The AlgoFinance research project sought answers to three questions: (1) How and with what consequences does the use of algorithms change the ways in which trading firms operate in financial markets?; (2) How and with what consequences do automated trading algorithms interact in financial markets?; and (3) What forms of sociality characterize algorithmic finance?
To answer the first question, we conducted almost 200 interviews with market participants active in algorithmic trading. The majority of the informants were working in firms in Chicago, New York, London or Amsterdam, all central hubs for this form of trading. In addition to trading firms, we interviewed exchange officials, regulators, and non-algorithmic firms such as institutional investors who might be adversely affected by algorithmic trading. In addition to interviews, we did observational studies at two trading firms in Chicago and London, respectively. The interviews and the fieldwork gave us unique insights into the world of algorithmic finance. Specifically, we demonstrated that new types of expertise are getting traction in financial markets; that the advent of opaque machine-learning models has produced a rift among algorithmic traders, with some enthusiastically endorsing such models and with others being skeptical about their non-intuitive nature; and that the turn of machine-learning-based algorithmic trading produces new types of challenges for trading firms and therefore demand new forms of internal organization to meet these challenges.
We published 10 journal articles on this, and an additional five papers are currently in process. Furthermore, one full-length single-authored book manuscript is currently under consideration; another one is invited at proposal stage; and a further single-authored monograph is in progress.
To answer the second research question, we developed a semi-realistic simulation framework capable of simulating the interaction of thousands of algorithms with realistic properties. This simulation framework is the most complex and realistic one existing in the academic world. We consider having developed this simulation framework a core achievement of the project.
We are currently completing one journal paper based on the simulation work and have three more in the pipeline.
In answering the third research question, the project found that the interaction among fully automated trading algorithms can be understood through a comparison with the fleeting encounters among urban inhabitants. However, the project also demonstrated that, at least for machine-learning-based algorithmic trading systems, these can only be understood to a limited degree on the basis of sociological notions developed to understand inter-human behaviour. Examining this third research question led to one single-authored monograph and two journal articles. Three additional papers are in review/progress.
