Computer Algorithms, Market Manipulation and the Institutionalization of High Frequency Trading

Abstract

The article discusses the use of algorithmic models in finance (algo or high frequency trading). Algo trading is widespread but also somewhat controversial in modern financial markets. It is a form of automated trading technology, which critics claim can, among other things, lead to market manipulation. Drawing on three cases, this article shows that manipulation also can happen in the reverse way, meaning that human traders attempt to make algorithms ‘make mistakes’ by ‘misleading’ them. These attempts to manipulate are very simple and immediately transparent to humans. Nevertheless, financial regulators increasingly penalize such attempts to manipulate algos. The article explains this as an institutionalization of algo trading, a trading practice which is vulnerable enough to need regulatory protection.

How Algorithms Interact: Goffman's ‘Interaction Order’ in Automated Trading

“…When I began interviewing in 2010, spoofing seemed a routine market practice, at least in futures trading: 'most new orders [in the futures market] are fake', a trader in Chicago told me in 2014. There was a long tradition of spoofing being acceptable -in Chicago's trading pits, I was told by another interviewee, a successful spoofer was even admired, much as a skilled bluffer in poker would beand a tolerant attitude continued in the early years of the transition to electronic trading (Zaloom, 2006;Arnoldi, 2015). Recently, however, disapproval has grown sharply, even though two of the more libertarian-minded of my interviewees still felt strongly that it was quite wrong for the state to try to take action against spoofing.…”

‘Crises of Modernity’ Discourses and the Rise of Financial Technologies in a Contested Mechanized World

“…Such pattern recognition, Amoore (2011: 32) stresses sceptically, embodies 'an indifference to conventional Galilean scientific notions of evidence and accuracy'. Strategic priority of the current algorithmic technologies used to more and more financialize the world is speed, not knowledge or philosophy, let alone democratic culture or Bildung (Arnoldi 2016). Moreover, Mackenzie and Vurdubakis (2011), Amoore (2011), and Marc Lenglet (2011 stress, in a technological pessimist fashion, that algorithmic codes are by definition ambiguous.…”

“…Liberal scholars like MacKenzie (2015) and Bjerg (2016) argue that although it may be true that, given the crisis of the mind, no new alternative world can be imagined beyond the financialized world, new financial technologiesincluding cryptocurrencies, blockchain technology and high frequency trading-have emerged from the global financial crisis that may well disrupt the post-Keynesian order. Algorithmic financial technologies, the argument goes, radicalize the mechanization of the financial world from below, often via start-up companies (Mackenzie and Vurdubakis 2011;Arnoldi 2016). The hundreds of cryptocurrencies that exist today make it possible to bypass the established power complex: these are types of money that are produced and validated in global networks without banks or government (MacKenzie 2015;Bjerg 2016).…”

What is a 'Real' Transaction? Infrastructures, relations and Spoofing in High-Frequency Trading

“…This is certainly part of the story-but what an infrastructural inversion would advise is looking further than front-stage struggles. Definitional battles over spoofing were surely shaped by overt institutional politics, for example (see Arnoldi 2015); but practical instances of spoofing depended on an altogether different articulation of intent on the basis of overlapping (yet disparate) forms of knowledge and expertise. Spoofing was not the object of ideological intervention-rather, it was at best a boundary object (Star and Griesemer 1996), linking the different ecologies of trading platform designers, lawyers, market microstructure economists, regulators, and a mixed gamut of market participants.…”