Regulators have had their sights on automated trading since the Flash Crash in May of 2010 when stock prices swooned 9% in mere minutes. In August of 2015, the same phenomenon occurred again as the price of exchange-traded funds (ETFs) became unhinged from their underlying value due to automated trading. Recent research has shown that such flash crashes may actually be occurring much more frequently on a smaller scale.
Regulators responded with a series of new rules eliminating techniques like spoofing, layering, and front-running. More recently, they proposed a new rule that would require the source code for trading algorithms to be open to inspection by the Commodity Futures Trading Commission (CFTC) and the U.S. Department of Justice without a subpoena. The hope is that regulators would be able to identify any potential issues and avoid these flash crashes.
The proposed rule is controversial given the government’s battered track record of keeping confidential information out of third-party hands. In addition, trading firms argue that individuals who reviewed the source code for the government could wind up back in the private sector. Many trading firms rely on the source code to generation millions or billions of dollars per year in profit and consider it their most important intellectual property.
Automated trading accounts for more than 70% of all futures trading, according to the CFTC, which has created a lot of fear among regulators. Many experts believe that this number will only increase over time since computers can interpret and act on information much more quickly and objectively than a human. The advent of complex software applications – including deep learning and artificial intelligence – could accelerate adoption even more.
The new rules being proposed by the CFTC and those already in place are designed to reduce the incidence of flash crashes and bring the futures market in line with other highly regulated markets like the Treasury market. Despite these efforts, many experts are skeptical that the new rules will effectively solve the problem of sudden and unexpected movements in securities markets since software will always be an inexact science.
Impact on Traders
There are a few ways that automated trading might impact commodities trading.
First, there has been a growing correlation over short periods of time between different commodities. A 2012 research paper found a sharp increase in the correlations between commodities like corn and soybeans and the U.S. equity market over five-minute, 10-second, and one-second frequencies. The researchers attributed these correlations to the rise in algorithmic trading and warned that it could potentially cause issues.
Second, the growing prevalence of algorithmic trading means that traders are likely to see a greater number of sudden and unexpected price movements. While regulators are introducing rules to combat these trends, software is prone to glitches and it’s nearly impossible to entirely avoid any of these problems. The good news is that safeguards could be put in place in order to limit the impact of these movements via fail-safes on an exchange level.
And third, the ongoing automation of trading may take away opportunities for human traders to generate a consistent profit in a zero-sum market like commodities. The ability of computers to quickly analyze, interpret, and act on information greatly surpasses that of any human.
The Bottom Line
Regulators have set their sights on regulating automated trading in the commodities market with controversial new rules. Many experts believe that these new rules could expose algorithms to third parties, but will do little to stop so-called flash crashes from occurring. In the meantime, algorithms already account for more than 70% of futures trading and that number is likely to continue rising over the coming years.
The increase in algorithmic trading will have a number of impacts on the market, including increased correlations, greater numbers of less severe flash crashes, and fewer opportunities for human traders who may struggle to compete.