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To understand the impact of high-frequency trading (HFT) systems on financial-market dynamics, a series of controlled real-time experiments involving humans and automated trading agents were performed. These experiments fall at the interdisciplinary boundary between the more traditional fields of behavioural economics (human-only experiments) and agent-based computational economics (agent-only simulations). Experimental results demonstrate that: (a) faster financial trading agents can reduce market efficiency—a worrying result given the race towards zero-latency (ever faster trading) observed in real markets; and (b) faster agents can lead to market fragmentation, such that markets transition from a regime where humans and agents freely interact to a regime where agents are more likely to trade between themselves—a result that has also been observed in real financial markets. It is also shown that (c) realism in experimental design can significantly alter market dynamics—suggesting that, if we want to understand complexity in real financial markets, it is finally time to move away from the simple experimental economics models first introduced in the 1960s.
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Angel, J., Harris, L., & Spratt, C. (2010). Equity trading in the 21st century. Working Paper FBE-09-10, Marshall School of Business, University of Southern California, February 2010. Available via SSRN https://ssrn.com/abstract=1584026 Accessed 22.03.2017
Arthur, W. B. (2014). Complexity and the economy. Oxford: Oxford University Press.
Battiston, S., Farmer, J. D., Flache, A., Garlaschelli, D., Haldane, A. G., Heesterbeek, H., et al. (2016). Complexity theory and financial regulation: Economic policy needs interdisciplinary network analysis and behavioral modeling. Science, 351(6275), 818–819 CrossRef
Baxter, G., & Cartlidge, J. (2013). Flying by the seat of their pants: What can high frequency trading learn from aviation? In G. Brat, E. Garcia, A. Moccia, P. Palanque, A. Pasquini, F. J. Saez, & M. Winckler (Eds.), Proceedings of 3rd International Conference on Applied and Theory of Automation in Command and Control System (ATACCS), Naples (pp. 64–73). New York: ACM/IRIT Press, May 2013.
Berger, S. (Ed.), (2009). The foundations of non-equilibrium economics. New York: Routledge.
Bisias, D., Flood, M., Lo, A. W., & Valavanis, S. (2012). A survey of systemic risk analytics. Annual Review of Financial Economics, 4, 255–296. CrossRef
Bouchaud, J. P. (2008). Economics needs a scientific revolution. Nature, 455(7217), 1181. CrossRef
Cartlidge, J. (2016). Towards adaptive ex ante circuit breakers in financial markets using human-algorithmic market studies. In Proceedings of 28th International Conference on Artificial Intelligence (ICAI), Las Vegas (pp. 77–80). CSREA Press, Athens, GA, USA. July 2016.
Cartlidge, J., & Cliff, D. (2012). Exploring the ‘robot phase transition’ in experimental human-algorithmic markets. In Future of computer trading. Government Office for Science, London, UK (October 2012) DR25. Available via GOV.UK https://www.gov.uk/government/publications/computer-trading-robot-phase-transition-in-experimental-human-algorithmic-markets Accessed 22.03.2017.
Cartlidge, J., & Cliff, D. (2013). Evidencing the robot phase transition in human-agent experimental financial markets. In J. Filipe & A. Fred (Eds.), Proceedings of 5th International Conference on Agents and Artificial Intelligence (ICAART), Barcelona (Vol. 1, pp. 345–352). Setubal: SciTePress, February 2013.
Cartlidge, J., De Luca, M., Szostek, C., & Cliff, D. (2012). Too fast too furious: Faster financial-market trading agents can give less efficient markets. In J. Filipe & A. Fred (Eds.), Proceedings of 4th International Conference on Agents and Artificial Intelligent (ICAART), Vilamoura (Vol. 2, pp. 126–135). Setubal: SciTePress, February 2012.
Chen, S. H., & Du, Y. R. (2015). Granularity in economic decision making: An interdisciplinary review. In W. Pedrycz & S. M. Chen (Eds.), Granular computing and decision-making: Interactive and iterative approaches (pp. 47–72). Berlin: Springer (2015)
Cliff, D., & Bruten, J. (1997). Minimal-Intelligence Agents for Bargaining Behaviours in Market-Based Environments. Technical Report HPL-97-91, Hewlett-Packard Labs., Bristol, August 1997.
