  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.
