Author:
J.-Emeterio Navarro-Barrientos
Affiliation:
Humboldt-Universität zu Berlin, Germany
Keyword(s):
Genetic algorithms, investment strategies, pattern recognition.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Decision Support Systems
;
Evolutionary Computation and Control
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Machine Learning in Control Applications
;
Optimization Algorithms
;
Soft Computing
Abstract:
In this paper, we present an investment strategy to control investment proportions for environments with cyclic changing returns on investment. For this, 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 modeled by stylized returns on investment (RoI). For comparison reasons, we present two reference strategies which represent agents with zero-knowledge and complete-knowledge of the dynamics of the RoI, and we consider an investment strategy based on technical analysis. To account for the performance of the strategies, we perform some computer experiments to calculate the average budget that can be obtained over a certain number of time steps. To assure for fair comparisons, we first tune the parameters of each strategy. Afterwards, we compare their performance for RoIs with fixed periodicity (stationary scenari
o) and for RoIs with changing periodicities (non-stationary scenario).
(More)