2 research outputs found
Adaptive Investment Strategies For Periodic Environments
In this paper, we present an adaptive investment strategy for environments
with periodic returns on investment. In our approach, we consider an investment
model 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 a stylized return on investment
function (RoI), which is modeled by a stochastic process with unknown
periodicities and levels of noise. For comparison reasons, we present two
reference strategies which represent the case of agents with zero-knowledge and
complete-knowledge of the dynamics of the returns. We consider also an
investment strategy based on technical analysis to forecast the next return by
fitting a trend line to previous received returns. To account for the
performance of the different strategies, we perform some computer experiments
to calculate the average budget that can be obtained with them over a certain
number of time steps. To assure for fair comparisons, we first tune the
parameters of each strategy. Afterwards, we compare the performance of these
strategies for RoIs with different periodicities and levels of noise.Comment: Paper submitted to Advances in Complex Systems (November, 2007) 22
pages, 9 figure
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, , and at each
time step invest a particular fraction, , of their budget. The return on
investment (RoI), , is characterized by a periodic function with
different types and levels of noise. Risk-avoiding agents choose their fraction
proportional to the expected positive RoI, while risk-seeking agents
always choose a maximum value 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 , 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 . 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
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