Cliff, D., & Northrop, L. (2017). The global financial markets: An ultra-large-scale systems perspective. In: Future of computer trading. Government Office for Science, London, UK (September 2011) DR4. Available via GOV.UK https://www.gov.uk/government/publications/computer-trading-global-financial-markets Accessed 22.03.2017
Das, R., Hanson, J., Kephart, J., & Tesauro, G. (2001) Agent-human interactions in the continuous double auction. In Nebel, B. (Ed.), Proceedings of 17th International Conference on Artificial Intelligence (IJCAI), Seattle (pp. 1169–1176). San Francisco: Morgan Kaufmann, August 2001
De Luca, M. (2015). Why robots failed: Demonstrating the superiority of multiple-order trading agents in experimental human-agent financial markets. In S. Loiseau, J. Filipe, B. Duval, & J. van den Herik, (Eds.), Proceedings of 7th International Conference on Agents and Artificial Intelligence (ICAART), Lisbon (Vol. 1, pp. 44–53). Setubal: SciTePress, January 2015.
De Luca, M., & Cliff, D. (2011). Agent-human interactions in the continuous double auction, redux: Using the OpEx lab-in-a-box to explore ZIP and GDX. In J. Filipe, & A. Fred (Eds.), Proceedings of 3rd International Conference on Agents and Artificial Intelligents (ICAART) (Vol. 2, pp. 351–358) Setubal: SciTePress, January 2011.
De Luca, M., & Cliff, D. (2011). Human-agent auction interactions: Adaptive-aggressive agents dominate. In Walsh, T. (Ed.), Proceedings of 22nd International Joint Conference on Artificial Intelligence (IJCAI) (pp. 178–185). Menlo Park: AAAI Press, July 2011.
De Luca, M., Szostek, C., Cartlidge, J., & Cliff, D. (2011). Studies of interactions between human traders and algorithmic trading systems. In: Future of Computer Trading. Government Office for Science, London, September 2011, DR13. Available via GOV.UK https://www.gov.uk/government/publications/computer-trading-interactions-between-human-traders-and-algorithmic-trading-systems Accessed 22.03.17.
Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge: Cambridge University Press CrossRef
Easley, D., Lopez de Prado, M., & O’Hara, M. (Winter 2011). The microstructure of the ‘flash crash’: Flow toxicity, liquidity crashes and the probability of informed trading. Journal of Portfolio Management, 37(2), 118–128 CrossRef
Farmer, J. D., & Foley, D. (2009). The economy needs agent-based modelling. Nature, 460(7256), 685–686 CrossRef
Farmer, J. D., & Skouras, S. (2011). An ecological perspective on the future of computer trading. In: Future of Computer Trading. Government Office for Science, London, September 2011, DR6. Available via GOV.UK https://www.gov.uk/government/publications/computer-trading-an-ecological-perspective Accessed 22.03.2017.
Feltovich, N. (2003). Nonparametric tests of differences in medians: Comparison of the Wilcoxon-Mann-Whitney and Robust Rank-Order tests. Experimental Economics, 6, 273–297. CrossRef
Foresight. (2012). The Future of Computer Trading in Financial Markets. Final project report, The Government Office for Science, London, UK (October 2012). Available via GOV.UK http://www.cftc.gov/idc/groups/public/@aboutcftc/documents/file/tacfuturecomputertrading1012.pdf Accessed 22.03.17
Giles, J. (2012). Stock trading ‘fractures’ may warn of next crash. New Scientist (2852) (February 2012). Available Online: http://www.newscientist.com/article/mg21328525.700-stock-trading-fractures-may-warn-of-next-crash.html Accessed 22.03.17.
Gjerstad, S., & Dickhaut, J. (1998). Price formation in double auctions. Games and Economic Behavior, 22(1), 1–29 CrossRef
Gode, D., & Sunder, S. (1993). Allocative efficiency of markets with zero-intelligence traders: Markets as a partial substitute for individual rationality. Journal of Political Economy, 101(1), 119–137. CrossRef
Gomber, P., Arndt, B., Lutat, M., & Uhle, T. (2011). High Frequency Trading. Technical report, Goethe Universität, Frankfurt Am Main (2011). Commissioned by Deutsche Börse Group.
Grossklags, J., & Schmidt, C. (2003). Artificial software agents on thin double auction markets: A human trader experiment. In J. Liu, B. Faltings, N. Zhong, R. Lu, & T. Nishida (Eds.), Proceedings of IEEE/WIC Conference on Intelligent Agent and Technology (IAT), Halifax (pp. 400–407). New York: IEEE Press.
Grossklags, J., & Schmidt, C. (2006). Software agents and market (in)efficiency: A human trader experiment. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Review) 36(1), 56–67. CrossRef
Holt, C. A., & Roth, A. E. (2004). The Nash equilibrium: A perspective. Proceedings of the National Academy of Sciences of the United States of America, 101(12), 3999–4002 CrossRef
Huber, J., Shubik, M., & Sunder, S. (2010). Three minimal market institutions with human and algorithmic agents: Theory and experimental evidence. Games and Economic Behavior, 70(2), 403–424 CrossRef
Johnson, N. (2017). To slow or not? Challenges in subsecond networks. Science, 355(6327), 801–802. CrossRef
Johnson, N., Zhao, G., Hunsader, E., Meng, J., Ravindar, A., Carran, S., et al. (2012). Financial Black Swans Driven by Ultrafast Machine Ecology. Working paper published on arXiv repository, Feb 2012.
