investment ba nks and hedge funds, espec ially as it allows to control for market impact i.e. to divide the order into blocks to minimize the shock to the assets price and prevent market players from guessing the size of the trade. Learn more about trading at Student 2 Trader (www.student2trader.com).
Algorithmic trading can be simultaneously used with a number of investment strategies such as arbitrage, speculation or market making. In terms of arbitrage, an algorithm can be used to identify mispriced assets based on different asset pricing models such as the Black-Scholes option pricing model and take advantage of this mispricing, quicker (fraction of a second) and more effectively than a trader would. In that sense, algorithmic trading is different to a strategy such as discretionary trading, which relies on the traders own judgement.
Algorithmic trading has been increasingly becoming more widespread currently close to half of all shares traded in the US are based on this strategy. The firms that utilise algo trading generally develop their own in-house programs e.g. Sniper or Guerrilla both by Credit Suisse as opposed buying from a third party.
There is still considerable debate as to the pros and cons of "algo trading" while it provides liquidity to the market, it has been blamed for higher volatility and the potential to exacerbate a downturn - in that case, retail traders trying to exit a position quickly would have no hope of competing against a computer that can dump large quantities of stock in a split second. To cite a recent example the May 6 2010 flash crash, whereby the US stock market crashed briefly only to rebound immediately after the Dow suffered its biggest intraday point swing of almost 10%. In this case, one of the causes of the crash was the fact that algorithmic trading initiated a dump of the Procter and Gamble stock following an unusually large sell order for the stock. Due to controversies such as these, algo trading has been closely monitored by regulatory bodies.
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