Nietzsche’s take on high-frequency trading
September 13, 2016
With the SEC’s recent approval of the Investors Exchange (IEX), a new national securities exchange has been added to the US financial-markets landscape. IEX is the first exchange to openly challenge the rise of automated, high-frequency trading (HFT), where computer algorithms buy and sell within seconds, and without any direct human intervention. Over the past decade, HFT has gained a foothold as a significant trading mechanism, and HFT firms are now estimated to be behind over half of the trading volume in U.S. equity markets. But like every hi-tech innovation intended to supplant human actors, HFT has triggered a fierce debate between its proponents and adversaries.
HFT’s proponents see it as a much welcomed rationalization of financial markets. HFT will take the emotional urges out of human traders. It is touted as being a cooler, more rational approach to trading. Indeed, HFT’s champions proudly claim that it will finally put an end to the human emotionality of financial markets. Critics of HFT, however, highlight the unfair advantage it gives fast market participants. HFT is also seen as a kind of techno nightmare where the wrong algorithm could cause new types of market crashes. Critics often cite the so-called Flash Crash of May 6th 2010, in which trading algorithms produced a dramatic drop on US markets until trading was suspended. Should investors welcome the entry of high frequency trading? Who benefits? And who shoulders the risks?
Part of the confusion lies in the lack of transparency in the HFT industry. Surprisingly little is known about the people who develop, program, and deploy these superfast algorithms. The industry is intensely secretive, and high-frequency traders prefer to operate under the radar of public scrutiny. This is understandable: in a field where the competitive edge of a new trading algorithm can rapidly dissolve if others copy it, who would want to disclose the source of their profitable trading? The problem, though, is that the lack of knowledge about what these traders are actually doing can easily lead to an overemphasis on the more spectacular aspects of HFT, whether it be its alleged crash-prone nature or the ostensible transformation of financial markets into an elite of HFT firms dominating an underclass of exploited non-HFTs. Of course, avoiding financial crashes is in everyone’s interest, and we also need to prevent any unfair or illegal market practices. However, we can only do this by truly understanding how HFT works and what it does.
In order to better understand HFT, and to provide a more complete picture of this new reality of financial markets, we have spent the past few years studying high-frequency traders and their work in especially Chicago and New York. Doing research among the HFT tribe is challenging, not only because of its secretive nature, but also because the field is rapidly changing. Despite these challenges, we have been able to gather some valuable insights into the world of algorithmic finance – some of which we discuss in a new article entitled ‘High-frequency trader subjectivity: emotional attachment and discipline in an era of algorithms’, just published in Socio-Economic Review.
In our research, we focused on HFT as a workshop where traders and programmers develop, test, monitor, refine, and deploy their trading algorithms. Despite their project to take the human emotions out of trading, our research reveals a complex relationship between rational-scientific analysis and emotional commitments. Successful traders are those who can navigate this relationship between the algorithmic and the emotion.
The emotionality of HFT is revealed in the way traders become attached to their algorithms. Similar to many other types of work, high-frequency traders invest a lot of themselves in their algorithms. It is not uncommon for traders to describe their algorithms as extensions of themselves, as babies in need of constant care and attention. As a result, traders are inclined to let their algorithms run longer than is strictly rational – in the hope that, while seemingly ‘misbehaving’, their ‘babies’ will eventually straighten up and yield the expected profits. Traders’ emotional attachment to their algorithms is in stark contrast to HFT’s own self-understanding as a means of avoiding the dreaded ‘emotional interference’ in financial markets. However, our research has shown that human emotions do not disappear with HFT. Rather, high-frequency traders deal with their emotionality in novel ways.
One way is to emphasize a strictly scientific ethos, seeing themselves as researchers in a discovery process rather than ordinary traders with yet another new magic bullet (several of our interviewees have PhDs in physics, computer science, engineering, etc.). This emphasis on the scientific approach entails an emphasis on the need to be able to explain profit and losses. Our traders are not afraid of losses. Rather, they accept losses if they are explainable. Likewise, high-frequency traders are not content with random profits; they only want the kind of profits that follow strictly from their algorithmic strategies.
The scientific ethos of high-frequency traders is also revealed in their continual back-testing of algorithmic strategies against historical market data. The challenge they face here is not only to demonstrate that a particular algorithmic strategy was viable in a historical market data. It is to ascertain whether future market situations adequately reflect the past ones on which the strategy was tested, giving rise to what they refer to as the ‘Heisenberg uncertainty principle in finance’. It seems that intuition and a (humanly emotional) feel for the market are required in such circumstances, which contrasts with the high-frequency traders’ faith that algorithmic trading is purely rational.
Like all traders, high-frequency traders must deal with the scale and risk of their investments. If an algorithm proves successful, traders, and especially their managers, will often be inclined to put more money behind it. But traders realize that such scaling up increases risk exponentially. Consequently, and testifying to the continuing role of human emotions in finance, several of our interviewees cautioned against being too euphoric about profitable algorithmic strategies; like all successful market actors, they balanced their risk-taking by advising careful and incremental steps forward.
While the SEC was carrying out its approval process of IEX over the past several months, Ernst and Young, one of the world’s largest financial service firms, has been running an advertising campaign with the slogan ‘How human is your algorithm?’ We can answer in the words of the German philosopher Nietzsche: ‘Human, all too human’. While high-frequency traders strive to develop algorithms that appear scientific and rational, purged of human emotion, the daily work of high-frequency traders has more in common with Nietzsche’s dictum than with a computer program. HFT is also High-Feelings Trading.