Risk-Seeking versus Risk-Avoiding Investments in Noisy Periodic Environments
J. Emeterio Navarro Barrientos,
Frank E. Walter and
Frank Schweitzer
Papers from arXiv.org
Abstract:
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.
Date: 2008-01, Revised 2008-09
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Published in International Journal of Modern Physics C vol. 19, no. 6 (2008) 971-994
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Related works:
Journal Article: RISK-SEEKING VERSUS RISK-AVOIDING INVESTMENTS IN NOISY PERIODIC ENVIRONMENTS (2008) 
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:0801.4305
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