Periodic Reporting for period 2 - AlgoFinance (Algorithmic Finance: Inquiring into the Reshaping of Financial Markets)
Reporting period: 2018-11-01 to 2020-04-30
The project is important for society because it offers new insights into an opaque field of algorithmic trading which is at the center of present-day financial markets, and because the research may help to better understand how we might design markets so that algorithmic market crashes can be avoided.
As per (1), we have conducted more than 170 interviews, mainly with firms specializing in algorithmic trading. The majority of these firms are from Chicago, New York, London or Amsterdam, all central hubs in financial markets. In addition to trading firms, we have 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 have been granted access to follow two trading firms ethnographically, one from Chicago and one from London. The interviews and the fieldwork have given us a unique view into the world of algorithmic finance. We are currently analyzing the data and hope to be able to publish findings over the next year.
As per (2), we have developed the contours of our simulation framework and its so-called agent-based model. Developing a realistic simulation of present-day financial markets is no trivial feat, given the complexity of these markets. We have a simple model in place now and hope to add further complexity to it over the next years.
As per (3), the PI has completed a monograph (forthcoming with Cambridge University Press), in which he theorizes the interaction of trading algorithms, arguing for conceiving of such interaction dynamics on the basis of a vocabulary of “social avalanching” that is derived from late-nineteenth-century reflections on crowd behavior.
Second, our simulation framework is truly beyond the state-of-the-art: it is an advanced agent-based model that contains dimensions of algorithmic trading never included in academic simulations of financial markets before. We expect to be able to explore an array of important features (network effects, market design effects, market crashes, etc.) once the framework is fully developed.
Third, the project offers a novel theorization of the interactions of algorithms. We have already presented novel theorization on this topic during the project’s first two years and expect to be able to add new significant findings on the basis of some of the simulation work.