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Risk-Seeking versus Risk-Avoiding Investments in Noisy Periodic Environments

  • J. Emeterio Navarro Barrientos
  • Frank E. Walter
  • Frank Schweitzer

We study the performance of various agent strategies in an artificial investment scenario. Agents are equipped with a budget, $x(t)$, and at each time step invest a particular fraction, $q(t)$, of their budget. The return on investment (RoI), $r(t)$, is characterized by a periodic function with different types and levels of noise. Risk-avoiding agents choose their fraction $q(t)$ proportional to the expected positive RoI, while risk-seeking agents always choose a maximum value $q_{max}$ if they predict the RoI to be positive ("everything on red"). In addition to these different strategies, agents have different capabilities to predict the future $r(t)$, dependent on their internal complexity. Here, we compare 'zero-intelligent' agents using technical analysis (such as moving least squares) with agents using reinforcement learning or genetic algorithms to predict $r(t)$. The performance of agents is measured by their average budget growth after a certain number of time steps. We present results of extensive computer simulations, which show that, for our given artificial environment, (i) the risk-seeking strategy outperforms the risk-avoiding one, and (ii) the genetic algorithm was able to find this optimal strategy itself, and thus outperforms other prediction approaches considered.

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File URL: http://arxiv.org/pdf/0801.4305
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Paper provided by arXiv.org in its series Papers with number 0801.4305.

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Date of creation: Jan 2008
Date of revision: Sep 2008
Publication status: Published in International Journal of Modern Physics C vol. 19, no. 6 (2008) 971-994
Handle: RePEc:arx:papers:0801.4305
Contact details of provider: Web page: http://arxiv.org/

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  1. Marco Raberto & Silvano Cincotti & Sergio Focardi & Michele Marchesi, 2003. "Traders' Long-Run Wealth in an Artificial Financial Market," Computational Economics, Society for Computational Economics, vol. 22(2), pages 255-272, October.
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  7. J.-Emeterio Navarro-Barrientos, 2008. "Adaptive Investment Strategies For Periodic Environments," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 11(05), pages 761-787.
  8. Hens, Thorsten & Schenk-Hoppe, Klaus Reiner, 2006. "Markets do not select for a liquidity preference as behavior towards risk," Journal of Economic Dynamics and Control, Elsevier, vol. 30(2), pages 279-292, February.
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  11. Tversky, Amos & Kahneman, Daniel, 1992. " Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
  12. Emeterio Navarro & Ruben Cantero & Joao Rodrigues & Frank Schweitzer, 2007. "Investments in Random Environments," Papers 0709.3630, arXiv.org, revised Sep 2008.
  13. Follmer, Hans & Horst, Ulrich & Kirman, Alan, 2005. "Equilibria in financial markets with heterogeneous agents: a probabilistic perspective," Journal of Mathematical Economics, Elsevier, vol. 41(1-2), pages 123-155, February.
  14. Hiroshi Takahashi & Takao Terano, 2003. "Agent-Based Approach to Investors? Behavior and Asset Price Fluctuation in Financial Markets," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 6(3), pages 3.
  15. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-37, February.
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