The speeds at which transactions are completed in global financial markets are accelerating and, in the process, connecting financial centers around the globe like never before. Algorithmic trading at high frequency is a form of automated trading in which machines, rather than humans, make the decision to buy or sell in spatio-temporal sequences. Insofar as they have agency of their own, their actions support the owners of the means of production. These techniques codevelop with new financial geographies. Accordingly, I examine technological change and speculative time-spaces of algorithmic strategies at stock exchanges. By analyzing algorithmic finance, I examine how—and to what extent—time, speed, location, and distance become critical for algorithmic finance by configuring time-spaces as competitive factors. The analysis interprets time-spaces of high-frequency trading strategies through the ways in which algorithmic finance constititutes what I term “mobile market-informational epicenters”. This paper discusses the spatio-temporalities of market information and examines whether space-times of privately owned high-frequency trading infrastructures result in a juxtaposition between “public” and “private” market information across digital/physical space. It thereby responds to the questions of what role geography plays when algorithms make money in microseconds and how techno-financial time-spaces turn into competitive advantage.
|Journal||Annals of the Association of American Geographers|
|Publication status||Accepted/In press - 2021|
- algorithmic finance
- financial geographies
- human-robotic interaction
- stock exchanges