- Algorithmic trading is when pre-programmed trading orders are carried out automatically;
- They are triggered without human interaction when a market reaches the programmed conditions;
- Often, algorithms trigger other algorithms and the market can move in one direction very fast.
In 1815, British and Prussian forces defeated Napoleon’s army at the Battle of Waterloo. Legend has it, before the dust had settled and the wounded were dragged from the battlefield, a carrier pigeon belonging to the House of Rothschild was already halfway across the Channel to London.
Nathan Rothschild found himself informed, ahead of all other traders, that the French were not about to romp freely throughout the United Kingdom. And as such, he promptly made a killing buying British government bonds.
The notion of obtaining information and acting quicker than anybody else is obviously appealing, especially it could lead to financial gains.
Fast forward to the present, and instead of fast flying carrier pigeons, traders rely on algorithmic software to deliver high frequency trades. Many believe the modern day trading practice is distorting markets, moving the eye of experts and regulators towards the lightning fast practice.
How does algorithmic trading work?
An algorithm is a step-by-step procedure to accomplish a task.
Algorithmic trading (or ‘algo’ trading) is a method of executing a large order on a financial market using pre-programmed instructions accounting for variables such as time, price and volume , i.e:
- Buy 50 shares of a stock when its 50-day moving average goes above the 200-day moving average.
- Sell shares of the stock when its 50-day moving average goes below the 200-day moving average.
Generally very large orders are broken up into smaller blocks, and the software calculates when the best, least disruptive, times to buy (or sell) the smaller parcels is.
Without human intervention, these calculations can happen very quickly and algo trading can be used to generate juicy profits at a speed and frequency beyond human capability.
Investment banks, pension funds, mutual funds and hedge funds use algorithmic trading to fill their very large orders.
Algorithmic trading has also been extremely cost effective for financial institutions, given software can follow explicit instructions, monitor prices and place orders endlessly, without hankering after a salary or bonus.
In 2011, The International Organization of Securities Commissions (IOSCO) Technical Committee noted that due to the strong links between financial markets, algorithms operating across markets could transmit shocks rapidly from one market to the next.
The example the IOSCO used was the Dow Jones Flash Crash in 2010.
Shortly after 2:30 pm Eastern Standard Time on May 6, 2010, the Dow Jones Industrial Average plunged more than 1000 points in ten minutes, the largest such drop in history, at that point.
Listen to these traders freak out:
Over one trillion dollars in equity evaporated, it was an absolute bloodbath and at the time, nobody could work out what triggered the wave upon wave of selling.
More than 20,000 sell-orders in 300 securities were executed at prices as much as 60% away from their values moments earlier. Some stocks sold at absurd prices, from as low as a penny or as high as $US100,000.
The bloodbath was swift, though before the end of the day, stocks had recovered around 70 per cent of their values.
But investors were left shocked.
In this instance, high frequency trading algorithms had kicked in following a minute trigger buried deeply in the market. (For the nerds: it was a large sell order in “e-mini” futures on the S&P 500 index by an unnamed mutual-fund group).
A combination of high volatility and low liquidity prompted trading bots to misread the e-mini sellorder as a much larger market move. As such, they were programmed to reduce their long exposure under such conditions. Once some started selling, it prompted thousands more to start selling.
On the 6th of February 2018, for 15 minutes just after 3pm in New York, the floodgates opened, and sell orders came so fast that it seemed like no human stock pickers could’ve been responsible.
Walter “Bucky” Hellwig, the Alabama-based senior vice president at BB&T Wealth Management, shared this sentiment.
“What was frightening was the speed at which the market tanked,” he said.
“The drop in the morning was caused by humans, but the free-fall in the afternoon was caused by the machines. It brought back the same reaction that we had in 2010, which was ‘What the heck is going on here?”
But the high speed and knee-jerk reactions to unforeseen events can cause glitches to cascade quickly throughout financial markets.
After waking up to the news that the Dow Jones has plunged by nearly 1,200 points or 4.6%, we saw a similar day for the ASX, with about $30 billion wiped off its total value.
Thanks to algo trading, the repetitive race to act on market sensitive information is run every minute.
Rather than relying on fast flying pigeons, we are now counting on algorithms that can place 400 orders before you blink your eye.
It's a frightening proposition, but one that is the norm in today's financial markets.