Skip to main content

V Mechanism Stability—Flash Crashes and Avalanche Effects

  • Chapter
  • First Online:
IT Solutions for the Smart Grid
  • 1009 Accesses

Abstract

Flash crashes, perceived as sharp drops in market prices that rebound shortly after, have turned the public eye towards the vulnerability of IT-based stock trading. In this paper, we explain flash crashes as a result of actions made by rational agents. We argue that the advancement of information technology, which has long been associated with competitive advantages, may cause ambiguities with respect to the game form that give rise to a Hypergame. We employ Hypergame Theory to demonstrate that a market crash constitutes an equilibrium state if players misperceive the true game. Once the ambiguity is resolved, prices readjust to the appropriate level, creating the characteristic flash crash effect. By analyzing the interaction with herd behavior, we find that flash crashes may be unavoidable and a systemic problem of modern financial markets. Furthermore, we outline that flash-crash-like effects are also relevant in other applications that rely on increasing automation, such as the automated management of energy demand.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Aldridge, I. (2010). High-frequency trading: A practical guide to algorithmic strategies and trading systems. Hoboken, NJ: Wiley.

    Google Scholar 

  • Barlevy, G. & Veronesi, P. (2003). Rational panics and stock market crashes. Journal of Economic Theory, 110 (2), 234–263.

    Google Scholar 

  • Bennett, P. G. (1977). Toward a theory of hypergames. Omega, 5 (6), 749–751.

    Google Scholar 

  • Bennett, P. G. (1980). Hypergames: Developing a model of conflict. Futures, 12 (6), 489–507.

    Google Scholar 

  • Budish, E. B., Cramton, P. & Shim, J. J. (2013). The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response. SSRN Working Paper Series.

    Google Scholar 

  • Carr, N. G. (2004). Does IT matter? Information technology and the corrosion of competitive advantage. Boston, MA: Harvard Business School Press.

    Google Scholar 

  • Carr, N. G. (2008). Is Google Making Us Stupid? Yearbook of the National Society for the Study of Education, 107 (2), 89–94.

    Google Scholar 

  • CFTC and SEC. (2010). Findings Regarding the Market Events of May 6, 2010. Report of the Staffs of the CFTC and SEC to the Joint Advisory Committee on Emerging Regulatory Issues. Washington, DC.

    Google Scholar 

  • Chlistalla, M. (2011). High-frequency trading: Better than its reputation? Clemons, E. K. & Weber, B. W. (1990). London’s Big Bang: A Case Study of Information Technology, Competitive Impact, and Organizational Change. Journal of Management Information Systems, 6 (4), 41–60.

    Google Scholar 

  • Copeland, T. E. & Friedman, D. (1987). The Effect of Sequential Information Arrival on Asset Prices: An Experimental Study. The Journal of Finance, 42 (3), 763–797.

    Google Scholar 

  • Darby, S. (2006). The Effectivesness of Feedback on Energy Consumption. Devenow, A. & Welch, I. (1996). Rational herding in financial economics. European Economic Review, 40 (3-5), 603–615.

    Google Scholar 

  • Easley, D., Lopez de Prado, M. M. & O’Hara, M. (2012). Flow Toxicity and Liquidity in a High-frequency World. Review of Financial Studies, 25 (5), 1457–1493.

    Google Scholar 

  • Easley, D., Lpez de Prado, Marcos M & O’Hara, M. (2011). The Microstructure of the “Flash Crash”: Flow Toxicity, Liquidity Crashes, and the Probability of Informed Trading. The Journal of Portfolio Management, 37 (2), 118–128.

    Google Scholar 

  • Feuerriegel, S., Bodenbenner, P. & Neumann, D. (2013). Is More Information Better Than Less? Understanding The Impact Of Demand Response Mechanisms In Energy Markets. ECIS 2013 Completed Research, Paper 167.

    Google Scholar 

  • Feuerriegel, S., Str¨uker, J. & Neumann, D. (2012). Reducing Price Uncertainty through Demand Side Management. ICIS 2012 Proceedings, Paper 7.

    Google Scholar 

  • Filimonov, V. & Sornette, D. (2012). Quantifying reflexivity in financial markets: Toward a prediction of flash crashes. Physical Review E, 85 (5), 056108.

    Google Scholar 

  • Gomber, P. & Haferkorn, M. (2013). High-Frequency-Trading - High-Frequency-Trading Technologies and Their Implications for Electronic Securities Trading. Business & Information Systems Engineering, 5 (2), 97–99.

    Google Scholar 

  • Gottwalt, S., Ketter, W., Block, C., Collins, J. & Weinhardt, C. (2011). Demand side management—A simulation of household behavior under variable prices. Energy Policy, 39 (12), 8163–8174.