Johnson, N., Zhao, G., Hunsader, E., Qi, H., Johnson, N., Meng, J., et al. (2013). Abrupt rise of new machine ecology beyond human response time. Scientific Reports, 3(2627), 1–7 (2013)
Joint CFTC-SEC Advisory Committee on Emerging Regulatory Issues. (2010). Findings Regarding the Market Events of May 6, 2010. Report, CTFC-SEC, Washington, DC, September 2010. Available via SEC https://www.sec.gov/news/studies/2010/marketevents-report.pdf Accessed 22.03.2017.
Kahneman, D. (2011). Thinking, fast and slow. New York, NY: Farrar, Straus and Giroux.
Keim, B. (2012). Nanosecond trading could make markets go haywire. Wired (February 2012). Available Online: http://www.wired.com/wiredscience/2012/02/high-speed-trading Accessed 22.03.2017.
Leinweber, D. (2009). Nerds on wall street. New York: Wiley.
May, R. M., Levin, S. A., & Sugihara, G. (2008) Complex systems: Ecology for bankers. Nature, 451, 893–895 CrossRef
Nelson, R. H. (2001). Economics as religion: From Samuelson to Chicago and beyond. University Park, PA: Penn State University Press.
Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Harvard: Harvard University Press.
Perez, E. (2011). The speed traders. New York: McGraw-Hill.
Price, M. (2012). New reports highlight HFT research divide. Financial News (February 2012). Available Online: https://www.fnlondon.com/articles/hft-reports-highlight-research-divide-cornell-20120221 Accessed 22.03.2017.
Schweitzer, F., Fagiolo, G., Sornette, D., Vega-Redondo, F., & White, D. R. (2009). Economic networks: what do we know and what do we need to know? Advances in Complex Systems, 12(04n05), 407–422
Smith, V. (1962). An experimental study of comparative market behavior. Journal of Political Economy, 70, 111–137 CrossRef
Smith, V. (2006). Papers in experimental economics. Cambridge: Cambridge University Press.
Stotter, S., Cartlidge, J., & Cliff, D. (2013). Exploring assignment-adaptive (ASAD) trading agents in financial market experiments. In J. Filipe & A. Fred (Eds.), Proceedings of 5th International Conference on Agents and Artificial Intelligence (ICAART), Barcelona. Setubal: SciTePress, February 2013.
Stotter, S., Cartlidge, J., & Cliff, D. (2014). Behavioural investigations of financial trading agents using Exchange Portal (ExPo). In N. T. Nguyen, R. Kowalczyk, A. Fred, & F. Joaquim (Eds.), Transactions on computational collective intelligence XVII. Lecture notes in computer science (Vol. 8790, pp. 22–45). Berlin: Springer.
Tesauro, G., & Bredin, J. (2002). Strategic sequential bidding in auctions using dynamic programming. In C. Castelfranchi & W. L. Johnson (Eds.), Proceedings of 1st International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Bologna (pp. 591–598). New York: ACM.
Tesauro, G., & Das, R. (2001). High-performance bidding agents for the continuous double auction. In Proceedings of the ACM Conference on Electronic Commerce (EC), Tampa, FL (pp. 206–209), October 2001.
Treanor, J. (2017). Pound’s flash crash ‘was amplified by inexperienced traders’. The Guardian, January 2017. Available Online https://www.theguardian.com/business/2017/jan/13/pound-flash-crash-traders-sterling-dollar Accessed 22.03.2017.
Vytelingum, P. (2006). The Structure and Behaviour of the Continuous Double Auction. PhD thesis, School of Electronics and Computer Science, University of Southampton.
Vytelingum, P., Cliff, D., & Jennings, N. (2008). Strategic bidding in continuous double auctions. Artificial Intelligence, 172, 1700–1729 CrossRef
Widrow, B., & Hoff, M. E. (1960). Adaptive switching circuits. Institute of Radio Engineers, Western Electronic Show and Convention, Convention Record, Part 4 (pp. 96–104).
Zhang, S. S. (2013). High Frequency Trading in Financial Markets. PhD thesis, Karlsruher Institut für Technologie (KIT).
- Modelling Complex Financial Markets Using Real-Time Human–Agent Trading Experiments
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