Fundamentals of Algorithmic Markets: Liquidity, Contingency, and the Incomputability of Exchange
- 674 Downloads
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.
KeywordsMarket liquidity Contingency Incomputability Ontogenesis Algorithmic trading Market rationality Financial crises
- Amato, M., & Fantacci, L. (2011). The end of finance. Cambridge: Polity.Google Scholar
- Ayache, E. (2010). The blank swan: The end of probability. Chichester: Wiley.Google Scholar
- Ayache, E. (2011). In the middle of the event. In R. Mackay (Ed.), The medium of contingency (pp. 19–35). Falmouth: Urbanomic.Google Scholar
- Ayache, E. (2014). A formal deduction of the market. In R. Mackay (Ed.), Collapse: Casino real. Urbanomic: Falmouth.Google Scholar
- Browning, L. (2007). The subprime loan machine. The New York Times, 23 March. http://www.nytimes.com/2007/03/23/business/23speed.html. Accessed 28 June 2015.
- Carlson, M. (2006). A brief history of the 1987 stock market crash with a discussion of the federal reserve response. Finance and Economics Discussion Series. Washington: Divisions of Research & Statistics and Monetary Affairs, Federal Reserve Board. http://www.federalreserve.gov/pubs/feds/2007/200713/200713pap.pdf. Accessed 1 March 2016.
- Chaitin, G. (2006a). Meta math!: The quest for omega. New York: Vintage Books.Google Scholar
- Commodity Futures Trading Commission (2015). CFTC orders Bitcoin options trading platform operator and its CEO to cease illegally offering Bitcoin options and to cease operating a facility for trading or processing of swaps without registering. Press Release. http://www.cftc.gov/PressRoom/PressReleases/pr7231-15. Accessed 25 September 2015.
- Deleuze, G., & Guattari, F. (2005). A thousand plateaus: capitalism and schizophrenia. Minneapolis: University of Minnesota Press.Google Scholar
- Fosback, N. G. (2005). Stock market logic. New Delhi: Vision Books Pvt.Google Scholar
- Holmes, B. (2014). Money unlimited: Consequences of quantitative easing. Amsterdam: Institute of Network Cultures. http://networkcultures.org/moneylab/2014/03/27/brian-holmes-consequences-of-quantitative-easing/. Accessed 14 April 2016.
- Keynes, J. M. (2013). A treatise on money in two volumes. 2 The applied theory of money. Cambridge: Cambridge University Press.Google Scholar
- MacKenzie, D. (2008). An engine, not a camera: how financial models shape markets. Cambridge: The MIT Press.Google Scholar
- MacKenzie, D. (2014). A sociology of algorithms: High-frequency trading and the shaping of markets. Second Draft. Edinburgh. http://www.sps.ed.ac.uk/__data/assets/pdf_file/0004/156298/Algorithms25.pdf. Accessed 21 November 2014.
- Mirowski, P. (1991). More heat than light: Economics as social physics, physics as nature’s economics. Cambridge: Cambridge University Press.Google Scholar
- Mirowski, P. (2014). Never let a serious crisis go to waste: how neoliberalism survived the financial meltdown. London: Verso.Google Scholar
- Parisi, L. (2013). Contagious architecture: computation, aesthetics, and space. Cambridge: The MIT Press.Google Scholar
- Popper, N. (2012). Knight Capital says trading glitch cost it $440 million. The New York Times, 2 August. http://dealbook.nytimes.com/2012/08/02/knight-capital-says-trading-mishap-cost-it-440-million/. Accessed 20 April 2016.
- Ruggiero, M. A. (1997). Cybernetic trading strategies: developing a profitable trading system with state-of-the-art technologies. New York: Wiley.Google Scholar
- Salmon, F. (2009). Recipe for disaster: the formula that killed Wall Street. WIRED. 23 February. http://archive.wired.com/techbiz/it/magazine/17-03/wp_quant?currentPage=all. Accessed 8 July 2016.
- Simondon, G. (2013). L’individuation à la lumière des notions de forme et d’information. Grenoble: Millon.Google Scholar
- Simondon, G. (2014). Sauver l’objet technique (1983). In Sur la technique: 1953-1983 (pp. 447–454). Paris: Presses Universitaires de France.Google Scholar
- Sipser, M. (2013). Introduction to the theory of computation (3rd ed.). Boston: Cengage Learning.Google Scholar
- Skabar, A., & Cloete, I. (2001). Neural networks, financial trading and the efficient markets hypothesis. http://crpit.com/confpapers/CRPITV4Skabar.pdf. Accessed 3 August 2016.
- Vogl, J. (2012). Taming time: media of financialization (trans: Reid, C.). Grey Room, 72–83.Google Scholar
- Wigglesworth, R. (2016). Fintech: Search for a super-algo. Financial Times, 20 January. http://www.ft.com/intl/cms/s/0/5eb91614-bee5-11e5-846f-79b0e3d20eaf.html#axzz44sCMEouu. Accessed 20 April 2016.