    Google Scholar 

  • Groth, S. (2010). Enhancing Automated Trading Engines To Cope With News-Related Liquidity Shocks. ECIS 2010 Proceedings, Paper 111.

    Google Scholar 

  • Katz, R. H., Culler, D. E., Sanders, S., Alspaugh, S., Chen, Y., Dawson-Haggerty, S., … Shankar, S. (2011). An informationcentric energy infrastructure: The Berkeley view. Sustainable Computing: Informatics and Systems, 1 (1), 7–22.

    Google Scholar 

  • Kirilenko, A. A., Kyle, A. S., Samadi, M. & Tuzun, T. (2011). The Flash Crash: The Impact of High Frequency Trading on an Electronic Market. SSRN Working Paper Series.

    Google Scholar 

  • Lattemann, C., Loos, P., Gomolka, J., Burghof, H.-P., Breuer, A., Gomber, P., … Zajonz, R. (2012). High Frequency Trading - Costs and Benefits in Securities Trading and its Necessity of Regulations. Business & Information Systems Engineering, 4 (2), 93–108.

    Google Scholar 

  • Lee, H. G. & Clark, T. H. (1996/1997). Market Process Reengineering through Electronic Market Systems: Opportunities and Challenges. Journal of Management Information Systems, 13 (3), 113–136.

    Google Scholar 

  • Lucas, H., Agarwal, R., Sawy, O. E. & Weber, B. (2013). Impactful Research on Transformational Information Technology: An Opportunity to Inform New Audiences. Management Information Systems Quarterly, 37 (2), 371–382.

    Google Scholar 

  • Lux, T. (1995). Herd Behaviour, Bubbles and Crashes. The Economic Journal, 105 (431), 881–896.

    Google Scholar 

  • Premkumar, G., Ramamurthy, K. & Saunders, C. S. (2005). Information Processing View of Organizations: An Exploratory Examination of Fit in the Context of Interorganizational Relationships. Journal of Management Information Systems, 22 (1), 257–298.

    Google Scholar 

  • Ramchurn, S. D., Vytelingum, P., Rogers, A. & Jennings, N. (2011). Agent-based control for decentralised demand side management in the smart grid. In The 10th International Conference on Autonomous Agents and Multiagent Systems (pp. 5–12). Ann Arbor: IFAAMAS.

    Google Scholar 

  • Rapoport, A. & Chammah, A. M. (1966). The Game of Chicken. American Behavioral Scientist, 10 (3), 10–28.

    Google Scholar 

  • Sakaki, T., Okazaki, M. & Matsuo, Y. (2010). Earthquake shakes Twitter users: real-time event detection by social sensors. WWW ’10 Proceedings, 851–860.

    Google Scholar 

  • Sasaki, Y. & Kijima, K. (2012). Hypergames and bayesian games: A theoretical comparison of the models of games with incomplete information. Journal of Systems Science and Complexity, 25 (4), 720–735.

    Google Scholar 

  • Schelling, T. (1990). The Strategy of Conflict (Reprint). Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Strueker, J. & Dinther, C. (2012). Demand Response in Smart Grids: Research Opportunities for the IS Discipline. AMCIS 2012 Proceedings, Paper 7.

    Google Scholar 

  • Walczak, S. (1999). Gaining Competitive Advantage for Trading in Emerging Capital Markets with Neural Networks. Journal of Management Information Systems, 16 (2), 177–192.

    Google Scholar 

  • Wang, E., Tai, C.-F. & Grover, V. (2013). Examining the Relational Benefits of Improved Interfirm Information Processing Capability in Buyer–Supplier Dyads. Management Information Systems Quarterly, 37 (1), 149–173.

    Google Scholar 

  • Watson, R., Boudreau, M.-C. & Chen, A. (2010). Information Systems and Environmentally Sustainable Development: Energy Informatics and New Directions for the IS Community. Management Information Systems Quarterly, 34 (1), 23–38.

    Google Scholar 

  • White, M. (2011). Information anywhere, any when: The role of the smartphone. Business Information Review, 27 (4), 242–247.

    Google Scholar 

  • Xu, S. & Zhang, X. (2009). How Do Social Media Shape the Information Environment in the Financial Market? ICIS 2009 Proceedings, Paper 56.

    Google Scholar 

  • Zhang, S. & Riordan, R. (2011). Technology and Market Quality: The Case of High Frequency Trading. ECIS 2011 Proceedings, Paper 95.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tobias Brandt .

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Fachmedien Wiesbaden

About this chapter

Cite this chapter

Brandt, T. (2016). V Mechanism Stability—Flash Crashes and Avalanche Effects. In: IT Solutions for the Smart Grid. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-12415-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-658-12415-1_5

  • Published:

  • Publisher Name: Springer Vieweg, Wiesbaden

  • Print ISBN: 978-3-658-12414-4

  • Online ISBN: 978-3-658-12415-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics