In 2013, a false tweet sent from a hacked account owned by the Associated Press (AP) resulted in the Dow Jones Industrial Average dropping by 143.5 points and the Standard & Poor’s 500 Index loosing more than $136bn of its value in a matter of seconds.
Once the nature of the tweet was discovered, bosses corrected themselves almost as quickly as they were skewed by the bogus information, but the event, known as “Hack Crash”, demonstrates the need to better understand how social media data is linked to decision making.
This is according to Tero Karppi an assistant professor at the University at Buffalo College of Arts and Sciences, who claimed that based on its speed, Hack Crash was identified as a computer-based event – initiated by sophisticated algorithms designed to identify and evaluate Internet content that could influence markets. Those algorithms launched what amounted to a panicked trading spree, executing thousands of trades per second all because of the assumed gravity of one social media posting.
“We need to begin to identify the different ways social media is being connected to modern finance,” Karppi said. “This includes an understanding of how things spread online and how the Internet infrastructure is designed for things to spread.”
When hackers broke into the AP’s Twitter account and sent a message that a pair of explosions at the White House had injured President Barack Obama, people believed it given to the AP’s credibility as a trusted organisation – it was retweeted 4,000 times in less than five minutes.
The information spread into financial markets in micro-seconds and the markets responded. Nobody knows for sure what exactly caused the crash, but financial analysts argued that high-frequency traders, who use algorithms to execute trades and to get important signals of the future from social media feeds, were involved.
Financial algorithms execute trades based on many variables, sometimes performing autonomously, Karppi claimed, and they move faster than human thought. Since the markets operate on uncertainties and probabilities, the algorithms presumably responded to the uncertainties and probabilities implied by the false tweet, he said.
“We know the principles of algorithmic trading, such as they operate based on timing, price and volume and they rely on the speed of the network infrastructure,” he explained. “But to know exactly what particular financial algorithms do is almost impossible because of their proprietary nature. Since we do not have access to these algorithms we need to find alternative ways to understand how they work.
“Social media is still a relatively new area of research and a majority of that research is focused on everyday users. Only recently have we begun to realise other actors that have tremendous power are monitoring social media feeds. These players come from financial markets, but also from the security industry and the public sector, to name a few. In general there seems to be this neo-positivist belief that social media data represents our reality and can be used to make accurate decision making.”
It’s all happening quickly, and that’s a problem, according to Karppi. “When computation systems begin to analyse what spreads on Twitter and then makes decisions based on these predictions faster than human response time we will see unpredictable consequences.”
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