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

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.Comment: 27 pp. v2 with minor corrections. See http://www.sg.ethz.ch for more inf

ADAPTIVE INVESTMENT STRATEGIES FOR PERIODIC ENVIRONMENTS

In this paper, an adaptive investment strategy for environments with periodic returns on investment is presented. In this approach, an investment model is considered where the agent decides at every time step the proportion of wealth to invest in a risky asset, keeping the rest of the budget in a risk-free asset. Every investment is evaluated in the market via stylized return on investment function (RoI), which is modeled by a stochastic process with unknown periodicities and levels of noise. For comparison, two reference strategies are presented which represent the case of agents with zero knowledge and complete knowledge of the dynamics of the returns. An investment strategy based on technical analysis to forecast the next return is also considered. To account for the performance of the different strategies, some computer experiments are performed to calculate the average budget that can be obtained with them over a certain number of time steps. To assure fair comparisons, the parameters of each strategy are first tuned for budget maximization. Afterward, the performance of these strategies is compared for RoI's with different periodicities and levels of noise.Genetic algorithms, portfolio optimization, investment strategies, time series