Algorithmic trading programs that use automated pre-set trading instructions to execute orders have recently been blamed for the sharp fluctuations in global stock markets. While algorithmic trading programs offer the advantage of buying and selling assets without human intervention, they also have the drawback of triggering massive sell-offs whenever the set conditions are met, increasing market volatility.
Although there are various forms of algorithmic trading, they all use a computer program that follows a defined set of instructions to place a trade. For instance, a program might be designed to buy a stock if its price rises by 5% within the first two hours of trading and then automatically sell if it falls by the same percentage in the next two hours. When these programs are highly sophisticated, they can place trades in less than a second, making high-frequency trading possible.
One of the primary benefits of algorithmic trading is its ability to handle large volumes of trades simultaneously as long as the conditions are met. It can process hundreds of orders in a single second. This is particularly advantageous for institutional investors who can quickly execute “stop-loss” orders—selling off assets to prevent further losses—when the market shows signs of risk.
Investment bank Goldman Sachs estimated that algorithmic trading accounted for about 60% to 70% of all trades in the U.S. in 2016. Experts believe this figure has now risen to 70% to 80%. Considering current trading volumes, at least $3.7 trillion worth of global daily transactions in foreign exchange, derivatives, stocks, and bonds are carried out through algorithmic trading.
The recent surge in passive investments, such as exchange-traded funds (ETFs), is also cited as a reason for the rise in algorithmic trading. Passive investing involves automated trading based on the price movements of an index or stock rather than active management by a fund manager who analyzes data and makes investment decisions. Global ETF net assets had reached $12.851 trillion as of June, according to the global ETF research firm ETFGI.
But some experts are concerned that algorithmic trading, initially introduced to reduce risk for institutional investors, has led to increased market volatility, making the global stock markets more unpredictable.
Alison Nathan, a senior strategist within Global Macro Research at Goldman Sachs, pointed to the “flash crash” of 2010—when the New York Stock Exchange dropped 9% in minutes—as an example of how “volatility can cause liquidity to evaporate, leading to prices moving excessively away from fundamentals.”
Experts believe that algorithmic trading programs are driving the recent stock price fluctuations of large-cap companies such as Nvidia and Tesla in the U.S. stock market. Typically, stocks with large market capitalizations have been more stable, but lately, large-cap stocks have dropped by as much as 8% in a single day for no apparent reason. “Stocks like Nvidia are now more sensitive to price changes because they are linked to semiconductor ETFs and are actively traded by algorithms,” said a U.S. equity manager at a domestic brokerage firm.
