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
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