'Algorithmic Trading' is explained in detail and with examples in the Trading edition of the Herold Financial Dictionary, which you can get from Amazon in Ebook or Paperback edition.
Algorithmic trading has many names. Users also know it as black box trading and algo trading. This system of trading works with complicated and highly advanced mathematical formulas. It takes these models and engages in rapid decisions to execute transactions in various financial markets. This type of trading is only possible with super fast computers and programs. It combines these with the algorithms to come up with trading strategies that deliver the maximum returns.
There a variety of trading and investment strategies that can benefit from algorithmic trading. Some of these are inter-market spreads, speculation, market making, and arbitrage. Nowadays this style of trading is able to operate and automate trading and investment strategies by working on electronic platforms. This means that the programs themselves can carryout specific trading instructions. They can be set up to consider special scenarios like volume, price, and timing.
Bigger institutional investors who buy enormous amounts of shares are most likely to use algorithmic trading. These programs help them to gain the optimal price in the market without substantially impacting the price of the stock or making their buying costs higher.
Among the most popular types of algorithmic trading strategies are trading in advance of index fund re-balancing, scalping, arbitrage, and mean reversion. These are complex strategies that mostly require speed of discovery and instantaneous decision making to be effective.
Trading in advance of index fund re-balancing has to do with mutual funds. Pension funds and other retirement accounts are heavily invested in these vehicles. Because the underlying assets held in these funds constantly change, they must purchase or sell index funds to match. This algorithmic trading strategy looks for the points where the mutual funds are ready to re-balance. It then buys or sells first. In reality it makes money for the algorithmic traders at the expense of the mutual fund investors.
Scalpers look to make money on the bid versus ask spreads. If they trade the difference quickly enough repeatedly during the day, it can make them significant amounts of money. For it to work, the movement in the price of the stock has to be less than the spread of the security or stock in question. These kinds of movements happen anywhere from seconds to minutes. Only the quick decision making of the algorithm formulas is sufficient to maximize this strategy.
Arbitrage refers to finding the differences in pricing of two related entities. Global businesses utilize this effectively all the time. They can buy supplies for less or contract labor cheaper by getting it in different nations. It helps them to lower their expenses and boost their profits.
In algorithmic trading, arbitrage strategies work with examples like S&P 500 stocks versus S&P futures. These two markets for the securities or index will often encounter price differences. Stocks on NYSE or NASDAQ could move ahead of or behind the futures market for them. The powerful and fast algorithms are able to both track and trade such differences and profit as a result.
Mean reversion refers to coming up with the average for short term low and high prices on a security. The algorithms are able to compile such an average rapidly. When the price moves towards or away from this average mean, the programs can rapidly trade them as they move back towards it.
There are a few other strategies that algorithms are able to enhance. Some of these pertain to dark pools of capital and reducing their transaction costs. Dark pools are unregulated and off market trades created when institutional investors make their own private exchanges.