Based on famous stock related movies like The Wolf of Wall Street, its hard not to picture the stock exchange as a chaotic floor with hundreds of traders shouting their orders. But since the computerisation of orders in the 1970s, the world has gradually been moving towards automated trading.
To put it simply, algorithmic trading is placing a buy or sell order of a defined quantity in a model that automatically triggers an order based on the goals specified by the parameters of an algorithm. Let’s briefly examine how algo trading has impacted the financial markets.
Brief History Algo Trading
Back in the 1980s, program trading was used on the New York Stock Exchange, with arbitrage traders pre-programming orders to automatically trade when the S&P500’s future and index prices were far apart. As markets moved to becoming fully electronic, human presence on a trading floor gradually became redundant, and the rise of high frequency traders emerged. A special class of algo traders with speed and latency advantage of their trading software emerged to react faster to order flows.
By 2009, high frequency trading firms were estimated to account for as much as 73% of US equity trading volume.
Types of Algorithms Used By Traders
Execution
Imagine if you’re a huge sovereign wealth fund placing a 100 million order on Apple shares. Do you think there will be enough sellers at the price you chose? And what do you think will happen to the share price before the order gets filled?
This is where an algorithm can be used to break up orders and strategically place them over the course of the trading day. In this case, the trader isn’t exactly profiting from this strategy, but he’s more likely able to get a better price for his entry.
Arbitrage
If you see the price of a Chanel bag to be US$5000 in France and US$6000 in Singapore, what would you do? The obvious answer would be the buy in France and sell in Singapore. This is risk free profit at no cost, by earning a spread between the 2 countries. Similarly, if one spots a price difference in futures and cash markets, an algo trader can be alerted by this and take advantage.
Trend following
There are tons of investment gurus claiming to have the best strategies based on technical analysis, relying on indicators like moving averages, momentum, stochastics and many more. Some automated trading systems make use of these indicators to trigger a buy and sell order.
Scalping
A scalper’s trading algorithm relies on tiny price gaps and attempting to profit from these small moves.
Impact of Automation on the Markets
Volatility
Retail trading among super fast computers with well tested trading software is like jumping into shark infested waters. With heightened market volatility, it is more difficult now for fundamental investors to enter the market. As an exaggerated example, imagine calling to execute a buy order. Within those split seconds, a HFT could have executed multiple traders, profiting from your final entry price.
Market Crash
Suppose everyone sees this algo trading strategy and chooses to follow it. If for some reason the market falls slightly and a sell order is triggered to cut loss at once, prices can immediately collapse because there are no buyers in the market. Famous examples of crashes occurred in 1987 stock market, in 2010 flash crash and many more.
Liquidity
It’s natural to assume that with computers automatically carrying out trades, liquidity should increase. However, the issue comes when the markets are crashing. With major crashes, like the recent Swiss National Bank peg removal, there was simply no liquidity available for the CHF, causing prices to collapse rapidly. This even caused several major brokerage firms to go bankrupt instantly.
The impact of automation and artificial intelligence is so real that computers have been beating humans at our own games, controlling our roads, and certainly the financial markets.
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