Using agent-based artificial financial market to analyse market manipulation
Abstract
This work aims to evaluate price manipulation provided by investors with great amount of capital and its overall effect in the stock market. In order to do so, we have created an artificial financial market using NetLogo. The experiments were carried out in a closed environment, with technical analysis speculators and other three different groups of agents, each one with a unique investment strategy. This work provides inputs for the creation of an artificial financial market, in which other diverse agent strategies could be added, and evidences of a market manipulation caused by excess demand.References
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Moore, T., Gandal, N., Hamrick, J. T. and Oberman, T. (2018) “Price Manipulation in the Bitcoin Ecosystem”, Journal of Monetary Economics, DOI: 10.1016/j.jmoneco.2017.12.004.
Raberto, M., Cincotti, S., Focardi, S. M. and Marchesi, M. (2001) “Agent-based simulation of a financial market”, Physica A, vol. 299, Elsevier Science B.V., p. 319-327.
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Vishwanath, R and Krishnamurti, C. (2009) “Investment Management: A Modern Guide to Security Analysis and Stock Selection”. Springer-Verlag Berlin Heidelberg, 1st ed., p. 13-29.
Westerhoff, F. H., and Dieci, R. (2006) “The effectiveness of Keynes–Tobin transaction taxes when heterogeneous agents can trade in different markets: A behavioral finance approach”, Journal of Economic Dynamics & Control, vol. 30, Elsevier B. V., p. 293-322.
Wurman, P. R., Walsh, W. E and Wellman, M. P. (1998) “Flexible double auctions for electronic commerce: theory and implementation”, Decision Support Systems, vol. 24, Elsevier Science B.V., p. 17-27.
Asparouhova, E., Bossaerts, P. and Plott, C. (2003) “Excess demand and equilibration in multi-security financial markets: the empirical evidence”, Journal of Financial Markets, vol. 6, Elsevier Science B.V., p. 1-21.
Buss, A., Dumas, B., Uppal, R. and Vilkov, G. (2016) “The intended and unintended consequences of financial-market regulations: A general-equilibrium analysis”, Journal of Monetary Economics, vol. 81, Elsevier B.V., p. 25-43.
Castro, P. A. and Sichman, J. S. (2009) “Agex: a financial market simulation tool for software agents”, In: Aalst W., Mylopoulos J., Sadeh N. M., Shaw M. J., Szyperski C., Filipe J., Cordeiro J. (eds) LNBIP, vol 24, Springer, Berlin, p. 704–715.
Castro, P. A. and Sichman, J. S. (2013) “Automated asset management based on partially cooperative agents for a world of risks”, Appl Intell, vol 38, Springer, Berlin, p. 210-225.
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P. and Oliveira, A. L. I. (2016) “Computational Intelligence and Financial Markets: A Survey and Future Directions”, Expert Systems With Applications, vol. 55, Elsevier Ltd., p. 194-211.
CVM, C. d. V. M. (2013) “Mercado de valores mobiliários brasileiro.” [S.l.]: Comissão de valores mobiliários, 1 st ed., ISBN 978-85-67896-00-7.
Immonen, E. (2017) “Simple agent-based dynamical system models for efficient financial markets: Theory and examples”, Journal of Mathematical Economics, vol. 69, Elsevier B. V., p. 38-53.
Kirkpatrick II, C. D. and Dahlquist, J. A. (2010) “Technical analysis: the complete resource for financial market technicians”. FT press.
LeBaron, B. (2000) “Agent-based computational finance: Suggested readings and early research”, Journal of Economic Dynamics & Control, vol. 24, Elsevier Science B.V., p. 679-702.
Marchesi, M., Mannaro, K. and Setzu, A. (2008) “Using an artificial financial market for assessing the impact of Tobin-like transaction taxes”, Journal of Economic Behavior & Organization, vol. 67, Elsevier B.V., p. 445-462.
Moore, T., Gandal, N., Hamrick, J. T. and Oberman, T. (2018) “Price Manipulation in the Bitcoin Ecosystem”, Journal of Monetary Economics, DOI: 10.1016/j.jmoneco.2017.12.004.
Raberto, M., Cincotti, S., Focardi, S. M. and Marchesi, M. (2001) “Agent-based simulation of a financial market”, Physica A, vol. 299, Elsevier Science B.V., p. 319-327.
Reilly, F. K. and Brown, K. C. (2011) “Investment analysis and portfolio management”. Cengage Learning.
Reis, E. R., Castro, P. A. and Sichman, J. S. (2016) “Enhancing Classification Accuracy Through Feature Selection Methods”, XIII Encontro Nacional de Inteligência Artificial e Computacional, SBC ENIAC, Recife – PE, Brazil.
Vishwanath, R and Krishnamurti, C. (2009) “Investment Management: A Modern Guide to Security Analysis and Stock Selection”. Springer-Verlag Berlin Heidelberg, 1st ed., p. 13-29.
Westerhoff, F. H., and Dieci, R. (2006) “The effectiveness of Keynes–Tobin transaction taxes when heterogeneous agents can trade in different markets: A behavioral finance approach”, Journal of Economic Dynamics & Control, vol. 30, Elsevier B. V., p. 293-322.
Wurman, P. R., Walsh, W. E and Wellman, M. P. (1998) “Flexible double auctions for electronic commerce: theory and implementation”, Decision Support Systems, vol. 24, Elsevier Science B.V., p. 17-27.
Published
2018-05-02
How to Cite
BIASOTO, Luiza P.; REIS, Everton R.; SICHMAN, Jaime S..
Using agent-based artificial financial market to analyse market manipulation. In: WORKSHOP-SCHOOL ON AGENTS, ENVIRONMENTS, AND APPLICATIONS (WESAAC), 12. , 2018, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 154-166.
ISSN 2326-5434.
DOI: https://doi.org/10.5753/wesaac.2018.33263.
