14 Jan 16. Cyberwarfare: Will terrorists blow up the global finance system with weaponised algorithms? High frequency trading (HFT) and other ways to cheat markets are now being surpassed by advancements in machine learning which are now growing within financeGetty Images
Cyberwarfare waged by terrorists using weaponised algorithms and artificial intelligence – it sounds like something straight out of “The Fear Index” by Robert Harris, or maybe the plot of a James Bond flick. But this is the new reality and we should be scared – very scared.
High frequency trading technologies have infiltrated markets over the last decade leading to unprecedented episodes of volatility and flash crashes, leaving regulators as bewildered as everyone else. Technology which was once confined to investment banks or hedge fund managers has made its way into the retail space, and it can be relatively easily modified. In addition, recent advancements in machine learning and AI are entering a number of fields, including finance.
“”We have not yet seen criminals or terrorists weaponise algorithms for attacks, but the future will surely bring this at some point. When it happens, the safest thing for investors is to have their investments in other algorithms.””
– Kristian Kristiansen
Kristian Kristiansen of QuantAlliance, which develops trading algorithms focused on Forex and commodities, told IBTimes UK: “Weaponised algos [algorithms] are software specifically designed to manipulate stock markets and start a market crash. The effect of this is not only a loss of assets, but the creation of fear that will manifest in investors. It will prevent them from creating further investment, or they will make significant downgrades in the amounts they invest. This in return will have a serious impact on any economy, even one as big as the US.
“The scary thing is that software like this is pretty easy to make, and can be constructed for a very low cost. It’s only possible because the majority of trades are currently performed by algos. The software I’m talking about will simply ‘trick’ the algos to sell and this will create a chain reaction in other algos and spark a crash.”
Kristiansen believes there are too many blue chip algorithms with vast sums under management, relying on analysis of market orders. The problem is that orders can be made without being executed. “I can place an order to sell stocks worth hundreds of millions of dollars, and never actually do it. But other algos can see I placed the order and many of them will react to this,” said Kristiansen.
Flash crash
“If just one big algorithm reacts and actually starts selling as a result of my actions, this will start a chain reaction with others. And you have to keep in mind many algos are specifically designed to follow other algos. There are even algos designed to follow the ones who are following. It could get very bad very fast. Look at the flash crash of 2010.”
The 2010 trillion dollar flash crash lasted approximately 36 minutes. Navinder Singh Sarao, a trader who operated from his parents’ house in suburban West London, is being blamed for “spoofing” the markets and triggering the crash. Sarao amassed nearly £30m using his moderated trading software. But market commentators have said blaming a single trader for the crash is like blaming lightning for starting a fire.
How Do Trading Algorithms Work?
Here is an example of a simple algorithm investment thesis: “The top-performing stocks from last week will do worse this week, and vice-versa.”
So when the markets open on a Monday, the system ranks high-volume stocks based on their previous five day returns, and goes long (bets on a price increase) on the bottom 20% of stocks with the worst returns over the past five days, and goes short (bets against) the top 20% of stocks with the best returns over the past five days.
High frequency trading (HFT) and other ways to cheat markets are now being surpassed by advancements in machine learning