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

TL;DR

This paper compares risk-seeking and risk-avoiding investment strategies in noisy periodic environments, showing risk-seeking strategies perform better and genetic algorithms can discover optimal strategies through simulation.

Contribution

It demonstrates that risk-seeking strategies outperform risk-avoiding ones in noisy periodic settings and that genetic algorithms can autonomously find optimal investment strategies.

Findings

Risk-seeking strategies outperform risk-avoiding strategies.

Genetic algorithms can discover optimal investment strategies.

Risk-seeking agents achieve higher average budget growth.

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.