Abstract
In light of the structural role of computational technology in the expansion of modern global finance, this essay investigates the ontology of contemporary markets starting from a reformulation of liquidity—one of the tenets of financial trading. Focusing on the nexus between financial and algorithmic flows, the paper complements contemporary philosophies of the market with insights into recent theories of computation, emphasizing the functional role of contingency, both for market trading and algorithmic processes. Considering the increasing adoption of advanced computational methods in automated trading strategies, this article argues that the event of price is the direct manifestation of the incomputability at the heart of market exchange. In doing so, it questions the ontological assumptions of “flow” and “fluidity” underpinning traditional conceptions of liquidity and challenges the notion of rationality in market behavior. Ultimately, the paper gestures toward some of the social and political consequences of this reformulation.
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Notes
Quantitative easing has been criticized for fostering financial instability, inflation (by reducing the value of money), and the unequal distribution of wealth. Furthermore, coupled with austerity, these measures are said to have reinvigorated the same financial powers and dynamics that led to the 2008 recession, for they answer liquidity crisis with even more trading and more financialization instead of curbing the speculative attitude of financial institutions (Amato and Fantacci 2011; Mirowski 2014).
Translations from French are mine throughout the paper, unless stated in the bibliography.
“What if the future contingent event had a place instead of a time or a timing, a place we could inhabit independently of time?” (Ayache 2011, p. 32).
Mortgage-backed securities and CDOs differ from other types of derivatives such as options and futures because the underlying asset is not constituted by commodities or shares, but by a tranche of a pool of loans (e.g., residential loans, bank loans, or bonds). For this reason, they are also called structured products.
“What makes the price a price, its past, is what virtually it will be. The price is not. It is insofar as it becomes” (Ayache 2010, p. 58).
One early example of market timing strategy is the Seasonality Timing System (STS), conceived by Norman Fosback (2005) in the early 1970s. As the name suggests, STS is based on discovering seasonal patterns in equity prices in large datasets. Note the algorithmic premises of this method, based on the assumption of recursive dynamics in market trading.
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Lotti, L. Fundamentals of Algorithmic Markets: Liquidity, Contingency, and the Incomputability of Exchange. Philos. Technol. 31, 43–58 (2018). https://doi.org/10.1007/s13347-016-0249-8
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DOI: https://doi.org/10.1007/s13347-016-0249-8